variación genética en el genoma

Variación genética en el genoma

A G A G T T C T G C T C GA G G G T T A T G C G C G




International HapMap Project (http://www.hapmap.org)

http://www.hapmap.org/index.html.en


1. Disponer datos genotípicos diferentes grupos

étnicos

2. Selección TagSNPs estudio asociación -> Potencial para Whole Genome Association studies

3. Evaluación significación estadística e interpretación resultados

4. Estudio de los alelos menos comunes

5. Estudio variación estructural

6. Farmacogenómica

Aplicaciones biomédicas

Bases de datos de variación genética

Online Mendelian Inheritance in Man

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=OMIM

Catalog of human genetic and genomic disorders

International HapMap Project

http://www.hapmap.org

Personalized Genomes: J. Watson’s genome

http://jimwatsonsequence.cshl.edu/cgi-perl/gbrowse/jwsequence/

Entrez dbSNP http://www.ncbi.nlm.nih.gov/projects/SNP/

Database of Genotype and Phenotype

http://view.ncbi.nlm.nih.gov/dbgap

MamPol

DPDB

http://mampol.uab.cat

http://dpdb.uab.cat

Human Genome Variation Database

http://hgvbase.cgb.ki.se/








http://www.hapmap.org/

http://www.hapmap.org/



http://www.ncbi.nlm.nih.gov/projects/SNP/




http://mampol.uab.cat/

http://dpdb.uab.cat/





Human genetic & phenotypic diversity database

SNP1 SNP2 SNP3

Secuenceindividual 1

Secuence individual 2

...

...

A/A

A/C

G/C

C/C

G/T

T/T

...

Disease 1

Healthy

Phenotype

Estimation phenotypic effect

Association studies: Phenotpyic effect of SNPs

Genotype

...

Trait i

x2

x1

Cervical

Cancer

BioBanks: Studies of cohorts at a great scale

•deCODE (Islandia)•Estonia•Germany•Canada•Japan•China

USA

Association Studies

Association Studies

•Study design

•Statistical analyses

1st phase: DesignStudy designs

Statistical analysis methods

2nd phase: Statistical analysis

Statistical analyses

in Association Studies

1. Data validation

2. Genetic description1. Unidimensional (snp by snp)2. Multidimensional

3. Test for association genotype-phenotype1. snp by snp2. Multisnp / haplotype /tagSNP3. Power assessment

4. Predictive model

Steps

2nd phase


in Association Studies

1. Data validation (error sources: sampling, genotyping)

• Checking with SNPref • Hardy-Weinberg proportions (separately for controls and

cases)• Consistence among samples• Stratification (genetic markers)

Step

Hardy-Weinberg Test

SNP rs1137933

Genotype frequencies

SNP diallelic: A & a with p and q relative freq.

Genotypic HW proportions AA, Aa & aa p2, 2pq & q2

Three statistics:

(i) That based on the Pearson (χ2) test statistic(ii) That based on the Likelihood ratio test statistic (G test). (iii) An exact test

CT CC TT

Control 38

(50.1) 76

(70.0) 15 (8.9)

Case 105 (95.2)

122 (126.9)

13 (17.9)

http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=1137933

Example of Hardy-Weinberg Test

CT CC TT

Control 38

(50.1) 76

(70.0) 15 (8.9)

SNP rs1137933

Genotypes

Pearson (χ2) test statistic

X2 = Σ (Oi-Ei) 2 / Ei

p = f(C)= f(CC) + f(CT)/2q = 1 – p

--------- Genotype SS SF FF Total

Number, obs 38 76 15 = 129 = N Frequency, exp p2 2pq q2 = 1,00 Number, exp p2N 2pqN q2N = N Number, exp 50.1 70.0 8.9 = 129 ----------

Control

Likelhood ratio (G) test statistic

G = - 2 Σ ln (Oi / Ei)


SNP rs1137933

Control

Genotypes = 129

p1 = f(C)= 0,736p2 = f(T)= 0,264

ChiSquare (1 gl) = 7,5** p = 0,00617

G (Likelihood ratio) (1 gl) = 7,06** p = 0,00788

Case

genotypes = 240

p1 = f(C)= 0,727p2 = f(T)= 0,273

ChiSquare (1 gl) = 2,52 ns p = 0,11241

G (Likelihood ratio) (1 gl) = 2,63 ns p = 0,10486

CT CC TT

Control 38

(50.1) 76

(70.0) 15 (8.9)

Case 105 (95.2)

122 (126.9)

13 (17.9)

Example of Hardy-Weinberg Test


Genetic description:

SNP by SNP

CT CC TT

Control 38

(29,5%) 76

(58,9%) 15

(11,6%)

Case 105

(43,8%) 122

(50,8%) 13

(5,4%)

