Identifying Causal Variants at Loci with Multiple Signals of Association

Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility…

• NS
  National Science Foundation

  Awards: 1320589, 1065276, 1302448, 0731455, 0513612, 0729049
• NI
  National Institutes of Health

  Awards: HL28481, U01-DA024417, P30 NS062691, K25-HL080079, R01-ES022282, HL30568, DA024417, R01-GM083198, R01 GM053275, P01-HL28481, GM053275, P01-HL30568, R01-MH101782