Explaining additional genetic variation in complex traits.
Explaining additional genetic variation in complex traits.
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DOI：
10.1016/j.tig.2014.02.003
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Visscher PM
中科院分区：
文献类型：
--
作者：
Robinson MR;Wray NR;Visscher PM
全基因组关联研究（GWAS）为复杂性状的遗传基础提供了有价值的见解，发现了bbb6000个与bbb500个数量性状和人类常见复杂疾病相关的变异。到目前为止，确定的关联仅代表影响表型的关联的一小部分，因为在整个频谱中可能存在非常多的变体，每个变体影响多个性状，对表型方差的平均贡献很小。这对进一步解剖种群内剩余的无法解释的遗传变异提出了相当大的挑战，这限制了我们预测疾病风险、确定新的药物靶点、改善和维持食物来源以及理解自然多样性的能力。这一挑战将在目前的框架内通过更大的样本量、更好的表型（包括记录非遗传风险因素）、重点研究设计以及多种表型和遗传信息来源的整合来应对。目前的证据支持定量遗传方法的应用，我们认为应该保留更简单的理论，直到简单可以换取更大的解释力。
Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those which influence phenotype, as there are likely to be very many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping including recording of non-genetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power.
影响因子：
3.7
作者：
Cheng KF;Chen JH
通讯作者：
Chen JH