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A Possible Mechanism behind Autoimmune Disorders Discovered By Genome-WideLinkage and Association Analysis in Celiac Disease

Ostensson, Malin; Montén, Caroline; Bacelis, Jonas; Gudjonsdottir, Audur H.; Adamovic,Svetlana; Ek, Johan; Ascher, Henry; Pollak, Elisabet; Arnell, Henrik; Browaldh, Lars; Agardh,Daniel; Wahlstrom, Jan; Nilsson, Staffan; Torinsson-Naluai, AsaPublished in:PLoS ONE

DOI:10.1371/journal.pone.0070174

2013

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Citation for published version (APA):Ostensson, M., Montén, C., Bacelis, J., Gudjonsdottir, A. H., Adamovic, S., Ek, J., Ascher, H., Pollak, E., Arnell,H., Browaldh, L., Agardh, D., Wahlstrom, J., Nilsson, S., & Torinsson-Naluai, A. (2013). A Possible Mechanismbehind Autoimmune Disorders Discovered By Genome-Wide Linkage and Association Analysis in CeliacDisease. PLoS ONE, 8(8), [e70174]. https://doi.org/10.1371/journal.pone.0070174

Total number of authors:14

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https://doi.org/10.1371/journal.pone.0070174https://portal.research.lu.se/portal/en/publications/a-possible-mechanism-behind-autoimmune-disorders-discovered-by-genomewide-linkage-and-association-analysis-in-celiac-disease(8dc2b002-ad5c-49ea-9c6d-2e735ee7b58b).htmlhttps://doi.org/10.1371/journal.pone.0070174

A Possible Mechanism behind Autoimmune DisordersDiscovered By Genome-Wide Linkage and AssociationAnalysis in Celiac DiseaseMalin Östensson1, Caroline Montén2, Jonas Bacelis3, Audur H. Gudjonsdottir4, Svetlana Adamovic3,

Johan Ek5, Henry Ascher6, Elisabet Pollak3, Henrik Arnell7, Lars Browaldh8, Daniel Agardh2,

Jan Wahlström3, Staffan Nilsson1, Åsa Torinsson-Naluai3,9*

1 Department of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden, 2 Diabetes and Celiac Disease Unit, Department of Clinical Sciences,

Lund University, Malmö, Sweden, 3 Institute of Biomedicine, Department of Medical and Clinical Genetics, Sahlgrenska Academy at the University of Gothenburg,

Gothenburg, Sweden, 4 Queen Silvia Children’s Hospital, Sahlgrenska Academy at the University of Gothenburg, Department of Pediatrics, Gothenburg, Sweden,

5 Buskerud Central Hospital, Department of Pediatrics, Drammen, Norway, 6 Sahlgrenska Academy at the University of Gothenburg, Department of Public Health and

Community Medicine, Unit of Social Medicine, Gothenburg, Sweden, 7 Department of Pediatric Gastroenterology, Hepatology and Nutrition, Karolinska University

Hospital and Division of Pediatrics, CLINTEC, Karolinska Institutet, Stockholm, Sweden, 8 Department of Clinical Science and Education, Karolinska Institutet

Sodersjukhuset, Stockholm, Sweden, 9 Systems Biology Research Centre, Tumor Biology, School of Life Sciences University of Skövde, Skövde, Sweden

Abstract

Celiac disease is a common autoimmune disorder characterized by an intestinal inflammation triggered by gluten, a storageprotein found in wheat, rye and barley. Similar to other autoimmune diseases such as type 1 diabetes, psoriasis andrheumatoid arthritis, celiac disease is the result of an immune response to self-antigens leading to tissue destruction andproduction of autoantibodies. Common diseases like celiac disease have a complex pattern of inheritance with inputs fromboth environmental as well as additive and non-additive genetic factors. In the past few years, Genome Wide AssociationStudies (GWAS) have been successful in finding genetic risk variants behind many common diseases and traits. Tocomplement and add to the previous findings, we performed a GWAS including 206 trios from 97 nuclear Swedish andNorwegian families affected with celiac disease. By stratifying for HLA-DQ, we identified a new genome-wide significant risklocus covering the DUSP10 gene. To further investigate the associations from the GWAS we performed pathway analysesand two-locus interaction analyses. These analyses showed an over-representation of genes involved in type 2 diabetes andidentified a set of candidate mechanisms and genes of which some were selected for mRNA expression analysis using smallintestinal biopsies from 98 patients. Several genes were expressed differently in the small intestinal mucosa from patientswith celiac autoimmunity compared to intestinal mucosa from control patients. From top-scoring regions we identifiedsusceptibility genes in several categories: 1) polarity and epithelial cell functionality; 2) intestinal smooth muscle; 3) growthand energy homeostasis, including proline and glutamine metabolism; and finally 4) innate and adaptive immune system.These genes and pathways, including specific functions of DUSP10, together reveal a new potential biological mechanismthat could influence the genesis of celiac disease, and possibly also other chronic disorders with an inflammatorycomponent.

Citation: Östensson M, Montén C, Bacelis J, Gudjonsdottir AH, Adamovic S, et al. (2013) A Possible Mechanism behind Autoimmune Disorders Discovered ByGenome-Wide Linkage and Association Analysis in Celiac Disease. PLoS ONE 8(8): e70174. doi:10.1371/journal.pone.0070174

Editor: Anna Carla Goldberg, Albert Einstein Institute for Research and Education, Brazil

Received March 18, 2013; Accepted June 14, 2013; Published August 2, 2013

Copyright: � 2013 Östensson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The principal funding for this study was provided through the regional agreement on medical training and clinical research (ALF) between GothenburgCounty Council and Gothenburg University. We also thank Bengt Ihre’s, Claes Groschinsky’s, Magnus Bergvall’s, Nilsson-Ehle’s, Tore Nilson’s and Professor NannaSvartz Foundations, Kungliga fysiografiska sällskapet in Lund, Frimurare barnhusdirektionen, Ruth and Richard Julin Foundation and the Swedish Society ofMedicine. The Swedish Medical Research Council and the Celiac Disease Foundation supported family sample collections. The funders had no role in study design,data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

Celiac disease (CD) is a common chronic disease and even

though most often diagnosed in early childhood, it can present

itself at any age. Most of the individuals with CD remain

undiagnosed and an estimated 2% of the Swedish population is

affected without having been diagnosed [1]. Ongoing disease will

increase the overall risk for developing other chronic inflammatory

diseases, neurological manifestations and malnutrition disorders.

