Developing Stats Methods to Detect Rare Genetics Variants in Human Pedigrees

下一代测序技术与高效的DNA捕获方法相结合，为研究复杂表型的遗传基础提供了外显子组测序方法。与全基因组关联研究(Gwas)不同，全基因组关联研究只能发现种群中频繁出现的DNA变异(大于1%)，外显子组测序对于今天可能有兴趣寻找罕见突变的科学家来说是一个很好的选择。此外，外显子组测序的优势是全面测试编码变异的作用，无论是常见的还是罕见的。据预测，每个基因都可能含有功能相关的变体。 最近，一些统计方法可用于分析稀有变异对复杂性状发育的贡献。这些方法包括多元折叠(CMC)方法、折叠子群的多元检验、HotelingT2检验、Manova、Fishers乘积方法、加权和方法和基于核的自适应检验。 虽然这些方法的优点在基于群体的关联研究中得到了广泛的评价，但这些方法目前的形式都不能用来分析基于家系的关联分析，使用外显子组测序数据。 我们将开发基于系谱的稀有变异分析方法，将每个受影响的亲属视为依赖对，并使用相关矩阵来解释依赖关系。 在一组罕见变异与疾病没有关联的零假设下，我们发展的新统计量是渐近分布的中心分布。 此外，我们将使用估计的基于IBD的权重来说明来自相同家系的相关受影响或未受影响的配对的相关性。这种方法将被用来分析具有外显组数据的大致双相情感障碍家系。将使用模拟研究来确定功率和I类错误。这种方法将被用来分析大约100个双相情感障碍家系的外显组数据，以及未来与其他精神障碍的数据集。

Next-generation sequencing technologies coupled with the efficient DNA capture methods provide exome sequencing approach to investigate the genetic basis of complex phenotypes. Unlike whole genome association studies (GWAS) which can only discover variation in DNA that is frequent in the population (great than 1%), exome sequencing is a great choice for scientists today who might be interested in looking for rare mutations. Furthermore, exome sequencing has the advantage of testing comprehensively the role of coding variation, both common and rare. It is anticipated that every gene may harbor functionally relevant variants. Recently, a number of statistical methods become available for analyzing the contribution of rare variants to the development of complex traits. These methods include Combined Multivariate and Collapsing (CMC) Method, Multivariate test of collapsed sub-groups Hotelling T2 test, MANOVA, Fishers product method, Weighted Sum Method and Kernel-based adaptive test. While the merits of these methods have been evaluated extensively for population-based association studies, none of these methods in their current form can be used to analyze the pedigree based association analysis using exome sequencing data. We will develop pedigree-based rare variants analysis approach by treating each affected relatives as dependent pairs and the dependency will be accounted for using correlation matrix. Under the null hypothesis of no association of a set of rare variants with the diseases, the new statistic we have developed is asymptotically distributed as a central distribution. Further, we will use the estimated IBD based weights to account for the dependency of the related affected or unaffected pairs generated from same pedigrees. This method will be used to analyze approximately bipolar disorder pedigrees with exome data. Simulation studies will be used for determining power and type I errors. This method will be used to analyze approximately 100 bipolar disorder pedigrees with exome data as well as data sets with other mental disorders in the future.