Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function.

Background Plasma levels of coagulation factors VII (FVII), VIII (FVIII), and von Willebrand factor (vWF) influence risk of hemorrhage and thrombosis. We conducted genome-wide association studies to identify new loci associated with plasma levels. Methods and Results Setting includes 5 community-based studies for discovery comprising 23,608 European-ancestry participants: ARIC, CHS, B58C, FHS, and RS. All had genome-wide single nucleotide polymorphism (SNP) scans and at least 1 phenotype measured: FVII activity/antigen, FVIII activity, and vWF antigen. Each study used its genotype data to impute to HapMap SNPs and independently conducted association analyses of hemostasis measures using an additive genetic model. Study findings were combined by meta-analysis. Replication was conducted in 7,604 participants not in the discovery cohort. For FVII, 305 SNPs exceeded the genome-wide significance threshold of 5.0×10−8 and comprised 5 loci on 5 chromosomes: 2p23 (smallest p-value 6.2×10−24), 4q25 (3.6×10−12), 11q12 (2.0×10−10), 13q34 (9.0×10−259), and 20q11.2 (5.7×10−37). Loci were within or near genes, including 4 new candidate genes and F7 (13q34). For vWF, 400 SNPs exceeded the threshold and marked 8 loci on 6 chromosomes: 6q24 (1.2×10−22), 8p21 (1.3×10−16), 9q34 (<5.0×10−324), 12p13 (1.7×10−32), 12q23 (7.3×10−10), 12q24.3 (3.8×10−11), 14q32 (2.3×10−10) and 19p13.2 (1.3×10−9). All loci were within genes, including 6 new candidate genes, as well as ABO (9q34) and VWF (12p13). For FVIII, 5 loci were identified and overlapped vWF findings. Nine of the 10 new findings replicated. Conclusions New genetic associations were discovered outside previously known biologic pathways and may point to novel prevention and treatment targets of hemostasis disorders.

In anticipation of the availability of next-generation sequencing data, there is increasing interest in investigating association between complex traits and rare variants (RVs). In contrast to association studies for common variants (CVs), due to the low frequencies of RVs, common wisdom suggests that existing statistical tests for CVs might not work, motivating the recent development of several new tests for analyzing RVs, most of which are based on the idea of pooling/collapsing RVs. However, there is a lack of evaluations of, and thus guidance on the use of, existing tests. Here we provide a comprehensive comparison of various statistical tests using simulated data. We consider both independent and correlated rare mutations, and representative tests for both CVs and RVs. As expected, if there are no or few non-causal (i.e. neutral or non-associated) RVs in a locus of interest while the effects of causal RVs on the trait are all (or mostly) in the same direction (i.e. either protective or deleterious, but not both), then the simple pooled association tests (without selecting RVs and their association directions) and a new test called kernel-based adaptive clustering (KBAC) perform similarly and are most powerful; KBAC is more robust than simple pooled association tests in the presence of non-causal RVs; however, as the number of non-causal CVs increases and/or in the presence of opposite association directions, the winners are two methods originally proposed for CVs and a new test called C-alpha test proposed for RVs, each of which can be regarded as testing on a variance component in a random-effects model. Interestingly, several methods based on sequential model selection (i.e. selecting causal RVs and their association directions), including two new methods proposed here, perform robustly and often have statistical power between those of the above two classes.

Venous thromboembolism (VTE), the third leading cause of cardiovascular mortality, is a complex thrombotic disorder with environmental and genetic determinants. Although several genetic variants have been found associated with VTE, they explain a minor proportion of VTE risk in cases. We undertook a meta-analysis of genome-wide association studies (GWASs) to identify additional VTE susceptibility genes. Twelve GWASs totaling 7,507 VTE case subjects and 52,632 control subjects formed our discovery stage where 6,751,884 SNPs were tested for association with VTE. Nine loci reached the genome-wide significance level of 5 × 10(-8) including six already known to associate with VTE (ABO, F2, F5, F11, FGG, and PROCR) and three unsuspected loci. SNPs mapping to these latter were selected for replication in three independent case-control studies totaling 3,009 VTE-affected individuals and 2,586 control subjects. This strategy led to the identification and replication of two VTE-associated loci, TSPAN15 and SLC44A2, with lead risk alleles associated with odds ratio for disease of 1.31 (p = 1.67 × 10(-16)) and 1.21 (p = 2.75 × 10(-15)), respectively. The lead SNP at the TSPAN15 locus is the intronic rs78707713 and the lead SLC44A2 SNP is the non-synonymous rs2288904 previously shown to associate with transfusion-related acute lung injury. We further showed that these two variants did not associate with known hemostatic plasma markers. TSPAN15 and SLC44A2 do not belong to conventional pathways for thrombosis and have not been associated to other cardiovascular diseases nor related quantitative biomarkers. Our findings uncovered unexpected actors of VTE etiology and pave the way for novel mechanistic concepts of VTE pathophysiology.

We report results from a genome wide association study (GWAS) of five quantitative indicators of behavioral disinhibition: Nicotine, Alcohol Consumption, Alcohol Dependence, Illicit Drugs, and non-substance related Behavioral Disinhibition. The sample, consisting of 7188 Caucasian individuals clustered in 2300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina’s Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and Illicit Drugs, with a p-value below the standard genome-wide threshold of 5 × 10-8. Because we had analyzed five separate phenotypes, we do not consider this single association to be significant. However, we report 13 SNPs that were associated at p < 10-5 for one phenotype and p < 10-3 for at least one other phenotype, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging from 49% to 70%, GCTA estimates of heritability for the same phenotypes ranged from 8% to 37%. Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains “missing”.