한빛사 논문
Taeyeop Lee1,†, Min Kyung Sung2,3,†, Seulkee Lee1,2,4,†, Woojin Yang2,5, Jaeho Oh2, Jeong Yeon Kim2, Seongwon Hwang6, Hyo-Jeong Ban7 and Jung Kyoon Choi2,*
1Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, Republic of Korea, 2Department of Bio and Brain Engineering, KAIST, Daejeon 34141, Republic of Korea, 3MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, UK, 4Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea, 5Korean Bioinformation Center (KOBIC), KRIBB, Daejeon 34141, Republic of Korea, 6Seminar for Statistics, Eidgen¨ossische Technische Hochschule (ETH) Zurich, CH-8092 Zurich, Switzerland and 7Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea
*Correspondence should be addressed.
†The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors
Abstract
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural network framework for combinatorial, nonlinear modeling of complex patterns shared by risk variants scattered among multiple associated loci. When applied for major psychiatric disorders and autoimmune diseases, neural and immune features, respectively, exhibited high explanatory power while reflecting the pathophysiology of the relevant disease. The predicted causal variants were concentrated in active regulatory regions of relevant cell types and tended to be in physical contact with transcription factors while residing in evolutionarily conserved regions and resulting in expression changes of genes related to the given disease. We demonstrate some examples of novel candidate causal variants and associated genes. Our method is expected to contribute to the identification and functional interpretation of potential causal noncoding variants in post-GWAS analyses.
논문정보
관련 링크
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기