Platelet function mediates both beneficial and harmful effects on human health, but few genes are known to contribute to variability in the process. We tested association of 2.5 million SNPs with platelet aggregation responses to 3 agonists (ADP, epinephrine and collagen) in two European-ancestry cohorts (N ≤ 2,753 in the Framingham Heart Study, N ≤ 1,238 in the Genetic Study of Atherosclerosis Risk), with replication (P Lys change at amino acid 323. The strong association of the Lys allele with decreased collagen response (increased lag time, Table 2) observed in FHS was weakly replicated in GS (EA, P = 0.037; AA, P = 0.048). Our first meta-analysis compared collagen doses of 190 ug/mL (FHS, calf-skin-derived collagen) with 2 ug/mL (GS, equine-tendon-derived collagen) since these provided the most similar lag time distributions (Supplementary Figure 3), consistent with several orders of magnitude higher efficacy of calf- vs. equine-derived collagen (pers. comm., BioData, Inc.). We additionally analyzed associations of the single FHS dose compared with results from three other doses in GS (1, 5, 10 ug/mL), but did not find any additional genome-wide significant loci or gain stronger replication evidence for the GP6 locus. Three additional loci with evidence of moderate association in the main meta-analysis for collagen lag time in the EA sample showed similar association in the AA sample (Supplementary Table 2). Given that the three platelet function agonists analyzed here target partially overlapping mechanisms of platelet aggregation, we inspected whether significantly associated loci overlapped across agonists. Four regions showed association with aggregation phenotypes in both the EA and AA samples and showed evidence for platelet responses to ≥2 different agonists (Supplementary Table 3). While an understanding of rare disorders of platelet aggregation has emerged6, the discovery of common genetic variations contributing to platelet aggregation has been marginally successful even though aggregation traits are heritable4,5. Prior studies were performed in modest sample sizes, utilized candidate gene approaches focusing on glycoprotein receptors, and often employed variable conditions in diseased populations. By adopting a GWAS approach in large cohorts of relatively healthy individuals and using similar platelet–rich plasma (PRP)-derived aggregation phenotypes, we discovered or replicated strong associations (P = 5.0 × 10−8) for 7 distinct loci with platelet aggregation, and found suggestive evidence for many additional loci (summarized in Table 4 and Supplementary Table 4). The findings for the PEAR1 8,9, ADRA2A 10,11 and GP6 12,13 regions provide strong evidence in a much larger sample than past studies, while the associations in the regions of MRVI1, SHH, JMJD1C, and PIK3CG are novel. Platelet endothelial aggregation receptor-1 (PEAR1) undergoes tyrosine phosphorylation after platelet-platelet contact14. A PEAR1 promoter region variant (rs2768759) was associated with increased aggregation in PRP, most strongly in response to epinephrine, and in both pre- and post-aspirin treatment conditions8. Recently a candidate gene study found association of PEAR1 SNPs with ADP and collagen responses in 500 whole blood-derived samples, and an increase in surface PEAR1 expression upon activation9. These candidate gene studies8,9 had limited coverage of the PEAR1 region. In our study, the prior SNPs8,9 were not among the strongest associations; instead, the peak associations with ADP and epinephrine response lie within a relatively conserved region of intron 1 of PEAR1. Variation in ADRA2A receptor numbers and polymorphisms in ADRA2A that influence epinephrine-induced aggregation in diverse populations were reported nearly 15 years ago10,11. The association of ADRA2A expression with epinephrine response is logical, given that ADRA2A serves as the primary receptor for epinephrine on platelets. Additional reports in small samples have reproduced ADRA2A associations15, including recognition of complex population patterns in the region and effects on RNA levels in vitro 16. Notably, unlike prior studies focused on the immediate gene region, the peak SNP associations we observed are somewhat distant and 3’ from the gene (EA, rs4311994, 63kb, P = 3.3 × 10−11; AA, rs869244, 70kb, P = 2.2 × 10−6) suggesting partial LD with causal variants close to the gene or possible long range regulatory elements. The association of GP6 variants with collagen lag time is biologically plausible, as GP6 is the primary glycoprotein receptor that mediates collagen responses in platelets. The peak GP6 SNP in FHS, a nonsynonymous variant (Thr323Lys), was strongly associated with collagen lag time (rs1671152, P = 9.1 × 10−14). Notably rs1671152 is in LD with rs1613662 (Ser219Pro, HapMap CEU r2=1.0). Both variants have been associated with diminished collagen expression or downstream responses (e.g.,13,17). Due to multiple GP6 protein isoforms formed by splicing and a frameshift, Thr323Lys is alternatively His322Aln in a shorter isoform. Five nSNPs are in LD, including Ser219Pro