Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
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DOI：
10.1093/bioinformatics/bts474
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发表时间：
2012-10-01
期刊：
影响因子：
5.8
通讯作者：
Wray, N. R.
中科院分区：
文献类型：
--
作者：
Lee, S. H.;Yang, J.;Wray, N. R.
遗传相关是影响多个性状的因果变异的全基因组聚集效应。传统上，复杂性状之间的遗传相关性是通过系谱研究来估计的，但这种估计可能会受到共享环境因素的干扰。此外，就疾病而言，低患病率意味着，即使疾病之间的真正遗传相关性很高，家庭中疾病的共同聚集也可能不会发生，或者无法与偶然性区分开来。我们已经开发并实施了基于线性混合模型的统计方法，以获得无偏估计的遗传相关性对数量性状或对二元性状的复杂疾病使用基于人群的病例对照研究与全基因组单核苷酸多态性数据。该方法在模拟研究中得到了验证，并应用于估计各种疾病之间的遗传相关性，从威康信托病例控制联盟的数据在一系列的双变量分析。我们估计2型糖尿病和高血压风险之间存在显著的正遗传相关性，近似为0.31（SE 0.14，P = 0.024）。
Genetic correlations are the genome-wide aggregate effects of causal variants affecting multiple traits. Traditionally, genetic correlations between complex traits are estimated from pedigree studies, but such estimates can be confounded by shared environmental factors. Moreover, for diseases, low prevalence rates imply that even if the true genetic correlation between disorders was high, co-aggregation of disorders in families might not occur or could not be distinguished from chance. We have developed and implemented statistical methods based on linear mixed models to obtain unbiased estimates of the genetic correlation between pairs of quantitative traits or pairs of binary traits of complex diseases using population-based case-control studies with genome-wide single-nucleotide polymorphism data. The method is validated in a simulation study and applied to estimate genetic correlation between various diseases from Wellcome Trust Case Control Consortium data in a series of bivariate analyses. We estimate a significant positive genetic correlation between risk of Type 2 diabetes and hypertension of similar to 0.31 (SE 0.14, P = 0.024).