CD is the only autoimmune disorder where the actual genes

responsible for the association in HLA are known (HLA-DQA1 and

HLA-DQB1) [2]. In the past few years Genome Wide Association

Studies (GWAS) have had tremendous success in identifying new

genes, or gene regions, that influence common diseases. These

studies use several hundreds of thousands of genetic markers

(single nucleotide polymorphisms, SNPs) across all human

chromosomes in order to pin down the chromosomal locations

of genes, which could influence the disease.

A large joint effort has been done, not the least in CD, and 40

new CD-associated genetic regions marked by SNPs have been

PLOS ONE | www.plosone.org 1 August 2013 | Volume 8 | Issue 8 | e70174

Celiac Disease Genome-Wide Linkage and Association


discovered [3–7]. However, these genes cannot account for all CD

heritability, and part of the genetic variance that influences disease

development is still unknown [8].

Most GWAS so far have been performed on case control

samples. A case control study design has some advantages

compared to using a family study design. For example, in a case

control design it is possible to select a perfectly matched set of

controls to increase the chance of discovering susceptibility genes,

and furthermore, cases and controls are usually easier to collect

than individuals from the same family. However, using a family

material can be a very good complement to a case control design.

First of all, families with several affected members are likely to

have a stronger genetic component compared to sporadic cases.

Familial cases tend to be enriched for disease-predisposing alleles

and there is an increased power especially for detecting rare

genetic variants [9]. Another important fact is that statistical

analyses based on family data are robust against population

stratification. Already in their paper from 1996, Risch and

Merikangas suggested that all sib-pair families collected for non-

parametric linkage analysis in complex diseases, should be re-run

‘‘Genome-Wide’’ using SNP markers and the potentially more

powerful Transmission Disequilibrium Test (TDT) [10]. The

TDT test in sib-pairs is a test of linkage in the presence of

association. Hereafter we refer to whole genome sibling TDT as

‘‘Linkage GWAS’’.

In this study, we aimed to uncover additional genetic factors in

CD by performing a Linkage GWAS using 206 affected children

(sib-pairs) within 97 nuclear families using the TDT test. In

addition to the Linkage GWAS we explored gene-gene interac-

tions and pathway analyses. We also performed a non-parametric

linkage (NPL) analysis and compared the results with the published

linkage analysis, with microsatellite markers, performed in the

same set of families previously [11]. Furthermore, quantitative

PCR was used to investigate levels of gene expression in small

intestinal biopsies from additional patients with CD autoimmunity

(CAI) and control patients. Finally, we stratified the TDT analysis

on HLA genotype. It has been shown that carrying DQB1*02 on

both chromosomes (i.e. being homozygous), confers higher risk of

developing CD as compared to heterozygote individuals [12]. It is

therefore conceivable that heterozygote individuals may require

more additional risk factors outside HLA, in order to accumulate

sufficient risk to develop CD, compared with homozygous

individuals. Based on this assumption we stratified the patient in

an HLA low-risk group and an HLA high-risk group. By

stratifying the Linkage GWAS, we expected to uncover even

more of the so-called ‘‘missing heritability’’ in CD. This strategy

could identify different risk factors all together or perhaps a more

likely scenario is that the same risk factors outside HLA would just

be more common in the HLA low-risk group.

Results

Genotyping and ImputationWe included single nucleotide polymorphism (SNP) markers

that had a call rate above 97%, which led to the exclusion of 1.3%

of the Omni Express and 0.6% of the 660W-Quad SNP markers.

Out of the 127,535,126 imputed genotypes, 88.3% had a posterior

probability of over 0.95. Approximately 90% of the 944,512 SNP

markers had a minor allele frequency of at least 0.01 after

imputation.

Transmission Disequilibrium Test (TDT)All markers from the TDT analysis are shown in Figure 1a. As

expected, the region around the CD associated HLA genes onchromosome 6 showed the strongest association with the most

significant p-value reaching 4.9610221 at marker rs424232. InTable 1, we present the 35 most significant associations found

outside of HLA (HLA defined as SNP markers located within 27–

34 Mb on chromosome 6). The most significant finding outside of

the HLA region was the marker rs12734338 on chromosome 1,

including the PPP1R12B gene.

HLA Stratified Transmission Disequilibrium Test (TDT)In Figure 1b and Table 2, we present results from the TDT

analysis stratified on the HLA-DQ risk factor. For this analysis 115affected offspring trios were included in the ‘‘low-risk’’ group and

88 trios were put in the ‘‘high-risk’’ group. A region including the

DUSP10 gene (also known as MKP5) reached genome-widesignificance (p-value = 3.861028) in the low-risk group. Figure 1cpresents this region including the most associated SNPs plotted on

the x-axis using SNAP.

Interaction AnalysesSince some markers just below genome-wide significance are

still expected to be true findings, we wanted to try and separate

these from the, in fact, true negative findings (those that show

linkage and association close to genome-wide significance just by

chance). In total, 603 SNP markers from 383 independent regions

and their surrounding genes were identified by three inclusion

criteria (Fig. 2 and Table S1). These genes were subsequently used

for pathway and two-locus interaction analyses.

