1. ESP6800 BP Analysis (recessive model)
Zhengzheng Tang and Danyu Lin
March 26, 2013

2. Variant QC
Started with “ESP6900 May02 release” vcf ( variants; 6823 individuals)
SVM filter
deleted variants that were flagged in vcf
Per-genotype depth filter
set genotype to missing if DP < 10
Bi-allelic filter
deleted non-bi-allelic variants
after this step, variants remain (18 variants deleted)
Per-variant depth filter
filtered out all variants with an average read depth > 500
applied Paul’s exclusion list Fail_DP500 (284 variants listed)
after this step, variants remain (284 variants deleted)
HWE filter
deleted variants if race-specific p-value < 5x10^-8
applied Paul’s race-specific exclusion lists Fail_hwe_AA (2779 variants listed) and Fail_hwe_EA (2592 variants listed)
after this step, variants remain (3718 variants deleted)

3. Sample QC I Started with 6823 subjects
Dropped 536 subjects in certain cohorts/phenotype groups
ESP_COHORT: PAH (80), CF (431)
ESP_PHENOTYPE: BP (1), Blind (22), EOMI_Case_Drop (2), SSC PAH (40), SSC no PAH (35), Other (5)
Dropped 34 subjects whose self-reported race is not missing and is not AA or EA
Dropped 52 subjects based on QC information contained in the phenotype file
intentional duplicates (22), high missing rates (1), sex mismatch (13), high homozygosity (1), poor concordance (5), no data to check concordance (3), unresolved ID (4), self-reported race is missing (30), PCA outliers (17), race mismatch (13)
Dropped 16 subjects due to duplications
for each duplicated pair, dropped the subject with the lower genotype call rate

4. Sample QC II Dropped 155 subjects due to relatedness
Only consider 1st and 2nd degree relatedness
For each related pair:
If one of the subjects in the pair is missing on SSR and the other is not, then drop the one with missing SSR.
If both of them are missing on SSR or neither of them is missing on SSR, then drop the one with the lower genotype call rate.

5. Data Phenotype groups (studies): Genetic variants:
Nonsynonymous T1/T5/VT burden scores
LOF T5
Single variants
Annotation: SeattleSeq
MAFs: extracted from vcf file
Study
LDL
BMI BP
EOMI
Stroke
DPR AA EA
286
318
6120
262
502
351
579 82
404
226
667
AA w/BP
EA w/BP
227
269
520
260
464 36
196
188
447

6. Data Processing Phenotypes Genotypes Genes SSR
Remove variants with call rates < 90%.
Impute missing values by expected number of minor alleles.
Remove variants with MAC<2 for single variant analysis.
Genes
Remove genes with MAC<2 for rare-variant analysis.

7. Analysis Perform race-specific analysis and meta analyze the results.
The total number of genes/variants being analyzed: 5658 (Nonsyn T1); 8116 (Nonsyn T5); 139 (LOF T5); (Single variant).
Covariates: pc1-2, sex, age, age square, target, cohorts

8. Analysis
Methods: For each study, calculated the score statistic based on full likelihood (MLE). Performed meta-analysis of the score statistics from the six studies. The MLE approach properly adjusts for trait-dependent sampling. It has the highest power among all valid tests and provides unbiased estimates of genetic effects.
Genetic models:
Recessive model

9. Nonsyn T1: combined

10. Nonsyn T1: AA

11. Nonsyn T1: EA

12. Nonsyn T1: Top 1-25 genes

13. Nonsyn T1: Top genes

14. Nonsyn T5: combined

15. Nonsyn T5: AA

16. Nonsyn T5: EA

17. Nonsyn T5: Top 1-25 genes

18. Nonsyn T5: Top genes

19. Nonsyn VT: combined

20. Nonsyn VT: AA

21. Nonsyn VT: EA

22. Nonsyn VT: Top 1-25 genes

23. Nonsyn VT: Top genes

24. LOF T5: combined

25. LOF T5: AA

26. LOF T5: EA

27. LOF T5: Top 1-25 genes

28. LOF T5: Top genes

29. Single Variant: combined

30. Single Variant: AA

31. Single Variant: EA

32. Single Variant: combined Top 1-25 SNPs

33. Single Variant: combined Top 26-50 SNPs

34. Single Variant: AA Top 1-25 SNPs

35. Single Variant: AA Top 26-50 SNPs

36. Single Variant: EA Top 1-25 SNPs

37. Single Variant: EA Top 26-50 SNPs