Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.
Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.
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全基因组测序以了解常见基因表达和生物标志物表型的遗传结构。
DOI：
10.1093/hmg/ddu560
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发表时间：
2015
影响因子：
3.5
通讯作者：
Curran,Jo
中科院分区：
文献类型：
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作者：
Wood,AndrewR;Tuke,MarcusA;Nalls,Mike;Hernandez,Dena;Gibbs,JRaphael;Lin,Haoxiang;Xu,ChristopherS;Li,Qibin;Shen,Juan;Jun,Goo;Almeida,Marcio;Tanaka,Toshiko;Perry,JohnRB;Gaulton,Kyle;Rivas,Manny;Pearson,Richard;Curran,Jo
测序研究的初步结果表明，相对较少的低频（<5%）变异对常见表型有较大影响。我们对来自InCHIANTI研究的680个个体进行了低通全基因组测序，以检验两个主要假设：(i)测序将检测单个低频大效应变异，这些变异解释了与单个常见变异相似的表型变异量，（ii）一些常见变异关联可以通过低频变异解释。我们测试了两组与疾病相关的常见表型，我们有统计能力在450个个体中检测到大量的常见变异-常见表型关联- 11个132cis基因表达性状，在所有680个个体中检测到93个循环生物标志物。在总共11 657 229个高质量变异中，其中6 129 221和5 528 008分别为常见变异和低频变异（<5%），低频-大效应关联包括7%的可检测基因表达性状[1314个cis- eqtls中有89个atP< 1 × 10−06（错误发现率~ 5%）]和8个生物标志物关联中的1个atP< 8 × 10−10]。很少（1232人中有30人，2%）常见变异关联完全可以用低频变异来解释。我们的数据表明，当样本量足够大，可以检测到大量的常见变异关联时，全基因组测序可以识别出基于基因分型的方法无法检测到的低频变异，而常见变异关联很少能用单个大影响的低频变异来解释。
Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectablecis-gene expression traits [89 of 1314cis-eQTLs atP< 1 × 10−06(false discovery rate ∼5%)] and one of eight biomarker associations atP< 8 × 10−10. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.