Two-locus interaction analysis. Two-locus interaction

analysis, identified 582 SNP pairs with a p-value of less than

1.061024 for the test comparing the model M0 of no associationand the general two-locus model MG. Out of these, 101 pairs from

87 regions deviated significantly (p,0.05) from a purely multipli-cative model (MM), which is the best fitting model when at least

one of the SNP markers is false. Under the null hypothesis we

expect to find 29 such pairs. The 101 pairs showed either epistasis

(individuals carry both risk alleles) or evidence of heterogeneity

(individuals carry either the one or the other risk allele from the

two loci).

The results with a p-value ,1.061024 for epistasis and thosewith high p-value (.0.05), which represent pairs that did not showconvincing deviation from the heterogeneity model are listed in

Table 3 and 4. Several loci were in an epistatic relationship with

HLA; rs4899272 (ACTN1), rs1073933 (COX7C), rs10482751(TGFB2), rs571879 (APPL1) and rs7590305 (FABP1). Also,previously identified susceptibility loci for CD were involved in

Figure 1. Manhattanplot of the TDT p-values. a) The location of all genotyped SNPs on chromosomes 1–22 and X plotted on the x-axis. –log10(p-value) result for each SNP and all transmissions on the y-axis. b) The location of all genotyped SNPs on chromosomes 1–22 and X plotted onthe x-axis. –log10(p-value) result for each SNP and all transmissions, to children in the low risk group, on the y-axis. c) Regional plot of associationresults and recombination rates, within the region surrounding DUSP10, generated by SNAP (http://www.broadinstitute.org/mpg/snap/ldplot.php).The x-axis show 500 kb around the most associated SNP. Genomic locations of genes within the region of interest (NCBI Build 36 human assembly)were annotated from the UCSC Genome Browser (arrows). The left y-axis show –log10(p-value) and estimated recombination rates (cM/Mb) fromHapMap Project (NCBI Build 36) are shown in light blue lines.doi:10.1371/journal.pone.0070174.g001



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several interactions: rs4899272 (ACTN1), rs6741418 (STAT1, GLS),

rs13096142 (CCR1,2,3,5), rs10197319 (ICOS, CTLA4) and

rs870875 (CD247).

Pathway analysis. Biological functions clustered by Ingenu-

ity Pathway Analysis (IPA) and Genetrail [13] are shown in

Table 5, 6 and 7. Several clusters were significant after correction

for multiple comparisons. The most significant network implicated

by IPA included DUSP10 (Fig. 3 and Table 8). The second top

network included the MHC complex (HLA) and the third top

network included LPP, which is located within the most

significantly non-HLA associated region identified in CD so far

[3].

Gene ExpressionOut of the 34 selected target genes, three were from the top

associated SNPs (DUSP10, SVIL and PPP1R12B) and the

remaining were genes identified from the two-locus and pathway

analysis. Eight genes showed significant up- or down-regulation

after correction for multiple testing using Bonferroni correction

(Fig. 4). For the top associated genes, several transcript variants

were tested (Table 9). For the PPP1R12B gene, Isoform c and d

(transcript variants NM032103.2 and NM032104.2) also known as

the small subunit (sm-M20) of myosin light chain phosphatase,

show significant up-regulation in patients with CD autoimmunity

compared to control patients. An additional ten genes showed

nominally significant differences in expression (Table 9).

Non-parametric Linkage (NPL)The strongest linkage outside of HLA was detected in

chromosome regions 5q23.2-q33.1, and 1q32.1. In total, thirteen

regions with an NPL point wise p-value below 0.01 were detected

(Fig. 5 and Table 10). In our previous linkage-scan, using almost

the same set of families, we detected only one region (11q23-25)

with a point wise p-value below 0.01 [14]. The reason for the

improved results is mainly the almost perfect information content

achieved by a dense set of highly successful SNP markers

compared to a relatively sparse set of less successful microsatellite

markers. Also in the NPL analysis, the PPP1R12B gene was

located in one of the top regions (1q32.1).

Discussion

This study confirmed some previous GWAS findings and in

addition, it established a new genome-wide significant region

containing the DUSP10 gene. The top markers, rs12144971 and

rs4240931 showed a substantial effect size in the HLA low-risk

group with a transmitted versus non-transmitted allele ratio of 3.11

(Table 2).

DUSP10, TNF-a and Tissue Transglutaminase (TGM2)The protein product of DUSP10 preferentially binds to the

stress-activated p38 MAPK (mitogen-activated protein kinase) and

plays an important role in regulating chemokine induction after

infection by various pathogens [15], and in coordinating MAPK

activity in response to oxidative stress [16]. In previous studies,

both p38 MAPK and DUSP10 have been shown to activate TNF-

a [17,18], of which one also demonstrates that TNF-a up-regulates TGM2 (the gene encoding the main autoantigen in CD

[19]) in intestinal mucosa from untreated CD patients [17].

Whether this up-regulation of TGM2 is of importance for the

immune response leading to formation of IgA-tTG and IgG-tTG

autoantibodies, the serological markers for CD is still unresolved.

Ta

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ble

2.

