Inference of disease associated genomic segments in post-GWAS analysis-侯琳
题目：Inference of disease associated genomic segments in post-GWAS analysis
Identification and interpretation of disease associated loci remain a paramount challenge in genome-wide association (GWAS) of complex disease. We develop post-GWAS analysis tools, which leverage pleiotropy and functional annotations to dissect the genetic architecture of complex traits. In this talk, I will first introduce LOGODetect, a powerful and efficient statistical method to identify small genome segments harboring local genetic correlation signals. LOGODetect automatically identifies genetic regions showing consistent association with multiple phenotypes through a scan statistic approach. Applied to seven neuropsychiatric traits, we identify hub regions showing concordant effect on five or more traits. Next, I will introduce Openness Weighted Association Studies (OWAS), a computational approach that leverage and aggregate predictions of chromatin accessibility in personal genomes for prioritizing GWAS signals. In extensive simulation and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways.
侯琳，清华大学统计学研究中心副教授、博士生导师，主要从事生物统计、生物信息、统计遗传学等方向的研究。担任中国现场统计研究会计算统计分会常务理事、秘书长；Statistics in Biosciences编委。研究成果发表在Nature Communications， PNAS，Bioinformatics， PLOS Computational Biology，Human Molecular Genetics等期刊。