Enhanced Gene Identification in Complex Traits Using Kernel Machines

使用内核机器增强复杂性状的基因识别

基本信息

批准号：
8598704
负责人：
MICHAEL PHILIP EPSTEIN
金额：
$ 35万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-04 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8598704
关键词：
Address Biological Chromosome Mapping Chromosomes Complex Computer software DNA Resequencing Data Data Set Disease Environmental Risk Factor Epilepsy Exhibits Family Gene Expression Genes Genetic Genome Genomics Goals Health Human Human Genome Individual Joints Link Linkage Disequilibrium Literature Machine Learning Maps Medical Methods Methylation Modeling Nature Non-Insulin-Dependent Diabetes Mellitus Nuclear Family Nucleotides Other Genetics Pattern Performance Play Post-Traumatic Stress Disorders Procedures Public Health Research Design Research Personnel Risk Role Scientific Advances and Accomplishments Sex Bias Simulate Source Statistical Methods Study models Technology Testing Variant Work X Chromosome X Inactivation X inherited trait abstracting base case control design flexibility gene interaction genetic analysis genetic pedigree genetic variant genome wide association study human disease improved interest next generation sequencing novel open source population based sex stem success tool trait user friendly software

项目摘要

DESCRIPTION (provided by applicant): Project Summary/Abstract Genome-wide association studies (GWAS) have mapped thousands of common trait-influencing variants yet the overwhelming majority of trait loci have yet to be discovered. The goal of this proposal is to develop and apply statistical approaches that move beyond the standard GWAS paradigm to map additional trait-influencing variation within the human genome. Most of our proposed tools are based on a flexible high-dimensional framework called kernel machine regression, which we have had past success employing for powerful gene mapping of complex traits in GWAS and next-generation sequencing (NGS) studies. We believe the inherent flexibility of the kernel framework makes it ideal for exploring new paradigms in gene mapping of complex human traits. Aim 1 proposes novel kernel methods for integrated analysis of both single-nucleotide variation data (derived from GWAS and/or NGS) and genomic data (such as gene-expression and methylation patterns) that we believe will provide improved power for trait mapping. Aim 2 proposes novel kernel methods for large scale gene-gene interaction analysis across the genome, as well as a computational approach that enables efficient adjustment for multiple testing when applying such exhaustive testing procedures. Aim 3 establishes novel kernel methods for association mapping of SNVs on the X chromosome. The flexible nature of kernel machines makes it ideal for modeling potential sex-specific effects on this chromosome and the methods further can accommodate random X inactivation. Aim 4 proposes novel kernel approach for robust analysis of rare trait-influencing variation within families; such family-based designs are generally not considered in current rare-variant procedures. We will evaluate these methods on large-scale datasets that we are actively involved in and will implement the methods in user-friendly software for public distribution (Aim 5).

描述（由申请人提供）：项目摘要/摘要全基因组关联研究（GWAS）已绘制了数千种常见的特质影响变体，但尚未发现绝大多数特质基因座。该提案的目的是开发和应用统计方法，这些方法超越标准GWAS范式来绘制人类基因组内的其他特征影响变化。我们提出的大多数工具都是基于一个称为内核机回归的灵活的高维框架，我们过去已经成功地利用了GWAS和下一代测序（NGS）研究的复杂特征的强大基因映射（NGS）。我们认为，内核框架的固有灵活性使其成为探索复杂人类特征基因映射的新范式的理想选择。 AIM 1提出了新的内核方法，用于综合分析单核苷酸变异数据（源自GWAS和/或NGS），以及我们认为我们认为将提供改进的性状映射功能的基因组数据（例如基因表达和甲基化模式）。 AIM 2提出了用于整个基因组大规模基因相互作用分析的新内核方法，以及一种计算方法，在应用此类详尽的测试程序时，可以有效调整多次测试。 AIM 3建立了用于X染色体上SNV关联映射的新内核方法。内核机的灵活性使其成为对该染色体的潜在性别特异性作用建模的理想选择，并且该方法进一步可容纳随机X失活。 AIM 4提出了新的内核方法，用于对家庭内部罕见性状影响变化的强大分析；这种基于家庭的设计通常在当前的稀有过程中不考虑。我们将在我们积极参与的大规模数据集上评估这些方法，并将在用户友好的软件中实施用于公共分发的方法（AIM 5）。