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stra

tifi

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Tra

nsm

issi

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Dis

eq

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um

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st(T

DT

). Hig

hR

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29

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41

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1.1

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MT

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35

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36

32

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35

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AC

36

34

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69

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15

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0.6

02

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1.9

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27

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91

8.3

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5

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00

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36

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5

4rs

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17

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D1

06

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65

55

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0.5

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E-0

12

24

0.0

81

8.6

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E-0

51

1.4

71

23

10

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8.4

03

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E-0

3

2rs

49

72

80

9D

LX1

DLX

2P

DK

1M

AP

1D

ITG

A6

17

29

25

33

7A

G1

61

21

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0.5

74

.50

E-0

14

11

13

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17

.31

3.1

8E-

05

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57

23

2.4

81

4.4

51

.44

E-0

4

6rs

13

20

75

43

ELO

VL4

SH3

BG

RL2

TT

K8

05

92

60

1A

C1

32

60

.50

4.3

33

.74

E-0

25

21

92

.74

15

.34

8.9

9E-

05

11

.44

65

45

1.4

43

.64

5.6

5E-

02

4rs

10

32

35

5R

G9

MT

D2

C4

orf

17

MT

TP

10

07

58

91

9C

T9

42

0.2

12

1.3

53

.82

E-0

62

43

70

.65

2.7

79

.60

E-0

21

1.2

33

38

10

.41

20

.21

6.9

4E-

06

5rs

11

95

26

77

SCA

MP

1LH

FPL2

77

82

36

90

GA

27

34

0.7

90

.80

3.7

0E-

01

55

17

3.2

42

0.0

67

.52

E-0

61

1.2

38

45

11

.65

8.0

74

.51

E-0

3

1rs

12

74

35

21

DU

SP1

02

20

16

75

71

AG

21

24

0.8

80

.26

.55

E-0

15

01

53

.33

18

.85

1.4

2E-

05

11

.22

74

40

1.8

51

0.1

41

.45

E-0

3

12

rs1

10

68

31

5FB

XW

8H

RK

TES

CR

NFT

21

15

99

49

35

TC

86

1.3

30

.28

5.9

3E-

01

42

60

.15

16

.13

5.9

0E-

05

11

.09

12

32

0.3

89

.09

2.5

7E-

03

6rs

77

45

05

2FB

XL4

C6

orf

16

8U

SP4

5C

OQ

3P

OU

3F2

SFR

S18

99

74

73

31

AG

54

17

3.1

81

9.2

81

.13

E-0

53

22

51

.28

0.8

63

.54

E-0

11

1.0

88

64

22

.05

15

.12

1.0

1E-

04

12

rs1

72

45

50

1P

PFI

A2

80

26

04

70

CT

17

18

0.9

40

.02

88

.66

E-0

14

51

33

.46

17

.66

2.6

5E-

05

11

.02

62

31

2.0

01

0.3

31

.31

E-0

3

1rs

75

44

50

1D

USP

10

22

01

68

98

5T

C2

12

40

.88

0.2

6.5

5E-

01

51

16

3.1

91

8.2

81

.90

E-0

51

1.0

27

54

11

.83

9.9

71

.60

E-0

3

1rs

78

56

27

LPH

N2

82

52

11

65

TC

30

17

1.7

73

.60

5.7

9E-

02

25

61

0.4

11

5.0

71

.04

E-0

41

1.0

25

67

90

.71

3.9

24

.78

E-0

2

8rs

72

01

31

RA

D2

11

17

98

51

96

AG

35

40

0.8

80

.33

5.6

4E-

01

79

34

2.3

21

7.9

22

.30

E-0

51

0.9

01

15

75

1.5

38

.42

3.7

1E-

03

12

rs1

11

04

36

5M

GA

T4

C8

61

64

65

0C

T4

15

70

.72

2.6

11

.06

E-0

13

47

90

.43

17

.92

2.3

0E-

05

10

.81

77

13

60

.57

16

.34

5.2

9E-

05



Pathway AnalysesIn order to discover possible functional connections between

DUSP10 and other genes, we analyzed genes surrounding the top

603 markers. A total of 845 genes were used in the analysis.

Ingenuity pathway analysis (IPA) included DUSP10 within the

most significant network. Also part of this network were GLS and

RGS1, two genes previously identified within significant GWAS

loci [3], as well as the insulin (INS) gene, and the immune

regulatory nuclear factor kappa B (NF-Kb) complex (Fig. 3 andTable 8). The second top network included the MHC complex

(HLA) and also several genes within already identified GWAS loci:

ACTN1, CD247, CCR5, ICOS and STAT1 [3]. In addition, both

IPA and GeneTrail [13] identified T2D genes as the most

significantly overrepresented gene cluster after correction for

multiple testing (Table 5 and 6). Among this set of genes

surrounding the 603 markers, many genes belonged to growth

and nutrient signaling pathways, for example, INS, INSR, EGF,

POMC, TIPRL and PRR5L. There were also related genes directly

involved in energy metabolism; PDK1, COX7C, COQ3 and GLS.

Overlapping Results with Other GWAS FindingsSurprisingly, four out of six top loci identified by a GWAS for

anorexia nervosa [20] and two out of three loci involved in plasma

glucose levels in type 1 diabetic patients [21] were among our 603

and 35 best SNP markers respectively. One of the genes in

anorexia, namely AKAP6, is also associated to fasting insulin-related

traits as well as the autoimmune disease Ankylosing spondylitis

[22]. Of the 40 identified regions in CD, seven regions overlap

with our 603 SNP list (LPP, STAT4/GLS, RGS1, CCR1/CCR3,

PUS10, ICOS/CTLA4 and CD247). Out of the 69 regions reported

in the GWAS catalog for type 1 diabetes, eight overlap with the

regions reported in this study and out of those eight, CTLA4/ICOS

also overlap with the previously reported CD associations.

We compared minor allele frequencies between the previous

CD GWAS by Dubois et al. and our GWAS. In their top 42

associations, there was no SNP below a minor allele frequency of

0.08. In our top 42 associations, we identified five SNPs with a

minor allele frequency below 0.06. This observation could just be

a chance finding or perhaps an indication that rare variants are

easier to discover using families. We also identified a relatively rare

variant in the LPP gene region (rs17283813), with a minor allele

frequency of 0.075. This SNP was not at all significant in the

GWAS by Dubois et al. (Table S1).

Neither was there an association with the DUSP10 region in the

GWAS by Dubois and co-workers. The associated markers in the

DUSP10 region in our GWAS have a minor allele frequency

around 0.5 and are hence very common in the population. It is

difficult to say if this is a population specific effect or if DUSP10

could be detected in an HLA stratified population from another

ethnicity. Interestingly, the DUSP10 region has also been identified

as a risk factor for colon cancer by a meta-analysis of three GWAS

from the UK. This is an indication that colon cancer and CD

could share genetic risk factors.

