RT Journal Article
SR Electronic
T1 Identifying Causal Variants by Fine Mapping Across Multiple Studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.01.15.908517
DO 10.1101/2020.01.15.908517
A1 Nathan LaPierre
A1 Kodi Taraszka
A1 Helen Huang
A1 Rosemary He
A1 Farhad Hormozdiari
A1 Eleazar Eskin
YR 2020
UL http://biorxiv.org/content/early/2020/01/16/2020.01.15.908517.abstract
AB Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.