Design and analysis of sequencing studies for gene mapping in families

描述（由申请人提供）：尽管在过去的几年中，人类复杂性状的许多常见变异已经被确定，但这些性状的很大一部分遗传能力仍未得到解释。下一代测序目前被用于揭示遗传变异的全谱，特别侧重于识别与复杂性状相关的低频变异（例如，小等位基因频率（MAF）在1-5%之间）和罕见变异（例如，MAF<1%）。然而，由于低频率和等位基因异质性，鉴定相关的罕见变异极具挑战性。因此，开发有效的设计和高效的分析和计算工具来解决这些困难至关重要。虽然病例对照研究被广泛用于常见变异的关联研究，但由于家谱中因果罕见变异的丰富，家族设计为罕见变异分析提供了有效的替代方法。此外，在存在群体分层的情况下，家庭研究是稳健的，这一特性是必不可少的，因为常规使用的常见变异方法可能无法纠正罕见变异的群体分层。为

DESCRIPTION (provided by applicant): Although numerous common variants have been identified for human complex traits in the past few years, a large proportion of the heritability o these traits remains unexplained. Next-generation sequencing is currently being employed to uncover the full spectrum of genetic variations with a particular focus on identifying low frequency variants (e.g. minor allele frequency (MAF) between 1-5%) and rare variants (e.g. MAF<1%) associated with complex traits. However identification of associated rare variants is extremely challenging due to the low frequency and allelic heterogeneity. Therefore it is crucial to develop effective designs and efficient analytical and computational tools to address these difficulties. Although case-control studies were extensively used for association studies of common variations, family designs provide an effective alternative for rare variant analysis due to the enrichment of causal rare variants in pedigrees. In addition, family studies are robust in the presence of population stratification, a property that is essential since routinely used methods for common variants may fail to correct for population stratification of rare variants. For family sequencing studies, a critical step is to infer underlying genotypes from sequence data and inaccurate genotype calls can lead to Mendelian inconsistencies and power loss of association studies. To address these challenges, in this application we propose to develop a comprehensive suite of statistical and computational methods for genotype/haplotype inference from family sequencing data and for rare variant association analysis in families. Using these methods we will carry out simulations to investigate cost efficiency of various family designs in comparison with case-control studies for improved power of detecting rare variant associations. We will also apply our methods to sequence data in our Amish family study and datasets from our collaborators on multiple complex traits. User-friendly and well-documented software packages will be released for public use.