Statistical Methods for Gene Mapping

描述(申请人提供)：现代遗传学的悖论之一是过去十年在测序和基因分型方面的巨大技术进步与识别复杂疾病的基因方面进展缓慢之间的对比。这些疾病涉及生化和发育途径的微妙破坏，并表现出显著的遗传异质性以及基因与基因和基因与环境的相互作用。为了应对这些挑战，遗传学家正在收集更大的样本，并对大量的SNPs(单核苷酸多态)进行基因分型。为了处理数据流的巨大增长并从可用的数据中提取最大数量的信息，必须向人类遗传学社区提供更好的统计分析工具。目前的赠款支持建立新的统计方法，并通过广泛分发的程序Mendel将其转化为用户友好的软件。在拨款的支持下，我们将处理一系列相关的项目，包括计算统计、关联映射、DNA拷贝数估计、种群遗传学，以及管理和显示人类谱系数据的软件。 我们在计算统计方面的研究围绕着三类优化算法--MM和EM算法、块松弛方法和套索惩罚估计。我们将把这些方法应用于随机图中的估计、非负矩阵因式分解和多分类判别分析。这些方法也适用于病例对照数据的快速Logistic回归和QTL(数量性状基因座)的快速定位。我们还计划开发基于列联表的快速关联性测试、针对多变量性状的稳健测试程序以及用于对基因与基因和基因与环境相互作用进行建模的算法。 我们在拷贝数变异方面的努力将集中在通过信号强度估计DNA拷贝数的惩罚，以及来自Illumina基因分型平台的拷贝数的隐马尔可夫模型。在群体遗传学方面，我们将开发在家系数据中检验Hardy-Weinberg平衡的方法和软件，单倍型频率的惩罚估计，以及种族混杂的估计。最后，我们的软件开发工作将集中在使Mendel更有利于密集的全基因组SNP数据，包括：现有Mendel代码的并行化；Mendel中数据结构的重组；使Mendel内更容易运行完整的分析例程；以及完善Mendel的图形用户界面MendelPro。 这一雄心勃勃的议程是我们一致努力的一部分，目的是为管理、显示和分析基因数据提供一个单一平台。如果遗传流行病学要在21世纪迅速向前发展，这种软件基础设施是必要的。

DESCRIPTION (provided by applicant): One of the paradoxes of modern genetics is the contrast between the tremendous technological advances in sequencing and genotyping during the past decade and the slow progress in identifying genes for complex diseases. These diseases involve subtle disruptions of biochemical and developmental pathways and display substantial genetic heterogeneity and gene-by-gene and gene-by-environment interactions. In response to these challenges, geneticists are collecting much larger samples and genotyping enormous numbers of SNPs (single nucleotide polymorphisms). To handle the massive increases in data flow and extract the maximum amount of information from available data, better statistical analysis tools must be made available to the human genetics community. The current grant supports construction of new statistical methods and their translation into user friendly software via the widely distributed program Mendel. Under the auspices of the grant, we will tackle a series of related projects on computational statistics, association mapping, estimation of DNA copy numbers, population genetics, and software for managing and displaying human pedigree data. Our research in computational statistics revolves around three classes of optimization algorithms - MM and EM algorithms, block relaxation methods, and lasso penalized estimation. We will apply these methods to estimation in random graphs, nonnegative matrix factorization, and multicategory discriminant analysis. These methods are also pertinent to fast logistic regression with case-control data and fast mapping of QTLs (quantitative trait loci). We further plan to develop fast tests of association based on contingency tables, robust testing procedures for multivariate traits, and algorithms for modeling gene-by-gene and gene-by-environment interactions. Our efforts on copy number variation will focus on penalized estimation of DNA copy number by signal intensity, and hidden Markov modeling of copy numbers from the Illumina genotyping platform. In population genetics we will develop methods and software for testing Hardy-Weinberg equilibrium in pedigree data, penalized estimation of haplotype frequencies, and estimation of ethnic admixture. Finally our software development efforts will concentrate on making Mendel more conducive to dense, genome-wide SNP data, including: parallelization of the existing Mendel code; restructuring of the data structures in Mendel; making it easier to run complete analysis routines within Mendel; and perfection of MendelPro, the graphical user interface to Mendel. This ambitious agenda is all part of our coherent effort to provide a single platform for managing, displaying, and analyzing genetic data. This kind of software infrastructure is necessary if genetic epidemiology is to move rapidly forward in the twenty-first century.