Design and analysis of sequencing studies for gene mapping in families

家族基因定位测序研究的设计和分析

• 批准号: 8668121
• 负责人: BINGSHAN LI
• 金额: $ 36.45万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8668121
• 关键词: Accounting; Address; Age related macular degeneration; Alleles; Alzheimer' s Disease; Amish; Area; Base Sequence; Case-Control Studies; Chromosome Mapping; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Disease; Dropout; Exhibits; Family; Family Study; Family member; Frequencies; Future; Gene Frequency; Genes; Genetic; Genetic Variation; Genotype; Goals; Guidelines; Haplotypes; Heritability; Heterogeneity; Human; Investigation; Large-Scale Sequencing; Lead; Methods; Minor; Modeling; Parkinson Disease; Population; Population Control; Property; Research; Research Design; Research Personnel; Resources; Sequence Analysis; Severities; Statistical Methods; Stratification; Testing; Uncertainty; Update; Variant; Weight; analytical tool; base; case control; computer framework; computerized tools; cost; deep sequencing; design; genetic pedigree; improved; next generation sequencing; open source; public health relevance; rare variant; simulation; tool; trait; transmission process; user friendly software; user-friendly

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.

描述（由申请人提供）：尽管在过去几年中已经确定了人类复杂性状的许多常见变体，但这些特征的遗传力中很大一部分仍无法解释。目前正在使用下一代测序来揭示各种遗传变异的范围，特别是识别与复杂性状相关的低频变异（例如，1-5％的次要等位基因频率（MAF））和稀有变体（例如MAF <1％）。但是，由于低频和等位基因异质性，对相关稀有变体的识别极具挑战性。因此，开发有效的设计以及有效的分析和计算工具以解决这些困难至关重要。尽管病例对照研究被广泛用于共同变化的关联研究，但由于富集了谱系的因果稀有变异，家庭设计为稀有变体分析提供了有效的替代方法。此外，在存在种群分层的情况下，家庭研究是强大的，这是一种必不可少的特性，因为常规使用的常见变体方法可能无法纠正稀有变体的种群分层。为了 家庭测序研究，一个关键的步骤是从序列数据中推断基本型，基因型调用不准确会导致孟德尔的不一致和关联研究的功率损失。为了应对这些挑战，在本应用程序中，我们建议开发一套全面的统计和计算方法，用于从家庭测序数据中的基因型/单倍型推断以及家庭中罕见的变体关联分析。使用这些方法，我们将进行模拟，以研究各种家庭设计的成本效率与病例对照研究相比，以提高检测稀有变体关联的能力。我们还将应用我们的方法在我们的Amish家庭研究中对数据进行测序，并从合作者那里获得有关多个复杂特征的数据集。将发布用户友好且有据可查的软件包供公众使用。