Key Metabolic Regulators as well as the Top Associatedgene PPP1R12B were Differently Expressed in CD CasesCompared to Controls

Another important finding was the difference between cases and

controls and their gene expression patterns in the small intestine.

Eight of the 34 candidate genes selected for quantitative

measurements of gene expression, including PPP1R12B, PDK1,

GLS, PRR5L and the INSR, showed significant up or down

regulation of mRNA levels in cases compared to controls (Fig. 4).

Ta

ble

2.

Co

nt.

Hig

hR

isk

Lo

wri

skA

ll

Ch

rS

NP

ge

ne

(s)

BP

A1

A2

TU

T/U

ch

isq

p-v

alu

eT

UT

/Uc

his

qp

-va

lue

we

igh

ted

ch

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Figure 2. Illustration of the three inclusion criteria used for pathway and interaction analyses. The first criteria of p-values less than3.061024 in the linkage TDT analysis resulted in a total of 477 markers. The second criteria included a comparison of the results from this study withthe results from the study by Dubois et al. [3]. We included 118 SNPs that had a simple score based on a combined p-value less than 5.061025 and inthe same allelic direction in both datasets. The third criteria involved selecting markers with a large effect size. We included 65 markers which had aratio of transmitted versus not transmitted (T/NT) alleles of over 5 or below 0.2, combined with a p-value of less than 2.061023.doi:10.1371/journal.pone.0070174.g002

Table 3. The top epistasis interaction results from the 101 two-locus interaction analysis.

Snp 1 Genes chr Snp 2 Genes chr N P02 P12 PM2

rs2187668 HLADQ 6 rs4899272 ACTN1 14 95 4.0E-17 1.42E-13 4.E-02

rs204034 SHISA9 16 94 1.3E-14 1.09E-12 5.E-02

rs571879 APPL1 HESX1 IL17RD. DNHD2. ASB14 3 94 2.3E-15 5.21E-11 3.E-02

rs204999 HLA 6 rs1073933 COX7C 5 94 9.9E-14 9.27E-12 3.E-02

rs11836636 ATXN7L3B KCNC2 12 91 1.1E-12 8.15E-11 4.E-02

rs7745052 FBXL4. C6orf168. USP45. COQ3. POU3F2. SFRS18 6 92 2.3E-05 1.79E-05 4.E-02

rs10749738 FOXD3 1 rs1373649 BMPR1B 4 93 2.7E-05 1.78E-05 4.E-02

rs3860295 RASSF5 IKBKE 1 rs13096142 CCR5 CCR3 LTF CCR2 CCR1 3 95 1.1E-05 6.48E-06 1.E-02

rs9396802 KIF13A NUP153 FAM8A1 6 rs2194633 NETO1 18 95 3.8E-06 6.82E-06 2.E-02

rs9296204 MTCH1 PI16 6 rs4385459 LY96 JPH1 GDAP1 TMEM70 TCEB1 8 95 2.8E-05 9.91E-06 3.E-02

rs9397928 ARID1B* 6 rs2415836 FSCB* 14 93 2.8E-05 1.75E-05 3.E-03

rs1145212 APOA5 ZNF259 BUD13 11 rs10083673 MYO5A 15 95 6.6E-05 1.77E-05 2.E-03

rs7756191 DNAH8 6 rs1108001 NAV2 HTATIP2 DBX1 PRMT3 11 95 3.5E-05 2.60E-05 3.E-03

rs10197319 ICOS CTLA4 2 rs882820 SRL TFAP4 16 94 1.4E-05 3.03E-05 3.E-05

rs4899272 ACTN1 14 rs17703807 C15orf41 15 83 2.9E-05 8.68E-05 1.E-02

All SNP pairs which reached an interaction p-value of P12,1.061024, in addition to PM2,0.05.

*closest known gene. located .500 kb from associated SNP.P02– p-value for the test statistic comparing the models M0 (no association) and the general model MG.P12– p-value for the test test comparing the models MR (heterogeneity) and the general model MG.PM2– p-value for the test comparing the models MM (multiplicative) and the general model MG.doi:10.1371/journal.pone.0070174.t003



This could very well be a consequence of an ongoing inflammation

or possibly also indicate an underlying metabolic difference.

Glutamine is converted to glutamate by the enzyme glutaminase

(GLS). In turn, glutamate can be converted to proline and

subsequently catabolized by the enzyme proline dehydrogenase

(PRODH) resulting in the production of reactive oxygen species

and apoptosis [23]. In the present study, we show that the

expression of GLS is down-regulated and PDK1 is up-regulated in

cases. Interestingly, a previous study has shown that cell lines with

a known familiar mutation for amyotrophic lateral sclerosis (ALS)

have the same expression pattern, with up-regulated PDK1 and

down-regulated GLS, as compared to the wild-type cell line [24].

PRR5L (also called Protor-2) belong to the TOR signaling

pathway. Our results show an up-regulated PRR5L expression in

cases (Fig. 4). Like DUSP10, the protein product from PRR5L has

been shown to stimulate an increased TNF-a expression [25].Another gene, connected to the MAPK pathway and which was

identified both by our two-locus interaction analysis and in

significant biological functions implied by IPA, was the APPL1

gene. APPL1 is a binding partner of the protein kinase Akt2 and a

key regulator of insulin signaling [26]. It takes part in adiponectin

signaling to stimulate activity of p38 MAPK in muscle cells [27]

and is a critical regulator of the crosstalk between adiponectin

signaling and insulin signaling pathways [28]. We could detect

expression of both APPL1 and APPL2 in small intestinal biopsies

and a significantly lower expression of APPL2 was detected in the

CD autoimmunity cases as compared to controls (Fig. 4). Lower

expression of APPL2 levels lead to enhanced adiponectin

stimulated glucose uptake and fatty acid oxidation [29]. A SNP

(rs10861406) included in the top 603 list was located upstream of

the APPL2 gene, however the promotor of this gene was on the

opposite side of a recombination hotspot and therefore not

included in the gene list for pathway analyses.

