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Genome-wide association studies
Misha Kapushesky
Slides: Johan Rung, EBISt. Petersburg Russia 2010
Overview
• Methods for genome-wide association studies
• Montreal GWAS for Type 2 Diabetes
• GWAS results - context and caveats
Study coverage
• Associating phenotype/disease state to genetic variation
• Cost per genotype has decreased
• Instead of a candidate gene approach, just scan the entire genome
• SNP microarrays covering up to 5M SNPs on one chip
• Increased sample sizes
Recombination
Linkage disequilibrium
Two markers on the genome are inherited together more often than would be expected by chance
This leads to high correlation between nearby markers in its haplotype block
Haplotypes and genotype tagging
Association studies
• Linkage disequilibrium enables association studies, because of detection by proxy - not every variant need to be typed
Study power
1
2
3
4
1
2
3
4
A B
Cases
Controls
• The power of a study is to correctly predict a true positive
• To calculate this, you need: • risk model• genotype relative risk• allele frequency• number of cases and controls• population penetrance• Acceptable rate of false positives
Study power
How many SNPs should be tested? Studies of small regions revealed linkage disequilibrium blocks in which common SNPs are highly correlated (usually <10,000–30,000 base pairs in African populations or 30,000–50,000 base pairs in the newer European and Asian populations) (22). This motivated the HapMap Project (www.hapmap.org [12]), which has validated approximately 4 million SNPs, including 2.8 million of the estimated 10 million common SNPs in major world populations, while creating competition among biotechnology companies to develop high-throughput genotyping technologies. Sequencing and genotyping studies showed that sets of 500,000 (European populations) to 1,000,000 (African populations) SNPs could "tag" (serve as proxies for) approximately 80% of common SNPs (23).
Quality controls
• Call rates for samples and SNPs
• Exclusion of low frequency SNPs
• Exclusion of SNPs out of Hardy-Weinberg Equilibrium
• Clean (or take into account) population stratification
Hardy-Weinberg Equilibrium
• If the alleles A and B have frequencies p and q, you would expect the following genotype frequencies:
AA: p2
AB: 2pq
BB: q2
Hardy-Weinberg Equilibrium
• When observed genotype frequencies deviate from the ones expected under HWE, this is indicative of
• population stratification
• different mutation rates between males and females
• different fitness between alleles
• genotype calling problems
• true association at the locus
• Binary traits are typically disease state labels (case or control)
• Real-valued traits are quantitatively measured phenotypes• blood sugar• lipids• height• BMI• gene expression
Binary or real-valued phenotypes
Molecular vs disease phenotypes
• Disease phenotypes are the result of combinations of molecular phenotypes in the body
• Progression with time
• Precision of phenotype measurement
Molecular vs disease phenotypes
• Many physiological phenotypes involved in disease dynamics
Molecular vs disease phenotypesMolecular phenotypes can give more precise information about disease state
• Association statistics for binary traits are most often based on a 2-statistic, based on the genotype count table, or a logistic regression model
2-statistic summarizes independence between disease state and genotype
Association statistics
aa aA AA Sum
Cases r0 r1 r2 R
Controls s0 s1 s2 S
Count n0 n1 n2 N
• For aa in cases, you would expect
N
n
N
RNr 0
0 **~
• The sum of the squares of the differences is 2-distributed
Association statistics
