Lloyd-Jones Luke R, Holloway Alexander, McRae Allan, Yang Jian, Small Kerrin, Zhao Jing, Zeng Biao, Bakshi Andrew, Metspalu Andres, Dermitzakis Manolis, Gibson Greg, Spector Tim, Montgomery Grant, Esko Tonu, Visscher Peter M, Powell Joseph E
Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD 4072, Australia; Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia.
Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia.
Am J Hum Genet. 2017 Feb 2;100(2):228-237. doi: 10.1016/j.ajhg.2016.12.008. Epub 2017 Jan 5.
We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (h) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (h) accounted for on average 48% (0.093/0.192) of h. Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.
我们分析了来自2765名个体的36778个转录本表达性状（探针）的mRNA水平，以全面研究外周血中基因表达的遗传结构和缺失遗传力程度。我们使用线性混合模型进行全基因组关联分析，鉴定出11204个顺式和3791个反式独立表达数量性状位点（eQTL）。此外，利用密切相关和远亲个体的信息，对所有表达性状进行了遗传力估计。在一组表达的探针（15966个）中，10580个（66%）的估计狭义遗传力（h）大于零，平均值（中位数）为0.192（0.142）。在这些探针中，所有eQTL解释的遗传方差比例（h）平均为31%（0.060/0.192），这意味着69%的遗传方差缺失，最大eQTL的哨兵SNP解释了所有已鉴定的顺式和反式eQTL所贡献方差的87%（0.052/0.060）。对于同一组探针，全基因组常见（MAF>0.01）的HapMap 3 SNP所贡献的遗传方差（h）平均占h的48%（0.093/0.192）。综上所述，有证据表明，基因表达的遗传方差中约有一半未被常见SNP标记，而在被常见SNP标记的方差中，很大一部分可归因于通常为顺式的可识别的大效应eQTL。最后，我们提供的证据表明，与荟萃分析相比，使用个体水平数据可使检测eQTL的效能提高约50%。
