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

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
依托单位国家:
美国
项目类别:
财政年份:
--
资助国家:
美国
项目状态:
未结题
起止时间:
至

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中文摘要
翻译
鉴定与多基因疾病相关的基因是具有挑战性的,部分原因是这些疾病也可能受到环境和行为因素的极大影响。 为了克服这些挑战,更深入地了解祖先和性别相关的遗传变异如何影响疾病风险的差异是值得的。 更好地了解这种遗传变异也可能有助于多基因疾病相关基因的鉴定。 我们假设,发现与多基因疾病相关的基因可能会受到限制,过度依赖单核苷酸多态性(SNP)为基础的基因组研究,因为在全基因组SNP关联研究中发现的最重要的变异映射到内含子和基因组的基因间区域。 为了克服这种潜在的限制,我们正在开发基因约束和基于功能的分析方法,这些方法以编码移码、终止或剪接位点破坏的高风险变体(hrV)为中心,但也可能考虑影响可能较小的变体,如编码错义突变的变体。 基于功能和祖先和性别特异性的遗传变异分析可以加速与多基因疾病相关的基因的鉴定,这些疾病也受到环境和行为因素的极大影响,如2型糖尿病(T2DM),肥胖,高血压和阿尔茨海默病。
英文摘要
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.
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