GENETIC VARIATION IN BITTER TASTE PERCEPTION,
FOOD PREFERENCE AND DIETARY INTAKE
by
Christine Rose Asik
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Graduate Department of Nutritional Sciences
University of Toronto
© Copyright by Christine Rose Asik (2010)
ii
GENETIC VARIATION IN BITTER TASTE PERCEPTION, FOOD
PREFERENCE AND DIETARY INTAKE
Christine Rose Asik
Master of Science
Graduate Department of Nutritional Sciences
University of Toronto
2010
Abstract
The role of variation in the TAS2R50 bitter taste receptor gene is unknown, but may influence
taste perception and dietary habits. Individuals (n=1171) aged 20 to 29, from the Toronto
Nutrigenomics and Health Study, completed a food preference checklist and a semi-quantitative
food frequency questionnaire to assess their preference and intake of potentially bitter foods and
beverages. DNA was isolated from blood and genotyped for 3 polymorphisms in the TAS2R50
gene (rs2900554 A>C; rs10772397 A>G; rs1376251 A>G). Taste intensity was examined using
taste strips infused with 3µg of naringin. The rs2900554 SNP was associated with naringin taste
intensity, grapefruit preference and grapefruit intake in females. Homozygotes for the C allele
reported the highest frequency of experiencing a high naringin taste intensity, disliking grapefruit
and not consuming grapefruit. The rs10772397 and rs1376251 SNPs were associated with
disliking grapefruit. These results suggest that naringin may be a ligand for the T2R50 receptor.
iii
ACKNOWLEDGEMENTS
I would like to thank my supervisor Dr. Ahmed El-Sohemy for believing in my abilities
as a graduate student and researcher. You are an outstanding mentor. Thank you for your
unconditional guidance and support, and for providing an environment that encouraged scientific
exploration and personal growth. Without you none of this would be possible.
I am also deeply grateful for the guidance and feedback I received from my advisory
committee, Dr. Anthony Hanley and Dr. Elena Comelli. You are truly caring and patient
teachers. Dr. Anthony Hanley, thank you for helping me develop the critical thought needed in
research. Furthermore, thank you to the Bazinet lab and Archer lab for their feedback on my
research presentations.
I feel blessed to have had the opportunity to work alongside the intelligent, kind and hard
working individuals in Dr. El-Sohemy‟s lab. Leah Cahill and Karen Eny, you are exceptional
researchers and mentors. Thank you for the great amount of guidance and support you provided
me with my thesis. Sarah Herd and Joanne Brathwaite, thank you for sharing with me your
knowledge in genetics. Erica Day-Tasevski, Francesca Garofalo and Cristina Cuda, I am so
grateful I got to begin graduate school with all of you. Thank you for all the encouragement,
advice and friendship this year. Hyeon-Joo Lee, thank you greatly for the much needed lab
support and Daiva Nielsen, thank you for coordinating the Toronto Nutrigenomics and Health
Study. Darren Brenner, thank you for your advice in statistics. Bibiana Garcia Bailo, Laura
DaCosta and Andre Dias thank you for your friendship and support. It has been a joy working
with all of you.
I would like to extend a heartfelt thank you to my father and mother, Dr. and Mrs. Masis
and Elizabeth Asik, for always believing in me and encouraging me to try my hardest in all my
endeavors. I would like to further thank my mother for spending countless hours proof-reading
my thesis. I am grateful to have such a wonderful and supportive family.
iv
TABLE OF CONTENTS
Abstract ........................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
Table of contents ............................................................................................................................ iv
Lists of tables ................................................................................................................................ vii
Lists of figures ............................................................................................................................... ix
Lists of abbreviations ....................................................................................................................... x
CHAPTER 1: INTRODUCTION .................................................................................................... 1
CHAPTER 2: LITERATURE REVIEW ......................................................................................... 4
2. 1 Taste .......................................................................................................................................... 5
2. 1. 1 Bitter compounds ............................................................................................................. 6
2. 1. 2 Bitter taste perception, dietary habits and health ............................................................. 6
2. 2 Bitter taste assessment .............................................................................................................. 8
2. 3 Food preference assessment .................................................................................................... 10
2. 4 Dietary intake assessment ....................................................................................................... 11
2. 5 Taste anatomy ......................................................................................................................... 12
2. 5. 1 T2R Bitter taste receptor family ..................................................................................... 13
2. 5. 2 Taste genotype – phenotype association ........................................................................ 16
2. 5. 3 TAS2R50 ......................................................................................................................... 16
2. 6 Grapefruit and naringin ........................................................................................................... 18
v
2. 7 Summary ................................................................................................................................. 19
CHAPTER 3: RATIONALE, HYPOTHESIS AND OBJECTIVES ............................................. 21
CHAPTER 4: MATERIALS AND METHODS ........................................................................... 23
4.1 Participants ............................................................................................................................... 24
4.2 Study protocol .......................................................................................................................... 25
4.3 Bitter taste assessment ............................................................................................................. 26
4.4 Food preference assessment ..................................................................................................... 26
4.5 Dietary intake assessment ........................................................................................................ 27
4.6 Anthropometrics ...................................................................................................................... 27
4.7 Physical activity questionnaire ................................................................................................ 28
4.8 Genotyping ............................................................................................................................... 28
4.9 Statistical analyses ................................................................................................................... 29
CHAPTER 5: RESULTS ............................................................................................................... 33
5.1 Genotype frequency, allele frequency and Hardy Weinberg Equilibrium ............................. 34
5.2 Linkage disequilibrium ............................................................................................................ 34
5.3 Subject Characteristics ............................................................................................................. 35
5.4 TAS2R50 genotype and food preference .................................................................................. 35
5.5 TAS2R50 genotype and dietary intake ..................................................................................... 38
5.6 TAS2R50 genotype and taste intensity ..................................................................................... 40
vi
5.7 Difference in food preferences between Caucasians and East Asians ..................................... 41
CHAPTER 6: DISCUSSION ......................................................................................................... 68
6. 1 Limitations .............................................................................................................................. 74
6. 2 Future direction ....................................................................................................................... 78
6. 3 Implications ............................................................................................................................. 79
6. 4 Conclusions ............................................................................................................................. 80
REFERENCES .............................................................................................................................. 81
vii
LIST OF TABLES
Table 1: Taste receptor genes and their ligands ............................................................................. 15
Table 2: Variable type and definition ............................................................................................ 32
Table 3: Genotype frequency, allele frequency and Hardy Weinberg Equilibrium value by
TAS2R50 genotype in the total population and in Caucasians and East Asians and other
ethnocultural groups ....................................................................................................................... 42
Table 4: Subject characteristics for East Asians and Caucasians .................................................. 45
Table 5: The frequency and odds of disliking grapefruit and grapefruit juice by TAS2R50
genotype in Caucasians and Asians ............................................................................................... 46
Table 6: The frequency and odds of disliking vegetables by TAS2R50 genotype in Caucasians
and East Asians .............................................................................................................................. 47
Table 7: The frequency and odds of disliking soy products by TAS2R50 genotypes in Caucasians
and East Asians .............................................................................................................................. 52
Table 8: The frequency and odds of disliking cocoa containing products by TAS2R50 genotype in
Caucasians and East Asians ........................................................................................................... 53
Table 9: The frequency and odds of disliking alcoholic beverages by TAS2R50 genotype in
Caucasians and East Asians ........................................................................................................... 54
Table 10: The frequency and odds of disliking caffeinated beverages by TAS2R50 genotype in
Caucasians and East Asians ........................................................................................................... 55
viii
Table 11: The frequency of disliking grapefruit by rs2900554 SNP and rs1376251 SNP genotype
combinations in Caucasians and East Asians ................................................................................ 56
Table 12: The frequency and odds of not consuming grapefruit in the past month by TAS2R50
genotype in Caucasians and East Asians ....................................................................................... 57
Table 13: The frequency and odds of not consuming grapefruit juice in the past month by
TAS2R50 genotype in Caucasians and East Asians ....................................................................... 58
Table 14: The frequency and odds of not consuming raw spinach, cooked spinach or Swish
chard, and kale, mustard, collard or turnip greens in the past month by TAS2R50 genotype in
Caucasians and East Asians ........................................................................................................... 59
Table 15: The frequency and odds of not consuming soymilk and chocolate in the past month by
TAS2R50 genotype in Caucasians and East Asians ....................................................................... 60
Table 16: Frequency and odds of high naringin or PTC intensity by TAS2R50 genotype in
Caucasians and East Asians ........................................................................................................... 61
Table 17: The frequency and odds of disliking fruits and vegetables by ethnocultural group ...... 62
Table 18: The frequency and odds of disliking soy products by ethnocultural group ................... 64
Table 19: The frequency and odds of disliking cocoa-containing products by ethnocultural group
........................................................................................................................................................ 65
Table 20: The frequency and odds of disliking alcoholic beverages by ethnocultural group ....... 66
Table 21: The frequency and odds of disliking caffeinated beverages by ethnocultural group .... 67
ix
LIST OF FIGURES
Figure 1: Scatter plot of TAS2R50 genotype for the rs2900553 SNP ............................................ 43
Figure 2: Linkage disequilibrium between TAS2R50 SNPs in the total population and in
Caucasians, East Asians and other ethnocultural groups ............................................................... 44
x
LIST OF ABBREVIATIONS
A adenine
AVI alanine, valine, isoleucine
A49P alanine 49 proline
BMI body mass index
C cytosine
CI confidence interval
CVD cardiovascular disease
CYP3A4 cytochrome P450 3A4
DNA Deoxyribonucleic acid
FFQ food frequency questionnaire
FPC food preference checklist
GHLQ general health and lifestyle questionnaire
gLMS generalized Linear Magnitude Scale
GPCR G-protein coupled receptor
HEK human embryonic kidney cells
HR hazard ratio
HWE Hardy Weinberg Equilibrium
I236V isoleucine 236 valine
LDL low density lipoprotein
MET metabolic equivalent of task
MI myocardial infarction
OR odds ratio
PAQ physical activity questionnaire
xi
PAV proline, alanine, valine
PROP 6-n-proplythiouracil
PTC phenylthiocarbamide
SNP single nucleotide polymorphism
T2R taste receptor, type 2, receptor
TAS2R taste receptor, type 2, gene
T2R50 taste receptor, type 2, member 50, receptor
TAS2R50 taste receptor, type 2, member 50, gene
TRC taste receptor cell
TNH Toronto Nutrigenomics and Health Study
UTR untranslated region
V26A valine 26 alanine
1
Chapter 1
Introduction
2
Food selection is influenced by a number of physiological, environmental, economic
and sociocultural factors1, however taste may be the primary determinate of food selection
2.
Bitter taste is thought to act as a warning sensor for noxious compounds3. Foods that are
perceived as extremely bitter are considered unpalatable and avoided3,4
. However, several
nutritious foods, such as some fruits and vegetables, contain bitter compounds and many of
these bitter compounds are phytonutrients5. Diets rich in phytonutrients have been associated
with a lower risk of heart disease and cancer6,7
. Individuals who perceive bitter compounds as
more intense may avoid the consumption of bitter foods and compromise their health.
Bitter taste is a variable trait both within and between populations8-10
. Variability in
bitter taste is influenced by genetic variation in the T2R bitter taste receptor gene family, which
mediate bitter taste perception11,12
. A large amount of variation exists within the 25 functional
bitter taste receptor genes, however very little is known about its affect on bitter taste
perception, dietary habits and health13
. Furthermore, more than half of the 25 functional bitter
taste receptor genes have not been deorphaned13
, limiting the advancement in the understanding
of the mode of bitter taste perception.
Recent studies have associated a adenine to guanine (rs1376251, A/G) single
nucleotide polymorphism (SNP) in the taste receptor, type 2, member 50 (TAS2R50) gene with
myocardial infarction (MI) risk14,15
. Genetic variation in the TAS2R50 gene could lead to MI by
influencing bitter taste perception and in turn food preferences and intake14
. However, to our
knowledge no studies have examined the functional or behavioral significance of genetic
variation in the TAS2R50 gene. Naringin, the primary bitter constituent of grapefruit has been
shown to have cardioprotective properties16
. It is possible that the bitter taste of phytonutrients,
such as naringin, are influenced by genetic variation in the TAS2R50 gene, leading to altered
dietary intake and increased MI risk. Since cardiovascular disease (CVD) is the leading cause of
3
hospitalization in Canada17,18
, with MI accounting for the majority of CVD related deaths in
200217
, research is needed to elucidate the affects of genetic variation in the TAS2R50 gene on
taste perception, food preference and dietary intake.
4
Chapter Two
Literature Review
5
2.1 TASTE
The gustatory system regulates taste perception or gustation. Taste is categorized
into five taste modalities, which are sweet, bitter, sour, salty and umami (or savory) 4,12,19
.
Emergent evidence suggests that fat may be the sixth taste modality 20
. Taste perception helps
individuals evaluate the nutrient content of food and discriminate between safe and harmful
foods 4,12,19
. In humans, taste contributes to the overall enjoyment of a meal.
It is thought that the role of sweet taste is to identify calorie dense foods, while the role
of umami taste is to identify protein rich foods 12,19
. Salty taste is thought to help maintain
sodium and other mineral levels within the body4. Sour taste may serve to warn against the
ingestion of spoiled food or unripened fruit21
. While bitter taste is thought to warn against the
ingestion of toxic or poisonous compounds12,19
.
The assumption that bitter taste is a warning sensory for noxious compounds is due in
part by the large number of naturally occurring noxious compounds that taste bitter5. Many of
these compounds are produced by plants as natural defense mechanisms, such as toxic
glycosides or alkaloids22
. Natural physiological responses to bitter compounds also help
validate this hypothesis. The rejection of bitter taste is thought to be innate4. Young children and
nonhuman primates find bitter compounds aversive and react with stereotypic rejection
responses 3,23
. Pregnant women have a higher sensitivity to bitter taste in their first trimester,
possibly warning against the ingestion of toxic compounds during a critical period of fetal
development24
. Furthermore, the threshold detection levels of bitter tastants are considerably
lower than the other taste modalities ensuring even low levels of potentially harmful compounds
are identified and avoided25
.
6
2. 1. 1 Bitter compounds
A vast number of structurally diverse compounds elicit bitter taste. Bitter compounds
can be found in a large number of chemical groups, such as amino acids and peptides, fatty
acids, amines, amides, sulfimides, ureas and thioureas, alkaloids, glycosides, carbamides, esters,
lactones, phenolic compounds, terpenes, diterpenes, triterpens, crown ethers, metal ions, etc.4,5
.
Bitter taste is thought to help warn against the ingestion of noxious compounds however, many
bitter compounds have been shown to be beneficial to health5.
Bitter tasting phytonutrients such as phenols, flavonoids, isoflavones, terpenes and
glucosinolates found in plants have been shown to have potential antioxidant and anti-cancer
properties5,16,26,27
. Furthermore, many bitter compounds are found in nutritious foods28
including: isothiocyanates resulting from the breakdown of glucosinolate in cruciferous
vegetables, such as broccoli, watercress, cabbage, cauliflower, bok choy, arugula, radish and
kale; caroteniods found in spinach, carrots and tomatoes; limoniods and naringin found in citrus
fruit; phenol flavonoids found in tea, berries, wine, citrus fruit, endive, cranberries, onion and
kale; isoflavones found in miso, soymilk, soy nut, tofu and licorice; phenolic acids found in tea,
berries, orange, grapefruit, grape juice and coffee; and polyphenols found in wine 4,27
. Other
common foods and beverages found to elicit bitter taste responses are sharp cheeses29
and beer
28. Individuals who perceive bitter compounds as more intense may avoid the consumption of
bitter foods and affect their nutritional or health status.
2. 1. 2 Bitter taste perception, dietary habits and health
Bitter taste perception is a variable trait both within and between populations8-10,30
. The
best known examples of variation in bitter taste perception are those of phenylthiocarbaminde
(PTC) and 6-n-proplythiouracil (PROP). PTC and PROP are structurally similar compounds
7
containing a thiourea group (N-C=S) that is responsible for their bitter taste3. About 25% of
individuals worldwide are unable to taste PTC and PROP, with the prevalence of “nontasters”
varying between ethnocultural groups31
. It has been reported that 30% of North American
Caucasians, 3% of West Africans, 6 to 23% of Chinese Asians and 40% of Indians are unable to
taste PTC and PROP28,32,33
. Tasters of PTC and PROP can be further subdivided into two groups
based on their perceived sensitivity to the bitter taste of PTC or PROP, “medium tasters” and
“supertasters”10
.
Variations in bitter taste intensity have been associated with differences in food
preference and intake in multiple populations. Sensitivity to PROP taste has been associated
with lower preferences for a variety of bitter foods such as cruciferous vegetables34
, Brussels
sprouts, cabbage, spinach, coffee 35
, asparagus, kale36
, grapefruit juice9, and beer
37.
Furthermore, dietary intake of raw watercress, cooked turnip 38
, olives, cucumber and broccoli 30
have been shown to be less in PROP taster individuals within certain populations. Although
several studies examining bitter taste sensitivity have found significant associations between
PTC/PROP bitter taste sensitivity and food preference and/or intake, these studies have been
inconsistent or non-significant warranting further investigation in the association between bitter
taste intensity and food acceptance and selection 39-42
. Most bitter taste research is focused on
PTC/PROP sensitivity and little is known about how variation in sensitivity to other bitter
tastants affects food preferences and dietary intake.
Variation in bitter taste intensity may influence disease risk, however studies
examining bitter taste sensitivity and disease risk are limited. PROP sensitivity was found to be
positively associated with number of colon polyps, a risk factor for colon cancer, in men
undergoing endoscopy8. In that study, men who were sensitive to PROP consumed the lowest
number of vegetable servings/day, suggesting that PROP sensitivity is related to vegetable
8
intake and colon cancer risk8. Cardiovascular disease
39 or lipid profile
41 have not been found to
be associated with bitter taste intensity. However, differences in bitter taste intensity have been
associated with body weight, which is a risk factor for cardiovascular disease43
. PROP nontaster
women were found to have a higher body mass index, percent body weight and triceps skinfold
thickness compared to supertasters of PROP in a group of overweight women43
. This may be
because PTC/PROP supertasters generally have a higher taste acuity, perceiving sucrose44
,
sourness45
, saltiness46
and oral irritants47
as more intense and having a greater ability to
differentiate between high and low fat foods, than PTC/PROP nontasters. It has been suggested
that this higher taste acuity may promote a lower intake of all foods3. However, studies
examining the association between BMI and PROP sensitivity in different populations have not
always found comparable or significant associations40,48,49
. To our knowledge, all studies
examining the association between health and bitter taste intensity have focused on PTC/PROP
sensitivity. Further studies are needed to investigate if sensitivity to other bitter tastants affects
disease risk.
2. 2 BITTER TASTE ASSESSMENT
One of the challenges in assessing bitter taste perception is that one cannot know for
certain the perceived bitter taste intensity or sensitivity experienced by another individual,
however many methods have been devised to help estimate an individual‟s bitter taste
experience. Bitter taste assessment methods can be divided into two categories, threshold
determination and suprathreshold assessment3. Threshold determination is a process used to
determine the lowest concentration of a bitter compound that an individual can detect3.
Suprathreshold assessment is the measurement of an individual‟s perceived bitter taste intensity
of a bitter compound above threshold detection levels3.
9
Traditionally, PTC/PROP threshold detection levels were used to categorize individuals
into two groups, tasters of PTC/PROP and non-tasters, based on the distribution of PTC/PROP
threshold levels50,51
. However, when suprathreshold assessment of PTC/PROP were examined it
became clear that threshold assessment of PTC/PROP did not adequately explain all variation in
the bitter taste perception of these compounds50
. It was found that PTC/PROP “taster”
individuals who had similar threshold detection levels varied in their perception of bitter taste
intensity when suprathreshold levels of PTC/PROP were assessed, which helped separate tasters
into “medium tasters” and “supertasters”51
. Since this discovery suprathreshold assessment has
been the favored method of bitter taste perception assessment.
