Bias-reduced methods in Genetic Epidemiology

我的研究领域是遗传流行病学中的生物统计方法。这项拟议的研究的动机是对复杂人类特征的遗传基础进行调查，事实证明，这比最初想象的要微妙得多。过去对常见疾病与基因组中数百万种常见遗传变异之间关系的研究发现，许多基因在疾病的发生和发展中起作用，但这些发现只是冰山一角。最近的研究旨在深入研究常见疾病的复杂遗传结构，需要强大的统计方法，能够应对罕见的遗传变异，发现基因与环境暴露之间的相互作用，并测量基因直接增加疾病风险的程度，或通过中间疾病状态间接起作用。我的研究建议可以总结如下。病例对照数据的惩罚似然方法：为了研究罕见变异关联，我们需要对主要为零的预测变量使用回归方法。我对罕见变异的工作重点是对从病例对照数据推断遗传效应的惩罚似然方法。病例-父母三人组对GxE的有力推断：在病例-父母三人组设计中，对受影响儿童（病例）及其父母的遗传变异进行了测量。也可以收集儿童的环境暴露情况。病例-父母三人组设计对遗传效应给出了正确的推断，即使在汇集了多个种族群体数据的研究中也是如此。然而，Shi等人（2011）表明，当被分析的遗传位点（测试位点）不是因果关系，而是与因果位点相关，并且这种相关性因种族而异时，从病例-父母三人组数据推断GxE可能存在偏差。我正在开发从病例-父母三人组数据推断GxE的方法，这些方法对这种人口分层偏差具有鲁棒性。稳健的中介分析方法：中介可以被认为是基因和疾病之间因果路径上的中间疾病状态。例如，基因对冠心病的影响可能是由脂质水平介导的。中介分析将暴露效应分解为估计的直接和间接分量。然而，这种效应估计在存在未测量的混杂变量（如人口分层）时容易产生偏差。基于我对GxE的稳健推断的工作，我计划开发对人口分层偏差稳健的中介分析方法。

My area of research is biostatistical methods in genetic epidemiology. The proposed research is motivated by investigations of the genetic basis of complex human traits, which has proven to be more subtle than initially thought. Past studies of associations between common diseases and millions of common genetic variants  across the genome have uncovered many genes that play a part in disease onset and progression, but these findings are just the tip of the iceberg. Recent studies that aim to delve deeper into the complex genetic architecture of common diseases require robust statistical methods able to cope with rare genetic variants, find interactions between genes and environmental exposures, and measure the extent to which a gene acts directly to increase disease risk, or acts indirectly, through an intermediate disease state. My proposed research can be summarized as follows. Penalized likelihood methods for case-control data: To study rare variant associations we require regression methods for predictor variables that are predominantly zeros. The focus of my work for rare variants is on penalized likelihood methods for inference of genetic effects from case-control data. Robust inference of GxE from case-parent trios: In the case-parent trio design genetic variants are measured on affected children (cases) and their parents. Environmental exposures on the child may also be collected. The case-parent trio design gives correct inference of genetic effects, even in studies that pool data across multiple ethic groups. However, Shi et al. (2011) have shown that showed that inference of GxE from case-parent trio data can be biased when the genetic locus being analyzed (the test locus) is not causal, but is correlated with a causal locus and this correlation varies from one ethnic group to another. I am developing methods for inference of GxE from case-parent trio data that are robust to such population stratification bias. Robust methods for mediation analysis: Mediators can be thought of as intermediate disease states on the causal path between a gene and a disease. For example, the effect of a gene on coronary heart disease may be mediated by lipid levels. Mediation analysis decomposes the exposure effect into estimated direct and indirect components. However, such effect estimates are prone to bias in the presence of unmeasured confounding variables, such as population stratification. Building on my work on robust inference of GxE, I plan to develop methods for mediation analysis that are robust to population stratification bias.