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숭실대학교 정보통계보험수리학과는 미래 사회의 핵심 역량인 자료분석 능력을 키우는 정보통계 트랙,
통계학과 보험수리 두 학문분야의 시너지를 극대화한 보험수리 트랙을 모두 가진 국내 유일한 학과입니다.

[2021. 12. 02] 학과세미나 안내
날짜 ㅣ 2022.02.16 조회수 ㅣ 536

1. 발표 제목: 최신 다인종 바이오뱅크 데이터 및 인종별 LD구조 차이를 이용한 교차 인종 다유전성 예측 방법인 Cross-ethnic penalized regression(CTPR)에 대한 연구

 

2. 발표내용 요약 : Polygenic risk prediction in diverse populations currently fall far behind risk prediction in populations of European descent. The use of European training samples for risk prediction in non-European populations reduces prediction accuracy due to different patterns of linkage disequilibrium (LD). The use of training samples from the target population generally implies a much lower sample size and decrease in prediction accuracy. Here, we propose a novel penalized regression based polygenic risk prediction method for cross-ethnic studies. We introduce a new cross-ethnic penalty function to incorporate different LD structure across multiple populations and evaluate its predictive performance with a sparsity penalty such as the Lasso and the minimax concave penalty (MCP). Assuming similar LD structure around a SNP between two distinct populations lead to similar coefficients of the SNP, the SNP specific adjacency coefficient in the cross-ethnic penalty is defined as functional form of LD score. This function can improve the prediction accuracy for complex traits of non-European ancestry (primary ethnicity) using European ancestry (secondary ethnicity) with large samples in Biobank. Furthermore, it can take advantage of the secondary ethnicities based on summary statistics and thus have the potential to utilize information from most published GWAS summary statistics. To apply our method to large-sample Biobank data, we utilize the parallel computing such as Message Passing Interface (MPI) which can enhance the speed of computation. We performed large-scale simulation studies to illustrate the excelling performance of our multi-ethnic approach compared to single-ethnic one. Our simulation studies showed the advantage of the cross-ethnic method over single population method.

 

3. 시간, 장소:  12월 2일 목요일 오후 12시 온라인 

 

4. 발표자: 정원일 교수