Statistical Methods for Gene Mapping

基因作图统计方法

• 批准号: 7989689
• 负责人: Kenneth L Lange
• 金额: $ 14.52万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7989689
• 关键词: Admixture; Algorithms; Applications Grants; Area; Biochemical; Bioinformatics; Chromosome Mapping; Code; Communities; Complex; Computer software; Computing Methodologies; Copy Number Polymorphism; DNA copy number; Data; Data Set; Detection; Development; Discriminant Analysis; Disease; Dose; Environment; Equilibrium; Faculty; Frequencies; Fruit; Genes; Genetic; Genetic Heterogeneity; Genotype; Grant; Graph; Hand; Haplotypes; Human; Human Genetics; Lasso; Logistic Regressions; Maps; Methods; Modeling; Pathway interactions; Population Genetics; Principal Investigator; Procedures; Quantitative Trait Loci; Relaxation; Research; Research Infrastructure; Research Personnel; Running; Sampling; Series; Signal Transduction; Single Nucleotide Polymorphism; Statistical Methods; Structure; Suggestion; Testing; Translations; base; case control; experience; gene environment interaction; genetic analysis; genetic epidemiology; genetic pedigree; genome-wide; graphical user interface; markov model; member; programs; response; software development; statistics; tool; trait; user friendly software

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.

描述（由申请人提供）：现代遗传学的矛盾之一是过去十年中测序和基因分型方面的巨大技术进步与鉴定复杂疾病基因方面的缓慢进展之间的对比。这些疾病涉及生物化学和发育途径的微妙破坏，并显示出相当大的遗传异质性和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SNP（单核苷酸多态性）进行基因分型。为了处理数据流的大量增加并从现有数据中提取最大量的信息，必须向人类遗传学界提供更好的统计分析工具。目前的赠款支持新的统计方法的建设和翻译成用户友好的软件通过广泛分布的程序孟德尔。在拨款的资助下，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、群体遗传学以及管理和显示人类谱系数据的软件。 我们的研究在计算统计围绕三类优化算法- MM和EM算法，块松弛方法，套索惩罚估计。我们将把这些方法应用于随机图的估计、非负矩阵分解和多类别判别分析。这些方法也适用于病例对照数据的快速逻辑回归和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测试，多变量性状的稳健测试程序，以及用于建模基因与基因和基因与环境相互作用的算法。 我们在拷贝数变异方面的努力将集中在通过信号强度对DNA拷贝数的惩罚性估计，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，我们将开发方法和软件，用于测试系谱数据中的Hardy-Weinberg平衡，单倍型频率的惩罚估计，以及种族混合的估计。最后，我们的软件开发工作将集中在使孟德尔更有利于密集的，全基因组SNP数据，包括：现有孟德尔代码的并行化;在孟德尔的数据结构的重组;使其更容易在孟德尔运行完整的分析例程;和MendelPro的完善，孟德尔的图形用户界面。 这一雄心勃勃的议程是我们一致努力的一部分，旨在为管理、显示和分析遗传数据提供一个单一的平台。如果遗传流行病学要在世纪迅速发展，这种软件基础设施是必要的。