The most significant finding from our non-stratified linkage

GWAS analysis was the association with the PPP1R12B gene

region. PPP1R12B is involved in smooth muscle contractibility and

mediates binding to myosin [30]. Myosin light chain phosphatase

from smooth muscle consists of a catalytic subunit (PP1c) and two

non-catalytic subunits, M130 and M20. The two non-catalytic

subunits are both encoded by the PPP1R12B gene. The M130

transcript was not differentially expressed between CD autoim-

munity and control patients while the small subunit ‘‘M20’’

showed a significantly higher expression in patients with CD

autoimmunity. (PPP1R12B_22 in Fig. 4) Several other genes

located close to top markers such as the PPP3CA, ACTN1, MYO1B,

MYO5A, MAPK1, PRKCH, PRKCQ, PRKACB, PRR5L and NTS

genes, are connected to smooth muscle when examining their

function by using KEGG [31] and Gene Ontology [32].

Table 4. The top heterogeneity results from the 101 two-locus interaction analysis.

SNP1 Genes chr SNP2 Genes chr N P02 P12 PM2

rs4899272 ACTN1 14 rs4820682 SRRD HPS4 TFIP11 ASPHD2 MIR548JTPST2 CRYBB1 CRYBA4

22 95 7.1E-06 6.97E-02 2.E-02

rs4426448 DOK6 18 94 9.5E-06 6.80E-01 1.E-02

rs870875 CD247 1 94 9.4E-05 7.19E-02 3.E-02

rs4842007 PAEP 9 95 8.6E-06 5.66E-01 4.E-02

rs571879 APPL1 HESX1 IL17RDDNHD2 ASB14

3 rs4385459 LY96 JPH1 GDAP1 TMEM70 TCEB1 8 94 4.1E-05 5.81E-01 5.E-02

rs7590305 FABP1 THNSL2 2 rs390495 MICAL3 22 93 7.0E-05 9.09E-01 3.E-03

rs7745052 FBXL4 C6orf168 USP45COQ3 POU3F2 SFRS18

6 rs4930144 IGF2AS TH MRPL23 TNNT3 SYT8 ASCL2TNNI2 LSP1 IGF2 INS-IGF2 INS H19

11 50 1.9E-05 5.30E-01 3.E-02

rs10749738 FOXD3 1 rs10498982 EPHA7* 6 93 2.0E-05 1.95E-01 4.E-02

rs2605393 STAC 3 63 7.3E-05 4.37E-01 4.E-02

rs2187668 HLADQ 6 rs11013804 KIAA1217 10 94 3.5E-14 8.40E-02 2.E-02

rs1676235 ESRRB ANGEL1 VASH1 14 43 2.0E-07 8.55E-02 3.E-02

rs958802 KANK4 L1TD1 INADL 1 rs2194633 NETO1 18 95 1.9E-05 5.55E-01 3.E-02

rs2345981 KHDRBS2 6 rs6495130 RYR3 15 94 6.1E-05 1.58E-01 3.E-02

rs11940562 PCDH7* 4 rs4905043 ITPK1 CHGA 14 44 4.6E-05 2.77E-01 2.E-02

rs4656538 POU2F1 1 rs2187668 HLADQ 6 94 3.0E-13 1.19E-01 5.E-02

rs3860295 RASSF5 IKBKE 1 rs7046385 SMC2 9 94 5.3E-05 1.07E-01 2.E-02

rs6741418 STAT1 GLS STAT4 2 rs10798004 C1orf25 C1orf26 IVNS1ABP RNF2 1 87 7.2E-05 7.68E-02 4.E-02

rs1571812 VLDLR 9 86 3.0E-05 9.19E-02 4.E-02

rs882820 SRL TFAP4 16 87 4.2E-05 3.52E-01 6.E-03

rs1470379 VIM 10 82 1.0E-05 3.70E-01 8.E-03

rs10946659 DCDC2 NRSN1 6 87 1.9E-06 6.64E-01 9.E-03

rs10482751 TGFB2 1 rs1571812 VLDLR 9 92 5.2E-05 1.86E-01 1.E-02

All SNP pairs which reached an interaction p-value of P12.0.05, in addition to PM2,0.05.*closest known gene located .500 kb from associated SNP.P02– p-value for the test statistic comparing the models M0 (no association) and the general model MG.P12– p-value for the test test comparing the models MR (heterogeneity) and the general model MG.PM2– p-value for the test comparing the models MM (multiplicative) and the general model MG.doi:10.1371/journal.pone.0070174.t004



The second most significant region in the HLA-stratified

analysis after DUSP10 contains the SVIL gene. The product of

this gene has been suggested to bind LPP [33]. In our two-locus

interaction analysis, the LPP locus and a locus containing KIF13A

was one of the 101 interaction pairs. KIF13A is a motor protein,

which shuttles vesicles containing AP-1 and the mannnose-6-

phosphate receptor [34]. KIF13A was significantly down-regulated

in intestinal biopsies from CD patients in our gene expression

analysis (Fig. 4). SVIL is associated with cell-focal adhesions

(substrate contacts), which are important for rapidly moving cells

such as for example immune cells but also for motility and polarity

of intestinal epithelial cells. SVIL mRNA was down-regulated in

our gene expression analysis, however, not significant after

correction for multiple testing.

Proline and Glutamine Metabolism - Part of a ‘‘DangerSignal’’

Amoebiasis was one of the nominally significant pathways in the

GeneTrail analysis of genes surrounding the two-locus interaction

SNPs (Table 7). Several of these genes were also present together

with DUSP10 and the MHC class II genes in the two most

significant IPA generated networks (marked in bold text in

Figure 3. Ingenuity network 1. The top network identified by the Ingenuity IPA software using genes surrounding all 603 most associated SNPsfrom the TDT analysis. Molecules in gray were present among the genes from our TDT analysis and molecules in white were added by the IPAsoftware. The DUSP10 gene is marked in yellow.doi:10.1371/journal.pone.0070174.g003



Table 5. Biological functions of genes surrounding the 603 top associated SNPs. Results from IPA.

