Genetic architecture of gene expression traits across diverse populations.
Genetic architecture of gene expression traits across diverse populations.
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DOI：
10.1371/journal.pgen.1007586
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发表时间：
2018-08
期刊：
影响因子：
4.5
通讯作者：
Wheeler HE
中科院分区：
文献类型：
--
作者：
Mogil LS;Andaleon A;Badalamenti A;Dickinson SP;Guo X;Rotter JI;Johnson WC;Im HK;Liu Y;Wheeler HE
对于许多复杂的性状，基因调控可能发挥着至关重要的机制作用。复杂性状的遗传结构如何在人群之间变化以及随后对遗传预测的影响尚不清楚，部分原因是历史上非欧洲血统人群中 GWAS 的缺乏。我们使用来自 MESA（动脉粥样硬化多种族研究）队列的数据来表征不同人群内部和之间基因表达的遗传结构。基因型和单核细胞基因表达在非裔美国人 (AFA, n = 233)、西班牙裔 (HIS, n = 352) 和欧洲人 (CAU, n = 578) 血统的个体中可用。我们在每个群体中进行了表达数量性状位点（eQTL）作图，并显示基因表达的遗传相关性取决于共同的祖先比例。使用具有交叉验证的弹性网络模型来优化每个群体中基因表达的基因型预测因子，我们表明大多数可预测基因的基因表达的遗传结构是稀疏的。我们发现每个人群中预测最好的基因，AFA 中的 TACSTD2 以及 CAU 和 HIS 中的 CHURC1，在各个人群中具有相似的预测性能，且 R2 > 0.8。然而，我们发现了一个基因子集，这些基因在一个群体中预测良好，但在另一个群体中预测较差。我们表明预测性能的这些差异是由于人群之间的等位基因频率差异造成的。使用在 MESA 中训练的基因型权重来预测独立群体中的基因表达表明，与测试集具有相似血统的训练集更能预测测试群体中的基因表达，这表明基因组学中迫切需要多样化群体采样。我们在不同群体中的预测模型和表现统计数据已公开用于转录组作图方法，网址为 https://github.com/WheelerLab/DivPop。大多数全基因组关联研究（GWAS）是在欧洲血统的人群中进行的，导致对不同人群之间复杂性状的遗传学的理解存在差异。对于许多复杂性状，基因调控至关重要，因为性状相关变异中的调控变异不断丰富。然而，目前尚不清楚这些关键变异的影响在不同人群中有何不同。我们使用来自 MESA 的数据，通过优化不同群体内部和不同群体之间的基因表达预测来研究基因表达的潜在遗传结构。具有可用基因型和基因表达数据的人群来自具有非裔美国人 (AFA, n = 233)、西班牙裔 (HIS, n = 352) 和欧洲人 (CAU, n = 578) 血统的个体。在计算预测性能后，我们发现许多在一个群体中预测良好的基因在另一群体中预测较差。我们进一步表明，与测试集具有相似血统的训练集可以产生更好的基因表达预测，这证明了在基因组研究中纳入不同群体的必要性。我们的基因表达预测模型和性能统计数据是公开的，以促进未来不同人群的转录组图谱研究。
For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene in each population, TACSTD2 in AFA and CHURC1 in CAU and HIS, had similar prediction performance across populations with R2 > 0.8 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at https://github.com/WheelerLab/DivPop. Most genome-wide association studies (GWAS) have been conducted in populations of European ancestry leading to a disparity in understanding the genetics of complex traits between populations. For many complex traits, gene regulation is critical, given the consistent enrichment of regulatory variants among trait-associated variants. However, it is still unknown how the effects of these key variants differ across populations. We used data from MESA to study the underlying genetic architecture of gene expression by optimizing gene expression prediction within and across diverse populations. The populations with genotype and gene expression data available are from individuals with African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578) ancestry. After calculating the prediction performance, we found that many genes that were well predicted in one population are poorly predicted in another. We further show that a training set with ancestry similar to the test set resulted in better gene expression predictions, demonstrating the need to incorporate diverse populations in genomic studies. Our gene expression prediction models and performance statistics are publicly available to facilitate future transcriptome mapping studies in diverse populations.
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影响因子：
4.5
作者：
Manor O;Segal E
通讯作者：
Segal E
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
9.8
作者：
Gusev, Alexander;Lee, S. Hong;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
30.8
作者：
Das, Sayantan;Forer, Lukas;Schoenherr, Sebastian;Sidore, Carlo;Locke, Adam E.;Kwong, Alan;Vrieze, Scott I.;Chew, Emily Y.;Levy, Shawn;McGue, Matt;Schlessinger, David;Stambolian, Dwight;Loh, Po-Ru;Iacono, William G.;Swaroop, Anand;Scott, Laura J.;Cucca, Francesco;Kronenberg, Florian;Boehnke, Michael;Abecasis, Goncalo R.;Fuchsberger, Christian
通讯作者：
Fuchsberger, Christian