Suprathreshold assessment is the measurement of an individual‟s perceived bitter taste
intensity of a bitter compound above threshold detection levels3. A large diversity of
suprathreshold assessment methods exist. Suprathreshold assessment methods track an
individual‟s perceived taste intensity over several concentrations of a bitter compound or
estimate the intensity of a bitter compound at a single concentration3. During these tests the
bitter compound may be presented as a solute in water or infused on filter papers which are
placed on the tongue3. The filter paper method of bitter tastant delivery is simple, cost effective
and suited for large epidemiological studies52,53
. Recently, a method of impregnating filter
papers has been devised that delivers a constant concentration of bitter tastant across filter
papers53
. Furthermore, the filter paper method has been shown to be a reliable and valid way to
assess taste intensity and identify PTC/PROP taster status, when compared to the three-solution
test, which is considered the standard53
. Taken together, the suprathreshold assessment method
of bitter taste perception using the filter paper method of tastant delivery may be the most
efficient and cost effective method of bitter taste assessment in large epidemiological studies.
10
2. 3 FOOD PREFERENCE ASSESSMENT
Food preference assessment is used to evaluate liking or disliking of a food54
. Taste
perception is generally assumed to predict food preference, however this assumption is not
always correct54
. Numerous factors influence food preference such as sensory attributes of food
(taste, texture, odor and appearance)55
as well as an individual‟s age56
, gender57
, ethnocultural
group58
, BMI54
and health consciousness54
.
There are two methods of food preference assessment. Food preference can be assessed
using taste tests of real food in a controlled laboratory environment or using a questionnaire54
.
Food preference questionnaires contain a list of foods and ask subjects to rate their liking or
disliking of the specific food on a hedonic scale. Food preference questionnaires have been
criticized because they rely on a subject‟s memory of a past sensory experience and may reflect
a subject‟s attitude towards the food rather than the taste of the food59
. However, a food
preference questionnaire is the ideal method of food preference assessment in large
epidemiologic studies because of its ease of use, low cost and ability to assess the preference of
a large number of foods.
The most widely used scale to assess food preference is the 9-point hedonic scale60
.
This 9-point scale is an ordinal scale ranging from 1= dislike extremely to 9=like extremely with
a neutral midpoint of 5=neither like nor dislike61
. A considerable amount of research was
invested into identifying the appropriate number of categories, verbal category descriptors and
scale midpoint, in order to achieve an assessment tool that was easy, quick to use, sensitive,
reliable and uniformly understood61
. The 9-point hedonic scale has been criticized because it
does not exhibit ratio properties, may reduce variability in responses due to the ceiling effect
and may not be uniformly understood due to the use of verbal descriptors60
. However, the
degree of variability in response, reliability of the scale, ease of use and ability to discriminate
11
preference between samples when using the 9-point hedonic scale was comparable and
occasionally superior to other food preference scales (line, scanner and magnitude estimation)62
.
Furthermore, similar food preference results are obtained with the use of the 9-point hedonic
scale and the labeled affective magnitude scale60
. The labeled affective magnitude scale is a
category scale with possible ratio properties and extreme anchors that may avoid limitations due
to the ceiling effect60
. These results indicate that the 9-point scale may be the most appropriate
scale for subject‟s to record food preference responses.
2. 4 DIETARY INTAKE ASSESSMENT
Dietary intake assessment is used to measure actual food consumption or estimate
habitual consumption over a specific duration of time. Food selection is a complex behavior that
is influenced by physiological, environmental, economic and sociocultural factors1. Taste
perception and food preferences are thought to have a strong influence on food selection54
. A
linear relationship is thought to exist between taste perception, food preferences and dietary
intake, however few studies have examined this relationship in the same population54
.
There are several tools used to assess diet including diet histories, diet records, 24-hour
recalls and food frequency questionnaires (FFQ). A diet history does not follow a set template,
but is used to obtain information about an individual‟s past dietary habits63
. A food record
requires trained individuals to record all foods and beverages consumed as they are consumed,
usually over a series of 3 to 5 days, with at least one weekend day64
. This method may require
preparation methods, eating times and brand names to be reported as well as food and beverage
portions to be measured or estimated63
. A 24-hour recall is administered by a trained interviewer
who asks subjects to recall all foods and beverages consumed and their portion sizes,
preparation methods and brand names, within a 24 hour period64
. If this method is used to
12
estimate usual consumption over a study period, multiple 24-hour recalls must be administered
since daily diet is highly variable64
. The FFQ measures usual eating habits by requiring
individuals to record the frequency of consumption of foods and beverages from a list, over a
specific period of time (usually a month or year)63,64
.
FFQs have been criticized because they collect less detailed information on foods and
beverages, portion sizes and preparation methods, and require the use of memory65
. However,
FFQs are beneficial because they can be self-administered and processed quickly through
optical scanning techniques, which can significantly reduce time and costs63
. Respondent burden
has been reported to be lower for FFQs than multiple dietary records or 24-hour dietary
recalls63
. FFQs have been validated by comparison to 3 day food records or multiple 24-hour
recalls and correlation coefficients for food and nutrients between these methods have been
found to range between 0.4 and 0.7 63
. These benefits make the FFQ ideal for assessing habitual
dietary intake in large epidemiological studies.
2. 5 TASTE ANATOMY
Taste is mediated by taste receptors that are located on the surface of taste receptor
cells (TRCs). TRCs cluster together in groups of approximately 60 -100 to form onion shaped
taste buds12,13
. Taste buds are embedded predominantly in the endothelial surface of the tongue,
and to a lesser extent soft palate, pharynx, larynx, and epiglottis 12,13,19,66
. TRCs interact with
molecules in the oral cavity by way of microvilli which are located in a depression called the
taste pore within a taste bud4,19
. It is thought that each TRC detects a single taste modality67-70
Taste buds are distributed within specialized folds and protrusions on the tongue called
papillae. There are three types of papillae on the tongue, which are called circumvallate, foliate
and fungiform papillae4. There are about 3 to 18 circumvallate papillae distributed in a v-shape
13
at the root of the tongue that contain about 250 taste buds each4,19,71
. Foliate papillae are made
up of about 2 to 19 alternating ridges and crevices containing about 120 taste bud pre ridge that
are present on the back edges of the tongue4,19,71
. There are about 200-300 fungiform papillae
containing a total of approximately 1 to 3 taste buds each distributed across the tongue, with the
highest density at the tip of the tongue4,19,71
. Based on the differences in papillae location on the
tongue, a „taste map‟ of the tongue had been proposed for the five taste modalities4,19
. However,
these „taste maps‟ have been dated since research has shown that all five taste modalities can be
perceived in all areas of the tongue19,69,70,72
and variation in threshold sensitivity to tastes do not
vary to a great extent across the tongue4.
2. 5. 1 T2R Bitter taste receptor family
Bitter taste perception is mediated by about 25 functional bitter taste receptor genes
located on chromosomes 5, 7 and 12 that make up the T2R bitter taste receptor family 19
. The
T2R genes encode G protein coupled receptors (GPCR), that consist of about 300 amino acids,
that create a protein with seven transmembrane domains and a short extracellular N-terminus 12
.
The T2R receptor family is expressed on TRCs of the circumvallate and foliate papillae and to a
lesser extent fungiform papillae of the tongue 12
. The T2R receptor family is also found in TRCs
of the epiglottis and palate12
. Non-gustatory cells of the digestive 73
and respiratory tract 74
have
been found to express T2Rs creating the possibility that all T2R genes do not function as taste
receptors. However, a recent study detected the mRNA of all the functioning T2R genes in
human circumvallate papillae using reverse transcriptase-PCR analysis and in situ hybridization
methods, validating a possible gustatory function for all T2R genes 75
.
The pattern of expression of T2R genes within bitter TRCs is unclear. Previous studies
have examined if each bitter TRC is the same, expressing all taste receptor genes, or not. The
14
prevailing model is one in which bitter TRCs are heterogeneous, each expressing a subset of
bitter taste receptors 23
. Evidence for this model was identified by Behrens et al. using in situ
hybridization experiments that found that the level of expression of T2R genes and number of
TRCs expressing T2R genes differed75
.
It is not understood how approximately 25 taste receptor genes identify and transmit
sensory information from thousands of structurally diverse bitter compounds13
. Only a few taste
receptor proteins have had ligands identified (Figure 1), leaving a majority of the taste receptor
proteins orphaned13
. A few bitter taste receptors seem to identify bitter molecules with structural
similarities13
. For example, TAS2R16 and TAS2R38 bitter taste receptors identify bitter
molecules with β-D-glycopyranoside and thiourea moieties, respectively13
. However, other taste
receptors, such as TAS2R7, TAS2R14 and TAS2R46, seem to identify a broad range of
structurally diverse bitter compounds13
. In order to fully understand how sensory information is
coded by bitter taste receptors, further research is needed to deorphanize all remaining taste
receptors13
.
Bitter taste is perceived when bitter agonists in the oral cavity come into contact with
the microvilli of TRCs, which activate bitter taste receptors12,19,66
. An activated bitter taste
receptor activates α-gustducin and other G proteins which release their βγ subunits causing
phospholipase C β2 to increase intracellular levels of inositol 1,2,5, triphosphate13,23
. This leads
to a release of intracellular calcium stores that stimulate the transient receptor potential channel
5 causing a change in membrane potential and release of adenosine triphosphate13,76
. These
changes cause sensory information to be sent to the gustatory cortex of the brain by way of
afferent facial (VII), glossopharyngeal (IX) and vagus (X) cranial nerves 12
.
15
Table 1: Taste receptor genes and their ligands
Taste Receptor
Ligand
TAS2R4 Denatonium, high concentrations of 6-n-propylthiouracil
77
TAS2R7 Strychnine, quinacrine, chloroquine, papaverine78
TAS2R8 Saccharin79
TAS2R10 strychnine80
TAS2R14
Aristolochic acid78
, 1-naphthoic acid, picrotoxinin, (-)-α-thujone,
1,8 – naphthalaldehydic acid, 1-nitronaphthalene, picrotin,
piperonylic acid, sodium benzoate81
TAS2R16
β-D-glucopyranosides moieties (Salicin, phenyl- β-D-
glucopyranosides, helicon, arbutin, 2-ntiro-phenyl- β-D-
glucopyranosides, methyl- β-D-glucopyranosides, amygdalin,
esulin)80
TAS2R38 Phenylthiocarbamide, Propylthiouracil82
TAS2R43, TAS2R44 Saccharin, acesulfame K, aristolochic acid83
TAS2R46
Sesquiterpene lactones (absinthin, arborescin, arglabin,
artemorin, peroxy-artemorin, cnicin, costunolide, crispolide,
cynaropicrin, epizaluzannin C, germacradien-6,11-dihydroxy-
8,12-olide, grosheimin, herbolide D, herbolide D acetate, nobilin,
parthenin, parthenolide, prcrotin, pcirotoxnin, santamarine,
sinternin, speciformin acetate, tatridin A, tatridin A acetate,
tatridin D, taurin, dihydro-taurin, umbellifolide, vulgarolide,
zaluzannin D), Diterpenoids (andrographolide, cascarillin,
marrubiin, teuflavin, teuflavoside, teumarin), brucine,
chloramphenicol, denatonium benzoate, strychnine, strychnine-N-
oxide, sucrose octacetate84
TAS2R47 denatonium, 6-nitro-saccharin79
16
2. 5. 2 Taste genotype-phenotype association
The majority of studies on bitter taste perception have focused on the TAS2R38 gene.
The TAS2R38 gene is a bitter taste receptor gene that belongs to the T2R receptor family and is
located on chromosome 7q3485
. This gene consists of one single 1002-bp long exon that
transcribes a 333-amino acid protein that forms a 7-transmemebrane G-protein coupled receptor
with short N and C terminal domains and is expressed on the surface of taste receptor cells 11,86
.
PTC/PROP sensitivity has been used as a marker for genetic variation, however with
the discovery of the T2R gene family the phenotype of PTC/PROP sensitivity was attributed to
genetic variation in the TAS2R38 gene11
. It has been suggested that three single nucleotide
polymorphisms (SNPs) in the TAS2R38 gene can explain 50 to 85% of the variability in PTC
and to a lesser extent PROP sensitivity3,11,28,87
. The three SNPs result in proline/ alanine,
alanine/valine and valine/ isoleucine amino acid substitutions at position 49, 262 and 296 in the
taste receptor protein, respectively, and result from cytosine to guanine, cytosine to thymine and
guanine to adenine substitutions in the taste receptor gene, respectively 3,11,28
. The most common
haplotypes of the TAS2R38 gene, named in the order of the three amino acid substitutions
(A49P, V262A and I236V), are AVI and PAV, which have a frequency of 47% and 49% in a
European sample 3,11,28
. The remaining 4% are considered rare haplotypes11
. PAV homozygotes
exhibit the greatest sensitivity to PTC and PROP, AVI homozygotes exhibit the least sensitivity,
while PAV/AVI heterozygotes exhibit an intermediate sensitivity, corresponding to the
nontaster, medium taster and supertaster phenotypes3,11,28,82
.
2. 5. 3 TAS2R50
The TAS2R50 gene is a bitter taste receptor gene that belongs to the T2R receptor
family and is located on chromosome 12p13.2 88
. This gene contains one exonic region that
17
transcribes a 299-amino acid protein that forms a 7-transmembrane G-protein coupled receptor
with short N terminal domain expressed on the surface of TRCs 88
.
A polymorphism in the TAS2R50 gene has been associated with risk of myocardial
infarction (MI)14
. A three-stage genome-wide association study was undertaken to determine if
11,053 SNPs in 9,891 genes were associated with MI14
. This three-stage genome wide
association study included two case-control studies to identify possible SNPs associated with
MI and a third study to test the hypothesis that the genes identified in the first two studies are
associated with MI. It was found that a SNP (rs1376251, A/G) in the TAS2R50 gene was
associated with MI 14
. Individuals with the (GG) genotype were 59% more likely to have MI
compared to individuals with the (AA) genotype (p=0.007) 14
. When risk factors were adjusted,
a similar trend was seen (OR 1.40, p=0.06) 14
. The association between MI and the TAS2R50
gene (rs1376251, A/G) was further validated in a subsequent study using a population-based
prospective study of 4522 Caucasian individuals 65years or older, however a weaker association
was seen (HR 1.14, p=0.04) 15
. No association was found between MI and the TAS2R50 gene in
a population with familial hypercholesterolemia 89
.
Variation in the TAS2R50 gene could lead to MI by influencing dietary intake, however
no studies have examined this association. The adenine to guanine polymorphism that is
associated with MI (rs1376251) is located within the exonic region of the TAS2R50 gene and
causes a cysteine to tyrosine amino acid change at position 203 in the taste receptor protein85
.
This SNP may affect the structure of the taste receptor protein causing a change in ligand
binding potential and as a result taste perception. The frequency of the ancestral allele (G) has
been found to be 65%, 29% and 96% in a European, Han Chinese and sub-Saharan African
population, respectively85
. Shiffman et al.14
explained that the association between MI and the
TAS2R50 gene may not be due to the A/G SNP (rs1376251) because TAS2R50 is inherited in a
18
~500 kb haplotype block that contains 13 taste-receptor homologs and three genes that encode
proline-rich proteins, warranting further investigation into the TAS2R50 gene14
.
Other SNPs may have a potential to influence the TAS2R50 taste receptor and in turn
bitter taste perception. The (rs10772397) SNP is a A/G substitution in the coding region of the
TAS2R50 gene and produces a silent mutation at position 259 in the taste receptor protein 85
.
This can affect mRNA stability and TAS2R50 taste receptor production. The frequency of the
ancestral A allele in a European, Han Chinese and sub-Saharan African population is 61%, 73%
and 20%85
. An A/C SNP (rs2900554) located 3859 base pairs downstream of the TAS2R50 gene
can also affect bitter taste perception through post translational modification of the T2R50
protein. The frequency of the ancestral A allele in a European, Han Chinese and West African
population is 49%, 78% and 64%85
.
2. 6 GRAPEFRUIT AND NARINGIN
Naringin (4‟,5,7-trihydroxy-flavanone-7-rhamnoglucoside) is a bitter flavonoid found
in grapefruit that is primarily responsible for its characteristic bitter taste90
. Naringin is a
glycoside that is made up of the flavanone moiety naringenin and disaccharide β-
neohesperidose90
. There is about 400 to 700 mg/l of naringin in grapefruit juice and this amount
varies depending on the maturity of the fruit, fraction of fruit and cultivar9,90,91
.
Naringin has been shown to have several cardio-protective properties such as anti-
inflammatory, cholesterol lowering92
, antiatherogenic93
and antioxidant94
as well as anticancer
properties95,96
, and hepatoprotective effects97,98
. Furthermore, naringenin the metabolic
breakdown product of naringin, has been shown to improve insulin sensitivity and glucose
tolerance by increasing hepatic fatty acid oxidation, decreasing hepatic cholesterol synthesis and
preventing lipogenesis in LDL null mice fed a high fat diet99
. Grapefruit juice has been shown to
19
interact with over 50 drugs leading to typically an increase in circulating drug levels, by
inhibiting metabolism and/or affecting intestinal absorption of certain drugs100,101
. It is thought
that naringin plays a role in this interaction by inhibiting drug transport by P-glycoprotein102,103
and metabolism by CYP3A4104
. Bitter taste perception of naringin may affect an individual‟s
grapefruit and grapefruit juice consumption and influence their health.
The bitter taste perception of naringin varies between individuals. Threshold detection
levels of naringin in water ranges from 1.5 mg/ l to 50 mg/l105
. Significant variation is also seen
with naringin suprathreshold intensity ratings9. Drewnowski et. al. examined if naringin taste
intensity and grapefruit preference varied between PROP taster status9. Naringin taste intensity
was not found to vary significantly with PROP taster status9. PROP taster status was only
weakly negatively correlated with naringin preference and grapefruit juice preference9. These
results allude to the possibility that naringin and PROP are detected by different taste receptors
and warrant further investigation.
2. 7 SUMMARY
Bitter taste is a variable trait that influences food preferences and dietary intake3. The
majority of the research that examines the affect of variation in bitter taste, on food preferences
and dietary habits, has used PTC or PROP sensitivity as a marker of genetic variation in bitter
taste. However, a considerable amount of genetic variation exists within the T2R bitter taste
receptor gene family88
and little is known about how this variation affects the expression and
function of the T2R bitter taste receptors, as well as bitter taste perception and behavioral
responses to foods. It is possible that certain variants of the T2R gene family increase bitter
sensitivity causing individuals to dislike and avoid the consumption of bitter foods. The
avoidance of bitter foods over time can be detrimental to health since many nutritious foods,
20
such as certain fruits and vegetables, contain bitter tastants, and many of these bitter tastants are
phytonutrients5. Research on how variation in the T2R gene family affects bitter taste perception
and dietary habits are important since diets high in fruits and vegetables, as well as
phytonutrients, have been thought to be protective against certain chronic diseases, such as
cancers6 and cardiovascular disease
7.
Recently, a genome-wide association study found that an adenine to guanine SNP
(rs1376251) in the TAS2R50 gene was associated with MI risk in three separate populations14
. It
is unlikely that the rs1376251 SNP directly affect MI risk. Rather, it is possible that the
rs1376251 SNP alters bitter taste perception causing certain individuals to undertake dietary
habits that may modify their risk of cardiovascular disease and MI14
. Since, cardiovascular
disease (CVD) is the leading cause of hospitalization and mortality in Canada17,18
with MI
accounting for the majority of CVD related deaths in 200217
, it is important to identify
modifiable risk factors associated with both CVD and MI. Understanding how taste may be a
barrier to healthy eating can help improve health promotion programs and create programs
targeted to bitter sensitive individuals.
To our knowledge, the T2R50 taste receptor has not been deorphaned. In order to
understand how the rs1376251 SNP may affect MI risk, research is needed to elucidate the
function of the T2R50 bitter taste receptor, as well as identify the functional and behavioral role
of SNPs in and around the TAS2R50 bitter taste receptor gene. The aim of this thesis will be to
examine how genetic variation in the TAS2R50 gene region affects bitter taste perception, food
preference and dietary intake.
21
Chapter Three
Rationale, Hypothesis and Objectives
22
Rationale: The majority of studies examining the association between genetic variation in bitter
taste and food selection have used PROP or PTC sensitivity as a marker for genetic variation.
