Development of statistical genetics methodology

这一部分的一个主要项目是开发新的统计遗传学方法，以满足我们应用研究的需要，并测试和比较新的和现有的统计方法。 与Betty Doan博士合作，继续开展在连锁分析中开发倾向评分作为纳入协变量影响的方法的项目。这种方法似乎很有希望，因为它通常比直接将协变量纳入模型更有效，并且没有强烈膨胀的I型错误率。我们目前正在使用计算机模拟研究来检查影响该方法性能的因素，并将其应用于Bailey-Wilsons博士的肺癌数据。 我们完成了使用HapMap数据库中独立SNP和区块的数量以及Affymetrix和Illumina GWA板为全基因组关联研究建立p值阈值的工作。由于密度的增加减少了独立测试的数量，使用像Bonferroni这样的修正是不准确的。相反，我们使用HapMap数据和基因组的连锁不平衡结构来确定整个基因组中独立SNP的真实数量。这项工作于今年发表(1)，为该领域的研究人员提供了在几个种族群体中适当的重要性阈值的指导方针，以及为其他种族群体重新计算这些阈值的算法，以及随着更新版本的HapMap的发布。 我们还探索了各种机器学习方法在全基因组关联研究中的应用，特别是在基因-基因和基因-环境相互作用的能力和检测方面。我们使用了来自弗雷明翰心脏研究数据库的GWAS型数据和计算机模拟的特征数据，从而使我们能够证明这些方法可能能够在适当的研究中检测相互作用的影响。一篇介绍这些结果的论文正在出版中。我们正在继续寻求在基因组学研究中使用机器学习方法。 其中许多项目都在进行中。

A major project of this section is the development of new statistical genetics methodology as prompted by the needs of our applied studies and the testing and comparison of novel and existing statistical methods. The project to develop propensity scores in linkage analyses as a method for inclusion of covariate effects has been continued in conjunction with Dr. Betty Doan. This method appears promising in that it is generally more powerful than including the covariates directly into the model, and does not have strongly inflated Type I error rates. We are currently using computer simulation studies to examine factors that affect the performance of this method and are applying it to Dr. Bailey-Wilsons lung cancer data. We are completed work on establishing a p-value threshold for genome wide association studies using the number of independent SNPs and blocks within the HapMap database, as well as the Affymetrix and Illumina GWAS panels. Since increased density reduces the number of independent tests, using corrections like Bonferroni are not accurate. Instead, we used HAPMAP data and the linkage disequilibrium structure of the genome to identify the true number of independent SNPs across the genome. This work was published this year (1), giving researchers in the field guidelines for appropriate significance thresholds in several ethnic groups plus algorithms for recomputing these thresholds for other ethnic groups and as newer versions of the HapMap are released. We also explored the utility of various machine learning methods in genome-wide association studies, particularly with respect to power and detection of gene-gene and gene-environment interactions. We used GWAS genotype data from the Framingham Heart Study data repository with computer simulated trait data, thus allowing us to show that these methods may be able to detect interaction effects in suitably-powered studies. A paper presenting these results is in press. We are continuing to pursue the use of machine learning methods in genomics studies. Many of these projects are ongoing.