Global adaptive rank truncated product method for gene-set analysis in association studies

Cytokine pathway variants modulate platelet production: IFNA16 is a thrombocytosis susceptibility locus in humans

Inflammatory stimuli have divergent effects on peripheral platelet counts, although the mechanisms of thrombocytopenic and thrombocytotic responses remain poorly understood. A candidate gene approach targeting 326 polymorphic genes enriched in thrombopoietic and cytokine signaling pathways was applied to identify single nucleotide variants (SNVs) implicated in enhanced platelet responses in cohorts with reactive thrombocytosis (RT) or essential (myeloproliferative neoplasm [MPN]) thrombocytosis (ET). Cytokine profiles incorporating a 15-member subset, pathway topology, and functional interactive networks were distinct between ET and RT, consistent with distinct regulatory pathways of exaggerated thrombopoiesis. Genetic studies using aggregate (ET + RT) or ET-restricted cohorts identified associations with 2 IFNA16 (interferon-α16) SNVs, and the ET associations were validated in a second independent cohort (P = .0002). Odds ratio of the combined ET cohort (n = 105) was 4.92, restricted to the JAK2V617F-negative subset (odds ratio, 5.01). ET substratification analysis by variant IFNA16 exhibited a statistically significant increase in IFN-α16 levels (P = .002) among 16 quantifiable cytokines. Recombinantly expressed variant IFN-α16 encompassing 3 linked non-synonymous SNVs (E65H95P133) retained comparable antiviral and pSTAT signaling profiles as native IFN-α16 (V65D95A133) or IFN-α2, although both native and variant IFN-α16 showed stage-restricted differences (compared with IFN-α2) of IFN-regulated genes in CD34+-stimulated megakaryocytes. These data implicate IFNA16 (IFN-α16 gene product) as a putative susceptibility locus (driver) within the broader disrupted cytokine network evident in MPNs, and they provide a framework for dissecting functional interactive networks regulating stress or MPN thrombopoiesis.

Efficient and Powerful Method for Combining P-Values in Genome-Wide Association Studies

The goal of Genome-wide Association Studies (GWAS) is the identification of genetic variants, usually single nucleotide polymorphisms (SNPs), that are associated with disease risk. However, SNPs detected so far with GWAS for most common diseases only explain a small proportion of their total heritability. Gene set analysis (GSA) has been proposed as an alternative to single-SNP analysis with the aim of improving the power of genetic association studies. Nevertheless, most GSA methods rely on expensive computational procedures that make unfeasible their implementation in GWAS. We propose a new GSA method, referred as globalEVT, which uses the extreme value theory to derive gene-level p-values. GlobalEVT reduces dramatically the computational requirements compared to other GSA approaches. In addition, this new approach improves the power by allowing different inheritance models for each genetic variant as illustrated in the simulation study performed and allows the existence of correlation between the SNPs. Real data analysis of an Attention-deficit/hyperactivity disorder (ADHD) study illustrates the importance of using GSA approaches for exploring new susceptibility genes. Specifically, the globalEVT method is able to detect genes related to Cyclophilin A like domain proteins which is known to play an important role in the mechanisms of ADHD development.