Studies that have examined the genetic variation of bitter taste using SNPs in taste receptor
genes have predominantly focused on the TAS2R38 gene, which has been shown to be
responsible for detecting PTC/PROP31
. A study examining genetic variation in the TAS2R50
bitter taste receptor gene will help advance knowledge and understanding of bitter taste
perception. A polymorphism (rs1376251 A/G) in the TAS2R50 gene has been associated with
MI risk14,15
, and it has been hypothesized that this association may be due to a difference in
dietary intake caused by variations in taste perception. However, this association has not yet
been examined. Naringin, the primary bitter compound in grapefruit90
, has been shown to have
potential cardioprotective properties92-94
and affect drug metabolism102-104
. Genetic variation in
the TAS2R50 gene may affect the bitter taste perception of naringin and other bitter tastants,
which can affect food selection and health. Research is needed to examine the association
between polymorphisms in the TAS2R50 gene region and bitter taste intensity, food preferences
and dietary intake.
Hypothesis: Genetic polymorphisms of the TAS2R50 gene region are associated with
differences in bitter taste perception, food preferences and dietary intake.
Objectives: To determine if polymorphisms in the TAS2R50 gene region (rs2900554 A/C ,
rs10772397 A/G and rs1376251 A/G) are associated with differences in naringin and PTC taste
intensity perception, food preferences, dietary intake.
23
Chapter Four
Materials and Methods
24
4.1 Participants
Participants are young men (n=358) and women (n=825) aged 20-29 years from the
ongoing Toronto Nutrigenomics and Health (TNH) study which is a cross sectional study with
an overall goal of investigating gene-diet interactions on biomarkers of chronic disease and
genetic determinants of food intake behaviors. A younger population is ideal for examining taste
and food preferences because taste diminishes with age106
. Participants were recruited from the
University of Toronto through campus newspaper, internet postings, and class room
announcements. Individuals who were unable to communicate in English were excluded from
this study since questionnaires were available only in English. Additionally, women who were
pregnant or breastfeeding were excluded because changes in taste, metabolism and dietary
habits occur with these life stages24
.
For the purpose of this study subjects with type 2 diabetes (n=1), inflammatory bowel
disease (n=2) and cancer (n=2) during study completion were excluded, since presence or
treatment of these diseases my affect taste perception and/or dietary habits. Subjects with
missing data for any of the variables of interest were excluded (n=6). One subject with
unreliable food preference checklist and food frequency questionnaire results was excluded.
Leaving a population of 1171 individuals (men=352, women=819) free of chronic disease.
Subjects were placed into ethnocultural groups based on self-reported ethnicities. There were
560 Caucasian (European, Hispanic or Middle-Eastern), 404 East Asian (Chinese, Japanese,
Korean, Vietnamese or Filipino), 120 South Asian (Indian, Pakistani and Sri Lankan) and 87
individuals grouped as “other” (African descent, First Nations or individuals of two or more
ethnocultures). Participants allergic or intolerant to alcohol (n=1), fruits (n=15) or vegetables
(n=10) were excluded when assessing food preferences and dietary intake since
allergies/intolerances to these food groups may affect preference or intake responses of many
25
bitter foods. Individuals (n=87) who reported consuming less than 800 kcal, women who
reported greater than 3500 kcal and men who reported greater than 4000 kcal on the food
frequency questionnaire (FFQ) were excluded when assessing dietary intake. These cut-points
were based on cut-points established by Walter Willet in order to avoid calorie over or under
reporters and individuals with disordered eating habits or unusual dietary needs64
.
4.2 Study protocol
Potential subjects were screened for age by phone or email and individuals who met the
study requirements were recruited. Subjects were required to visit the study office on two
separate occasions. On the first visit written consent was obtained, anthropometric
measurements were taken (height, weight, waist circumference and blood pressure) and naringin
taste intensity was assessed. Subjects were instructed on how to complete four questionnaires: a
general health and lifestyle questionnaire (GHLQ), a physical activity questionnaire (PAQ), a
food frequency questionnaire (FFQ) and a food preference checklist (FPC). The GHLQ and
PAQ were completed in the study office while the FFQ and the FPC were completed away from
the office. Subjects were given a requisition for a 12 hour fasting venous blood sample to be
drawn at a MDS laboratory (LifeLabs, Toronto) at 180 Bloor Street West between 8:30 and
10:30am, within 10 days of their first visit to the study office. Subjects were scheduled for a
second visit to the study office after blood samples were received from MDS laboratory. During
the second visit subjects returned completed FFQs and FPCs and they were reviewed for
completeness. Additionally, PTC taste intensity was assessed. Subjects received a $20
honorarium upon study completion.
26
4.3 Bitter taste assessment
Naringin and PTC infused taste strips were used to rank subjects‟ perceived bitter taste
intensity on a 9-level scale. Taste strips were infused with a solution containing 3 µg of naringin
or PTC. The intensity of the bitter taste perception of naringin and PTC were determined using a
standardized control/filter paper protocol (Precision Labs Inc, Cottenwood, AZ). Subjects were
asked to rinse their mouth with bottled spring water before the filter papers were administered.
Subjects placed a control strip with no bitter substance on the anterior medial surface of their
tongue until completely moistened (5-10 seconds). Next, subjects placed the bitter taste strip on
the anterior medial surface of their tongue until completely moistened (5-10 seconds). Subjects
were then asked to circle the number corresponding to their perceived bitterness of the taste strip
on a 9-point numbered scale ranging from “1=not at all bitter” to “9=extremely bitter” with a
central point labeled “5=moderately bitter”. This scale is a variant of the Natick 9-point
scale50,107
. Naringin and PTC taste intensity testing were done on two separate visits.
4.4 Food preference assessment
Subjects completed a 63-item food preference checklist to assess their preference of 25
potentially bitter foods and beverages. This food preference checklist was modeled after a 171-
item food preference checklist that is widely used to assess food preference in North
America35,41,108
. The number of food items was reduced to 63 commonly consumed fruits (12),
vegetables (21), beverages (11), breads (4), and other miscellaneous food items (9) to minimize
participant burden. The food preference checklist asked subjects to rank their liking/disliking of
common foods on an anchored 9-point hedonic scale ranging from dislike extremely to like
27
extremely. For each food, subjects were able to indicate if they never tried a food or would not
try the food.
4.5 Dietary intake assessment
A comprehensive, self-administered, semi-quantitative FFQ was used to assess habitual
diet during the month prior to study participation. This FFQ has been modified from the Willett
FFQ109,110
to improve dietary assessment of whole grains, fruits and vegetables, glycemic index,
and caffeine by the addition of questions on the consumption of 6 fruits, 7 vegetables, 6
cereals/breads, 4 beverages and 3 miscellaneous food items. The Willett FFQ has been
extensively used and validated for use in North America65
. The FFQ has 24 pages and contains
questions on the consumption of 189 food items and 12 vitamin/mineral supplements. For most
foods, 9 possible responses are given from never to 4 or more servings per day. Subjects were
given verbal instructions on how to complete the FFQ and examples of standard portion sizes
were shown to participants to help improve the accuracy of their food intake responses.
Completed FFQs were sent to Harvard University for dietary intake analysis. The nutrient
database that is used is based on the United States Department of Agriculture‟s Nutrient
Database for Standard Reference111
which is the source of 86% of non-zero nutrient data in
Health Canada‟s Canadian Nutrient Files112
. Average daily energy intake was calculated by
dividing monthly energy intake by 30 days.
4.6 Anthropometrics
Height (to the nearest 0.1cm) was measured using a wall-mounted stadiometer (model
Seca 206, Seca Corporation, Hanover, MD, USA) and body weight (to the nearest 0.1 kg) was
28
measured using a digital scale (model Bellissima 841, Seca Corporation, Hanover, MD, USA).
Body mass index (BMI) was calculated (kg/m2). Waist circumference was measured twice to the
nearest 0.1cm and averaged. Resting blood pressure was measured twice, one minute apart,
using the OMRON IntelliSense Blood Pressure Monitor (Model HEM-907XL, OMRON
Healthcare, Vernon Hills, Illinois, USA) and measurements were averaged.
4.7 Physical activity questionnaire
Metabolic equivalent of task (MET) can be used to express the estimated energy costs
of a specific task113
. One MET is approximately equal to 1 kcal/ kg/ hour sitting at rest113
. A
physical activity questionnaire was used to measure the number of hours spent sleeping (0.9
METs) or involved in sitting or reclining activities (1.0 METs), light activities (2.3 MET),
moderate activities (3.6 MET), and intense activities (7.5 MET) during a usual weekday and a
weekend day in the month prior to study participation. This questionnaire was taken from a
portion of the questionnaire created by Paffenbarger et. al., which is used to measure physical
activity to assess health effects in a healthy free-living population114
. Examples of physical
activities were provided and were grouped according to their intensities in metabolic equivalents
(MET). MET-hours per week was calculated by multiplying the time spent on each activity by
the intensity of the activity in METs and then multiplying the product by the frequency the
activity is preformed per week (5 for weekdays and 2 for weekends).
4.8 Genotyping
Approximately 44 ml of venous blood was collected from each participant at MDS
Laboratories (Toronto, Canada) in yellow citric acid-trisodium citrate-dextrose (ACD) treated
tubes (Becton Dickinson Vacutainer Tubes, Franklin Lakes, NJ). The blood samples were kept
29
at room temperature and delivered to the University of Toronto on the day of blood collection.
DNA was isolated from leukocytes in whole blood using the GenomicPrep™ Blood DNA
Isolation Kit (Amersham Pharmacia Biotech Inc, Piscataway, NJ).
Real time PCR was used to genotype the rs2900554, rs10772397 and rs1376251 SNPs
in the TAS2R50 gene region using TaqMan allelic discrimination assays, C___1326589,
C___1326594, C___12107274, respectively, from Applied Biosystems (Foster City, CA). A 10
µl reaction containing 0.25µl of TaqMan assay, 5.0 µl of TaqMan Mastermix, 2.75 µl of
distilled water and 2.0 µl DNA was used to genotype a SNP for each sample. Allelic
discrimination was performed using an ABI 7000 Sequence Detection System and PCR
conditions were set at 95οC for 15 minutes, followed by 40 cycles of 95
οC for 15 seconds and
60οC for 1 minute. 10% of genotyped results were replicated randomly for each SNP.
4.9 Statistical analysis
Statistical analyses were conducted using SAS version 9.1 (SAS Institute, Cary, NC,
USA) and a double sided p<0.05 was considered significant. Statistical analyses were performed
in Caucasians and Asians separately due to differences in allele frequencies and linkage
disequilibrium between ethnocultural groups. A gene-sex interaction was examined for bitter
taste, food preference and dietary intake analyses since women may be more sensitive to bitter
tastes3. Definitions of all variables used in the statistical analysis are provided in table 2.
Differences in population characteristics between TAS2R50 genotypes were assessed
using Pearson‟s test for categorical variables and analysis of variance (ANOVA) with
Tukey‟s post hoc test for continuous variables. Since bitter taste is generally disliked, food
preference responses (9 categories) were grouped into binary variables, disliking (category 1-3)
and neutral/liking (categories 4-9), to allow for the assessment of differences in the odds of
30
strongly disliking foods between TAS2R50 genotypes. Individuals who reported that they “never
tried” or “would not try” a specific food in the FPC were excluded from the analysis of food
preference by TAS2R50 genotype for the specific food in which they reported “never tried” or
“would not try”. Pearson‟s and binary logistic regression was used to assess if the odds of
strongly disliking 25 foods differed between TAS2R50 genotypes. Binary logistic regression was
adjusted for age, sex, BMI, physical activity and smoking status. BMI was log transformed
because it was not normally distributed. A variable was considered a confounding variable for
all food preference models (TAS2R50 genotype → disliking foods) if it changed the beta
estimate of the unadjusted logistic regression model by 10%, for any of the 25 food preference
variables, in order to standardize food preference models.
Dietary intake was grouped into a binary variable, did not consume in the past month
(category 1) and consumed in the past month (categories 2-9 or 2-10). Only foods that had a
significant difference in food preference between TAS2R50 genotypes were used to assess if an
association exists between dietary intake and TAS2R50 genotype using Pearson‟s 2 and binary
logistic regression. Age, sex, BMI, physical activity, smoking status, calorie intake and season
were considered confounding variables because they changed the beta estimate of the
unadjusted logistic regression model by10%, for any of the dietary intake variables, in order to
standardize logistic regression models. Since the objective of this study is to determine if
differences in taste affect food consumption, individuals who reported that they never tried a
food on the FPC were excluded from the dietary intake analysis of the specific food.
Naringin and PTC taste intensity ratings were grouped into a binary variable, high
intensity (7-9) and medium/low intensity (1-6). Pearson‟s and binary logistic regression was
used to assess if the odds of reporting a high taste intensity rating differed between TAS2R50
genotypes. Age, sex and smoking status were thought to confound the relationship between
31
TAS2R50 genotype and taste intensity if they changed the beta estimate of the unadjusted
regression model by 10%. Age and sex were found to confound the relationship between
TAS2R50 genotype and taste intensity of naringin and PTC.
Haploview Software115
was used to assess hardy Weinberg Equilibrium and linkage
disequilibrium between the three SNPs.
32
Table 2: Variable type and definition
Variable
Category Variable
Variable
Type
Variable Definition
Subject
Characteristics/
Potential
Confounding
Variables
Age
Continuous
Years
BMI
Continuous
Kg/m2
Physical activity
Continuous
MET×hours/week
Calorie intake
Continuous
Kcal/day
Sex
Categorical
Male and Female
Ethnocultural group
Categorical
Caucasian (European, Middle Eastern, Hispanic), East
Asian (Chinese, Japanese, Korean), South Asian
(Indian, Pakistani, Sri Lankan) and Other (African
descent, Aboriginal and mixed ethnocultural group)
Smoking status
Categorical
Smoker (at least 1 cigarette per day for 1 month or
longer) and Non-smoker
Season
Categorical
Spring (March, April, May), Summer (June, July,
August), Fall (September, October, November),
Winter (December, January, February)
Food Preference
Assessment
Grapefruit, Grapefruit Juice,
Asparagus, Broccoli, Brussels
sprouts, Cabbage, Cauliflower,
Endives, kale, Onion raw,
Parsley, Radicchio, Radish,
Rapini, Spinach, Soymilk, Tofu,
Dark Chocolate, Bitter-
Sweet/Semi-Sweet Chocolate,
Red Wine, White Wine, Beer,
Coffee, Green tea, Black Tea
Categorical
Dislike (1=dislike extremely, 2=dislike very much,
3=dislike moderately) and Neutral/Like (4=dislike
slightly, 5= neither like nor dislike, 6= like slightly, 7=
like moderately, 8= like very much, 9= like extremely)
Dietary Intake
Assessment
Foods that had a significant
association between TAS2R50
genotype and food preference
Categorical
Did not consume (Never) and Consumed (less than
once per month, 1-3 times per month, once per week,
2-4 times per week, 5-6 times per week, once per day,
2-3 servings per day, 4 or more servings per day) in
the past month
Bitter Taste
Intensity
Assessment
Naringin or PTC taste intensity
Categorical
High intensity (7-9)
Medium/Low intensity (1-6)
TAS2R50
Genotypes
rs2900554 A/C Categorical CC, AC and AA
rs10772397 A/G
Categorical GG, AG and AA
rs1376251 A/G Categorical GG, AG and AA
33
Chapter 5
Results
34
5.1 Genotype frequency, allele frequency and Hardy Weinberg Equilibrium
Genotype frequencies, allele frequencies and HWE for the three TAS2R50 SNPs are
presented in table 3 for the total population and stratified by Caucasian, East Asian and other
ethnocultural groups. The genotype frequencies and allele frequencies differed between
ethnocultural groups, particularly for the rs2900554 SNP and the rs1376251 SNP. In the East
Asian population allele frequencies for the rs10772397 SNP and the rs1376251 SNP were not in
Hardy-Weinberg equilibrium. However, the potential for genotyping error is unlikely since the
genotype and allele frequencies of the three SNPs are similar to the genotype and allele
frequencies reported in other Caucasian and Asian populations85
. Furthermore, real-time PCR
results produced clear genotype calls for all three SNPs (Figure 1) and 10% of samples were
randomly replicated producing identical genotype results. All subjects were genotyped
successfully for the three SNPs.
5.2 Linkage disequilibrium
Figure 2 shows linkage disequilibrium (D‟ and r2) between the three SNPs in the total
population and stratified by Caucasian, East Asians and other ethnocultural groups. When
linkage disequilibrium was measured using the D‟ statistic, SNPs rs2900554 and rs1376251, as
well as SNPs rs10772397 and rs1376251 are in high linkage disequilibrium in all samples.
However, the linkage disequilibrium between the rs2900554 SNP and the rs10772397 SNP
differs between ethnocultural groups with the SNPs being 98% in linkage disequilibrium in East
Asians, 23% in linkage disequilibrium in Caucasians and 34% in linkage disequilibrium in the
other ethnocultural group.
The correlations (r2) between the three SNPs are also different between ethnocultural
groups. A moderate correlation exists between the rs2900554 and rs1376251 SNPs in
35
Caucasians while the rs2900554 and rs1376251 SNPs, and the rs10772397 and the rs1376251
SNPs were not highly correlated. In contrast, the three TAS2R50 SNPs were highly correlated in
East Asians. Since the allele frequency and linkage disequilibrium (D‟ and r2) between the three
TAS2R50 SNPs varied highly between ethnocultural groups all subsequent analyses were
stratified by Caucasians and East Asians.
5.3 Subject Characteristics
Subject characteristics are shown in table 4. In Caucasians, a significant difference in
age was seen between genotypes of the rs2900554 SNP and was controlled for in all subsequent
multivariate analysis.
5.4 TAS2R50 genotype and food preference
The frequency and odds of disliking foods are presented in tables 5 to 10. Multivariate
analyses were adjusted for sex, BMI (kg/m2), physical activity (MET.hrs/wk), and smoking
status. The modifying effect of sex on the association between TAS2R50 genotypes and food
preference could not be examined for most food preference variables due to the limited number
of males who reported disliking foods.
The frequency and odds of disliking grapefruit and grapefruit juice is shown in table 5.
In Caucasians, genetic variation in the TAS2R50 gene region was significantly associated with
grapefruit disliking in the rs2900554 SNP and the rs1376251 SNP for individuals homozygous
for the C and G alleles, respectively. Heterozygous individuals had an intermediate odds of
grapefruit disliking, compared to individuals homozygous for the A allele, however only
individuals heterozygous for the rs2900554 SNP had an odds ratio that reached significance.
The rs10772397 SNP was only weakly associated with grapefruit disliking in Caucasians. In
36
contrast, all three TAS2R50 SNPs were associated with grapefruit disliking in East Asians. This
is because the rs10772397 SNP is almost in 100% linkage disequilibrium (r2) with the
rs1376251 SNP and is therefore a marker of this SNP. East Asians homozygous for the minor
alleles in all three SNPs disliked grapefruit significantly more than East Asians homozygous for
the A allele in all three SNPs. Heterozygous East Asians showed intermediate odds of disliking
grapefruit compared to East Asians homozygous for the A allele, however the odds of disliking
grapefruit did not reach significance. The rs2900554 SNP had the greatest influence on
grapefruit disliking in both Caucasians and Asians. After adjusting for sex, BMI, physical
activity and smoking status the odds ratio (95% confidence interval) of grapefruit disliking in
Caucasians and East Asians homozygous for the C allele was 6.6 (3.0, 14.3) and 5.9 (2.2, 15.6),
respectively, compared to individuals homozygous for the A allele. This SNP was also
significantly associated with grapefruit juice disliking in both Caucasians and East Asians with
individuals homozygous for the C allele having a two and three fold higher adjusted odd of
disliking grapefruit juice respectively, compared to individuals homozygous for the A allele.