Genotype frequencies

C T

Control 190

(73,6%) 68

(26,4%)

Case 349

(72,7%) 131

(27,3%)

Allele frequencies

SNP rs1137933


Haplotype inference

Haplotype 1 acgtagcatcgtatgcgttagacgggggggtagcaccagtacagHaplotype 2 acgtagcatcgtatgcgttagacgggggggtagcaccagtacagHaplotype 3 acgtagcatcgtatgcgttagacgggggggtagcaccagtacagHaplotype 4 acgtagcatcgtttgcgttagacgggggggtagcaccagtacagHaplotype 5 acgtagcatcgtttgcgttagacgggggggtagcaccagtacagHaplotype 6 acgtagcatcgtttgcgttagacggcatggcaccggcagtacagHaplotype 7 acgtagcatcgtttgcgttagacggcatggcaccggcagtacagHaplotype 8 acgtagcatcgtttgcgttagacggcatggcaccggcagtacagHaplotype 9 acgtagcatcgtttgcgttagacggcatggcaccggcagtacag


MultiSNP

a/t g/c ->

a) a g

t c

b) a c

t g

Genotypes Possible haplotypes

Frequency Haplotype estimates

Haplotype

SNPrs1042522

SNPrs12951053

SNPrs8064946

SNPrs6541003

SNPrs4846049

SNPrs4646421

SNPrs4986885

SNPrs91590

7

SNPrs4147567

SNPrs2266633

Total

1 G A G G T C G C G G 0.1056

2 G A G A G C G C G G 0.0767

3 G A G A G C G C A G 0.0485

4 G A G A G C G A G G 0.0423

5 G A C G G T G A A A 0.0378

6 G A C A G T A A A A 0.0282

7 G A G G G C G C A G 0.0276

Linkage disequilibrium measure (D’ Lewontin)

B1 B2 Total

A1 p11 = p1q1 + D p12 = p1q2 - D p1

A2 p21 = p2q1 - D p22 = p2q2 + D p2

Total q1 q2 1D’ = D / Dmax

r = D’ / square root (p1 p2 q1 q2)


MultiSNP

Linkage Disequilibrium representation

Linkage blocks

Recombination Hotspot

Associated SitesTagSNPs


in Association StudiesSteps

1. Data validation

2. Genetic description1. Unidimensional (snp by snp)2. Multidimensional

3. Test for association genotype-phenotype1. snp by snp2. Multisnp / haplotype /tagSNP3. Power assessment

4. Predictive model

Genetic - phenotype Association -> Guilty by association

Case vs Control

SNP2 (A/T) 100% A 0% T 0% A 100% T Mendelian SNP

SNP3 (T/G) 80% T 20% G 60% T 40% G QTL SNP

SNPn

SNP1 (G/C) 40% G 60% C 40% G 60% C Neutral SNP

Case – control study

CT CC TT

Control 38

(29,5%) 76

(58,9%) 15

(11,6%)

Case 105

(43,8%) 122

(50,8%) 13

(5,4%)

Genotypic

C T

Control 190

(73,6%) 68

(26,4%)

Case 349

(72,7%) 131

(27,3%)

Allele

Test for association (snp by snp)

ChiSquare (2 gl) = 9,71** p = 0,00779

G (Likelihood ratio) (2 gl) = 9,67** p = 0,00795

ChiSquare (1 gl) = 0,07 p = 0,79134 G (Likelihood ratio) (1 gl) = 0,07 p = 0,79134

Odds Ratio (OR) = 1,05

Risk Ratio (RR) = 1,02

SNP rs1137933

Chi-square Independence Test


Odds ratio (oportunidad relativa)

odds (oportunidad) is the ratio of probabilties for an event given by the quantity p / (1 − p), where p is the probability of the event

An disease with a 1 in 5 probability of occurring for a given genotype (i.e. 0.2 or 20%), then the odds are 0.2 / (1 − 0.2) = 0.2 / 0.8 = 0.25.

The odds ratio is defined as the ratio of the odds of an event occurring in one group to the odds of it occurring in another group. These groups might be case and control groups, or any other dichotomous classification. So if the probabilities of the event in each of the groups are p (first group) and q (second group), then the odds-ratio is:

p

1 - p

Odds ratio (razón de posibilidades)

Casos Controles Total

Alelo 1 SNP1a b a+b

Alelo 2 SNP1c d c+d

Total a+c b+d N

El cociente a/c es la Odds de exposición observada en el grupo de casos. El cociente b/d es la Odds de exposición en el grupo control

OR = 2,2 -> 2,2:1

Un efecto (enfermedad) aparece 2,2 veces más ante la presencia de otra variable (alelo SNP) que si esta variable no está presente

Riesgo relativo RR, Risk ratioRR= tasa de incidencia de expuestos/tasa de incidencia en no expuestos


