Haplotyping and QTL Mapping in Pedigrees with Missing Data

描述（由申请人提供）：家系中的统计基因作图推进了我们对复杂疾病（例如，糖尿病、阿尔茨海默病、哮喘）和数量性状（例如，骨密度、血压、产奶量）。不幸的是，大多数观察到的系谱数据是不完整的，有缺失的数据。通过使用观察到的基因型数据（特别是密集的单核苷酸多态性（SNP）标记）来有效地推断具有大量连锁位点和缺失标记数据的复杂家系的单倍型配置和计算血统同一性（IBD）概率是关键组成部分，并且在统计基因定位中仍然具有挑战性。广泛的，长期的目标，拟议的工作是开发有效的统计和计算方法，单倍型和基因定位在大型家系缺失和阶段未知的标记数据。具体目标是：1）将我们的条件枚举单倍型分析方法推广到具有缺失标记的家系，并改进该方法，使其能够处理标记间的连锁不平衡（LD）; 2）开发一种计算有效的方法，用于估计具有大量连锁基因座和缺失标记数据的大家系中的IBD概率，通过对密集（SNP）标记之间的LD信息进行建模，开发精细定位方法，并从连锁分析和精细定位中的数量性状基因座（QTL）定位准确性方面评估IBD概率估计方法的性能; 3）将所提出的方法应用于两个大的、真实的人类谱系数据集的连锁分析和精细作图（一个1623人的哈特派血统和一个1412人的阿米什人血统）;以及4）开发计算机软件来实现目标1和2。我们的方法，这些目标是基于条件概率的计算可能有序的基因型在相位未知的标记和计算的可能性单倍型配置。通过设置一个阈值的条件概率的有序基因型在相位未知的标记和单倍型配置的条件概率的阈值，所提出的单倍型分型方法确定一个子集的单倍型配置与最高的可能性的系谱。IBD概率估计的基础上，这个子集的单倍型配置，然后被用作输入方差分量为基础的QTL定位方法在大系谱。本研究开发的方法将提高我们在人类和动物群体中定位QTL和复杂疾病基因的能力。

DESCRIPTION (provided by applicant): Statistical gene mapping in pedigrees advances our knowledge of the genetic architecture of complex diseases (e.g., diabetes, Alzheimer's disease, asthma) and quantitative traits (e.g., bone density, blood pressure, milk production) in human and animal populations. Unfortunately most observed pedigree data are not complete and have missing data. Efficiently inferring haplotype configurations and calculating identity-by-descent (IBD) probabilities for complex pedigrees with large numbers of linked loci and missing marker data by using the observed genotype data (especially dense single nucleotide polymorphism (SNP) markers) are critical components and remain challenging in statistical gene mapping. The broad, long-term objectives of the proposed work are to develop efficient statistical and computational methods for haplotyping and gene mapping in large pedigrees with missing and phase unknown marker data. The specific aims are to 1) extend our conditional enumeration haplotyping method that currently works with complete pedigree data to pedigrees with missing marker data, and then improve the method so that it can handle linkage disequilibrium (LD) between markers; 2) develop a computationally efficient method for estimating IBD probabilities in large pedigrees with large numbers of linked loci and with missing marker data, develop a fine mapping method by modeling LD information between dense (SNP) markers, and evaluate the performance of the IBD probability estimation method in terms of quantitative trait loci (QTL) mapping accuracy in linkage analysis and fine mapping; 3) apply the proposed methods to linkage analysis and fine mapping of two large, real human pedigree data sets (a 1623-person Hutterite pedigree and a 1412- person Amish pedigree); and 4) develop computer software to implement aims 1 and 2. Our approach to these aims is based on the computation of conditional probabilities of possible ordered genotypes at phase unknown markers and the calculation of likelihood of haplotype configurations. By setting a threshold value for the conditional probabilities of ordered genotypes at phase unknown markers and a threshold value for the conditional probabilities of haplotype configurations, the proposed haplotyping method identifies a subset of haplotype configurations with the highest likelihoods for a pedigree. IBD probabilities are estimated based on this subset of haplotype configurations, and then are used as input to variance components based QTL mapping methods in large pedigrees. The methodologies developed in this research will enhance our ability to map QTL and complex disease genes in human and animal populations.