The frequency and odds of disliking vegetables is shown in table 6 for Caucasians and
East Asians. East Asians homozygous for the C allele in the rs2900554 SNP had significantly
higher odds of radish disliking compared to individuals homozygous for the A allele, however
this association was not seen in Caucasians. A greater proportion of Caucasians with the GG
genotype for the rs10772397 SNP disliked kale compared to individuals homozygous for the A
allele. In the East Asian population individuals heterozygous for the rs2900554 SNP and G
allele carriers for the rs10772397 SNP and the rs1376251 SNP significantly disliked kale greater
than individuals homozygous for the A alleles in all three SNPs. A greater proportion of East
Asians homozygous for the C allele in the rs2900554 SNP and G alleles in the rs10772397 SNP
and the rs1376251 SNP disliked spinach significantly more than individuals homozygous for the
37
A allele for all three SNPs, however this trend was not seen in Caucasians. Interpretation of kale
and spinach preference in the East Asian population must be cautioned due to the limited
number of individuals who reported disliking these foods. No other significant association was
seen between the TAS2R50 genotype and disliking of vegetables.
Table 7 shows the frequency and odds of disliking soy products by TAS2R50 genotype.
A borderline significant association was seen between the rs1376251 SNP and soymilk
preference in Caucasians, with heterozygotes for the rs1376251 SNP disliking soymilk less than
individuals homozygous for the A allele. A greater proportion of Caucasians with a C allele for
the rs2900554 SNP disliked bitter-sweet/semi-sweet chocolate more than individuals
homozygous for the A allele (Table 8). Too few East Asians reported disliking soymilk and
semi-sweet/bitter-sweet chocolate to examine this association in the East Asian population.
There were no significant associations between TAS2R50 genotype and preference for alcoholic
(Table 9) or caffeinated beverages (Table 10).
Cross tabular analyses of the three TAS2R50 SNPs by food preference was Bonferonni
corrected for 25 potentially bitter foods and beverages. Preference for grapefruit was the only
food that remained significantly associated with the genetic variation in the TAS2R50 gene
region, with the rs2900554 SNP and rs1376251 SNP in Caucasians and the rs2900554 SNP in
East Asians having p-values below 0.002.
The combined effect of the rs2900554 SNP and the rs1376251 SNP on the frequency of
grapefruit disliking was analyzed since both SNPs were strongly associated with grapefruit
disliking in Caucasians and East Asians. Table 11 shows all possible genotype combinations for
the rs2900554 SNP and the rs1376251 SNP and the frequency of individuals in the Caucasian
and East Asian population who dislike grapefruit by the rs2900554 SNP and the rs1376251 SNP
genotype combinations. Individuals with the CC/GG genotype were more likely to report
38
disliking grapefruit than all other genotype combinations, followed by genotypes GT/CC and
GT/CT. The frequency of individuals who reported disliking grapefruit reduced substantially in
the absence of a C allele at the rs2900554 SNP, suggesting the rs2900554 SNP may be driving
the association between genetic variation in the rs1376251 SNP and grapefruit disliking through
linkage disequilibrium.
5.5 TAS2R50 genotype and dietary intake
Since grapefruit preference showed a strong association with TAS2R50 genotype, the
association between TAS2R50 genotype and both grapefruit (1/2 fruit serving) (Table 12) and
grapefruit juice (100% juice, 1/2 cup serving) (Table 13) consumption was assessed in
Caucasians and East Asians. Sex was found to modify the association between the rs2900554
SNP and grapefruit intake (p=0.05) and the rs10772397 SNP and grapefruit juice intake
(p=0.04) in Caucasians, so all analyses for grapefruit and grapefruit juice intake were stratified
by sex. No significant association was seen between genetic variation in the rs2900554 SNP and
grapefruit consumption in Caucasians and East Asians. However, when the analysis was
stratified by sex a significant association was seen between the rs2900554 SNP and grapefruit
consumption in Caucasian females. Compared to individuals homozygous for the A allele, the
odds ratio (95% confidence interval) for individuals who did not consume grapefruit in the past
month was 2.1 (1.2, 4.0) for females homozygous for the C allele. East Asian females had a
similar trend in grapefruit consumption for the rs2900554 SNP, with individuals homozygous
for the C allele having the highest odds of never consuming grapefruit, however this result did
not reach significance. Genetic variation in the rs2900554 SNP was not associated with
grapefruit consumption in Caucasian or East Asian males.
39
A borderline significant association was seen with the rs1376251 SNP and grapefruit
consumption, with Caucasian heterozygote individuals having a lower odds of not consuming
grapefruit in the past month than individuals homozygous for the A allele. When the Caucasian
population was stratified by sex this association was pronounced in the male population. A
similar trend in grapefruit consumption for the rs1376251 SNP was seen in East Asian males,
however this trend did not reach significance. There was no association between the rs1376251
SNP and grapefruit consumption in Caucasian and East Asian females. Genetic variation in the
rs10772397 SNP was not associated with grapefruit consumption in any of the populations.
Furthermore, grapefruit juice consumption was not significantly associated with TAS2R50
genotype in any of the three SNPs in Caucasians and East Asians and this association remained
non-significant when the populations were stratified by sex. However, the trend in grapefruit
juice consumption for the rs2900554 SNP in Caucasian females was similar to that of grapefruit
consumption in the same population.
The association between genetic variation in the TAS2R50 gene region and the
consumption of spinach (raw in salad, 1 cup serving or cooked with Swiss chard, ½ cup
serving), kale (or mustard, collard or turnip greens, ½ cup serving) (table 14), soymilk (8 oz.
glass) and chocolate (candy bar or packet) (table 15) were assessed because preference for these
foods were found to be associated with genetic variation in the TAS2R50 gene. Sex did not
modify the association between genetic variation in the TAS2R50 gene region and consumption
of spinach, kale, soymilk or chocolate. Genetic variation in the rs2900554 SNP was associated
with cooked spinach and Swiss chard consumption, with 70% more East Asian heterozygotes
reporting to have not consumed cook spinach and Swiss chard in the past month compared to
individuals homozygous for the A allele. This association was not seen in Caucasians, or in the
40
rs10772397 and rs1376251 SNPs for any populations. Genetic variation in the TAS2R50 gene
region was not associated with raw spinach, kale, soymilk or chocolate consumption.
5.6 TAS2R50 genotype and taste intensity
The difference in naringin taste intensity was assessed in a subpopulation of 211
individuals because an association was found between genetic variation in the TAS2R50 gene
region and grapefruit preference and intake. The frequency and odds of experiencing a high
naringin and PTC taste intensity between TAS2R50 genotypes is shown in table 16. Sex did not
modify the association between TAS2R50 genotype and naringin or PTC taste intensity for any
of the TAS2R50 SNPs assessed.
Genetic variation in the rs2900554 SNP was found to be significantly associated with
naringin taste intensity in Caucasians. Caucasians homozygous for the C allele had a 5.4 (1.5,
9.3) fold higher odds (95% confidence interval) of experiencing a high naringin taste intensity
compared to individuals homozygous for the A allele. A similar trend was seen in East Asians
with a higher proportion of C allele carriers reporting a high naringin taste intensity, however
this did not reach significance due most likely to the low number of East Asians with the CC
genotype. Naringin taste intensity was not associated with genetic variation in the rs10772397
SNP or the rs1376251 SNP. In Caucasians, a significant association was seen between genetic
variation in the rs10772397 and rs1376251 SNPs, and PTC taste intensity with heterozygous
individuals experiencing a high PTC taste intensity 60% less than individuals homozygous for
the A allele. This trend was not seen in East Asians. No significant association was seen
between genetic variation in the rs2900554 SNP and PTC taste intensity.
41
5.7 Difference in food preferences between Caucasians and Asians
Tables 17 to 21 present the frequency and odds of disliking foods by ethnocultural
groups. The reason for examining if food preference differs between ethnocultural groups was to
determine if differences in allele frequencies for the three TAS2R50 SNPs between ethnocultural
groups can help predict food preferences between groups. The allele frequencies for the
rs2900554 SNP and the rs1376251 SNP differ greatly between Caucasians and East Asians,
with Caucasians having a higher C and G allele frequency, respectively. Since the C allele for
the rs2900554 SNP and the G allele for the rs1376251 SNP have been shown to be associated
with a higher disliking of grapefruit and other bitter foods, Caucasians may be more likely to
dislike these foods than East Asians. As expected, East Asians were found to be significantly
less likely to dislike grapefruit and grapefruit juice compared to Caucasians (table 17). East
Asians were also found to dislike cauliflower, turnip, radish (table 17), coffee, black tea (table
21) and soymilk (table 18) more than Caucasians. Broccoli, rapini (table 17), green tea (table
21) and tofu (table 18) were disliked less by East Asians than Caucasians, however these results
need to be interpreted with caution since fewer than 5 Asians disliked these foods. Parsley (table
17), beer and white wine (table 20) were disliked more by East Asians than Caucasians.
42
Table 3: Genotype frequency, allele frequency and Hardy Weinberg Equilibrium by TAS2R50
genotype in the total population and in Caucasians and East Asians and other ethnocultural
groups
Genotype Frequency
n (%)
Allele
Frequency (%)
HWE
rs2900554 CC AC AA Total C A p-value
Total Population 180 (15) 492 (42) 499 (43) 1171 (100) 0.36 0.64 0.002
Caucasian 131 (23) 268 (48) 161 (29) 560 (100) 0.47 0.53 0.4
East Asian 24 (6) 136 (34) 244 (60) 404 (100) 0.23 0.77 0.5
Others 25 (12) 88 (43) 94 (45) 207 (100) 0.33 0.67 0.6
rs10772397 GG AG AA Total G A p-value
Total Population 147 (12) 510 (44) 514 (44) 1171 (100) 0.34 0.66 0.3
Caucasian 84 (15) 264 (47) 212 (38) 560 (100) 0.39 0.61 1.0
East Asian 38 (9) 142 (35) 224 (56) 404 (100) 0.27 0.73 0.04
Others 25 (12) 104 (50) 78 (38) 207 (100) 0.37 0.63 0.4
rs1376251 GG AG AA Total G A p-value
Total Population 320 (27) 499 (42) 352 (30) 1171 (100) 0.49 0.51 5.6×10-7
Caucasian 227 (41) 253 (45) 80 (14) 560 (100) 0.63 0.37 0.5
East Asian 38 (10) 143 (35) 223 (56) 404 (100) 0.27 0.73 0.05
Others 55 (26) 103 (50) 49 (24) 207 (100) 0.51 0.49 1.0
Subjects were classified based on self-identified ethnocultural group. HWE was assessed using
Haploview software.
43
Figure 1: Scatter plot of TAS2R50 genotype for the rs2900554 SNP. Ninety-two individuals are represented in this scatter plot
which shows real-time PCR results of relative fluorescence given off by TaqMan allelic discrimination assay C_1326589. Grey
squares represent negative controls. Blue diamonds, green triangles and red diamonds represent individuals with AA, AC and CC
genotypes for the rs2900554 SNP.
C allele fluorescence signal
A a
llel
e fl
uore
scen
ce s
ign
al
44
TOTAL POPULATION CAUCASIANS
EAST ASIANS OTHERS
Linkage
Disequilibrium
D‟
Linkage
Disequilibrium
D‟
Linkage
Disequilibrium
D‟
Linkage
Disequilibrium
D‟
Correlation
Coefficient
r2
Correlation
Coefficient
r2
Correlation
Coefficient
r2
Correlation
Coefficient
r2
Figure 2: Linkage disequilibrium between TAS2R50 SNPs in the total population and
in Caucasians, East Asians and other ethnocultural groups. Squares indicate linkage
disequilibrium (D‟ or r2 values x 100%) between SNPs with a darker shade indicating
stronger linkage disequilibrium.
45
Table 4: Subject Characteristics for East Asians and Caucasians
rs2900554 rs10772397 rs1376251
CC AC AA p GG AG AA p GG AG AA p
Caucasians
n (%) 131 (23) 268 (48) 161 (29) 84 (15) 264 (47) 212 (38) 227 (41) 253 (45) 80 (14)
Age (y) 23.6±0.2a
23.1±0.2 22.7±0.2b
0.006 23.0±0.3 22.9±0.2 23.4±0.2 0.1 23.2±0.2 23.1±0.2 22.7±0.3 0.4
Female (%) 94 (72) 183 (68) 115 (71) 0.7 58 (69) 181 (69) 153 (72) 0.7 161 (71) 177 (70) 54 (68) 0.9
BMI ( kg/m2 )* 22.9±4.7 22.6±3.8 22.3±4.0 0.7 22.8±4.8 22.6±4.0 22.4±4.0 0.5 22.8±4.3 22.5 ±3.9 22.5±3.8 1.0
Non-Smoker (%) 120 (92) 245 (91) 143 (89) 0.6 74 (88) 244 (92) 190 (90) 0.4 209 (92) 229 (91) 70 (88) 0.5
Physical Activity
(MET.hrs/week)
12.4±0.2 12.7±0.1 12.5±0.2 0.5 12.5±0.3 12.4±0.1 12.7±0.2 0.3 12.6±0.2 12.5±0.2 12. 5±0.3 1.0
Calorie (kcal/day) 1945±56 2039±39 2003±51 0.4 1985±71 2007±40 2013±44 0.9 1957±43 2024±40 2089±72 0.2
East Asians
n (%) 24 (6) 136 (34) 244 (60) 38 (9) 142 (35) 224 (56) 38 (10) 143 (35) 223 (55)
Age (y) 21.9±0.4 22.2±0.2 21.9±0.1 0.5 21.7±0.3 22.1±0.2 22.0±0.1 0.6 21.7±0.3 22.1±0.2 22.0±0.1 0.6
Female (%) 20 (83) 96 (71) 178 (73) 0.4 29 (76) 103 (73) 162 (72) 0.9 29 (76) 104 (73) 161 (72) 0.9
BMI ( kg/m2 )* 20.6±4.6 21.4±3.9 21.2±3.3 0.9 21.2±3.7 21.3±3.9 21.3±3.3 0.9 21.2±3.7 21.2±3.9 21.3±3.3 0.9
Non-Smoker (%) 23 (96) 132 (97) 232 (95) 0.7 37 (97) 136 (96) 214 (96) 0.9 37 (97) 137 (96) 213 (96) 0.9
Physical Activity
(MET.hrs/week)
12.0±0.5 11.9±0.1 11.5±0.1 0.1 11.9±0.4 11.7±0.2 11.6±0.2 0.6 11.9±0.4 11.8±0.2 11.5±0.2 0.5
Calorie (kcal/day)
1717±135 1844±56 1863±42 0.6 1798±106 1832±55 1867±43 0.8 1798±106 1830±55 1869±43 0.8
Values shown are mean ± SE for normally distributed continuous variables, *median ± quartile range for continuous variables that are not normally distributed
and n (%) for categorical variables. Differences between TAS2R50 groups were compared using an analysis of variance for normally distributed continuous
variables, Kruskal-Wallis test for continuous variables that were not normally distributed and a Pearson chi-square test for categorical variables. Means with
different letters are significantly different following a Tukey correction (P <0.05). When assessing calorie intake between genotypes, individuals who consumed
less than 800 kcal/day and more than 3500 kcal/day for women or 4000 kcal/day for men were excluded leaving n=530 Caucasians and n=365 East Asians.
46
Table 5: The frequency and odds of disliking grapefruit and grapefruit juice by TAS2R50
genotype in Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Grapefruit n=539 n=386
rs2900554
AA 9 (6) 146 (94) 1 1 21 (9) 214 (91) 1 1
AC 46 (18) 204 (82) 3.6 (1.7 ,7.5) 3.6 (1.7, 7.6) 18 (13) 111 (87) 1.6 (0.8, 3.1) 1.6 (0.8, 3.2)
CC 38 (29) 91 (71) 6.7 (3.1, 14.7) 6.6 (3.0,14.3) 8 (35) 15 (65) 5.4 (2.1, 14.3) 5.9 (2.2, 15.6)
rs10772397
AA 27 (13) 177 (87) 1 1 21 (10) 195 (90) 1 1
AG 52 (21) 201 (79) 1.7 (1.0, 2.8) 1.7 (1.0, 2.9) 16 (12) 119 (88) 1.2(0.6,2.5) 1.3 (0.6 ,2.6)
GG 14 (17) 68 (83) 1.4 (0.7, 2.7) 1.4 (0.7, 2.8) 9 (26) 26 (74) 3.2 (1.3, 7.8) 3.5 (1.4, 8.5)
rs1376251
AA 6 (8) 70 (92) 1 1 21 (10) 194 (90) 1 1
AG 33 (14) 208 (86) 1.9 (0.7, 4.6) 1.9 (0.7, 4.6) 16 (12) 120 (88) 1.2 (0.6, 2.5) 1.3 (0.6, 2.5)
GG 54 (24) 168 (76) 3.8 (1.5, 9.1) 3.8 (1.5, 9.2) 9 (26) 26 (74) 3.2 (1.3, 7.7) 3.5 (1.4, 8.5)
Grapefruit
Juice
n=535
n=374
rs2900554
AA 28 (18) 124 (82) 1 1 27 (12) 202 (88) 1 1
AC 53 (21) 203 (79) 1.2 (0.7, 1.9) 1.2 (0.7, 2.0) 14 (11) 110 (89) 1.0 (0.5, 2.0) 0.9 (0.4, 1.8)
CC 43 (34) 84 (66) 2.3 (1.3, 3.9) 2.3 (1.3, 4.1) 6 (29) 15 (71) 3.0 (1.1, 8.4) 3.0 (1.1, 8.8)
rs10772397
AA 42 (21) 162 (79) 1 1 27 (13) 182 (87) 1 1
AG 65 (26) 186 (74) 1.4 (0.9, 2.1) 1.3 (0.9, 2.1) 12 (9) 120 (91) 0.7 (0.3, 1.4) 0.7 (0.3, 1.4)
GG 17 (21) 63 (79) 1.0 (0.6, 2.0) 1.0 (0.5, 1.9) 8 (24) 25 (76) 2.2 (0.9, 5.3) 2.4 (0.9, 5.9)
rs1376251
AA 15 (20) 61 (80) 1 1 27 (13) 181 (87) 1 1
AG 49 (20) 191 (80) 1.0 (0.6, 2.0) 1.1 (0.6, 2.1) 12 (9) 121 (91) 0.7 (0.3, 1.4) 0.7 (0.3, 1.3)
GG 60 (27) 159 (73) 1.5 (0.8, 2.9) 1.6 (0.8, 3.0) 8 (24) 25 (76) 2.2 (0.9, 5.2) 2.3 (0.9, 5.8)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
47
Table 6: The frequency and odds of disliking vegetables by TAS2R50 genotype in Caucasians
and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Asparagus
n=528
n=372
rs2900554
AA 8 (5) 140 (95) 1 1 9 (4) 216 (96) 1 1
AC 21 (8) 231 (92) 1.6 (0.7, 3.7) 1.7 (0.7, 4.0) 9 (6) 116 (93) 1.7 (0.6, 4.4) 1.6 (0.6, 4.3)
CC 8 (6) 120 (94) 1.2 (0.4, 3.2) 1.4 (0.5, 3.8) 2 (9) 21 (91) 2.3 (0.5, 11.3) 2.2 (0.5, 11.1)
rs10772397
AA 14 (7) 185 (93) 1 1 8 (4) 200 (96) 1 1
AG 18 (7) 229 (93) 1.0 (0.5, 2.1) 1.0 (0.5, 2.0) 8 (6) 120 (94) 1.7 (0.6, 4.6) 1.6 (0.6, 4.5)
GG 5 (6) 77 (94) 0.9 (0.3, 2.5) 0.8 (0.3, 2.3) 3 (8) 33 (92) 2.3 (0.6, 9.0) 2.2 (0.6, 8.9)
rs1376251
AA 6 (9) 64 (91) 1 1 8 (4) 199 (96) 1 1
AG 14 (6) 223 (94) 0.7 (0.3, 1.8) 0.7 (0.3, 2.0) 8 (6) 121 (94) 1.7 (0.6, 4.5) 1.6 (0.6, 4.4)
GG 17 (8) 204 (92) 0.9 (0.3, 2.4) 1.0 (0.4, 2.7) 3 (8) 33 (92) 2.3 (0.6, 9.0) 2.2 (0.6, 8.9)
Broccoli
n=547
n=391
rs2900554
AA 3 (2) 154 (98) 1 1 3 (1) 233 (99)
AC 14 (5) 246 (95) 2.9 (0.8, 10.3) 3.0 (0.8, 10.6) 1 (1) 131 (99) n/a n/a
CC 5 (4) 125 (96) 2.1 (0.5, 8.8) 2.0 (0.5, 8.6) 0 (0) 23 (100)
rs10772397
AA 9 (4) 198 (96) 1 1 3 (1) 214 (99)
AG 9 (3) 248 (97) 0.8 (0.3, 2.1) 0.8 (0.3, 2.1) 1 (1) 137 (99) n/a n/a
GG 4 (5) 79 (95) 1.1 (0.3, 3.7) 1.0 (0.3, 3.5) 0 (0) 36 (100)
rs1376251
AA 2 (3) 76 (97) 1 1 3 (1) 213 (99)
AG 12 (5) 233 (95) 2.0 (0.4, 8.9) 2.0 (0.4, 9.3) 1 (1) 138 (99) n/a n/a
GG 8 (4) 216 (96) 1.4 (0.3, 6.8) 1.5 (0.3, 7.0) 0 (0) 36 (100)
Brussels
Sprouts
n=486
n=321
rs2900554
AA 25 (18) 111 (82) 1 1 22 (11) 171 (89) 1 1
AC 50 (22) 180 (78) 1.2 (0.7, 2.1) 1.2 (0.7, 2.1) 16 (13) 97 (87) 1.1 (0.6, 2.3) 1.1 (0.5, 2.2)
CC 31 (26) 89 (74) 1.6 (0.9, 2.8) 1.5 (0.8, 2.7) 2 (12) 15 (88) 1.0 (0.2, 4.8) 0.9 (0.2, 4.5)
rs10772397
AA 44 (24) 139 (76) 1 1 21 (12) 156 (88) 1 1
AG 45 (20) 182 (80) 0.8 (0.5, 1.3) 0.8 (0.5, 1.3) 15 (13) 101 (86) 1.1 (0.5, 2.5) 1.1 (0.5, 2.2)
GG 17 (22) 59 (78) 0.9 (0.5, 1.7) 0.9 (0.5, 1.7) 2 (7) 26 (93) 0.6 (0.1, 2.6) 0.5 (0.1, 2.5)
rs1376251
AA 14 (21) 53 (79) 1 1 21 (12) 156 (88) 1 1
AG 42 (20) 170 (80) 1.0 (0.5, 1.8) 0.9 (0.5, 1.8) 15 (13) 101 (87) 1.1 (0.5, 2.2) 1.1 (0.5, 2.2)
GG 50 (24) 157 (76) 1.2 (0.6, 2.4) 1.2 (0.6, 2.3) 2 (7) 26 (93) 0.6 (0.1, 2.6)
0.5 (0.1, 2.5)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
n/a = Odds ratio is not estimable due to one or more empty cells.