• For real-valued phenotypes, use linear regression• For binary phenotypes, use logistic regression
Regression
• Population stratification occurs when groups or subpopulations within your sample are more related than would be expected by random
• This introduces correlations and inflates association p-values and need to be corrected for
Population stratification
Genomic control
Eigenstrat
Imputation
• Using a reference population (like HapMap or 1000 genomes) we can infer the genotype of SNPs that were not tested
• IMPUTE or MACH commonly used
• Yields probabilistic genotypes that need special treatment
Imputation
Wu et al, Nat. Genet. 41, 991-995, 2009
Montreal GWASMontreal GWAS
Type 2 diabetes
• Blood glucose levels are regulated by insulin release
• Increased blood glucose levels triggers release of insulin, that signals to the cells in muscle for glucose intake
• Through -cell dysfunction or insulin resistance, insulin regulation is impaired, leading to increased glucose levels and eventually type 2 diabetes
Type 2 diabetes
Genetics of type 2 diabetes
• Before GWAS, T2D genetics was studied with linkage studies and candidate gene approaches
• Results in particular for MODY variants, caused by disruptions of single genes
• Genome-wide association studies and SNP arrays made it possible to study complex diseases
• Five large GWAS for T2D in 2007
• DIAGRAM meta-analysis in 2008
Montreal GWAS
• Part of a larger T2D project at McGill and Genome Quebec
• After initial planning for candidate gene genotyping, we switched to a GWAS strategy
Multi-stage GWAS
• Two main strategies for increasing study power
• Meta-analyses increase effective sample size by combining results from different studies
• Multi-stage approaches scan the whole genome with relatively low power, followed by focusing in on the hits with higher power
• Maximizing power in a single study in a cost-effective way
Multi-stage GWAS
Study design
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 3 - 28 SNPs
Danish (N=7,698)3,334 cases, 4,364 controls
Focused Stage 3 - 28 SNPs
Danish (N=7,698)3,334 cases, 4,364 controls
Stage 4: population effect study - 1 SNP (rs2943641)
Population based study samplesFrench (N=3,351), Finnish (N=5,183), Danish (N=5,824)
Stage 4: population effect study - 1 SNP (rs2943641)
Population based study samplesFrench (N=3,351), Finnish (N=5,183), Danish (N=5,824)
CASE-CONTROLT2D ASSOCIATION
QT ASSOCIATIONIN POPULATIONS
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 3 - 28 SNPs
Danish (N=7,698)3,334 cases, 4,364 controls
Focused Stage 3 - 28 SNPs
Danish (N=7,698)3,334 cases, 4,364 controls
Stage 4: population effect study - 1 SNP (rs2943641)
Population based study samplesFrench (N=3,351), Finnish (N=5,183), Danish (N=5,824)
Stage 4: population effect study - 1 SNP (rs2943641)
Population based study samplesFrench (N=3,351), Finnish (N=5,183), Danish (N=5,824)
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fasting glucoseNormoglycemic individuals
Stage 1: French (N=654)
Stage 2: rs560887 (N=9,353)
Previously published,Science, May 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Fast-track confirmation - 57 SNPs
French (N=5,511)2,617 cases, 2,894 controls
Previously published,Nature, Feb 2007
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Stage 1: Genome-wide scan - 392,365 SNPs
French (N=1,376)679 cases, 697 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Focused Stage 2 - 16,273 SNPs
French (N=4,977)2,245 cases, 2,732 controls
Stage 1 samples
• French individuals: 690 cases, 670 controls
• Criteria for cases:• T2D• First degree relative with T2D• Non-obese (BMI < 31 kg/m² , 25.8 ± 2.8 kg/m²)
• Controls from DESIR, a prospective French cohort• Normal glucose tolerance for the 9 years of the study
