Joint Identification of Multiple Genetic Variants via Elastic-Net Variable Selection in a Genome-Wide Association Analysis

P>Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome-wide association (GWA) studies have been conducted with large-scale data sets of genetic variants. Most of those studies have relied on single-marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi-stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic-net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false-positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population.