Multiple Imputation in Genetic Studies

描述（由申请人提供）： 最近，关联研究比传统的大家族连锁分析更受青睐，以确定复杂疾病的遗传学。由于双等位基因单核苷酸多态性（SNP）在整个基因组中的可用性和高频率，使用双等位基因单核苷酸多态性（SNP）作为关联/连锁研究的标记已经变得更加普遍。随着诸如Perlgen芯片等新技术的出现，可以对每个个体的全基因组中约160万个SNP进行基因分型。然而，这在分析具有缺失值的数据时产生了一系列新的问题和挑战。缺失值在基因型和表型数据中是不可避免的，明显不完整的数据可能会导致基因作图中的不可靠和/或偏倚结果。 表型和大量可用基因型的家族数据中的缺失数据问题尚未得到充分解决。大多数统计分析软件使用列表或成对删除，这可能会删除大部分分析数据，从而导致严重的偏倚。目前，没有软件可用于家族或病例对照遗传数据的多重插补。本提案的总体目标是在这些公认的缺失数据估计技术与最常用的遗传学数据统计分析方法之间架起一座桥梁。具体而言，该提案的具体目标是： 具体目标1：基于真实的数据，对家系和病例对照设计的多变量表型和基因型数据进行建模和模拟。 具体目标2：使用模拟比较缺失数据程序的稳健性和可靠性，然后开发多重插补软件。

DESCRIPTION (provided by applicant): Recently, association studies are being favored over traditional linkage analyses of extended families to determine the genetics of complex diseases. The use of bi-allelic single nucleotide polymorphisms (SNPs) as markers for association/linkage studies has become more common due to their availability and high frequency throughout the genome. With the advent of such new technologies as the Perlgen chip, it is possible to genotype about 1.6 million SNPs on the whole genome on each individual. However, this creates a new set of problems and challenges in analyzing data with missing values. Missing values are inevitable in genotypic as well as phenotypic data, and clearly incomplete data could give rise to undependable and/or biased results in gene mapping. The missing data problems in familial data with phenotypes and the large number of available genotypes have not been adequately addressed. Most statistical analysis software uses list-wise or pair-wise deletion that can potentially create a problem by deleting most of the data for the analysis thus resulting in serious bias. Currently, there is no software available for Multiple Imputation for familial or case-control genetic data. The overall goal of this proposal is to provide a bridge between, on the one hand, these well-established techniques for estimation in the presence of missing data and, on the other hand, the most commonly used statistical analysis methods of genetics data. In particular, the Specific Aims of this proposal are: Specific Aim 1: To model and simulate multivariate phenotypic and genotypic data for family and case-control design based on real data. Specific Aim 2: To compare missing data procedures for robustness and reliability using simulation and then to develop Multiple Imputation software.