FunctionAnnotation

p-value(Raw)

B-H p-value* Molecules Molecules

non-insulin-dependentdiabetesmellitus

0.0000057 0.025 ABCC8. ADRA1B. ADRA1D. AGT. APOA5. ATP10A. BCL2L11. CCR5. CD38. CNTNAP2. FOXP1. FTO. HFE.HFE2. INS. INSR. KCNJ11. KIRREL3. KLF10. mir-154. mir-448. MTTP. PBX3. PIEZO2. PPARA. PPP3CA. PRDM10.RGS5. VEGFA. ZMYM2

30

quantity ofmetal

0.0000082 0.025 ABCC8. ADRA1B. AGT. APLP2. ATP2B3. BCL2. BMP2. BTK. CAMLG. CCR5. CD247. CD38. CHGA. CX3CR1.CXCL13. DARC. DCN. DVL1. EGF (includes EG:13645). FBXL5. FCER1A. GNA14. GNB1. HFE. HFE2. IGF2. INS.INSR. KCNJ11. LTF. NTS. NUCB2. POMC. PRL. PRNP. PTGDR2. RGS1. RYR3. SELL. SOD1. TRPM8. TXNIP.VAV3. VEGFA

44

incorporationof thymidine

0.000010 0.025 AGT. AKAP13. BMP2. CD40. EGF (includes EG:13645). IGF2. INS. INSR. PRL. THBS2. TNFSF13B. VEGFA. WT1 13

quantity ofCa2+

0.000018 0.033 ABCC8. ADRA1B. AGT. ATP2B3. BCL2. BTK. CAMLG. CCR5. CD247. CD38. CHGA. CX3CR1. CXCL13. DARC.DCN. DVL1. EGF (includes EG:13645). FCER1A. GNA14. GNB1. IGF2. INS. INSR. KCNJ11. NTS. NUCB2. POMC.PRL. PRNP. PTGDR2. RGS1. RYR3. SELL. SOD1. TRPM8. VAV3. VEGFA

37

eyedevelopment

0.000022 0.033 BID. BMPR1B. CD247. CHD7. CRYBB2. CX3CR1. DLX1. DNMT3A. EBF3. EGF (includes EG:13645). FJX1. FTO.GJA3. H19. HESX1. IFT88. IGF2. IRX3. ITGA6. LUM. MITF. OGN. PAX5. PROM1. PRRX2. PYGO1. SEMA5A. SOD1.STAT1. TGFB2. TH. THBS2. THRB. TUB. USH2A. VEGFA. WT1

37

diabetesmellitus

0.000027 0.034 ABCC8. ABCG1. ABT1. ADRA1B. ADRA1D. AGT. APOA5. ATP10A. BCL2. BCL2L11. BTC. BTN2A1. BTN3A2. CBLB.CCR5. CD200. CD38. CD40. CNTNAP2. CYBA. E2F3. ENAH. FOXP1. FTO. GABRD. HFE. HFE2. HIST1H3A(includes others). HTR2C. ICOS. IGF2-AS1. INS. INSR. KCNJ11. KIRREL3. KLF10. mir-154. mir-448. MTTP. PBX3.PDE8A. PGM1. PIEZO2. PPARA. PPP3CA. PRDM10. PRSS16. PRUNE2. PXDNL. RGS1. RGS5. SELL. SOD1. TH.THRB. TSPO. VEGFA. ZMYM2

58

angiogenesisof bone

0.000032 0.034 BMP2. NOG. TGFB2. VEGFA 4

quantity ofmetal ion

0.000071 0.043 ABCC8. ADRA1B. AGT. ATP2B3. BCL2. BTK. CAMLG. CCR5. CD247. CD38. CHGA. CX3CR1. CXCL13. DARC.DCN. DVL1. EGF (includes EG:13645). FCER1A. GNA14. GNB1. IGF2. INS. INSR. KCNJ11. NTS. NUCB2. POMC.PRL. PRNP. PTGDR2. RGS1. RYR3. SELL. SOD1. TRPM8. TXNIP. VAV3. VEGFA

38

developmentof head

0.000069 0.043 BCL2. BCL2L11. BID. BMP2. BMPR1B. CD247. CHD7. CRYBB2. CX3CR1. DLX1. DNMT3A. EBF3. EGF (includesEG:13645). FJX1. FTO. GJA3. H19. HESX1. IFT88. IGF2. IRX3. ITGA6. LUM. MITF. MYO5A. NOG. OGN. PAX5.PROM1. PRRX2. PYGO1. SEMA5A. SOD1. STAT1. TGFB2. TH. THBS2. THRB. TUB. USH2A. VEGFA. WT1

42

migration ofcells

0.000057 0.043 ADI1 (includes EG:104923). AGT. APLP2. APPL1. ARHGAP5. B3GAT1. BCL2. BGN. BID. BMP2. BTC. BTK. CBLB.CCR5. CD200. CD247. CD36. CD38. CD40. CD99. CHGA. CMA1. CNTNAP2. CSF2RA. CTBP2. CTNNA2. CTSG.CX3CR1. CXCL13. DARC. DCDC2. DCN. DISC1. DLX1. DYX1C1. E2F3. EBF3. EGF (includes EG:13645). ELMO2.FCER1A. FH. FHL2. GFRA1. GNA12. GRIA2. GZMB. HTATIP2. ICOS. IGF2. INS. INSR. ITGA6. KIAA0319. LAMA2. LPP.LSP1 (includes EG:16985). LTF. LUM. LY96 (includes EG:17087). MAPK1. MNX1. MTAP. MYO10. MYO1F. NAV1.NOG. NPTX2. NTS. NUCB2. PAEP. PDPN (includes EG:10630). PEX11B. PEX13. POMC. POU3F2. PPARA. PPM1F.PRKCQ. PRKCZ. PRL. PRNP. PROK2. PTGDR2. PTGES. PTK2 (includes EG:14083). PVR. RASSF5. RGS1. SATB2. SELL.SEMA5A. SOD1. STAT1. TDP2. TGFB2. THBS2. TIAM1. TNFAIP8. TNFRSF18. TNFRSF4. TNFSF13B. TSPO. UNC5C.VASH1. VAV3. VEGFA. VIM. WWOX