Alelo 1 SNP1 a b a+b

Alelo 2 SNP1 c d c+d

Total a+c b+d N

Riesgo Relativo


Alelo 1 SNP1 210 250 460

Alelo 2 SNP1 100 300 400

Total 310 550 860

Riesgo Relativo = 210/460

100/400= 1,83

Razón Odds = 210/100

250/300= 2,52

Genotypic

Controling for other independent variables

SNP rs1137933

♀

♂ CC CT

TT

Control 15 (62,5%

)

6 (25%)

3 (12,5%

)

Case 16 (51,6%

)

13 (41,9%

)

2 (6,5%

ChiSquare (2 gl) = 1,95 p = 0,37719

G (Likelihood ratio) (2 gl) = 1,98 p = 0,37158

CC CT

TT

Control 61

(58,1%) 32

(30,5%) 12

(11,4%)

Case 106

(50,7%) 92

(44%) 11

(5,3%)

ChiSquare (2 gl) = 7,59*

p = 0,02248

G (Likelihood ratio)(2 gl) = 7,5*

p = 0,02352


Test for association (multisnp)

Test for association among haplotype and response (diseases) or TagSNP and response

Haplotypec

SNPrs1042522

SNPrs12951053

SNPrs8064946

SNPrs6541003

SNPrs4846049

SNPrs4646421

SNPrs4986885

SNPrs915907

SNPrs4147567

SNPrs2266633

Case

1 G A G G T C G C G G 0.1056

2 G A G A G C G C G G 0.0767

3 G A G A G C G C A G 0.0485

4 G A G A G C G A G G 0.0423

5 G A C G G T G A A A 0.0378

6 G A C A G T A A A A 0.0282

7 G A G G G C G C A G 0.0276

Haplotypec

SNPrs1042522

SNPrs12951053

SNPrs8064946

SNPrs6541003

SNPrs4846049

SNPrs4646421

SNPrs4986885

SNPrs915907

SNPrs4147567

SNPrs2266633

Control

1 G A G G T C G C G G 0.1168

2 G A G A G C G C G G 0.0657

3 G A G A G C G C A G 0.0405

4 G A G A G C G A G G 0.0345

5 G A C G G T G A A A 0.0275

6 G A C A G T A A A A 0.0185

7 G A G G G C G C A G 0.0134

Logistic regressionLogistic regression modelo de regresión estadística de variables dependientes binarias. Puede considerarse un modelo lineal generalizado que usa la función logit como función de enalce (link), y sus errores están distribuidos binomialmente.

El modelo se expresa en la forma

i, = 1, ..., n, donde

El logaritmo de odds (probabilidad dividida por uno menos la probabilidad) del resultado se modela como una función lineal de variables explicativas, X1 a Xk. Puede escribirse como

La interpretación de las estimas de los parámetros β es el efecto multiplicativo sobre la razón de odds. En el caso de variables dicotómicas explicativas, por ejemplo sexo, eβ (el antilog de β) es la estima del odds-ratio of tener el resultado según se compare machos y hembras. Los parámetros α β1, ..., βk se estiman normalmente por máxima verosimilitud.

Logistic regression is a predictive tool

if the logit β1 = 2.303, then the corresponding odds ratio (the exponential function, eβ1 ) is 10, then we may say that when the independent variable increases one unit, the odds that the dependent = 1 increase by a factor of 10, when other variables are controlled.

Links

•http://bioinfo.iconcologia.net/SNPstats (Web tool for association studies)

•http://www.mep.ki.se/genestat/tl/genass_ldmap (Tutorial for association studies)

•http://linkage.rockefeller.edu/soft (Software for genetic analysis)

•http://www.broad.mit.edu/personal/jcbarret/haploview (Haploview)

•http://www.genome.gov/26525384 (Catálogo de estudios de GWA publicados)

•http://geneticassociationdb.nih.gov (Base de datos de estudios de asociación de enfermedades humana)

http://bioinfo.iconcologia.net/SNPstats

Association studies: Recurso Web

http://bioinfo.iconcologia.net/index.php?module=Snpstats

http://bioinfo.iconcologia.net/index.php?module=Snpstats

Asociación genética -> Culpable por asociación

Pacientes vs Control

SNP2 (A/T) 100% A 0% T 0% A 100% T

SNP3 (T/G) 80% T 20% G 60% T 40% G

SNPn

SNP1 (G/C) 40% G 60% C 40% G 60% C

Hoy podemos abordar el análisis de asociación de miles de SNPs, pudiendo

desvelar la base genética de las enfermedades.

Translation of genetic-

phenotypic information

into the clinical practise

D.R. Bentley. 2004 Nature 429: 440-445

Translation of genetic-

phenotypic information

into the clinical practise

variación genética en el genoma

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