Genetic Association and Personalized Medicine

 描述（申请人提供）：尽管最近发表的全基因组关联研究（GWAS）已经定位了许多疾病相关的遗传变异，但它们仅占可遗传表型变异的很小比例，这表明由于遗传异质性，仅确定了一小部分致病基因座（即与复杂性状相关的多种遗传变异），证实了常见遗传变异的小到中等效应大小，并且当前分析方法的统计功效有限。另一方面，GWAS数据也为个性化医疗提供了一个令人兴奋的机会，旨在根据个人的临床和遗传信息为他/她分配最合适的治疗或干预。然而，在将GWAS数据转化为个性化医疗实践方面仍有相当大的距离，主要是由于缺乏强大的分析方法。这项研究致力于个性化医疗与高维遗传和临床数据的几个新兴课题。基于上一个资助期内在惩罚回归和分类方面取得的进展，我们建议为GWAS数据开发创新和强大的统计方法，以发现新的基因通路并将其用于个性化医疗。特别是，我们的目标是发现包含SNP的从头基因途径，这些SNP对复杂的疾病和性状具有单独弱，但集体强的影响。我们联合收割机现有的肺部健康研究（LHS）临床数据和来自两个不同来源的GWAS数据，应用开发的统计方法来探索遗传变异和基线临床变量如何可能改变吸烟干预的效果，以及如何确定任何给定受试者的最佳个性化干预规则。

 DESCRIPTION (provided by applicant): Although recently published genome-wide association studies (GWASs) have localized many disease-associated genetic variants, they only account for tiny proportions of heritable phenotypic variations, suggesting that only a small fraction of causal loci have been identified, due to genetic heterogeneity (i.e. multiple genetic variants associated with a complex trait), confirmed small to modest effect sizes of common genetic variants and limited statistical power of current analysis methods. On the other hand, GWAS data also offer an exciting opportunity for personalized medicine, aiming to assign the most suitable treatment or intervention to an individual based on his/her clinical and genetic information. However, there is still quite a distance in translating GWAS data to practice of personalized medicine, largely due to the paucity of powerful analysis methods. This research is devoted to several emerging topics in personalized medicine with high-dimensional genetic and clinical data. Building on the advances in penalized regression and classification made during the previous funding period, we propose developing innovative and powerful statistical methods for GWAS data to discover novel gene pathways and utilize them in personalized medicine. In particular, we aim to discover de novo gene pathways containing SNPs with individually weak, but collectively strong, effects on complex disease and traits. We combine the available Lung Health Study (LHS) clinical data and GWAS data from two different sources, applying the developed statistical methods to explore how genetic variants and baseline clinical variables possibly modify the effects of smoking interventions, and how to determine an optimal individualized intervention rule for any given subject.