and Thr323Lys/His322Aln, making it difficult to determine which are functional13,17, although a recent study supports an effect on receptor binding of Thr323Lys/His322Aln within this haplotype17. GP6 plays a role in thrombus formation18. Interestingly, two studies recently replicated association of the 219Pro allele with reduced risk for deep vein thrombosis, indicating potential clinical relevance for genetic findings in GP6 19,20. In our study, both Thr323Lys and Ser219Pro were similarly associated with collagen lag time (EA, P = 4.6 × 10−13 vs. P = 4.7 × 10−12, AA, P = 0.048 vs. P = 0.08). MRVI1 (also known as IRAG), which showed both ADP- and epinephrine-induced associations (Table 3, Supplementary Table 3), has prior evidence of functions in platelet aggregation. MRVI1 is a member of a signaling complex which influences smooth muscle cell relaxation through negative regulation of INP3-induced calcium signaling21. In mice MRVI1 plays a direct role in the inhibition of platelet aggregation and in vivo thrombosis22. There is also prior evidence for platelet-related functions for some genes at other novel loci we report. In a human heterologous system SHH+ microvesicles induce differentiation along a megakaryocyte lineage suggesting a link to platelet biology23. Polymorphisms near PIK3CG (rs342293) were recently associated with decreased mean platelet volumes24. The SNP rs342286, associated here with epinephrine-induced aggregation (P 50% aggregation. Testing was not conducted at higher concentrations if >50% aggregation was observed. The maximal aggregation response (% aggregation) was also determined for each participant at each concentration tested. GS recorded maximal aggregation (% aggregation) for periods 5 min post-ADP (2.0, 10.0 uM) and post-epinephrine (2.0, 10.0 uM), and lag time (s) to aggregation with equine-tendon-derived Type-I collagen (1, 2, 5 and 10 ug/mL, Chronolog Corp., Havertown, PA). Genotyping and imputation DNA was extracted and genotyped for consenting FHS participants with the Affymetrix 500K array and an additional gene-focused 50K array as part of the SNP Health Association Resource (SHARe) project. DNA was extracted and genotyped for the GS samples with the Illumina 1M (duo) array at deCODE Genetics (Reykjavik, Iceland). FHS and GS both used MACH to impute ~2.54 million SNPs based on the HapMap CEU phased haplotypes (release 22). SNPs were excluded from imputation in FHS that had MAF 100, or were missing from the HapMap CEU population release 22. Two hundred unrelated individuals were selected from FHS who had low SNP missingness, low numbers of Mendelian errors and who did not show up as outliers in EIGENSTRAT 2.047 (default parameters). The 200 individuals were used to infer MACH model parameters first (MACH flags used: --rounds 100 –greedy), and subsequently applied on all 8,481 individuals (MACH flags used: --greedy --mle –crossovermap –errormap). FHS samples were excluded from GWAS analysis if they had genome-wide call rates 5 s.d. away from the mean. In GS, participants with sex discrepancies or Mendelian errors > 2% were excluded from imputation. SNPs excluded from imputation had MAF = 1.0% and an imputation observed to expected ratio >= 0.30 in both cohorts. After this QC filtering ~2.33 million SNPs were included in the meta-analysis for each trait. Sample-size weighted meta-analysis was conducted with the software METAL combining the GS and FHS. The phenotypes used in meta-analyses were for the same agonists at the concentrations with the best available overlap (see Supplementary Table 1). Additionally, when meta-analyzing FHS threshold response (EC50) associations for ADP and epinephrine and GS maximal aggregation, the sign of the beta in FHS was flipped, since threshold response and maximal aggregation are inversely related. Results presented are based on individual cohort age-, sex- and PC-adjusted analyses, and meta-analyses corrected for individual study genomic control inflation rates. Regional association plots (Supplementary Figure 1a–h) were generated with SNAP41. Replication analysis We conducted testing for replication in an independent, African-ancestry sample within GS. Since LD patterns in general for African-ancestry individuals at the genome level are more complex and diverse than in populations that are primarily of European-ancestry, relying on single sentinel SNPs from European-ancestry individuals or on imputed or proxy SNPs in African-ancestry individuals for replication comparisons could lead to spurious associations. Thus, we chose to focus replication efforts on all SNPs in regions with evidence for association in the EA meta-analyses (P<1.0×10−4) that were directly genotyped with the Illumina 1M (duo) array and had MAF ≥ 1.0% in the AA replication sample. We searched for evidence of age- and sex-adjusted association in the AA samples only for the same platelet aggregation phenotypes corresponding to those in the main scan. Replication evidence was defined by SNPs with effects in the same direction in AA samples as in EA samples at a P<0.05 threshold. Supplementary Material 1