48
Table 6 continued: The frequency and odds of disliking vegetables by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Cabbage
n=523
n=369
rs2900554
AA 22 (15) 127 (85) 1 1 33 (15) 191 (85) 1 1
AC 40 (16) 207 (84) 1.1 (0.6, 2.0) 1.2 (0.7, 2.0) 15 (11) 109 (87) 0.7 (0.4, 1.5) 0.8 (0.4, 1.6)
CC 22 (17) 105 (83) 1.2 (0.6, 2.3) 1.2 (0.6, 2.4) 4 (18) 18 (82) 1.3 (0.4, 4.1) 1.3 (0.4, 4.1)
rs10772397
AA 31 (15) 170 (85) 1 1 32 (16) 174 (84) 1 1
AG 44 (18) 196 (82) 1.2 (0.7, 2.0) 1.2 (0.7, 2.1) 15 (12) 114 (88) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)
GG 9 (11) 73 (89) 0.7 (0.3, 1.5) 0.6 (0.3, 1.4) 4 (12) 30 (88) 0.7 (0.2, 2.2) 0.7 (0.2, 2.2)
rs1376251
AA 9 (12) 66 (88) 1 1 32 (16) 173 (84) 1 1
AG 39 (17) 191 (83) 1.5 (0.7, 3.3) 1.6 (0.7, 3.5) 15 (12) 115 (88) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)
GG 36 (17) 182 (83) 1.5 (0.7, 3.2) 1.6 (0.7, 3.5) 4 (12) 30 (88) 0.7 (0.2, 2.2) 0.7 (0.2, 2.2)
Cauliflower
n=539
n=382
rs2900554
AA 10 (6) 145 (94) 1 1 5 (2) 227 (98) 1 1
AC 22 (9) 234 (91) 1.4 (0.6, 3.0) 1.4 (0.6, 3.1) 5 (4) 123 (96) 1.8 (0.5, 6.5) 2.1 (0.6, 7.9)
CC 11 (9) 117(91) 1.4 (0.6, 3.3) 1.3 (0.5, 3.3) 1 (5) 21 (95) 2.2 (0.2, 19.4) 2.0 (0.2, 18.9)
rs10772397
AA 18 (9) 188 (91) 1 1 4 (2) 210 (98) 1 1
AG 16 (6) 235 (94) 0.7 (0.3, 1.4) 0.7 (0.4, 1.4) 6 (5) 127 (95) 2.5 (0.7, 9.0) 2.6 (0.7, 9.8)
GG 9 (11) 73(89) 1.3 (0.6, 3.0) 1.2 (0.5, 2.9) 1 (3) 34 (97) 1.5 (0.2, 14.2) 1.5 (0.2, 15.0)
rs1376251
AA 5 (6) 72 (94) 1 1 4 (2) 209 (98) 1 1
AG 18 (7) 223 (93) 1.2 (0.4, 3.2) 1.2 (0.4, 3.4) 6 (4) 128 (96) 2.5 (0.7, 8.9) 2.5 (0.7, 9.7)
GG 20 (9) 201 (91) 1.4 (0.5, 4.0) 1.5 (0.5, 4.2) 1 (3) 34 (97) 1.5 (0.2, 14.2) 1.5 (0.2, 15.0)
Endives
n=290
n=174
rs2900554
AA 7 (9) 63 (91) 1 1 8 (7) 100 (93)
AC 8 (6) 130 (94) 0.6 (0.2, 1.7) 0.6 (0.2, 1.7) 2 (3) 57 (97) n/a n/a
CC 7 (9) 70 (91) 1.0 (0.3, 2.9) 0.9 (0.3, 2.7) 0 (0) 7 (100)
rs10772397
AA 11 (10) 96 (90) 6 (6) 92 (94)
AG 11 (8) 128 (92) n/a n/a 4 (7) 55 (93) n/a n/a
GG 0 (0) 44 (100) 0 (0) 17 (100)
rs1376251
AA 3 (8) 36 (92) 1 1 6 (6) 92 (94)
AG 10 (8) 109 (92) 1.1 (0.3, 4.2) 1.1 (0.3, 4.3) 4 (7) 55 (93) n/a n/a
GG 9 (7) 123 (93) 0.9 (0.2, 3.4) 0.8 (0.2, 3.3) 0 (0) 17 (100)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
n/a = Odds ratio is not estimable due to one or more empty cells.
49
Table 6 continued: The frequency and odds of disliking vegetables by TAS2R50 genotype in
Caucasians and East Asian
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Kale
n=294
n=215
rs2900554
AA 6 (7) 75 (93) 1 1 2 (2) 131 (98) 1 1
AC 11 (8) 135 (92) 1.0 (0.4, 2.8) 1.0 (0.3, 2.8) 6 (8) 66 (92) 6.0 (1.2 , 30.3) 6.4 (1.2, 34.4)
CC 9 (13) 58 (87) 1.9 (0.7, 5.8) 1.9 (0.6, 5.8) 1 (10) 9 (90) 7.3 (0.6, 88.1) 10.3 (0.7, 145.3)
rs10772397
AA 8 (7) 108 (93) 1 1 1 (1) 122 (99) 1 1
AG 10 (8) 121 (92) 1.1 (0.4, 3.0) 1.3 (0.5, 3.4) 6 (8) 67 (92) 10.9 (1.3, 92.6) 12.4 (1.4, 110.5)
GG 8 (17) 39 (83) 2.8 (0.97, 7.9) 3.1 (1.1, 9.2) 2 (11) 17 (89) 14.4 (1.3, 167) 20.8 (1.6, 277.9)
rs1376251
AA 2 (5) 38 (95) 1 1 1 (1) 122 (99) 1 1
AG 8 (6) 123 (94) 1.2 (0.3, 6.1) 1.2 (0.2, 5.8) 6 (8) 67 (92) 10.9 (1.3, 92.6) 12.4 (1.4, 110.5)
GG 16 (13) 107 (87) 2.8 (0.6, 12.9) 2.9(0.6,13.4) 2 (11) 17 (89) 14.4 (1.2, 167) 20.4 (1.6, 269.9)
Onion Raw
n=540
n=389
rs2900554
AA 34 (22) 122 (78) 1 1 56 (24) 180 (76) 1 1
AC 56 (22) 198 (78) 1.0 (0.6, 1.6) 1.1 (0.6, 1.7) 28 (22) 102 (78) 0.9 (0.5, 1.5) 0.9 (0.5, 1.5)
CC 18 (14) 112 (86) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1) 7 (30) 16 (70) 1.4 (0.6, 3.6) 1.4 (0.5, 3.5)
rs10772397
AA 45 (22) 161 (78) 1 1 50 (23) 167 (77) 1 1
AG 50 (20) 202 (80) 0.9 (0.6, 1.4) 0.9 (0.5, 1.4) 30 (22) 107 (78) 0.9 (0.6, 1.6) 0.9 (0.6, 1.6)
GG 13 (16) 69 (84) 0.7 (0.3, 1.3) 0.6 (0.3, 1.3) 11 (31) 24 (69) 1.5 (0.7, 3.3) 1.5 (0.7, 3.3)
rs1376251
AA 18 (23) 60 (77) 1 1 50 (23) 166 (77) 1 1
AG 50 (21) 189 (79) 0.9 (0.5, 1.6) 0.9 (0.5, 1.7) 30 (22) 108 (78) 0.9 (0.6, 1.5) 0.9 (0.6, 1.6)
GG 40 (18) 183 (82) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4) 11 (31) 24 (69) 1.5 (0.7, 3.3) 1.5 (0.7, 3.2)
Parsley
n=535
n=375
rs2900554
AA 7 (5) 147 (95) 1 1 18 (8) 206 (90) 1 1
AC 10 (4) 243 (96) 0.9 (0.3, 2.3) 0.9 (0.3, 2.5) 16 (12) 113 (88) 1.6 (0.8, 3.3) 1.7 (0.8, 3.5)
CC 6 (5) 122 (95) 1.0 (0.4, 3.2) 1.1 (0.4, 3.4) 1 (95) 21 (95) 0.6 (0.1, 4.5) 0.6 (0.1, 4.5)
rs10772397
AA 13 (6) 191 (94) 1 1 16 (8) 191 (92) 1 1
AG 9 (4) 242 (96) 0.6 (0.2, 1.3) 0.5 (0.2, 1.3) 16 (12) 117 (88) 1.6 (0.8, 3.4) 1.7 (0.8, 3.5)
GG 1 (1) 79 (99) 0.2 (0.02, 1.5) 0.2(0.02,1.4) 3 (9) 32 (91) 1.1 (0.3, 4.1) 1.2 (0.3, 4.3)
rs1376251
AA 4 (5) 73 (95) 1 1 16 (8) 190 (92) 1 1
AG 12 (5) 227 (95) 1.0 (0.3, 3.1) 1.0 (0.3, 3.2) 16 (12) 118 (88) 1.6 (0.8, 3.3) 1.7 (0.8, 3.4)
GG 7 (3) 212 (97) 0.6 (0.2, 2.1) 0.6 (0.2, 2.2) 3 (9) 32 (91) 1.1 (0.3, 4.0) 1.2 (0.3, 4.3)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
50
Table 6 continued: Frequency and odds of disliking vegetables by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Radicchio
n=346
n=199
rs2900554
AA 11 (12) 80 (88) 1 1 5 (4) 118 (96) 1 1
AC 13 (8) 154 (92) 0.6 (0.3, 1.4) 0.6 (0.3, 1.4) 5 (7) 65 (93) 1.9 (0.5, 6.7) 1.9 (0.5, 7.3)
CC 9 (10) 79 (90) 0.8 (0.3, 2.1) 0.8 (0.3, 2.0) 1 (13) 7 (87) 3.4 (0.4, 32.9) 3.7 (0.3, 42.6)
rs10772397
AA 14 (11) 112 (89) 1 1 5 (5) 106 (95) 1 1
AG 12 (7) 150 (93) 0.6 (0.3, 1.4) 0.7 (0.3, 1.5) 5 (7) 65 (93) 1.6 (0.5, 5.9) 1.6 (0.4, 6.1)
GG 7 (12) 51 (88) 1.1 (0.4, 2.9) 1.1 (0.4, 3.0) 1 (6) 17 (94) 1.3 (0.1, 11.3) 1.3 (0.1, 13.2)
rs1376251
AA 3 (7) 40 (93) 1 1 5 (5) 106 (95) 1 1
AG 13 (9) 137 (91) 1.3 (0.3, 4.7) 1.2 (0.3, 4.6) 5 (7) 65 (63) 1.6 (0.5, 5.8) 1.6 (0.4, 6.1)
GG 17 (11) 136 (89) 1.7 (0.5, 6.0) 1.6 (0.5, 5.9) 1 (6) 17 (94) 1.3 (0.1, 11.3) 1.3 (0.1, 13.2)
Radish
n=517
n=365
rs2900554
AA 33 (23) 113 (77) 1 1 18 (8) 204 (92) 1 1
AC 39 (16) 208 (84) 0.6 (0.4, 1.1) 0.6 (0.4, 1.1) 15 (12) 108 (88) 1.6 (0.8, 3.2) 1.7 (0.8, 3.6)
CC 25 (20) 99 (80) 0.9 (0.5, 1.6) 0.9 (0.5, 1.6) 5 (23) 17 (77) 3.3 (1.1, 10.1) 3.6 (1.2, 11.4)
rs10772397
AA 35 (18) 161 (82) 1 1 18 (9) 187 (91) 1 1
AG 47 (19) 195 (81) 1.1 (0.7, 1.8) 1.1 (0.7, 1.8) 14 (11) 111 (89) 1.3 (0.6, 2.7) 1.4 (0.6, 2.9)
GG 15 (19) 64 (81) 1.1 (0.6, 2.1) 1.1 (0.6, 2.1) 5 (14) 30 (86) 1.7 (0.6, 5.0) 2.0 (0.7, 5.8)
rs1376251
AA 13 (18) 58 (82) 1 1 18 (9) 186 (91) 1 1
AG 42 (18) 189 (82) 1.0 (0.5, 2.0) 1.0 (0.5, 2.0) 15 (11) 112 (89) 1.3 (0.6, 2.7) 1.3 (0.6, 2.8)
GG 42 (20) 173 (80) 1.1 (0.5, 2.2) 1.1 (0.5, 2.1) 5 (14) 30 (86) 1.7 (0.6, 5.0) 2.0 (0.7, 5.8)
Rapini
n=215
n=130
rs2900554
AA 6 (13) 40 (87) 1 1 0 (0) 76 (100)
AC 14 (13) 98 (88) 1.0 (0.3, 2.7) 1.0 (0.4, 2.9) 2 (4) 44 (96) n/a n/a
CC 6 (11) 51 (89) 0.8 (0.2, 2.6) 0.9 (0.3, 3.2) 0 (0) 8 (100)
rs10772397
AA 9 (11) 74 (89) 1 1 0 (0) 67 (100)
AG 10 (11) 81 (89) 1.0 (0.4, 2.6) 0.9 (0.3, 1.5) 2 (4) 46 (96) n/a n/a
GG 7 (17) 34 (83) 1.7 (0.6, 4.9) 1.6 (0.5, 5.0) 0 (0) 15 (100)
rs1376251
AA 3 (12) 23 (88) 1 1 0 (0) 67 (100)
AG 10 (11) 80 (89) 1.0 (0.2, 3.8) 1.0 (0.2, 4.0) 2 (4) 46 (96) n/a n/a
GG 13 (13) 86 (87) 1.2 (0.3, 4.4) 1.3 (0.3, 5.0) 0 (0) 15 (100)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
n/a = Odds ratio is not estimable due to one or more empty cells.
51
Table 6 continued: Frequency and odds of disliking vegetables by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Spinach
n=548
n=390
rs2900554
AA 5 (3) 152 (97) 1 1 3 (1) 232 (99)
AC 13 (5) 248 (95) 1.6 (0.6, 4.6) 1.6 (0.6, 4.8) 5 (4) 127 (96) 3.0 (0.7, 13.0) 3.3 (0.8, 14.6)
CC 8 (6) 122 (94) 2.0 (0.6, 6.3) 2.3 (0.7, 7.2) 2 (9) 21 (91) 7.4 (1.2, 46.6) 9.9 (1.4, 69.3)
rs10772397
AA 9 (4) 198 (96) 1 1 3 (1) 213 (99) 1 1
AG 14 (5) 244 (95) 1.3 (0.5, 3.0) 1.2 (0.5, 2.8) 4 (3) 134 (97) 2.1 (0.5, 9.6) 2.2 (0.5, 10.4)
GG 3 (4) 80 (96) 0.8 (0.2, 3.1) 0.8 (0.2, 3.0) 3 (8) 33 (92) 6.5 (1.3, 33.3) 7.0 (1.3, 39.3)
rs1376251
AA 3 (4) 75 (96) 1 1 3 (1) 212 (99) 1 1
AG 10 (4) 236 (96) 1.1 (0.3, 4.0) 1.1 (0.3, 4.1) 4 (3) 135 (97) 2.1 (0.5, 9.5) 2.3 (0.5, 10.4)
GG 13 (6) 211 (94) 1.5 (0.4, 5.6) 1.7 (0.5, 6.0) 3 (8) 33 (92) 6.4 (1.2, 33.2) 7.0 (1.3, 39.3)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
52
Table 7: The frequency and odds of disliking soy products by TAS2R50 genotype in Caucasians
and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Soymilk
n=454
n=385
rs2900554
AA 33 (26) 96 (74) 1 1 8 (3) 223 (97)
AC 48 (22) 171 (78) 0.8 (0.5, 1.4) 0.9 (0.5, 1.5) 10 (8) 121 (92) n/a n/a
CC 26 (25) 80 (75) 1.0 (0.5, 1.7) 1.0 (0.6, 1.9) 0 (0) 23 (100)
rs10772397
AA 44 (26) 127 (74) 1 1 8 (4) 203 (96)
AG 48 (23) 162 (77) 0.9 (0.5, 1.4) 0.8 (0.5, 1.3) 10 (7) 128 (93) n/a n/a
GG 15 (21) 58 (79) 0.8 (0.4, 1.5) 0.7 (0.4, 1.4) 0 (0) 36 (100)
rs1376251
AA 20 (33) 41 (67) 1 1 8 (4) 202 (96)
AG 39 (19) 165 (81) 0.5 (0.3, 0.9) 0.5 (0.3,0.95) 10 (7) 129 (93) n/a n/a
GG 48 (25) 141 (75) 0.7 (0.4, 1.3) 0.7 (0.4, 1.4) 0 (0) 36 (100)
Tofu
n=502
n=391
rs2900554
AA 28 (20) 112 (80) 1 1 2 (1) 234 (99)
AC 35 (15) 205 (85) 0.7 (0.4, 1.2) 0.7 (0.4, 1.3) 0 (0) 132
(100)
n/a n/a
CC 13 (11) 109 (89) 0.5 (0.2, 1.0) 0.5 (0.3, 1.1) 0 (0) 23 (100)
rs10772397
AA 27 (14) 160 (86) 1 1 2 (1) 215 (99)
AG 35 (15) 202 (85) 1.0 (0.6, 1.8) 1.0 (0.6, 1.7) 0 (0) 141
(100)
n/a n/a
GG 14 (18) 64 (82) 1.3 (0.6, 2.6) 1.2 (0.6, 2.4) 0 (0) 36 (100)
rs1376251
AA 12 (18) 53 (82) 1 1 2 (1) 213 (99)
AG 33 (15) 194 (85) 0.8 (0.4, 1.6) 0.8 (0.4, 1.7) 0 (0) 140
(100)
n/a n/a
GG 31 (15) 179 (85) 0.8 (0.4, 1.6) 0.9 (0.4, 1.8) 0 (0) 36 (100)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
n/a = Odds ratio is not estimable due to one or more empty cells.
