※ [本文转录自 NCTU-STAT97G 看板] 作者: pei16 (^^) 看板: NCTU-STAT97G 标题: [演讲公告] 1204 统计所专题演讲 时间: Tue Dec 1 00:11:49 2009 交通大学、清华大学 统计学研究所 专题演讲 题　目：Assessment of Gene-gene and Gene-environment Interactions by Incorporating Covariates into Association Mapping 主讲人：邱燕枫教授 (国家卫生研究院群体健康科学研究所生物统计与生物资讯研究组) 时　间：98年12月04日(星期五)上午10:40-11:30 (上午10:20-10:40茶会於交大统计所429室举行) 地　点：交大综合一馆427室 Abstract Case-control designs are commonly adopted in genetic epidemiological studies because they are cost effective and offer powerful tests for genetic and environmental risk factors, as well as their interactions. However, concerns often rise about case-control designs including possible false positives or bias due to confounders, heterogeneity or interactions among genes and between genes and environments. The present study develops a robust multipoint fine-mapping approach to incorporate covariates into the association mapping of case-control designs. Incorporating quantitative or qualitative covariates into this fine mapping through parametric and non-parametric modeling makes it possible to assess or account for main covariate effects and gene-covariate interaction effects while localizing the disease locus. Simulation results indicate that the efficiency in estimating the disease locus increases considerably when incorporating a covariate associated with the disease. This is especially true when the genetic effect of the disease locus is small. The proposed approach was applied to a case-control study of diabetes. It shows that a strong association between diabetes and a candidate gene, SCL2A10, was detected for the non-obese patients, whereas no evidence was found for either the obese patients or the whole sample when analyzed together. Additionally, the genome-wide association study (GWAS) of rheumatoid arthritis (RA) released for genetic analysis workshop (GAW) 16 was used to illustrate the application of this approach. Simulation studies and these data both demonstrate that with the incorporation of covariates, the proposed method can not only improve efficiency in estimating disease loci, but also elucidate the etiology of a complex disease. --

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