Developing new statisical methods to detect rare variants involved in neuropsychiatric disorders

我们开发了一种分析工具，并在同行评审的期刊上发表了我们的结果。 我们开发了一种基于单体型的方法（H-PDT）来分析复杂人类疾病家系中的罕见变异。在测序环境中进行了广泛的模拟，以评估和比较基于单体型的方法与罕见的变异方法，该方法采用更传统的折叠策略。如通过各种情况评估的，当感兴趣的疾病主要由罕见单倍型（具有多个罕见等位基因）引起时，与基于罕见变体的谱系测试相比，基于单倍型的谱系测试具有增强的统计功效，并且当疾病由独立作用的罕见变体引起时，反之亦然。对于大多数其他情况下，当疾病是由具有多个罕见等位基因的单倍型和具有相似效应的罕见变异引起时，这两种方法在检测相关性方面提供了相似的能力。所有测试均已在软件中实施，该软件已提交给综合R存档网络（CRAN），作为名为rvHPDT的计算机程序进行通用。

We developed one analytical tool and published our results in peer-reviewed journals. We developed a haplotype-based approach(H-PDT) to analyze rare variants within pedigrees with complex human disorders. Extensive simulations in the sequencing setting were carried out to evaluate and compare the haplotype-based approach with the rare variant methods that drew on a more conventional collapsing strategy. As assessed through a variety of scenarios, the haplotype-based pedigree tests had enhanced statistical power compared with the rare variants based pedigree tests when the disease of interest was mainly caused by rare haplotypes (with multiple rare alleles), and vice versa when disease was caused by rare variants acting independently. For most of other situations when disease was caused both by haplotypes with multiple rare alleles and by rare variants with similar effects, these two approaches provided similar power in testing for association. All tests have been implemented in a software, which was submitted to the Comprehensive R Archive Network (CRAN) for general use as a computer program named rvHPDT.