Genome-wide association studies (GWAS) represent an agnostic approach to screen for loci associated with complex traits and diseases by taking into account the genetic variation.To achieve the required large sample size, GWAS were usually performed by combining the association results of several smaller studies in a meta-analysis.In contrast, combining the genotypes before running the GWAS (mega-analysis) has several advantages compared to a classical meta-analysis, but can also introduce a severe technical bias when the genetic variants were obtained from genotyping arrays of different types.The aim of this proposal is to provide a workflow for processing, imputing and analyzing combined data from SNP arrays of different technology and genomic coverage for a GWAS mega-analysis while removing technology-related batch effects. This will help to remove false positive association results and to increase statistical power while making use of the benefits of GWAS conducted as mega-analyses especially for analyzing low-frequency variants.
DFG Programme
Research Grants
International Connection
Italy