53
Table 8: Frequency and odds of disliking cocoa-containing products by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Dark
Chocolate
n=548
n=393
rs2900554
AA 8 (5) 149 (95) 1 1 17 (7) 221 (93) 1 1
AC 20 (8) 241 (92) 1.6 (0.7, 3.6) 1.6 (0.7, 3.7) 12 (8) 121 (92) 1.2 (0.5, 2.6) 1.2 (0.5, 2.6)
CC 10 (8) 120 (92) 1.6 (0.6, 4.1) 1.6 (0.6, 4.3) 2 (9) 21 (91) 1.2 (0.3, 5.8) 1.3 (0.3, 6.1)
rs10772397
AA 16 (8) 91 (92) 1 1 15 (7) 203 (93) 1 1
AG 16 (6) 242 (94) 0.8 (0.4, 1.6) 0.8 (0.4, 1.6) 12 (9) 127 (91) 1.3 (0.6, 2.8) 1.3 (0.6, 2.8)
GG 6 (7) 77 (93) 0.9 (0.4, 2.5) 0.9 (0.3, 2.4) 3 (8) 33 (92) 1.2 (0.3, 4.5) 1.4 (0.4, 5.2)
rs1376251
AA 3 (4) 75 (96) 1 1 15 (7) 202 (93) 1 1
AG 19 (8) 227 (92) 2.1 (0.6, 7.2) 2.1 (0.6, 7.3) 12 (9) 128 (91) 1.3 (0.6, 2.8) 1.3 (0.6, 2.0)
GG 16 (7) 208 (93) 1.9 (0.6, 6.8) 2.0 (0.6, 6.9) 3 (8) 33 (92) 1.2 (0.3, 4.5) 1.4 (0.4, 5.2)
Bitter-Sweet
or Semi-
Sweet
Chocolate
n=544
n=390
rs2900554
AA 3 (2) 153 (98) 1 1 9 (4) 228 (96) 1 1
AC 16 (6) 244 (94) 3.3 (1.0, 11.7) 3.5 (1.0, 12.4) 8 (6) 122 (94) 1.7 (0.6, 4.4) 1.5 (0.5, 4.0)
CC 8 (6) 120 (94) 3.4 (0.9, 13.1) 3.8 (1.0, 14.8) 1 (4) 22 (96) 1.2 (0.1, 9.5) 1.1 (0.1, 9.8)
rs10772397
AA 10 (5) 196 (95) 1 1 9 (4) 209 (96) 1 1
AG 12 (5) 243 (95) 1.0 (0.4, 2.3) 1.0 (0.4, 2.3) 8 (6) 128 (94) 1.5 (0.6, 3.9) 1.4 (0.5, 3.7)
GG 5 (6) 78 (94) 1.3 (0.4, 3.8) 1.2 (0.4, 3.6) 1 (3) 35 (97) 0.7 (0.08, 5.4) 0.7 (0.08,
5.8)
rs1376251
AA 1 (1) 76 (99) 1 1 9 (4) 208 (96) 1 1
AG 15 (6) 230 (94) 5.0 (0.6, 38.1) 5.3 (0.7, 40.6) 8 (6) 129 (94) 1.4 (0.5, 3.8) 1.3 (0.5, 3.6)
GG 11 (5) 211 (95) 4.0 (0.5, 31.2) 4.3 (0.5, 33.9) 1 (3) 35 (97) 0.7 (0.08, 5.4) 0.7
(0.08,5.9)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
54
Table 9: The frequency and odds of disliking alcoholic beverages by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Red Wine
n=528
n=369
rs2900554
AA 27 (18) 121 (82) 1 1 40 (18) 183 (82) 1 1
AC 27 (11) 226 (89) 0.5 (0.3, 1.0) 0.5 (0.3, 1.0) 25 (19) 100 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 2.0)
CC 23 (18) 104 (82) 1.0 (0.6, 1.8) 1.0 (0.6, 1.9) 6 (27) 16 (73) 1.7 (0.6, 4.7) 1.7 (0.6, 4.6)
rs10772397
AA 28 (14) 170 (86) 1 1 37 (18) 167 (82) 1 1
AG 38 (15) 212 (85) 1.1 (0.6, 1.9) 1.0 (0.6, 1.8) 25 (19) 105 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 2.0)
GG 11 (14) 69 (86) 1.0 (0.5, 2.1) 1.0 (0.5, 2.0) 8 (23) 27 (77) 1.3 (0.6, 3.2) 1.3 (0.6, 3.2)
rs1376251
AA 12 (17) 59 (83) 1 1 37 (18) 166 (82) 1 1
AG 30 (13) 210 (87) 0.7 (0.3, 1.5) 0.7 (0.3, 1.4) 25 (19) 106 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 1.9)
GG 35 (16) 182 (84) 1.0 (0.5, 1.9) 0.9 (0.5, 1.9) 8 (23) 27 (77) 1.3 (0.6, 3.2) 1.3 (0.6, 3.2)
White Wine
n=523
n=353
rs2900554
AA 18 (12) 128 (88) 1 1 37 (17) 176 (83) 1 1
AC 24 (10) 226 (90) 0.8 (0.4, 1.4) 0.8 (0.4, 1.5) 18 (14) 102 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)
CC 16 (13) 111 (87) 1.0 (0.5, 2.1) 1.0 (0.5, 2.1) 3 (14) 18 (86) 0.8 (0.2, 2.8) 0.8 (0.2, 2.8)
rs10772397
AA 22 (11) 174 (89) 1 1 33 (17) 161 (83) 1 1
AG 28 (11) 219 (89) 1.0 (0.6, 1.8) 0.9 (0.5, 1.7) 19 (14) 107 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)
GG 8 (10) 72 (90) 0.9 (0.4 , 2.1) 0.8 (0.4, 2.0) 6 (18) 28 (82) 1.1 (0.4, 2.7) 1.1 (0.4, 2.8)
rs1376251
AA 9 (13) 62 (87) 1 1 33 (17) 160 (83) 1 1
AG 24 (10) 211 (90) 0.8 (0.3, 1.8) 0.8 (0.3, 1.8) 18 (14) 108 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)
GG 25 (12) 192 (88) 0.9 (0.4, 2.0) 0.9 (0.4, 2.0) 6 (18) 28 (82) 1.0 (0.4, 2.7) 1.1 (0.4, 2.8)
Beer
n=530
n=374
rs2900554
AA 38 (26) 109 (74) 1 1 79 (35) 147 (65) 1 1
AC 64 (25) 192 (75) 1.0 (0.6, 1.5) 1.0 (0.6, 1.6) 41 (32) 85 (68) 0.9 (0.6, 1.4) 0.9 (0.6, 1.5)
CC 32 (25) 95 (75) 1.0 (0.6, 1.7) 1.0 (0.5, 1.7) 5 (22) 18 (78) 0.5 (0.2, 1.5) 0.5 (0.2, 1.3)
rs10772397
AA 46 (23) 154 (77) 1 1 70 (34) 137 (66) 1 1
AG 73 (29) 177 (71) 1.4 (0.9, 2.1) 1.4 (0.9, 2.2) 44 (34) 87 (66) 1.0 (0.6, 1.6) 1.0 (0.6, 1.6)
GG 15 (19) 65 (81) 0.8 (0.4, 1.5) 0.8 (0.4, 1.6) 10 (28) 26 (72) 0.8 (0.3, 1.7) 0.7 (0.3, 1.6)
rs1376251
AA 20 (28) 52 (72) 1 1 70 (34) 136 (66) 1 1
AG 57 (24) 183 (76) 0.8 (0.5, 1.5) 0.7 (0.4, 1.4) 44 (33) 88 (67) 1.0 (0.6, 1.5) 1.0 (0.6, 1.6)
GG 57 (26) 161 (74) 0.9 (0.5, 1.7) 0.8 (0.4, 1.5) 10 (28) 26 (72) 0.7 (0.3, 1.6) 0.7 (0.3, 1.6)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
55
Table 10: The frequency and odds of disliking caffeinated beverages by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Coffee
n=541
n=390
rs2900554
AA 34 (22) 123 (78) 1 1 40 (17) 196 (83) 1 1
AC 52 (20) 204 (80) 0.9 (0.6, 1.5) 0.9 (0.6, 1.5) 14 (11) 119 (89) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1)
CC 31 (24) 97 (76) 1.2 (0.7, 2.0) 1.3 (0.7, 2.2) 1 (4) 22 (96) 0.2 (0.03,
1.7)
0.2 (0.03, 1.7)
rs10772397
AA 42 (20) 164 (80) 1 1 36 (17) 180 (83) 1 1
AG 61 (24) 191 (76) 1.3 (0.8, 2.0) 1.1 (0.7, 1.8) 18 (13) 120 (87) 0.8 (0.4, 1.4) 0.7 (0.4, 1.4)
GG 14 (17) 69 (83) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5) 1 (3) 35 (97) 0.1(0.01,1.1) 0.2 (0.02, 1.2)
rs1376251
AA 21 (27) 57 (73) 1 1 36 (17) 179 (83) 1 1
AG 44 (18) 199 (82) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1) 18 (13) 121 (87) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)
GG 52 (24 ) 168 (76) 0.8 (0.5, 1.5) 0.8 (0.5, 1.5) 1 (3) 35 (97) 0.1(0.02,1.1) 0.2 (0.02, 1.2)
Green tea
n=525
n=395
rs2900554
AA 12 (8) 136 (92) 1 1 1 (1) 236 (99)
AC 19 (8) 234 (92) 0.9 (0.4, 2.0) 0.9 (0.4, 2.0) 0 (0) 132 (100) n/a n/a
CC 10 (8) 114 (92) 1.0 (0.4, 2.4) 1.0 (0.4, 2.5) 0 (0) 23 (100)
rs10772397
AA 16 (8) 182 (92) 1 1 0 (0) 217 (100)
AG 20 (8) 226 (92) 1.0 (0.5, 2.0) 0.9 (0.5, 1.9) 1 (1) 138 (99) n/a n/a
GG 5 (6) 76 (94) 0.8 (0.3, 2.1) 0.7 (0.3, 2.1) 0 (0) 36 (100)
rs1376251
AA 5 (7) 68 (93) 1 1 0 (0) 216 (100)
AG 17 (7) 220 (93) 1.1 (0.4, 3.0) 1.1 (0.4, 3.0) 1(1) 139 (99) n/a n/a
GG 19 (9) 196 (91) 1.3 (0.5, 3.7) 1.4 (0.5, 3.9) 0 (0) 36 (100)
Black Tea
n=500
n=362
rs2900554
AA 12 (8) 131 (92) 1 1 4 (2) 215 (98)
AC 22 (9) 215 (91) 1.1 (0.5, 2.3) 1.1 (0.5, 2.4) 5 (4) 116 (96) n/a n/a
CC 9 (8) 111 (92) 0.9 (0.4, 2.2) 0.9 (0.4, 2.3) 0 (0) 22 (100)
rs10772397
AA 13 (7) 181 (93) 1 1 3 (2) 196 (98)
AG 23 (10) 206 (90) 1.6 (0.8, 3.2) 1.4 (0.7, 3.0) 6 (5) 123 (95) n/a n/a
GG 7 (9) 70 (91) 1.4 (0.5, 3.6) 1.4 (0.5, 3.7) 0 (0) 34 (100)
rs1376251
AA 5 (7) 63 (93) 1 1 3 (2) 195 (98)
AG 21 (9) 207 (91) 1.3 (0.5, 3.5) 1.3 (0.5, 3.6) 6 (5) 124 (95) n/a n/a
GG 17 (8) 187 (92) 1.2 (0.4, 3.2) 1.2 (0.4, 3.4) 0 (0) 34 (100)
* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity
(MET.hrs/wk), smoking status.
n/a = Odds ratio is not estimable due to one or more empty cells.
56
Table 11: Frequency of disliking grapefruit by rs2900554 SNP and rs1376251 SNP genotype
combinations in Caucasians and East Asians
Caucasian East Asian
Combination rs2900554 rs1376251 Dislike Neutral/ Like Dislike Neutral/ Like
n (%) n (%)
1 CC GG 38 (29) 91 (71) 8 (35) 15 (65)
2 CC AG - - - -
4 CC AA - - - -
3 AC GG 16 (20) 66 (80) 1 (9) 10 (91)
5 AC AG 30 (17) 143 (83) 16 (14) 100 (86)
7 AC AA - - 0 (0) 1 (100)
6 AA GG 0 (0) 11 (100) 0 (0) 1 (100)
8 AA AG 3 (4) 65 (96) 0 (0) 20 (100)
9 AA AA 6 (8) 70 (92) 21 (10) 193 (90)
Genotype combinations that were not possessed by any subjects are indicated by -.
57
Table 12: The frequency and odds not consuming grapefruit in the past month by TAS2R50
genotype in Caucasians and East Asians
Caucasian East Asian
Food No Yes Unadjusted Adjusted* No Yes Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
rs2900554 n=509 n=350
AA 76 (52) 69 (48) 1 1 144 (68) 69 (32) 1 1
AC 138 (57) 103 (43) 1.2 (0.8, 1.8) 1.2 (0.8, 1.9) 73 (62) 44 (38) 0.8 (0.5, 1.3) 0.8 (0.5, 1.3)
CC 79 (64) 44 (36) 1.6 (1.0, 2.7) 1.5 (0.9, 2.6) 17 (85) 3 (15) 2.7 (0.8,9.6) 2.6 (0.7, 9.5)
Male n=149 n=92
AA 28 (67) 14 (33) 1 1 42 (75) 14 (25)
AC 48 (64) 27 (36) 0.9 (0.4, 2.0) 0.9 (0.4, 2.2) 20 (61) 13 (39) n/a n/a
CC 19 (59) 13 (41) 0.7 (0.3, 1.9) 0.9 (0.3, 2.4) 3 (100) 0 (0)
Female n=360 n=258
AA 48 (47) 55 (53) 1 1 102 (65) 55 (35) 1 1
AC 90 (54) 76 (46) 1.4 (0.8, 2.2) 1.4 (0.8, 2.5) 53 (63) 31 (37) 0.9 (0.5, 1.6) 0.9 (0.5, 1.7)
CC 60 (66) 31 (34) 2.2 (1.2, 4.0) 2.1 (1.2, 4.0) 14 (82) 3 (18) 2.5 (0.7, 9.1) 2.1 (0.6, 8.1)
rs10772397 n=509 n=350
AA 115 (59) 80 (41) 1 1 137 (69) 61 (31) 1 1
AG 136 (57) 102 (43) 0.9 (0.6, 1.4) 0.9 (0.6, 1.3) 75 (62) 46 (38) 0.7 (0.5, 1.2) 0.7 (0.5, 1.2)
GG 42 (55) 34 (45) 0.9 (0.5, 1.5) 0.8 (0.5, 1.4) 22 (71) 9 (29) 1.1 (0.5, 2.5) 1.2 (0.5, 2.8)
Male n=149 n=92
AA 39 (70) 17 (30) 1 1 41 (75) 14 (25) 1 1
AG 45 (63) 27 (37) 0.7 (0.4, 1.5) 0.7 (0.3, 1.6) 19 (63) 11 (37) 0.6 (0.2, 1.5) 0.6 (0.2, 1.5)
GG 11 (52) 10 (48) 0.5 (0.2, 1.3) 0.7 (0.2, 1.9) 5 (71) 2 (29) 0.9 (0.2, 4.9) 1.0 (0.2, 6.6)
Female n=360 n=257
AA 76 (55) 63 (45) 1 1 96 (67) 47 (33) 1 1
AG 91 (55) 75 (45) 1.0 (0.6, 1.6) 0.9 (0.6, 1.5) 55 (61) 35 (39) 0.8 (0.5, 1.4) 0.8 (0.4, 1.4)
GG 31 (56) 24 (44) 1.1 (0.6, 2.0) 1.1 (0.6, 2.1) 17 (71) 7 (29) 1.2 (0.5, 3.1) 1.1 (0.4, 3.0)
rs1376251 n=509 n=350
AA 45 (63) 26 (34) 1 1 136 (69) 61 (31) 1 1
AG 119 (52) 109 (48) 0.6 (0.4, 1.1) 0.5 (0.3,0.95) 76 (62) 46 (38) 0.7 (0.5, 1.2) 0.8 (0.5, 1.2)
GG 129 (61) 81 (39) 0.9 (0.5, 1.6) 0.8 (0.5, 1.5) 22 (71) 9 (29) 1.1 (0.5, 2.5) 1.2 (0.5, 2.8)
Male n=149 n=92
AA 20 (83) 4 (17) 1 1 41 (75) 14 (25) 1 1
AG 39 (57) 30 (43) 0.3 (0.08, 0.8) 0.2 (0.07,0.8) 19 (63) 11 (37) 0.6 (0.2, 1.5) 0.6 (0.2, 1.5)
GG 36 (64) 20 (36) 0.4 (0.1, 1.2) 0.4 (0.1, 1.4) 5 (71) 2 (29) 0.9 (0.2, 4.9) 1.0 (0.2, 6.6)
Female n=360 n=258
AA 25 (53) 22 (47) 1 1 95 (67) 47 (33) 1 1
GA 80 (50) 79 (50) 0.9 (0.5, 1.7) 0.7 (0.4, 1.4) 57 (62) 35 (38) 0.8 (0.5, 1.4) 0.8 (0.5, 1.5)
GG 93 (60) 61 (40) 1.3 (0.7, 2.6) 1.2 (0.6, 2.4) 17 (71) 7 (29) 1.2 (0.5, 3.1) 1.1 (0.4, 3.1)
*Logistic regression model adjusted for age, sex, BMI (kg/m2), physical activity (MET.hrs/wk),
smoking status, caloric intake (kcal/day) and season.
n/a = Odds ratio is not estimable due to one or more empty cells.
