1. Genome-wide association studies (GWAS) Thomas Hoffmann Department of Epidemiology and Biostatistics, and Institute for Human Genetics

2. Outline GWAS Overview Study design Plink overview QC Analysis If we have time

3. Main idea ● Correlation between genotype (SNP) and trait of interest (e.g., LDL/HDL, blood pressure) ● Test ~1 Million SNPs to see if any are correlated with the phenotype. ● Agnostic “shotgun” approach across the genome Hirschhorn & Daly, Nat Rev Genet 2005

5. Size matters Visscher, AJHG 2012, ● Large # SNPs tested, multiple comparison correction ● Very small effect sizes ● TagSNP, rather than actual SNP

6. Outline GWAS Overview Study design gPlink overview QC Analysis If we have time

7. Study design with Quanto http://biostats.usc.edu/software ➢ Disease: case-control (unmatched/matched), continuous ➢ Hypothesis: Gene only (also does GxE) ➢ Outcome model: Baseline Risk ➢ Genetic Effect: many GWAS ORs have actually been very small ➢ Parameters: Power/Sample size ➢ Type I error: 5e-8 (=5x10 -8, bonferroni correction for 1 million tests) What parameters seem reasonable? How would you explain this in a grant?

8. Outline GWAS Overview Study design gPlink overview QC Analysis If we have time

9. Plink overview ● Command-line driven code [You have seen this, right?] ● Start > All Programs > Accessories > Command Prompt ● Rock solid ● Graphical interface [You haven't seen this?] ● Pretty, but a little unstable, especially if you kill something ● May not allow you to delete some things until restart system ● Great documentation http://pngu.mgh.harvard.edu/~purcell/plink/reference.shtml ● Apply an operation to data, which produces output. Manually looking at output much more difficult because of quantity of it for GWAS; use other programs (Haploview [today], stata, R, e.g.) to display that output better

10. gPLINK setup Click “File > Open project”, and then click “Browse” in the dialogue that pops up, and navigate to where you have your plink file located, then click OK. Ignore any “stream error” errors you get, they don't seem to cause any problems.

11. gPLINK Setup (2) Click “File > Configure”, then click the two “Browse” buttons, navigating to the haploview and plink executable files, respectively.

12. Outline GWAS Overview Study design Plink overview QC Analysis If we have time

13. QC Steps (1) Remove SNPs with low call rate (e.g., <95%), Individuals with too much missing data Proportion of SNPs actually called by software If it's low, the clusters aren't well defined, artifacts PLINK produce summary: – plink --bfile ceu --missing --out ceu-miss PLINK on the fly / to produce a new dataset – plink --bfile ceu --geno 0.05 --mind 0.05

18. WARNING: It will look like it is not doing anything!!! In the background a plink.exe process has been launched. You need to wait for the green circle.

20. This creates a new dataset based on the filter, but this can also be done on the fly with some of the association analysis options as well...

21. QC Steps (2) Remove those with low minor allele frequency? Rarer variants more likely artifacts / underpowered Sometimes fit is unstable Other approaches to deal with rare variants than single SNP at a time (combine them) Produce summary: plink --bfile ceu --freq --out ceu- freq Filter: plink --bfile ceu --maf 0.05 --make-bed --out ceu-0.05

23. QC Steps (3) SNPs that fail Hardy-Weinberg Suppose a SNP with alleles A and B has allele frequency of p. If random matting, then  AA has frequency p*p  AB has frequency 2*p*(1-p)  BB has frequency (1-p)*(1-p) Test for this (e.g., chi-squared test), if deviating too strongly, likely a bad SNP In practice do for homogeneous populations (more later), controls only

24. > hwe = read.table("hwe.hwe", header=TRUE, stringsAsFactors=FALSE, fill=TRUE) > head(hwe) CHR SNP TEST GENO O.HET. E.HET. P_HWD 1 1 rs3131968 ALL 0/15/45 0.2500 0.2188 0.5795 2 1 rs3131968 AFF 0/0/0 -1.0000 NA NA 3 1 rs3131968 UNAFF 0/0/0 -1.0000 NA NA 4 1 rs12562034 ALL 0/11/49 0.1833 0.1665 1.0000 5 1 rs12562034 AFF 0/0/0 -1.0000 NA NA 6 1 rs12562034 UNAFF 0/0/0 -1.0000 NA NA >

25. QC Steps (4) Check genotype gender from X heterozygosity plink --bfile ceu --check-sex --out ceu-check-sex ● Microarrays often have another gender check ceu-check-sex.sexcheck FID IID PEDSEX SNPSEX STATUS F 1341 NA06985 2 2 OK -0.06403 1341 NA06993 1 1 OK 1 1340 NA06994 1 1 OK 1 1340 NA07000 2 2 OK -0.03363 1340 NA07022 1 1 OK 1 1341 NA07034 1 1 OK 1 1341 NA07055 2 2 OK -0.06594 1340 NA07056 2 2 OK -0.02845 1345 NA07345 2 2 OK 0.00151 F: The actual X chromosome inbreeding (homozygosity) estimate. Which ones are female? Has this data been already cleaned?

