Analysis of polygenic disease risk and etiology by indexing ancestry- and gender-specific gene variants predicted to impact function

通过索引预计影响功能的祖先和性别特异性基因变异来分析多基因疾病风险和病因

• 批准号: 10919535
• 负责人: WILLIAM F SIMONDS
• 金额: $ 16.59万
• 依托单位: NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10919535
• 关键词: Acceleration; Affect; Alzheimer' s Disease; Behavioral; Disease; Disparity; Etiology; Gender; Genes; Genetic; Genetic Variation; Genome; Genomics; Hypertension; Intercistronic Region; Introns; Investigation; Maps; Missense Mutation; Non-Insulin-Dependent Diabetes Mellitus; Obesity; RNA Splicing; Risk; Single Nucleotide Polymorphism; Site; Variant; analytical method; disorder risk; gene discovery; genetic analysis; genetic variant; genome-wide; high risk; indexing; risk variant

Identification of genes associated with polygenic diseases is challenging in part because such diseases may be also greatly influenced by environmental and behavioral factors. To overcome these challenges, a deeper understanding of how ancestry-and gender-associated genetic variance affects disparities in the disease risk is merited. Better understanding of such genetic variance may also facilitate the identification of polygenic disease-associated genes. We hypothesize that the discovery of genes associated with polygenic diseases may be limited by over-reliance on single-nucleotide polymorphism (SNP)-based genomic investigation, since most significant variants identified in genome-wide SNP association studies map to introns and intergenic regions of the genome. To overcome such potential limitation, we are developing gene-constrained and function-based analytical methods centered on high-risk variants (hrV) that encode frameshifts, stopgains, or splice site disruption, but that may also consider variants with potentially lesser impact such as variants that encode missense mutations. Function-based and ancestry- and gender-specific analysis of genetic variations may accelerate the identification of genes associated with polygenic diseases that are also greatly influenced by environmental and behavioral factors, such as type 2 diabetes mellitus (T2DM), obesity, hypertension, and Alzheimers disease.

识别与多基因疾病相关的基因在一定程度上是具有挑战性的，因为此类疾病也可能受到环境和行为因素的很大影响。为了克服这些挑战，有必要更深入地了解与祖先和性别相关的基因差异如何影响疾病风险的差异。更好地了解这种遗传变异也可能有助于识别多基因疾病相关基因。我们推测，与多基因疾病相关的基因的发现可能受到过度依赖基于单核苷酸多态(SNP)的基因组研究的限制，因为在全基因组SNP关联研究中发现的大多数重要变异都映射到基因组的内含子和基因间区。为了克服这种潜在的限制，我们正在开发以高危变异(HRV)为中心的基因约束和基于功能的分析方法，这些变异编码移码、权值或剪接位点中断，但也可能考虑影响较小的变异，如编码错义突变的变异。基于功能和祖先和性别的遗传变异分析可能会加速识别与多基因疾病相关的基因，这些疾病也受到环境和行为因素的很大影响，如2型糖尿病(T2 DM)、肥胖症、高血压和阿尔茨海默病。