58
Table 13: The frequency and odds of not consuming grapefruit juice in the past month by
TAS2R50 genotype in Caucasians and East Asians
Caucasian East Asian
Food No Yes Unadjusted Adjusted* No Yes Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
rs2900554 n=505 n=341
AA 109 (77) 33 (23) 1 1 166 (80) 41 (20) 1 1
AC 188 (78) 54 (22) 1.1 (0.6, 1.7) 1.0 (0.6, 1.7) 88 (77) 27 (23) 0.8 (0.5, 1.4) 0.8 (0.5, 1.4)
CC 99 (82) 22 (18) 1.4 (0.8, 2.5) 1.2 (0.6, 2.2) 16 (83) 3 (16) 1.3 (0.4, 4.7) 1.1 (0.3, 4.2)
Male n=150 n=92
AA 34 (83) 7 (17) 1 1 43 (75) 14 (25)
AC 58 (75) 19 (25) 0.6 (0.2, 1.7) 0.6 (0.2, 1.6) 22 (69) 10 (31) n/a n/a
CC 27 (84) 5 (16) 1.1 (0.3, 3.9) 1.1 (0.3, 4.3) 3 (100) 0 (0)
Female n=355 n=249
AA 75 (74) 26 (26) 1 1 123 (82) 27 (18) 1 1
AC 130 (79) 35 (21) 1.3 (0.7, 2.3) 1.3 (0.7, 2.3) 66 (80) 17 (20) 0.9 (0.5, 1.8) 0.9 (0.4, 1.3)
CC 72 (81) 17 (19) 1.5 (0.7, 2.9) 1.3 (0.6, 2.6) 13 (81) 3 (19) 1.0 (0.3, 3.6) 0.8 (0.2, 3.0)
rs10772397 n=505 n=341
AA 150 (77) 45 (23) 1 1 154 (81) 37 (19) 1 1
AG 188 (80) 48 (20) 1.2 (0.7, 1.9) 1.1 (0.7, 1.8) 92 (77) 28 (23) 0.8 (0.5, 1.4) 0.8 (0.4, 1.3)
GG 58 (78) 16 (22) 1.1 (0.6, 2.1) 1.0 (0.5, 2.0) 24 (80) 6 (20) 1.0 (0.4, 2.5) 0.9 (0.3, 2.5)
Male n=150 n=92
AA 49 (87) 7 (13) 1 1 41 (75) 14 (25) 1 1
AG 54 (74) 19 (26) 0.4 (0.2, 1.1) 0.4 (0.1,1.0) 21 (70) 9 (30) 0.8 (0.3, 2.1) 0.8 (0.3, 2.4)
GG 16 (76) 5 (24) 0.5 (0.1, 1.6) 0.5 (0.1, 1.8) 6 (86) 1 (14) 2.1 (0.2, 18.5) 1.8(0.2,19.3)
Female n=355 n=249
AA 101 (73) 38 (27) 1 1 113 (83) 23 (17) 1 1
AG 134 (82) 29 (18) 1.7 (1.0, 3.0) 1.7 (0.9,2.9) 71 (79) 19 (21) 0.8 (0.4, 1.5) 0.7 (0.3, 1.4)
GG 42 (79) 11 (21) 1.4 (0.7, 3.1) 1.5 (0.7, 3.2) 18 (78) 5 (22) 0.7 (0.3, 2.2) 0.7 (0.2, 2.0)
rs1376251 n=505 n=341
AA 56 (79) 15 (21) 1 1 153 (81) 37 (19) 1 1
AG 175 (77) 52 (23) 0.9 (0.5, 1.7) 0.8 (0.4, 1.6) 93 (77) 28 (23) 0.8 (0.5, 1.4) 0.8 (0.4, 1.4)
GG 165 (80) 42 (20) 1.1 (0.5, 2.0) 0.9 (0.5, 1.8) 24 (80) 6 (20) 1.0 (0.4, 2.5) 0.9 (0.3, 2.5)
Male n=150 n=92
AA 22 (92) 2 (8) 1 1 41 (75) 14 (25) 1 1
AG 53 (76) 17 (24) 0.3 (0.06, 1.3) 0.2 (0.04, 1.1) 21 (70) 9 (30) 0.8 (0.3, 2.1) 0.8 (0.3, 2.4)
GG 44 (79) 12 (21) 0.3 (0.07, 1.6) 0.3 (0.06,1.5) 6 (86) 1 (14) 2.1 (0.2, 18.5) 1.8(0.2,19.3)
Female n=355 n=249
AA 34 (72) 13 (28) 1 1 112 (83) 23 (17) 1 1
AG 122 (78) 35 (22) 1.3 (0.6, 2.8) 1.1 (0.5, 2.5) 72 (79) 19 (21) 0.8 (0.4, 1.5) 0.7 (0.4, 1.4)
GG 121 (80) 30 (20) 1.5 (0.7, 3.3) 1.3 (0.6, 2.8) 18 (78) 5 (23) 0.7 (0.3, 2.2) 0.7 (0.2, 2.1)
*Logistic regression model adjusted for age, sex, BMI (kg/m2), physical activity (MET.hrs/wk),
smoking status, caloric intake (kcal/day) and season.
n/a = Odds ratio is not estimable due to one or more empty cells.
59
Table 14: The frequency and odds of not consuming raw spinach, cooked spinach or Swiss
chard, and kale, mustard, collars, or turnip greens in the past month by TAS2R50 genotypes in
Caucasians and East Asians
Caucasian East Asian
Food No Yes Unadjusted Adjusted* No Yes Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Spinach,
Raw in Salad
n=518
n=354
rs2900554
AA 51 (35) 96 (65) 1 1 109 (51) 105 (49) 1 1
AC 77 (31) 170 (69) 0.9 (0.6, 1.3) 0.8 (0.5, 1.3) 62 (52) 58 (48) 1.0 (0.7, 1.6) 1.1 (0.7, 1.7)
CC 44 (35) 80 (65) 1.0 (0.6, 1.7) 1.0 (0.6, 1.6) 9 (45) 11 (55) 0.8 (0.3, 2.0) 0.7 (0.3, 1.8)
rs10772397
AA 56 (28) 142 (72) 1 1 100 (51) 98 (49) 1 1
AG 88 (36) 155 (64) 1.4 (1.0, 2.2) 1.4 (0.9, 2.1) 64 (52) 60 (48) 1.1 (0.7, 1.6) 1.1 (0.7, 1.7)
GG 28 (36) 49 (64) 1.5 (0.8, 2.5) 1.4 (0.8, 2.5) 16 (50) 16 (50) 1.0 (0.5, 2.1) 0.9 (0.4, 2.0)
rs1376251
AA 24 (33) 49 (67) 1 1 99 (50) 98 (50) 1 1
AG 77 (33) 156 (67) 1.0 (0.6, 1.8) 1.0 (0.5, 1.7) 65 (52) 60 (48) 1.1 (0.7, 1.7) 1.1 (0.7, 1.8)
GG 71 (33) 141 (67) 1.0 (0.6, 1.8) 1.0 (0.6, 1.8) 16 (50) 16 (50) 1.0 (0.5, 2.1) 0.9 (0.4, 2.0)
Spinach or
Swiss Chard,
Cooked
n=518
n=354
rs2900554
AA 50 (34) 97 (66) 1 1 55 (26) 159 (74) 1 1
AC 90 (36) 157 (64) 1.1 (0.7, 1.7) 1.1 (0.7, 1.6) 42 (35) 78 (65 ) 1.6 (0.98, 2.6) 1.7 (1.0, 2.8)
CC 43 (35) 81 (65) 1.0 (0.6, 1.7) 0.9 (0.6, 1.6) 6 (30) 14 (70) 1.3 (0.5, 3.5) 1.2 (0.4, 3.3)
rs10772397
AA 66 (33) 132 (67) 1 1 53 (27) 145 (73) 1 1
AG 90 (37) 153 (63) 1.2 (0.8, 1.7) 1.1 (0.8, 1.7) 39 (31) 85 (69) 1.2 (0.8, 2.1) 1.4 (0.8, 2.3)
GG 27 (35) 50 (65) 1.1 (0.6, 1.9) 1.1 (0.6, 1.9) 11 (34) 21 (66) 1.4 (0.7, 3.2) 1.4 (0.6, 3.1)
rs1376251
AA 24 (33) 49 (67) 1 1 53 (27) 144 (73) 1 1
AG 79 (34) 154 (66) 1.0 (0.6, 1.8) 0.9 (0.5, 1.6) 39 (31) 86 (69) 1.2 (0.8, 2.0) 1.3 (0.8, 2.2)
GG 80 (38) 132 (62) 1.2 (0.7, 2.2) 1.1 (0.6, 1.9) 11 (34) 21 (66) 1.4 (0.6, 3.2) 1.3 (0.6, 3.1)
Kale,
mustard,
collard, or
turnip greens
n=284
n=354
rs2900554 62 (50) 61 (50) 1 1
AA 46 (59) 32 (41) 1 1 37 (55) 30 (45) 1.2 (0.7, 2.2) 1.2 (0.6, 2.3)
AC 71 (50) 71 (50) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 2 (25) 6 (75) 0.3 (0.06, 1.7) 0.3(0.05,1.4)
CC 43 (67) 21 (33) 1.4 (0.7, 2.8) 1.4 (0.7, 3.0)
rs10772397
AA 58 (51) 55 (49) 1 1 61 (53) 54 (47) 1 1
AG 73 (58) 53 (42) 1.3 (0.8, 2.2) 1.4 (0.8, 2.4) 34 (52) 32 (48) 0.9 (0.5, 1.7) 0.9 (0.5, 0.7)
GG 29 (64) 16 (36) 1.7 (0.8, 3.5) 1.9 (0.9, 3.9) 6 (35) 11 (65) 0.5 (0.2, 1.4) 0.4 (0.1, 1.4)
rs1376251
AA 21 (55) 17 (45) 1 1 61 (53) 54 (47) 1 1
AG 64 (50) 65 (50) 0.8 (0.4, 1.7) 0.8 (0.4, 1.5) 34 (52) 32 (48) 0.9 (0.5, 1.7) 0.9 (0.5, 1.7)
GG 75 (64) 42 (36) 1.5 (0.7, 3.0) 1.5 (0.7, 3.2) 6 (35) 11 (65) 0.5 (0.2, 1.4) 0.5 (0.2, 1.1)
*Logistic regression model adjusted for age, sex, BMI (kg/m2), physical activity (MET.hrs/wk),
smoking status, caloric intake (kcal/day) and season.
60
Table 15: The frequency and odds of not consuming soymilk and chocolate in the past month by
TAS2R50 genotypes in Caucasians and East Asians
Caucasian East Asian
Food No Yes Unadjusted Adjusted* No Yes Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Soymilk
n=437
n=350
rs2900554
AA 75 (61) 47 (39) 1 1 93 (44) 118 (56) 1 1
AC 130 (62) 79 (38) 1.0 (0.7, 1.6) 1.1 (0.7, 1.8) 51 (43) 68 (57) 1.0 (0.6, 1.5) 0.9 (0.6, 1.5)
CC 68 (64) 38 (36) 1.1 90.7, 1.9) 1.2 (0.7, 2.1) 6 (30) 14 (70) 0.5 (0.2, 1.5) 0.5 (0.2, 1.4)
rs10772397
AA 105 (63) 61 (67) 1 1 88 (45) 107 (55) 1 1
AG 125 (61) 79 (39) 0.9 (0.6, 1.4) 0.9 (0.6, 1.3) 53 (43) 70 (57) 0.9 (0.6, 1.5) 0.9 (0.6, 1.4)
GG 43 (64) 24 (36) 1.0 (0.6, 1.9) 1.0 (0.5, 1.8) 9 (28) 23 (72) 0.5 (0.2, 1.1) 0.5 (0.2, 1.1)
rs1376251
AA 37 (63) 22 (37) 1 1 88 (45) 106 (55) 1 1
AG 120 (62) 74 (38) 1.0 (0.5, 1.8) 1.0 (0.5, 1.8) 53 (43) 71 (57) 0.9 (0.6, 1.4) 0.9 (0.6, 1.4)
GG 116 (63) 68 (37) 1.0 (0.6, 1.9) 1.1 (0.6, 2.0) 9 (28) 23 (72) 0.5 (0.2, 1.1) 0.5 (0.2, 1.1)
Chocolate
candy bar or
packet
n=518
n=356
rs2900554
AA 31 (21) 116 (79) 1 1 36 (17) 180 (83) 1 1
AC 39 (16) 208 (84) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 24 (20) 96 (80) 1.3 (0.7, 2.2) 1.2 (0.7, 2.2)
CC 21 (17) 103 (83) 0.8 (0.4, 1.4) 0.8 (0.4,1 .5) 1 (5) 19 (95) 0.3 (0.03, 2.0) 0.3 (0.03, 2.0)
rs10772397
AA 30 (15) 168 (85) 1 1 34 (17) 166 (83) 1 1
AG 45 (19) 198 (81) 1.3 (0.8, 2.1) 1.3 (0.8, 2.2) 25 (20) 99 (80) 1.2 (0.7, 2.2) 1.3 (0.7, 2.3)
GG 16 (21) 61 (79) 1.5 (0.8, 2.9) 1.6 (0.8, 3.1) 2 (6) 30 (94) 0.3 (0.07, 1.4) 0.3 (0.07, 1.4)
rs1376251
AA 16 (22) 57 (78) 1 1 34 (17) 165 (83) 1 1
AG 41 (18) 192 (82) 0.8 (0.4, 1.5) 0.8 90.4, 1.5) 25 (20) 100 (80) 1.2 (0.7, 2.2) 1.2 (0.7, 2.3)
GG 34 (16) 178 (84) 0.7 (0.4, 1.3) 0.7 (0.3, 1 .3) 2 (6) 30 (94) 0.3 (0.07, 1.4) 0.3 (0.07, 1.3)
*Logistic regression model adjusted for age, sex, BMI (kg/m2), physical activity (MET.hrs/wk),
smoking status, caloric intake (kcal/day) and season.
61
Table 16: Frequency and odds of high naringin or PTC taste intensity by TAS2R50 genotype in
Caucasians and East Asians
Caucasian East Asian
High Medium/
Low
Unadjusted Adjusted* High Medium/
Low
Unadjusted Adjusted*
n (%) OR (95% CI) n (%) OR (95% CI)
Naringin
Taste
Intensity
n=108
n=103
rs2900554
AA 6 (20) 24 (80) 1 1 14 (24) 45 (76) 1 1
AC 14 (25) 41 (75) 1.4 (0.5, 4.0) 1.5 (0.5, 4.8) 15 (39) 23 (61) 2.1 (0.9, 5.1) 2.2 (0.9, 5.4)
CC 13 (57) 10 (43) 5.2 (1.5, 7.6) 5.4 (1.5, 9.3) 2 (33) 4 (67) 1.6 (0.3, 9.7) 1.5 (0.2, 8.8)
rs10772397
AA 16 (31) 36 (69) 1 1 14 (26) 39 (74) 1 1
AG 13 (32) 28 (68) 1.1 (0.4, 2.5) 1.1 (0.4, 2.8) 15 (38) 25 (62) 1.7 (0.7, 4.1) 1.7 (0.7, 4.1)
GG 4 (27) 11 (73) 0.8 (0.2, 3.0) 0.8 (0.2, 3.1) 2 (20) 8 (80) 0.7 (0.1, 3.7) 0.7 (0.1, 3.6)
rs1376251
AA 4 (21) 15 (79) 1 1 14 (26) 39 (74) 1 1
AG 14 (30) 33 (70) 1.6 (0.5, 5.7) 1.4 (0.4, 5.2) 15 (38) 25 (62) 1.7 (0.7, 4.0) 1.7 (0.7, 4.1)
GG 15 (36) 27 (64) 2.1 (0.6, 7.4) 2.0 (0.5, 7.4) 2 (20) 8 (80) 0.7 (0.1, 3.7) 0.7 (0.1, 3.6)
PTC Taste
Intensity
n= 560
n=404
rs2900554
AA 54 (34) 107 (66) 1 1 111 (45) 133 (55) 1 1
AC 80 (30) 188 (70) 0.8 (0.6, 1.3) 0.9 (0.6, 1.3) 69 (51) 67 (49) 1.2 (0.8, 1.9) 1.3 (0.8, 2.0)
CC 45 (34) 86 (66) 1.0 (0.6, 1.7) 1.1 (0.6, 1.7) 9 (38) 15 (62) 0.7 (0.3, 1.7) 0.7 (0.3, 1.6)
rs10772397
AA 80 (38) 132 (62) 1 1 103 (46 ) 120 (54) 1 1
AG 68 (26) 196 (74) 0.6 (0.4, 0.9) 0.6 (0.4, 0.9) 72 (50) 71 (50) 1.2 (0.8, 1.8) 1.2 (0.8, 1.8)
GG 31 (37) 53 (63) 1.0 (0.6, 1.6) 1.0 (0.6, 1.6) 14 (37) 24 (63) 0.7 (0.3, 1.4) 0.6 (0.3, 1.3)
rs1376251
AA 33 (41) 47 (59) 1 1 103 (46) 120 (54) 1 1
AG 71 (28) 182 (72) 0.6 (0.3, 0.9) 0.6 (0.3, 0.9) 72 (50) 71 (50) 1.2 (0.8, 1.8) 1.2 (0.8, 1.8)
GG 75 (33) 152 (67) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 14 (37) 24 (63) 0.7 (0.3, 1.4) 0.6 (0.3, 1.3)
*Binary logistic regression adjusted for age and sex.
62
Table 17: Frequency and odds of disliking fruits and vegetables by ethnocultural group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI)
Grapefruit
925
Caucasian 93 (17) 446 (83) 1 1
Asian 46 (12) 340 (88) 0.7 (0.4, 0.95) 0.6 (0.4, 0.94)
Grapefruit Juice 909
Caucasian 124 (23) 411 (77) 1 1
Asian 47 (13) 327 (87) 0.5 (0.3, 0.7) 0.5 (0.3, 0.7)
Asparagus 900
Caucasian 37 (7) 491 (93) 1 1
Asian 19 (5) 353 (95) 0.7 (0.4, 1.3) 0.7 (0.4, 1.3)
Broccoli 938
Caucasian 22 (4) 525 (96) 1 1
Asian 4 (1) 387 (99) 0.3 (0.08, 0.7) 0.3 (0.08, 0.7)
Brussels Sprouts 807
Caucasian 106 (22) 380 (78) 1 1
Asian 38 (12) 283 (88) 0.5 (0.3, 0.7) 0.5 (0.4, 0.8)
Cauliflower 921
Caucasian 43 (8) 496 (92) 1 1
Asian 11 (3) 371 (97) 0.3 (0.2, 0.7) 0.3 (0.2, 0.6)
Endives 464
Caucasian 22 (8) 268 (92) 1 1
Asian 10 (6) 164 (94) 0.7 (0.3, 1.6) 0.8 (0.3, 1.7)
Kale
509
Caucasian 26 (9) 268 (91) 1 1
Asian 9 (4) 206 (96) 0.5 (0.2, 1.0) 0.5 (0.2, 1.2)
Onion Raw 929
Caucasian 108 (20) 432 (80) 1 1
Asian 91 (23) 298 (77) 1.2 (0.9, 1.7) 1.1 (0.8, 1.6)
Parsley 910
Caucasian 23 (4) 512 (96) 1 1
Asian 35 (9) 340 (91) 2.3 (1.3, 4.0) 2.1 (1.2, 3.8)
63
Table 17 continued: Frequency and odds of disliking fruits and vegetables by ethnocultural
group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI)
Radicchio 501
Caucasian 33 (10) 313 (90) 1 1
Asian 11 (6) 188 (94) 0.6 (0.3, 1.1) 0.6 (0.3, 1.4)
Radish 882
Caucasian 97 (19) 420 (81) 1 1
Asian 37 (10) 328 (90) 0.5 (0.3, 0.7) 0.5 (0.3, 0.8)
Rapini 345
Caucasian 26 (12) 189 (88) 1 1
Asian 2 (2) 128 (98) 0.1 (0.03, 0.5) 0.09 (0.02, 0.4)
Spinach 938
Caucasian 26 (5) 522 (95) 1 1
Asian 10 (3) 380 (97) 0.5 (0.3, 1.1) 0.5 (0.2, 1.0)
Turnip 775
Caucasian 84 (18) 379 (82) 1 1
Asian 28 (9) 284 (91) 0.5 (0.3, 0.7) 0.4 (0.3, 0.7)
64
Table 18: Frequency and odds of disliking soy products by ethnocultural group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI)
Soymilk
839
Caucasian 107 (24) 347 (76) 1 1
Asian 18 (5) 367 (95) 0.2 (0.1, 0.3) 0.2 (0.09, 0.3)
Tofu 893
Caucasian 76 (15) 426 (85) 1 1
Asian 2 (1) 389 (99) 0.03 (0.007,0.1) 0.03(0.007,0.1)
65
Table 19: Frequency and odds of disliking cocoa-containing products by ethnocultural group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI)
Dark Chocolate
941
Caucasian 38 (6) 510 (93) 1 1
Asian
30 (8) 363 (92) 1.1 (0.7, 1.8) 1.3 (0.6, 2.3)
Bitter-Sweet or
Semi-Sweet
Chocolate
934
Caucasian 27 (5) 517 (95) 1 1
Asian 18 (5) 372 (95) 0.9 (0.5, 1.7) 1.2 (0.6, 2.3)
66
Table 20: Frequency and odds of disliking alcoholic beverages by ethnocultural group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted
n (%) OR (95% CI)
Beer
904
Caucasian 134 (25) 396 (75) 1 1
Asian 124 (33) 250 (67) 1.5 (1.1, 2.0) 1.3 (0.96, 1.8)
Red Wine 987
Caucasian 77 (15) 451 (85) 1 1
Asians 70 (19) 299 (81) 1.4 (1.0, 2.0) 1.3 (0.9, 1.8)
White Wine 876
Caucasian 58 (11) 465 (89) 1 1
Asian 57 (16) 296 (84) 1.5 (1.0, 2.3) 1.5 (1.0, 2.3)
67
Table 21: Frequency and odds of disliking caffeinated beverages by ethnocultural group
*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level
(MET.hrs/wk), smoking status.
