서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

세미나는 Zoom을 통한 온라인 강의로 예정되어 있으며 강의에 참여하고자 하시는 분께서는 아래의 링크로 참여 부탁드리겠습니다.

일시: 2021년 12월 2일 목 요일 오전 11시

연사: 선호근 연사님 (부산대학교)

Zoom link:　https://snu-ac-kr.zoom.us/j/88441994899

Title:　New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits

ABSTRACT :

Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Genetic overlap between multiple traits can raise the chance of detecting weak genetic associations missed by single-trait analyses. There are relatively many statistical methods to identify pleiotropic variants but most of them are limited to quantitative traits, even though pleiotropic effects on both quantitative and qualitative traits are often observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied. They basically integrate summary statistics of individual variants associated with either a quantitative or qualitative trait. However, meta-analysis methods cannot account for correlations among genetic variants. We propose new statistical selection method based on a selection score to prioritize pleiotropic variants associated with both quantitative and qualitative traits. For each trait, regression coefficients of elastic-net regularization are first estimated and then the number of nonzero estimates is additively combined to compute unified selection score of individual variants. In our extensive simulation studies, where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to peanut data with 6 quantitative and 2 qualitative traits, and cowpea data with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses.