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）：现代遗传学的悖论之一是在过去十年中测序和基因分型的巨大技术进步与鉴定复杂疾病基因的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，并显示出实质性的遗传异质性以及基因by-by-Gene和基因 - 环境相互作用。为了应对这些挑战，遗传学家正在收集更大的样本和基因分型的SNP（单核苷酸多态性）。为了处理数据流的大量增加并从可用数据中提取最大信息量，必须向人类遗传学社区提供更好的统计分析工具。当前的赠款支持新的统计方法的构建及其通过广泛分布的Mendel转换为用户友好软件。在赠款的主持下，我们将针对一系列有关计算统计，关联映射，DNA拷贝数量的估算，人口遗传学和用于管理和显示人类血统数据的软件的相关项目。 我们的计算统计研究研究围绕三类优化算法 - MM和EM算法，阻止松弛方法和套索惩罚估计。我们将将这些方法应用于随机图，非负矩阵分解和多级判别分析中的估计。这些方法还与案例对照数据和QTL的快速映射（定量性状基因座）相关。我们进一步计划基于意外表，多元特征的可靠测试程序以及用于建模基因基因和基因与环境相互作用的算法进行快速测试。 我们对拷贝数变化的努力将集中于通过信号强度对DNA拷贝数的惩罚估计，以及从Illumina基因分型平台对拷贝数进行隐藏的Markov建模。在种群遗传学中​​，我们将开发用于测试谱系数据中强硬式均衡的方法和软件，对单倍型频率的估算以及民族混合物的估计。最后，我们的软件开发工作将集中于使Mendel更有利于密集的，全基因组的SNP数据，包括：现有Mendel代码的并行化； Mendel中数据结构的重组；更容易在门德尔内运行完整的分析例程；以及MendelPro的完美，MendelPro是Mendel的图形用户界面。 这个雄心勃勃的议程是我们为提供一个用于管理，显示和分析遗传数据的单个平台的一致努力的一部分。如果遗传流行病学要在二十一世纪迅速发展，则需要这种软件基础设施。