Food N Dislike Like Unadjusted Adjusted*
n (%) OR (95% CI)
Coffee
931
Caucasian 117 (22) 424 (78) 1 1
Asian 55 (14) 335 (86) 0.6 (0.4, 0.9) 0.5 (0.4, 0.8)
Green Tea 917
Caucasian 41 (8) 484 (92) 1 1
Asian 1 (1) 391 (99) 0.03 (0.004, 0.2) 0.02 (0.003 ,0.2)
Black Tea 862
Caucasian 43 (9) 457 (91) 1 1
Asian 9 (2) 353 (98) 0.3 (0.1, 0.6) 0.2 (0.1, 0.5)
68
Chapter 6
Discussion
69
The results of this study demonstrate that genetic variation in the genomic region of the
TAS2R50 gene is associated with differences in bitter taste perception, food preferences and
dietary intake in a young Caucasian and East Asian population. The main finding of this study
was that genetic variation in the TAS2R50 gene region was found to be associated with naringin
taste intensity in Caucasians, grapefruit and grapefruit juice preference in Caucasians and East
Asians, and grapefruit intake in Caucasian females, with the rs2900554 SNP accounting for the
association. A greater proportion of individuals homozygous for the C allele for the rs2900554
SNP reported a high naringin taste intensity, disliking of grapefruit and grapefruit juice and not
consuming grapefruit in the past month compared to individuals homozygous for the A allele.
These results indicate that the T2R50 taste receptor may be associated with naringin sensing.
Food preference analyses revealed that the genetic variation in the TAS2R50 gene
region was also associated with preference of spinach, kale, radish, soymilk and bitter-
sweet/semi-sweet chocolate. Associations seen between genetic variation in the TAS2R50 gene
region and food preferences were limited to one ethnocultural group for these foods, except for
kale. This was unlike the association between grapefruit/grapefruit juice preference and
TAS2R50 genotype, which was found to be significant in both East Asians and Caucasians, thus
strengthening this finding. The differences in associations between ethnocultural groups for
TAS2R50 genotype and food preference may be due to cultural differences in food practices or
differences in linkage disequilibrium between SNPs. However, a possibility exists that some
food preference associations are false positives due to multiple comparisons between TAS2R50
genotype and food preferences. After a Bonferroni correction of food preference results for 25
potentially bitter foods or beverages (p<0.002), only grapefruit preference remained
significantly associated with the TAS2R50 gene region. Furthermore, a limited number of
70
individuals reported disliking spinach, kale, radish, soymilk and bitter-sweet/semi-sweet
chocolate to adequately assess the significance of the association between TAS2R50 genotype
and food preference in one or both ethnocultural groups.
Food preference analyses revealed multiple bitter tastants may be ligands of the T2R50
taste receptor. To our knowledge, no study has attempted to deorphanize the T2R50 taste
receptor. Since we found that genetic variation in the TAS2R50 gene region was associated with
the preference of grapefruit, grapefruit juice, kale, spinach, radish, soymilk, and bitter-
sweet/semi-sweet chocolate, it is possible that bitter tastants within these foods bind to the
T2R50 taste receptor. Naringin, a bitter compound in grapefruit, has the greatest potential of
being a ligand for the T2R50 taste receptor, since naringin taste intensity, grapefruit preference
and grapefruit consumption were all associated with the genetic variation in the TAS2R50 gene
region.
It is not known which bitter compounds in kale, spinach, radish, soymilk and bitter-
sweet/semi-sweet chocolate are driving the association between genetic variation in the
TAS2R50 gene region and preference for these foods. It is possible that these foods contain
naringin or structurally similar bitter compounds, however, structural similarity to naringin is
not a requirement for T2R receptor activation. In vitro cell studies have found that several bitter
taste receptors are broadly tuned, detecting a variety of structurally diverse compounds78,84
. Kale
and radishes are part of the cruciferous vegetable family and contain glucosinolates which are
responsible for their characteristic bitter taste38
. Like PTC/PROP, glucosinolates contain
thiocyanate moieties that may be decoded by the same taste receptors3. Spinach and kale are
high in carotenoids such as lutein and β-carotene116
, soymilk contains isoflavones such as
genistein and daidzein5, and bitter-sweet/semi-sweet chocolate contain theobromine, caffeine, l-
71
leucine and catechin flavonoids117
. All these compounds have been reported to elicit a bitter
taste in humans, and may be responsible for their association with genetic variation in the
TAS2R50 gene region.
Research has shown women to be more sensitive to bitter tastes than men50,118,119
. A
greater proportion of females have been found to be PROP supertasters and have been shown to
have a higher density of fungiform papillae and a greater number of taste pores per taste
papillae50,118
. Our study found a significant association between the rs2900554 SNP and
grapefruit consumption in females but not males, with females homozygous for the C allele not
consuming grapefruit in the past month more than females homozygous for the A allele. This
suggests that the bitter taste of naringin may influence the consumption of grapefruit to a greater
extent in women than it does in men. It is possible that among C allele carriers for the
rs2900554 SNP, women are more sensitive to the bitter taste of naringin and thus avoid the
consumption of grapefruit.
Bitter taste intensity analyses revealed that genetic variation in the TAS2R50 gene
region was associated with both naringin and PTC taste intensity. The trend in naringin taste
intensity by TAS2R50 genotype followed the trend in grapefruit and grapefruit juice preference
as well as grapefruit consumption in females for the rs2900554 SNP, thus strengthening the
possibility that this SNP is associated with naringin sensing. Furthermore, this SNP was not
associated with PTC taste intensity.
The rs10772397 SNP and the rs1376251 SNP were found to be associated with PTC
taste intensity in Caucasians, with heterozygotes reporting a significantly lower odds of
experiencing a high PTC taste intensity compared to individuals homozygous for the A allele.
However, this trend was not seen in the East Asian population. Furthermore, food preference
72
analyses of glucosinolate containing foods did not follow a similar trend in association with
TAS2R50 genotype as PTC taste intensity did. Previous research does not support an
association between PTC taste intensity and the TAS2R50 gene region, or any loci on
chromosome 12 where the TAS2R50 gene region is situated. Variation in the TAS2R38 gene,
located on chromosome 7, has been thought to account for up to 85% of PTC threshold
sensitivity87
. Furthermore, loci on chromosome 5120
and 16121
have also been associated with
PTC/PROP sensitivity in genome-wide scans. While an in vitro cell study found that human
embryonic kidney (HEK) 293 cells transfected with the T2R4 taste receptor, located on
chromosome 7, was activated by high levels of PTC77
.
Previous work has found an association between genetic variation in the TAS2R50
gene and myocardial infarction risk (MI). A three stage genome-wide association study14
and a
population-based prospective study15
found an association between the rs1376251 SNP and MI
risk, with G allele carriers experiencing an increased risk of MI compared to individuals
homozygous for the A allele. It was suggested that the rs1376251 SNP may lead to MI by
influencing bitter taste perception and in turn dietary choices14,15
. Our study found that G allele
carriers for the rs1376251 SNP disliked grapefruit significantly more than individuals
homozygous for the A allele in both Caucasians and East Asians, however this observation was
not related to a significant reduction in grapefruit consumption by G allele carriers. The dislike
of kale and spinach were also found to be associated with the rs1376251 SNP in East Asians,
with G allele carriers disliking kale and spinach at a greater frequency than individuals
homozygous for the A allele. Differences in the consumption of kale and spinach were however
not observed, perhaps due to limitations of the FFQ (discussed in section 6.1). Although a clear
trend in dietary consumption by rs1376251 genotype was not observed, our study still shows
73
that grapefruit, kale and spinach preference differ by rs1376251 genotypes. Difference in the
consumption of these types of foods may influence one‟s risk of CVD and MI. Studies
examining food consumption over time in relation to CVD endpoints found that the
consumption of citrus fruit and green leafy or cruciferous vegetables are particularly protective
against CVD122,123
.
Dietary intake is a complex behavior that is affected by physiological, environmental,
economic and sociocultural factors1. A relationship is thought to exist between taste perception,
food preferences and dietary intake, however this relationship is often assessed indirectly,
through multiple studies on different populations54
. A strength of our study is that it assesses
bitter taste perception, food preferences and dietary intake in one large, healthy, multi-
ethnocultural population. We found that genetic variation in the TAS2R50 gene region
influenced bitter taste intensity, food preferences and dietary intake, specifically naringin taste
intensity, grapefruit preference and grapefruit intake. Individuals who had the CC genotype for
the rs2900554 SNP were more likely to experience a high naringin taste intensity and dislike
grapefruit. In females, these individuals were also more likely to never consume grapefruit.
Barriers to grapefruit and bitter food consumption are important to health promotion and disease
prevention. Naringin, the major flavonoid that provides grapefruit with its characteristic bitter
taste, is an antioxidant and has been shown to have the potential to be protective against diseases
such as CVD92-94
and cancer95,96
. The preference of kale, spinach, radish, soymilk and bitter-
sweet/semi-sweet chocolate were associated with genetic variation in the TAS2R50 gene. These
foods have also been shown to contain phytonutrients and consumption of these foods may offer
additional health benefits5.
74
6.1 Limitations
Consideration must be given to potential limitations in the design of this study.
Subjects reported bitter taste intensity on an anchored 9-point category scale which has been
criticized because it may reduce, obscure or reverse variability in responses due to the ceiling
effect limiting response range and use of verbal descriptors that may not be uniformly
understood60,107
. Since there was no definite trend between PTC taste intensity and preference or
intake for glucosinolate containing vegetables by TAS2R50 genotype, it is possible that
misclassification of PTC taste intensity resulted with the use of the 9-point category rating scale.
However, since naringin taste intensity was found to vary by TAS2R50 genotype in the same
direction as grapefruit preference and intake, it is unlikely that severe misclassification in
naringin taste intensity resulted. It is possible that with the use of the generalized Linear
Magnitude Scale (gLMS), which is considered the gold standard50
, further variability in naringin
taste intensity response will be seen between TAS2R50 genotype groups. The gLMS is a scale
with seven descriptive adjectives ranging from “nothing” (or zero) to “strongest imaginable
sensation of any kind”, spaced empirically allowing the scale to have ratio properties50,124
.
Furthermore, the gLMS scale is also thought to avoid bias caused by the ceiling effect and the
use of descriptive adjectives50,107
.
The Toronto Nutrigenomics and Health (TNH) study uses a 196-item, self-
administered, semi-quantitative FFQ, modified from the Willet FFQ109,110
, to assess habitual
consumption over a one month period. Although the Willet FFQ has been extensively validated
for use in North America65
and possesses multiple benefits for use in large epidemiological
studies64
, it may not have been the best method to assess intake of bitter foods in our study. Our
63-item food preference checklist identified preference for grapefruit, spinach, kale, radish,
75
soymilk and bitter-sweet/semi-sweet chocolate as being associated with genetic variation in the
TAS2R50 gene region. Our intent was to then assess if the consumption of these foods were also
associated with TAS2R50 genotype, however the FFQ used to assess habitual consumption in
the TNH study did not measure radish or bitter-sweet/semi-sweet chocolate consumption. A
question that assessed the consumption of chocolate candy bars or packets was used as a
substitute to assess bitter-sweet/semi-sweet chocolate, however chocolate candy bars and
packets may differ significantly in theobromine, caffeine, l-leucine and catechin flavonoids
levels, which are responsible for the bitter taste in bitter-sweet/semi-sweet chocolate117
.
Furthermore, questions assessing kale and cooked spinach consumption grouped these foods
with nutritiously similar foods and therefore direct assessment of these foods was not possible64
.
Finally, assessment of dietary intake over the past month may not take into account seasonal
variation in food intake64
. However, a cross-tabular analysis of grapefruit (p=0.4), grapefruit
juice (p=0.4), kale, spinach (raw p=0.6 and cooked p=0.4), chocolate (p=0.9) and soymilk (0.04)
consumption by season reveals that the consumption of most foods do not vary greatly by
season in our population (data not shown).
Despite the FFQ‟s limitations, a satisfactory analysis of grapefruit and grapefruit juice
intake may have been accomplished. This is because the FFQ used separate questions to assess
consumption of grapefruit and grapefruit juice. Furthermore, the association between grapefruit
consumption in Caucasian females and genetic variation in the TAS2R50 gene region mirrored
the associations between TAS2R50 genotype and grapefruit preference as well as naringin taste
intensity, alluding to the possibility of adequate assessment in this population. Additional
studies examining the association between genetic variation in the TAS2R50 gene region and
76
dietary intake may benefit from using multiple diet records, collected seasonally, over a one
year period, to assess dietary intake of bitter foods.
Our research demonstrated that three SNPs in the TAS2R50 gene region are associated
with, bitter taste intensity, food preferences and dietary intake, however, the functional
significance of these three SNPs is unknown. The rs2900554 SNP is located 3859 base pairs
downstream of the TAS2R50 gene and may be located in the 3‟UTR region of the gene85
.
Genetic variation in the 3‟UTR region of a gene can affect mRNA folding and translational
efficiency. Since a greater proportion of G allele carriers for the rs2900554 SNP reported having
a high naringin taste intensity, disliking grapefruit and never consuming grapefruit, it is possible
that these individuals translate and express a greater number of T2R50 taste receptors. The
rs1376251 and the rs10772397 SNPs are located within the exonic region of the TAS2R50
gene85
. The rs1376251 SNP causes a cysteine to tyrosine amino acid change at position 203 in
the taste receptor protein, which may affect bitter taste perception by changing the structure of
the bitter taste receptor protein85
. While the rs10772397 SNP is a synonymous substitution that
may affect bitter taste perception by influencing the mRNA stability and T2R50 translation85
.
Although a possible genetic function exists for the association between genetic
variation in the TAS2R50 gene region and differences in bitter taste perception, food preference
and dietary intake, there is still a possibility that this association is due to linkage
disequilibrium. Haploview software115
was used to assess the CEPH (Utah residents with
ancestry from northern and western Europe) population from the International HapMap Project
(release 22) and it was found that the TAS2R50 gene was located within a ~ 229 kb haplotype
block that contains eight bitter taste receptor genes and 6 genes that encode proline-rich
protein125
. Since there is high linkage disequilibrium within the haplotype block a possibility
77
exists that the three SNPs examined in our study are not functionally significant, but are in
linkage disequilbrium with functionally significant SNPs in other genes within the haplotype
block. A pair-wise tag SNP analysis (r2
≥ 0.8) of the CEPH population (release 22) revealed that
the rs2900554 and rs1376251 SNPs were markers of genetic variation at other SNPs located in
coding or regulatory regions of additional bitter taste receptor genes125
. The rs2900554 SNP
was found to be strongly linked to a SNP (rs10772420)125
in the coding region of the TAS2R48
gene that causes a missense mutation (Cys299Arg) in the taste receptor protein85
. To our
knowledge no study has attempted to deorphan this receptor. The rs10845293 and rs7134036
SNPs, found in the 5‟ region of the TAS2R44 gene, were also found to be captured by the
rs2900554 SNP125
. The T2R44 receptor has been associated with the sensing of saccharine,
acesulfamce K and aristolochic acid79,83
, however to our knowledge no study has tested naringin
as a possible ligand to the T2R44 receptor. The rs10772420, rs10845293 and rs7134036 SNPs
were also strongly linked to the rs2900554 SNP in a Japanese population in Tokyo and a Han
Chinese population in Beijing (release 22)125
. The possibility that these SNPs are driving the
association between the TAS2R50 gene region and differences in naringin taste perception,
grapefruit preference and grapefruit intake cannot be ruled out in our study.
The rs1376251 SNP was found to be strongly linked (r2
≥ 0.8) to 3 SNPs in the coding
region of the TAS2R49 gene125
, causing missense mutations85
, as well as 6 SNPs in the 5‟ region
of the TAS2R49 gene125
. One SNP in the 5‟ region of the TAS2R48 gene was also strongly
linked to the rs2900554 SNP125
. To our knowledge, no study has identified ligands associated
with the TAS2R48 or TAS2R49 genes. The rs10772397 SNP was not found to capture the
variation of any other SNP within the haplotype block that the TAS2R50 gene is located, in the
CEPH population125
. Additional studies are needed to determine if linkage disequilibrium is
78
responsible for the association between the rs2900554 and rs1376251 SNPs, and bitter taste
intensity, food preferences and dietary intake.
6.2 Future Direction
Deorphanizing the T2R50 bitter taste receptor would help address issues relating to the
possible function of the T2R50 taste receptor. Heterologous expression studies of TAS2R-
cDNA in HEK 293 cells have been used previously to deorphanize T2Rs with success77,80
.
Numerous bitter tastants can be exposed to HEK 293 cells expressing T2R50 taste receptors and
activation of the T2R50 receptor can be monitored through calcium imaging analysis77,80
.
Furthermore, the response of common variants of the T2R50 protein, to bitter tastants, could be
assessed in order to determine if genetic variation in the TAS2R50 gene affects T2R50 receptor
function.
Although studies are needed to directly identify the function of the T2R50 taste
receptor and its variants, additional studies are needed to determine to what extent these variants
influence human sensory perception and dietary behaviors. To our knowledge, the present study
is the first to examine if genetic variation in the TAS2R50 gene region is associated with bitter
taste perception, food preferences and dietary intake. Further studies may examine if genetic
variants in the TAS2R50 gene region affect the perception of additional bitter tastants, as well as
the preference and consumption of additional bitter foods. Our study examined if genetic
variants in the TAS2R50 gene were associated with bitter taste intensity, however bitter taste
thresholds may also be important in explaining how genetic variation in the TAS2R50 gene
affects bitter perception. Furthermore, additional SNPs exist within the TAS2R50 gene and their
effects have yet to be determined.
79
6.3 Implications
Research on how genetic variation in bitter taste influences taste perception, food
preferences and dietary intake in humans is important because it is necessary to explain the
behavioral significance of common variation in the T2R gene family. Results of this study
demonstrate that common variants in the TAS2R50 gene region are associated with differences
in bitter taste perception, food preferences and dietary intake, specifically naringin taste
intensity, grapefruit preference and grapefruit intake. Preferences for other bitter foods were also
associated with genetic variation in the TAS2R50 gene region, however to a lesser extent than
grapefruit preference. These results suggest possible ligands of the T2R50 taste receptor.
Moreover, these results help further the understanding of the association between the TAS2R50
gene and MI risk by establishing a possible explanation for the association between the
rs1376251 SNP and MI risk.
Little is known about how genetic variation in the T2R gene family affects bitter taste
perception. Research in this area may help identify bitter sensitive populations who are at risk of
avoiding the consumption of bitter tasting foods with putative health benefits. Understanding
how bitter taste may be a barrier to a healthy diet can help in the creation of health promotion
programs targeted to bitter sensitive populations. Many phytonutrients have been shown to have
health benefits and are thus sought after in foods5. However, phytonutrients may cause a bitter
taste and are thus removed, or masked using flavorants such as sugar or salt, in order to improve
palatability5. Improving the understanding of how genetic variation affects taste receptor
function and bitter taste perception can help the food industry create novel methods of masking
or modulating bitter tastes that do not compromise the nutritional value of the food.
80
6.4 Conclusion
Bitter taste perception is a variable trait that is influenced by genetic variation in the
T2R bitter taste receptor gene family11
. This study assessed the association between genetic
variation in the genomic region of the TAS2R50 bitter taste receptor gene and differences in
bitter taste perception, food preference and dietary intake. Genetic variation in the TAS2R50
gene region was found to be associated with bitter taste intensity, food preferences and dietary
intake. A higher frequency of C allele carriers for the rs2900554 gene reported experiencing a
high naringin taste intensity, disliking grapefruit and not consuming grapefruit in the past
month, compared to individuals with the AA genotype. This suggests that naringin may be a
ligand of the T2R50 bitter taste receptor. In order to understand the function of the TAS2R50
bitter taste receptor gene and its variants, additional studies should be undertaken to deorphan
the T2R50 taste receptor.
81
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