Stage 1 SNPs
• Tested on Illumina Human1 (100k) and HumanHap300 (300k)
• 392,935 unique SNPs from the combined arrays
Stage 1 results
Fast-track validation
• Top 57 fast-tracked and tested on a Sequenom panel on 2,617 cases, 2,894 controls
• Relaxed criteria for cases• BMI < 35 kg/m² (28.9 ± 3.7 kg/m²)
• Sladek et al., Nature 445, 881-885, 2007
Results
SNP Chr Position pMAXClosest
gene
rs7903146 10 114748339 1.5 x 10-34 TCF7L2
rs13266634 8 118253964 6.1 x 10-8 SLC30A8
rs1111875 10 94452862 3.0 x 10-6 HHEX
rs7923837 10 94471897 7.5 x 10-6 HHEX
rs7480010 11 42203294 1.1 x 10-4 LOC387761
rs3740878 11 44214378 1.2 x 10-4 EXT2
rs11037909 11 44212190 1.8 x 10-4 EXT2
rs1113132 11 44209979 3.3 x 10-4 EXT2
SLC30A8
Chimienti et al. Biometals 18:313
HHEX
**
**
**
-log10(p) 024
* *
rs2
4907
45 r
s242
2067
rs1
1187
182
rs2
4907
51 r
s424
4932
rs1
4183
88 r
s115
9206
7 r
s111
8717
3 r
s193
5492
rs2
4880
62 r
s105
0964
6 r
s249
7351
rs9
4205
92 r
s153
9330
rs2
4880
71 r
s947
591
rs2
4973
04 r
s249
7311
rs7
9238
37 r
s111
1875
rs2
2757
29 r
s791
7359
rs7
9024
36 r
s658
3830
rs7
0709
90 r
s791
4814
rs1
0882
091
rs2
2752
19 r
s108
8208
8 r
s460
4791
rs3
8247
35 r
s122
5643
5 r
s658
3826
rs3
7585
05 r
s199
9763
rs7
9081
11 r
s242
1943
rs1
1187
064
rs1
1187
060
rs1
8321
97 r
s707
8413
rs6
5838
20 r
s111
8702
5 r
s373
7225
rs2
4219
40 r
s214
9632
rs1
8879
22 r
s551
266
rs7
9109
77 r
s107
8604
4 r
s122
5705
3 r
s708
6285
rs2
9015
87 r
s225
9049KIF11 HHEXIDE
D'0 0.2 0.4 0.6 0.8 1
HHEX controls pancreatic development
Habener Endocrinology 146:1025
Hex homeobox gene-dependent tissue positioning is required for organogenesis of the ventral pancreas. Bort (2004)
Heart induction by Wnt antagonists depends on the homeodomain transcription factor Hex. Foley (2005)
The homeobox gene Hex is required in definitive endodermal tissues for normal forebrain, liver and thyroid formation. Martinez Barbera (2000)
Stage 2
• Top 5% of GWAS hits were selected for design of a focused Stage 2
• Control for population bias with EIGENSTRAT
• iSelect array with 16,405 SNPs, tested on 2,245 cases, 2,732 controls (French)
• Analysis with EIGENSTRATand selection of 28 SNPs for a focused Stage 3
QC
Exclusion criterion Samples
Call rate < 95% 27
Continental stratification
296
Sex mismatch 64
Related individuals 70
Total 457
Chromosome SNPs Failed HWE Failed MAF Successful
TOTAL 16,360 48 43 16,273
EIGENSTRATcorrection
filters for MAF, HWE, call rate filters for MAF, HWE, call rate and r2
Results - stage 1 vs stage 2
Results - taking out known loci
Stage 3
• The top 28 SNPs were tested using a Sequenom panel in ~7,700 Danish cases and controls
• We confirm association of TCF7L2, WFS1, CDKAL1 and find one new association: rs2943641 near IRS1
rs2943641
• We studied the effect of variation in rs2943641 on T2D risk and metabolic phenotypes in general populations:
• DESIR: 3,351 French adults
• Inter99: 5,183 Danish adults
• NFBC 1986: 5,824 Finnish adolescents
Metabolic traits
• A variety of indexes to capture -cell function and insulin resistance
• HOMA-B and HOMA-IR based on fasting levels of glucose and insulin
• For Inter99, we had access to OGTT data and could calculate other measures of insulin response • time course data• AUC• corrected insulin response (CIR)• disposition indexes
Oral Glucose Tolerance Test
Metabolic traits 1
Metabolic trait
Cohort
rs2943641
P add P dom P recC/C C/T T/T
Age
NFBC 1986 16 16 16
DESIR 47.1 ± 9.8 47.5 ± 9.9 47.6 ± 10.1
INTER99 44.9 ± 7.9 45.4 ± 7.8 45.2 ± 7.6
Sex
NFBC 1986 1062/1092 1153/1208 322/346
DESIR 645/728 728/812 216/222
INTER99 776/942 974/1070 307/354
BMI (kg/m2)
NFBC 1986 21.3 ± 3.8 21.3 ± 3.7 21.1 ± 3.5 0.24 0.43 0.21
DESIR 24.5 ± 3.7 24.4 ± 3.5 24.4 ± 3.4 0.55 0.63 0.61