26. (5) Check for relatedness, e.g., HapMap Pemberton et al., AJHG 2010 ● Take overall average of all SNPs of how many alleles are shared. ● E.g., parent-offspring never shares zero alleles. ● HapMap was supposed to be unrelateds (this was a bit of a surprise!) ● “MZ twins” sometime a duplicated sample...

27. PLINK Identity By State (IBS) Calculations plink --bfile ceu --genome --out ceu-genome FID1 Family ID for first individual IID1 Individual ID for first individual FID2 Family ID for second individual IID2 Individual ID for second individual RT Relationship type given PED file EZ Expected IBD sharing given PED file Z0 P(IBD=0) Z1 P(IBD=1) Z2 P(IBD=2) PI_HAT P(IBD=2)+0.5*P(IBD=1) ( proportion IBD ) PHE Pairwise phenotypic code (1,0,-1 = AA, AU and UU pairs) DST IBS distance (IBS2 + 0.5*IBS1) / ( N SNP pairs ) PPC IBS binomial test RATIO Of HETHET : IBS 0 SNPs (expected value is 2)

28. PLINK Identity By State (IBS) Calculations plink --bfile ceu --genome --out ceu-genome FID1 Family ID for first individual IID1 Individual ID for first individual FID2 Family ID for second individual IID2 Individual ID for second individual RT Relationship type given PED file EZ Expected IBD sharing given PED file Z0 P(IBD=0) Z1 P(IBD=1) Z2 P(IBD=2) PI_HAT P(IBD=2)+0.5*P(IBD=1) ( proportion IBD ) PHE Pairwise phenotypic code (1,0,-1 = AA, AU and UU pairs) DST IBS distance (IBS2 + 0.5*IBS1) / ( N SNP pairs ) PPC IBS binomial test RATIO Of HETHET : IBS 0 SNPs (expected value is 2)

29. QC Notes ● Exact thresholds differ based on dataset, how many subjects you have ● GWAS of 1,000 subjects vs. GWAS of 100,000 subjects ● Ad-hoc look at QQ-plots (later) to asses some QC issues ● Look at cumulative distributions may be slightly easier than histograms (what we saw earlier) for cutoffs in the tails

30. QC Summary ● Filter SNPs and individuals ● MAF (very sample size dependent) ● Low call rates ● Test HWE ● Relatedness – remove first degree relatives at least, or account for ● Check genotypic gender (e.g., mislabeled samples) ● Anything extra you could do with family data? ● Mother AA, Father BB, Child AA

31. Outline GWAS Overview Study design Plink overview QC Analysis If we have time

32. GWAS analysis Most common approach: look at each SNP one-at-a-time – Additive coding of SNP most common, e.g., # of A alleles – Just a covariate in a regression framework Dichotomous phenotype: logistic regression Continuous phenotype: linear regression – {BMI}=B 1 {SNP}+ B 2 {Age}+... Can use residuals/propensity score to speed up analysis

33. What is population stratification? Balding, Nature Reviews Genetics 2010 ● If allele frequency is different in two populations (races/ethnicities), and a disease prevalence is different in the two... ● If not corrected for, what is your test actually telling you? ● Generally inflates all test statistics if not accounted for

34. Adjusting for PC's Li et al., Science 2008 Maximize variance using all SNPs between subjects – pulls out clusters of individuals of different populations/subp opulations.

35. Adjusting for PC's Razib, Current Biology 2008

36. PCs ● Eigenstrat most common (linux only) ● Plink can still run something similar, two steps: plink --bfile ceu --genome --out ceu-genome plink --bfile ceu --read-genome ceu- genome.genome --cluster --mds-plot 10 --out ceu-mds

37. Running the analysis Continuous phenotype: linear regression {BMI}=B 1 {SNP}+ B 2 {Age}+...

39. plink --bfile ceu --linear --covar ceu.cov --covar-name PC1,PC2,PC3,PC4 --pheno ceu.phe --pheno-name phe --out ceu-analysis --hide-covar --hide-covar: supresses lots of output (every covariate for every regression), unfortunately, cannot do in gPLINK

40. plink --bfile ceu --linear --covar ceu.cov --covar-name PC1,PC2,PC3,PC4 --pheno ceu.phe --pheno-name phe --out ceu-analysis --hide-covar --adjust --adjust: Get the genomic inflation factor lambda, and inflate the test statistics slightly for it (otherwise can inflate later with other packages; especially if you want to run in parallel) Do you really want to adjust? More later...

