推 helloboy :這個老師可以找當指導教授喔 對生統有興趣的記得要聽 12/01 01:34
※ [本文轉錄自 NCTU-STAT97G 看板]
作者: pei16 (^^) 看板: NCTU-STAT97G
標題: [演講公告] 1204 統計所專題演講
時間: Tue Dec 1 00:11:49 2009
交通大學、清華大學 統計學研究所 專題演講
題 目：Assessment of Gene-gene and Gene-environment Interactions by
Incorporating Covariates into Association Mapping
主講人：邱燕楓教授 (國家衛生研究院群體健康科學研究所生物統計與生物資訊研究組)
時 間：98年12月04日(星期五)上午10:40-11:30
(上午10:20-10:40茶會於交大統計所429室舉行)
地 點：交大綜合一館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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