INTER99 25.6 ± 3.9 25.4 ± 4.1 25.7 ± 4.2 0.57 0.094 0.24
Fasting plasmaglucose(mmol/l)
NFBC 1986 5.13 ± 0.41 5.14 ± 0.40 5.13 ± 0.41 0.77 0.62 0.90
DESIR 5.21 ± 0.44 5.20 ± 0.42 5.18 ± 0.43 0.05 0.32 0.07
INTER99 5.31 ± 0.40 5.31 ± 0.41 5.33 ± 0.39 0.66 0.93 0.32
Fasting serum insulin(pmol/l)
NFBC 1986 78.7 ± 48.6 76.8 ± 44.5 71.7 ± 32.1 0.001 0.03 0.0009
DESIR 50.6 ± 32.9 48.4 ± 29.7 49.1 ± 29.1 0.05 0.003 0.76
INTER99 38.8 ± 24.7 36.4 ± 21.9 37.6 ± 23.3 0.018 0.0043 0.49
Metabolic traits 2
HOMA-B
NFBC 1986 141 ± 95.1 136 ± 80.1 131 ± 91.6 0.006 0.05 0.009
DESIR 109 ± 87.0 103 ± 64.8 108 ± 92.2 0.16 0.006 0.24
INTER99 75.2 ± 65.6 68.3 ± 42.2 71.0 ± 49.9 0.005 0.0011 0.32
HOMA-IR
NFBC 1986 2.52 ± 1.63 2.47 ± 1.58 2.29 ± 1.06 0.007 0.07 0.005
DESIR 1.95 ± 1.35 1.86 ± 1.20 1.88 ± 1.17 0.03 0.004 0.95
INTER99 1.54 ± 1.00 1.44 ± 0.89 1.49 ± 0.95 0.026 0.0058 0.59
Insulin 30’
INTER99
300 ± 183 277 ± 172 281 ± 169 0.0019 8.1 x 10‑4 0.14
Insulin 120’ 176 ± 138 163 ± 127 162 ± 124 0.0059 0.011 0.057
AUC insulin 22000 ± 13800 20300 ± 12900 20500 ± 12700 6.9 x 10‑4 2.2 x 10‑4 0.12
Glucose 30’ 8.19 ± 1.53 8.17 ± 1.56 8.22 ± 1.50 0.72 0.34 0.55
Glucose 120’ 5.51 ± 1.11 5.51 ± 1.11 5.47 ± 1.15 0.54 0.99 0.23
AUC glucose 182 ± 101 181 ± 102 180 ± 99.5 0.44 0.48 0.59
AUC insulin / AUC glucose
32.5 ± 17.4 30.1 ± 16.2 30.6 ± 16.1 6.0 x 10‑4 1.6 x 10‑4 0.13
CIR 1140 ± 4210 1000 ± 1130 1000 ± 1060 0.045 0.066 0.17
ISI 0.151 ± 0.095 0.16 ± 0.098 0.156 ± 0.096 0.026 0.0058 0.59
Disp. Index (CIR * ISI)
180 ± 1610 147 ± 220 143 ± 174 0.73 1.0 0.50
IRS1 locus - rs2943641
IRS1
• G972R is a missense polymorphism in IRS1 that is known to impair insulin signalling (rs1801278) (Almind 1993)
• G972R associated to insulin resistance and insulin release (Clausen 1995, Sesti 2001)
• In mice, IRS1 disruption causes disrupted insulin action, both in target tissues and in -cells (Nandi 2004)
• Also linked to insulin resistance, glucose intolerance, islet hyperplasia (Tamemoto 1994, Araki 1994, Terauchi 1997, Withers 1998)
• G972R not conclusively associated to T2D (Florez 2004, Florez 2007, Jellema 2003, Zeggini 2004)
• We detect no epistasis between rs2943641 and G972R in DESIR or NFBC, only nominal significance in Inter99
• Evidence for link between rs2943641 and IRS1?
rs2943641 - IRS1 protein association
rs2943641 - IRS1 protein association
rs2943641CC
rs2943641CT
rs2943641TT
PAdd PDom PRec
n (male/female) 74 (35/39) 88 (51/37) 28 (10/18)
Age (years) 42.5 ± 17.1 43.5 ± 16.9 43.2 ± 17.6
BMI (kg/m2) 25.0 ± 3.8 24.9 ± 3.9 25.3 ± 4.1 0.3 0.7 0.2
Rd insulin clamp
(mg/kgFFM/min)10.4 ± 3.5 11.0 ± 3.2 11.7 ± 3.7 0.2 0.2 0.4
Di (x 10‑7) 1.7 ± 1.1 1.8 ± 1.3 1.8 ± 1.1 0.8 0.8 0.9
IRS-1 protein basal (AU) 296.7 ± 167.7 314.0 ± 155.1 413.1 ± 227.6 0.03 0.3 0.009
IRS-1 protein insulin (AU)
276.6 ± 143.6 280.9 ± 156.4 313.3 ± 147.9 0.3 0.7 0.2
IRS-1-associated PI3K activity basal (AU)
25.0 ± 12.6 26.6 ± 15.4 30.1 ± 17.2 0.3 0.4 0.4
IRS-1-associated PI3K activity insulin (AU)
47.1 ± 29.9 56.6 ± 32.1 72.2 ± 41.3 0.001 0.02 0.002
Conclusions
• The multi-stage study detected T2D risk loci that were later confirmed in other cohorts (SLC30A8, HHEX)
• Variation in rs2943641 is associated to • T2D risk• increased insulin levels• impaired insulin sensitivity• IRS1 protein levels• IRS1 activity in insulin signaling pathway
• Study provided a ”full story” from GWAS scan to functional evidence thanks to rich phenotyping
Paper
Rung et al., Nature Genetics, 41, 1110-1115, 2009
Acknowledgements
Johan Rung
Rob Sladek
Philippe Froguel
Oluf Pedersen
Constantin Polychronakos Ghislain Rocheleau
Alexander Mazur
Lishuang Shen
David Serre
Philippe Boutin
Daniel Vincent
Alexandre Belisle
Samy Hadjadj
Beverley Balkau
Barbara Heude