41. Format of covariate files Phenotype and covariate files not structurally different from each other: FID IID LDL HDL BMI ID1 ID1 120 160 200 … FID and IID match things in the.fam file of the plink formatted files (which generally calling software produces)

42. View analysis results

44. Manhattan plot

45. Multiple comparison correction If you conduct 20 tests at  =0.05, one true by chance http://xkcd.com/882/. If you conduct 1 million tests... http://xkcd.com/882/ Correct for multiple comparisons – e.g., Bonferroni, 1 million,  =5x10 -8

46. Multiple comparison correction If you conduct 20 tests at  =0.05, one true by chance http://xkcd.com/882/. If you conduct 1 million tests... http://xkcd.com/882/ Correct for multiple comparisons – e.g., Bonferroni, 1 million,  =5x10 -8

47. Multiple comparison correction If you conduct 20 tests at  =0.05, one true by chance http://xkcd.com/882/. If you conduct 1 million tests... http://xkcd.com/882/ Correct for multiple comparisons – e.g., Bonferroni, 1 million,  =5x10 -8

48. Multiple comparison correction If you conduct 20 tests at  =0.05, one true by chance http://xkcd.com/882/. If you conduct 1 million tests... http://xkcd.com/882/ Correct for multiple comparisons – e.g., Bonferroni, 1 million,  =5x10 -8

49. QQ-plots and PC adjustment Wang, BMC Proc 2009 Lambda quantifies how much it is deviating from what we would expect. Want it to be roughly 1, but I don't see too many objections for 1.04, 1.06, e.g., for ~10,000 subjects.

50. Genomic inflation factor Why might the genomic inflation factor be elevated?

51. Genomic inflation factor Why might the genomic inflation factor be elevated? No, there was nothing done wrong with the analysis...

52. Genomic inflation factor Why might the genomic inflation factor be elevated? There are hundreds of loci known for height...

53. Genomic inflation factor Why might the genomic inflation factor be elevated? There are hundreds of loci known for height...

54. After you found a hit: Check cluster plot (where genotype calls made) Good calls! Bad calls! Laird and Lange

55. Outline GWAS Overview Study design Plink overview QC Analysis If we have time

56. http://cgems.cancer.gov chromosome Advanced: Conditional analysis Prostate cancer, Witte, Nat Genet 2007 Multiple prostate cancer loci on 8q24 Y = b0 + b 1 X 1 + b 2 X 2 In practice, take residual of Y on X 1, then fit that to X 2 (avoid issues of multicollinearity)

57. Conditional Analysis (continued) ● Different approaches – what question is being asked? ● Rerun only within region ● Genome-wide ● Hard to coordinate in consortium

58. Imputation of SNP Genotypes Combine data from different platforms (e.g., Affy & Illumina) (for replication / meta-analysis). Estimate unmeasured or missing genotypes. Based on measured SNPs and external info (e.g., haplotype structure of HapMap). Increase GWAS power (impute and analyze all), e.g. Sick sinus syndrome, most significant was 1000 Genomes imputed SNP (Holm et al., Nature Genetics, 2011) HapMap as reference, now 1000 Genomes Project

59. Imputation Example Li et al., Ann Rev Genom Human Genet, 2009 What would you do?

60. Imputation Example Li et al., Ann Rev Genom Human Genet, 2009

61. Imputation Example Li et al., Ann Rev Genom Human Genet, 2009

62. Imputation Application Chromosomal Position Marchini Nature Genetics2007 http://www.stats.ox.ac.uk/~marchini/#software TCF7L2 gene region & T2D from the WTCCC data Observed genotypes black Imputed genotypes red.

63. Imputation Software ● SHAPE-IT to phase http://www.shapeit.fr/http://www.shapeit.fr/ ● Impute2 to impute (+ 1000 genomes):http://mathgen.stats.ox.ac.uk/impute/impute _v2.htmlhttp://mathgen.stats.ox.ac.uk/impute/impute _v2.html ● Analyze similar to typed genotypes, with plink –dosage ● Data subset, etc., not as flexible with dosages (i.e., don't work) ● 1000 Genomes: Tens of millions of SNPs...

64. Replication To replicate: – Association test for replication sample significant at 0.05/{Number of SNPs replicating} alpha level – Same genetic model (e.g. additive, dominant) – Same direction – Sufficient sample size for replication Non-replications not necessarily a false positive – LD structures, different populations (e.g., flip-flop) – covariates, phenotype definition, underpowered

65. Meta-analysis Combine multiple studies to increase power Either combine p-values (Fisher’s test), – when might you use this? (tricky question) or z-scores (better) plink --meta-analysis study1.assoc study2.assoc study3.assoc – Tip: Run previous analysis with --ci 0.95

66. Extras ● If using separate control group (“convenience controls”), need to be extra careful with QC (give example). ● Why?

67. Summary ● GWAS successful in finding large numbers of variants associated with disease. ● GWAS not as successful as people hoped ● Not as much of the heritability / variance explained. ● Difficult to find true functional SNP? ● What do we do after we find them?