108

cell movement0.000073 0.043 ADCY10. ADI1 (includes EG:104923). AGT. APLP2. APPL1. ARHGAP5. B3GAT1. BCL2. BGN. BID. BMP2. BTC.BTK. CATSPER3. CBLB. CCR5. CD200. CD247. CD36. CD38. CD40. CD99. CHGA. CMA1. CNTNAP2. CSF2RA.CTBP2. CTNNA2. CTSG. CX3CR1. CXCL13. DARC. DCDC2. DCN. DISC1. DLX1. DYX1C1. E2F3. EBF3. EGF (includesEG:13645). ELMO2. ENAH. FCER1A. FH. FHL2. GFRA1. GNA12. GNB1. GRIA2. GZMB. HTATIP2. ICOS. IFT88. IGF2.INS. INSR. ITGA6. KIAA0319. LAMA2. LPP. LSP1 (includes EG:16985). LTF. LUM. LY96 (includes EG:17087). MAPK1.MNX1. MTAP. MYO10. MYO1F. NAV1. NCK2. NOG. NPTX2. NTS. NUCB2. PAEP. PDPN (includes EG:10630). PEX11B.PEX13. POMC. POU3F2. PPARA. PPM1F. PRKCQ. PRKCZ. PRL. PRNP. PROK2. PTGDR2. PTGES. PTK2 (includesEG:14083). PVR. RASSF5. RGS1. RGS10. SATB2. SELL. SEMA5A. SOD1. SPAG16. STAT1. TAS1R3. TDP2. TGFB2. THBS2.THRB. TIAM1. TNFAIP8. TNFRSF18. TNFRSF4. TNFSF13B. TSPO. UNC5C. VASH1. VAV3. VEGFA. VIM. WWOX

118

apoptosis 0.000069 0.043 ABCG1. ADCY10. ADI1 (includes EG:104923). ADRA1B. ADRA1D. AGPAT2. AGT. APPL1. ATXN1. BCL2. BCL2L11.BCL2L13. BGN. BID. BIK. BMP2. BMPR1B. BTC. BTK. CACNA1A. CBLB. CCDC86. CCNI. CCNL2. CCR5. CD200.CD247. CD36. CD38. CD40. CD5L. CD99. CDK11A/CDK11B. CSF2RA. CTBP2. CTSG. CX3CR1. CYBA. DACH1. DCN.DLX1. DNMT3A. DUSP10. DVL1. E2F3. EGF (includes EG:13645). EPHA7. EPHX1. EPM2A. FABP1. FANCC. FBXL5.FCER1A. FHL2. FOXP1. FSTL3. GFRA1. GNA12. GRIA2. GZMB. HFE. HSF2. HTATIP2. ICOS. IFNE. IGF2. IKBKE.IL17RD. INS. INSR. IPPK. ITGA6. ITGB3BP. ITPK1. IVNS1ABP. KIFAP3. KLF10. LAMA2. LIG4. LSP1 (includes EG:16985).LTF. LUM. MAGED1. MAGEH1. MAPK1. mir-154. mir-506. MITF. MLLT3. MNAT1. MTCH1. NELL1. NOG. NPTX2. NTS.PAEP. PAWR. PAX5. PDCD6IP. PEX11B. PKN2. POLH. POMC. PPARA. PPM1F. PPP2R4. PPP3CA. PRAME. PRKCH.PRKCQ. PRKCZ. PRL. PRNP. PRPF19. PRUNE2. PTGES. PTK2 (includes EG:14083). PUS10. RASSF5. RGS5. RNASEH1.SELL. SGCG. SLC25A6. SMARCA2. SMOX. SOD1. SPAG16. ST14. STAT1. TFAP4. TGFB2. THBS2. TIAM1. TMEM109.TMEM132A. TNFAIP8. TNFRSF18. TNFRSF4. TNFSF13B. TREX2. TRPS1. TSPO. TUB. TXNIP. UNC5C. VEGFA. VIM.VPS13A. WT1. WWOX. XPO1. ZMYM2

153

quantity ofleukocytes

0.000076 0.043 AGT. BCL2. BCL2L11. BID. BIK. BST1 (includes EG:12182). BTK. CARD11. CBLB. CCR5. CD200. CD247. CD36.CD38. CD40. CD5L. CRLF2. CX3CR1. CXCL13. DCN. DMD. DUSP10. FABP1. FANCC. FCER1A. FOXP1. GNA12.HESX1. ICOS. IGF2. INS. ITGA6. KDM5A. KIFAP3. KLF10. LAMA2. LIG4. LSP1 (includes EG:16985). LUM. NOG.PAWR. PAX5. PRKCQ. PRL. PRNP. PROK2. PTGDR2. PTGES. PTK2 (includes EG:14083). RASSF5. RGS1. RGS10.SELL. SOD1. ST14. STAM. STAT1. TNFRSF4. TNFSF13B. TOX. TXNIP. VAV3. VEGFA. VPREB1. WWOX

65

A total of 823 genes surrounding the 603 top associated SNPs were put into the IPA software.Surrounding genes were defined by either Grail (www.broadinstitute.org/mpg/grail/) or the Genome Browser (http://genome.ucsc.edu/). Gene families located in thesame region were manually curated so that only one gene in each family remained in each region, based on a similar official gene symbol.*Hochberg Y, Benjamini Y. Statistics in medicine 1990; 9:811–8.doi:10.1371/journal.pone.0070174.t005



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a possible mechanism behind autoimmune disorders discovered...

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