Powerful Inference and Prediction for Genetic Association

描述（由申请人提供）：遗传关联研究旨在通过比较受影响和未受影响个体之间的遗传变异频率来绘制疾病基因图谱。由于遗传变异与疾病之间的关联通常较弱，因此应用强大的统计检验来最大限度地定位疾病基因座的机会至关重要。我们建议开发新的和强大的多位点方法的基础上惩罚回归检测遗传关联的人口或家庭为基础的研究与非阶段的基因型数据。具体来说，统计方法和理论将开发和评估统计推断和预测的基础上惩罚回归与新的非凸惩罚线性模型，广义线性模型和广义估计方程。我们将开发的方法应用于候选基因关联资源（CARe）中的大型队列，进行多位点分析，以发现房颤（AF）相关变异，可能通过考虑基因与基因和基因与环境的相互作用。已知和新发现的遗传和其他风险因素将用于预测AF。 公共卫生相关性：这项拟议中的研究不仅有望为阐明复杂人类疾病和性状的遗传成分提供有价值的分析工具，而且还将推进高维数据的统计方法和理论，

DESCRIPTION (provided by applicant): Genetic association studies aim to map disease genes through comparisons of frequencies of genetic variants among affected and unaffected individuals. Due to usually weak associations between genetic variants and disease, it is critical to apply powerful statistical tests to maximize the chance to locate disease loci. We propose developing novel and powerful multi-locus methods based on penalized regression to detect genetic association for population- or family-based studies with un- phased genotype data. Specifically, statistical methods and theory will be developed and evaluated for statistical inference and prediction based on penalized regression with novel nonconvex penalties for linear models, generalized linear models and generalized estimating equations. We will apply the developed methods to large cohorts in the Candidate gene Association Resource (CARe) for multi- locus analysis to discover atrial fibrillation (AF)-associated variants, possibly by considering gene by gene and gene by environment interactions. Known and newly discovered genetic and other risk factors will be used to predict AF. PUBLIC HEALTH RELEVANCE: This proposed research is expected not only to contribute valuable analysis tools to the elucidation of genetic components of complex human diseases and traits, but also to advance statistical methodology and theory for high-dimensional data,