Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples.

Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples.
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DOI:
10.1371/journal.pgen.1004269
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发表时间:
2014-04
期刊:
影响因子:
4.5
通讯作者:
Yang J
Yang J
中科院分区:
生物学2区
文献类型:
--
作者:
Visscher PM;Hemani G;Vinkhuyzen AA;Chen GB;Lee SH;Wray NR;Goddard ME;Yang J

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我们最近开发了分析方法 (GREML),利用不相关个体的全基因组单核苷酸多态性 (SNP) 数据来估计复杂性状/疾病的遗传方差以及两个复杂性状/疾病之间的遗传相关性。在这里,我们使用分析推导和模拟来量化所有 SNP 捕获的表型方差比例估计的抽样方差,用于数量性状和病例对照研究。当在相同或不同个体上测量两个复杂性状时,我们还得出双变量分析中遗传相关性估计的近似抽样方差。我们表明,采样方差与分析中成对对比的数量以及 SNP 衍生的遗传关系的方差成反比。对于双变量分析,遗传相关性的采样方差还取决于两个性状的 SNP 解释的方差比例的调和平均值以及性状之间的遗传相关性,并且取决于在同一个体上测量性状时的表型相关性。我们提供了一个在线工具,用于计算使用全基因组 SNP 数据检测遗传(共)变异的能力。新理论和在线工具将有助于规划实验设计,以估计尚未通过全基因组关联研究完全揭示的缺失遗传力,并估计复杂性状(疾病)之间的遗传重叠,特别是当性状(疾病)不是在同一样本上测量时。全基因组关联研究 (GWAS) 已识别出数百种性状和疾病的数千种遗传变异。然而,GWAS发现的遗传变异仅解释了遗传力的一小部分,从而产生了“遗传力缺失”的问题。我们最近开发了方法(称为 GREML)来估计所有 SNP 对性状(疾病)表型变异的总体贡献以及性状(疾病)之间遗传重叠的比例。一个经常被问到的问题是,需要多少样本才能以有用的精度估计归因于所有 SNP 的方差比例和遗传重叠比例。在本研究中,我们从理论推导了估计参数的标准误差,发现它们与已发表结果的观测值和模拟获得的值高度一致。该理论与在线应用工具将有助于规划实验设计,以量化缺失的遗传力,并估计性状(疾病)之间的遗传重叠,特别是当无法在同一个体上测量性状(疾病)时。
We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples. Genome-wide association studies (GWAS) have identified thousands of genetic variants for hundreds of traits and diseases. However, the genetic variants discovered from GWAS only explained a small fraction of the heritability, resulting in the question of “missing heritability”. We have recently developed approaches (called GREML) to estimate the overall contribution of all SNPs to the phenotypic variance of a trait (disease) and the proportion of genetic overlap between traits (diseases). A frequently asked question is that how many samples are required to estimate the proportion of variance attributable to all SNPs and the proportion of genetic overlap with useful precision. In this study, we derive the standard errors of the estimated parameters from theory and find that they are highly consistent with those observed values from published results and those obtained from simulation. The theory together with an online application tool will be helpful to plan experimental design to quantify the missing heritability, and to estimate the genetic overlap between traits (diseases) especially when it is unfeasible to have the traits (diseases) measured on the same individuals.
DOI: 10.1146/annurev-genet-111212-133258
发表时间: 2013
影响因子: 11.1
作者:
Vinkhuyzen AA;Wray NR;Yang J;Goddard ME;Visscher PM
通讯作者: Visscher PM
DOI: 10.1038/nature10781
发表时间: 2012-02-09
期刊: NATURE
影响因子: 64.8
作者:
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通讯作者: Visscher, Peter M.
DOI: 10.1038/ng.2711
发表时间: 2013-09
期刊: NATURE GENETICS
影响因子: 30.8
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发表时间: 2001-06-01
影响因子: 9.8
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期刊: BIOINFORMATICS
影响因子: 5.8
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