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Genomic approaches for understanding the genetics of complex disease

机译:了解复杂疾病遗传学的基因组方法

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There are thousands of known associations between genetic variants and complex human phenotypes, and the rate of novel discoveries is rapidly increasing. Translating those associations into knowledge of disease mechanisms remains a fundamental challenge because the associated variants are overwhelmingly in noncoding regions of the genome where we have few guiding principles to predict their function. Intersecting the compendium of identified genetic associations with maps of regulatory activity across the human genome has revealed that phenotype-associated variants are highly enriched in candidate regulatory elements. Allele-specific analyses of gene regulation can further prioritize variants that likely have a functional effect on disease mechanisms; and emerging high-throughput assays to quantify the activity of candidate regulatory elements are a promising next step in that direction. Together, these technologies have created the ability to systematically and empirically test hypotheses about the function of noncoding variants and haplotypes at the scale needed for comprehensive and systematic follow-up of genetic association studies. Major coordinated efforts to quantify regulatory mechanisms across genetically diverse populations in increasingly realistic cell models would be highly beneficial to realize that potential.
机译:遗传变异和复杂的人类表型之间有成千上万的已知关联,并且新发现的比率正在迅速增加。将这些关联转化为疾病机制的知识仍然是一项根本性的挑战,因为相关的变异绝大多数出现在基因组的非编码区域,在该区域我们几乎没有指导原则来预测其功能。与已鉴定的遗传关联的纲要与整个人类基因组的调控活性图相交,揭示了与表型相关的变体高度富集候选调控元件。基因调控的等位基因特异性分析可以进一步确定可能对疾病机制产生功能影响的变异体。新兴的高通量分析方法可以量化候选调控元件的活性,是朝着这个方向迈出的有希望的下一步。这些技术共同创造了以系统的和经验的方式对基因关联研究进行全面而系统的后续跟踪所需的非编码变体和单倍型功能假设的能力。在日益逼真的细胞模型中,为协调遗传多样性人群中调控机制的量化而进行的主要协调工作,将对实现这一潜力非常有益。

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