Guillaume Charpentier
Tom Hudson
Sebastien Brunet
François Bacot
Rosalie Frechette
Valérie Catudal
Philippe Laflamme
Stephane Cauchi
Christian Dina
David Meyre
Christine Cavalcanti-Proença
Anders Albrechtsen
Torben Hansen
Knut Borch-Johnsen
Torsten Lauritzen
Marjo-Riitta Järvelin
Jaana Laitinen
Emmanuelle Durand
Paul Elliott
Samy Hadjadj
Michel Marre
Alexander Montpetit
Charlotta Pisinger
Barry Posner
Anneli Pouta
Marc Prentki
Rasmus Ribel-Madsen
Aimo Ruokonen
Anelli Sandbaek
Jean Tichet
Martine Vaxillaire
Jorgen Wojtaszewski
Allan Vaag
GWAS into context
Complexity of interactions in biological systems...
Complexity
...a lot of complexity
AA BB
GG
BB
EEFF
DD
AA
CC
Redundancy
Network structure
• Biological networks have a scale-free structure
Log(#edges)
Log(# genes)
Most genes have few connections
Few genes have many connections
Signal propagation
• The structure of biological networks result in robustness against random errors
• Most mutations, even knockouts, can go by unnoticed because of redundancy and network wiring
• Low probability to knock out a hub
Common diseases
• What is most common - disease cause by many variants with low effect, or few rare variants with strong effects?
• GWAS so far have by necessity focused on common variants
• Many known rare variants associated with common diseases - or phenotypes that may contribute and progress to disease
Common disease / common variant
• The hypothesis that most common diseases are caused by a large number of variants, common in a general population, but each adding just a small risk
• GWAS results find many loci for common complex diseases, with small risk
• But... GWAS detected loci so far only explain a very small fraction of the observed variation
Rare variants
• With improved and lower cost sequencing, we can address rare variants
• Not just SNPs
• Utility of “extreme cohorts”
• Ex. “A new highly penetrant form of obesity due to deletions on chromosome 16p11.2” (Nature Feb 4, 2010)
Polygenic contributions
• Groups of non-genomewide significant SNPs proven to be associated with phenotype
• Individual SNPs can not be inferred, just “group action”
• Supports the idea of many weak variants responsible for effect
• Ex. “Common polygenic variation contributes to risk of schizophrenia and bipolar disorder” (Nature 460, 748-752)
Meta-analysis caveats
• Meta-analysis on heterogeneous data
• Phenotypes
• Quality control
• Platforms
• Genotype calling
• Analysis
Future directions for GWAS
• Sequencing is cheaper and yielding higher quality data
• Better basis for studying and detecting rare variants and their effect on diseases or phenotypes
• Copy number variants
• Genetic interactions, GxE interactions
• More samples => higher power
Future directions for GWAS
• Complex phenotypes
• Association of genetic loci to
• genome-wide expression levels
• protein levels
• metabolite levels
Future directions for GWAS
• More data shared => better quality of results
• As in other branches of science, data sharing, transparency and openness should be promoted
Resources• Analysis software packages
• PLINK - http://pngu.mgh.harvard.edu/~purcell/plink/ • *Abel - http://mga.bionet.nsc.ru/~yurii/ABEL/• MERLIN - http://www.sph.umich.edu/csg/abecasis/merlin/
• Imputations• IMPUTE - http://mathgen.stats.ox.ac.uk/impute/impute.html• MACH - http://www.sph.umich.edu/csg/abecasis/MACH/
• Population structure• Eigenstrat - http://genepath.med.harvard.edu/~reich/Software.htm• EMMA(X) - http://genetics.cs.ucla.edu/emmax/index.html
• Meta-analysis• METAL - http://www.sph.umich.edu/csg/abecasis/METAL/• GWAMA - http://www.well.ox.ac.uk/gwama/index.shtml
• Data• EGA - http://www.ebi.ac.uk/ega/• dbGAP - http://www.ncbi.nlm.nih.gov/gap
Recommended