Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.
Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs.
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DOI：
10.1038/s41467-021-23134-8
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发表时间：
2021-06-07
影响因子：
16.6
通讯作者：
Finucane HK
中科院分区：
文献类型：
--
作者：
Wang QS;Kelley DR;Ulirsch J;Kanai M;Sadhuka S;Cui R;Albors C;Cheng N;Okada Y;Biobank Japan Project;Aguet F;Ardlie KG;MacArthur DG;Finucane HK
通过GWAS识别的绝大多数变异是非编码的，这激发了对非编码变异功能的详细表征。在天然染色质环境下，通过直接扰动评估变异对基因表达的影响的实验方法是低通量的。因此，现有的高通量计算预测器缺乏大量用于训练和验证的调节变量的金标准集。在这里，我们利用通过统计精细定位获得的14,807个假定的人类因果方程，我们使用6121个特征直接训练一个预测变量是否改变附近基因表达的预测器。我们将结果预测称为表达修饰符评分（EMS）。我们通过比较EMS与其他主要分数优先考虑功能变量的能力来验证EMS。然后，我们使用EMS作为eqtl的统计精细定位的先验，以确定额外的20,913个推定因果性的eqtl，并将EMS纳入共定位分析，以确定英国生物银行表型中的310个额外候选基因。从GWAS基因座结果中寻找因果变异和基因仍然是一个挑战。在这里，作者训练了一个模型来预测一个变异是否会影响附近的基因表达，并应用该方法来识别新的可能的因果等式和GWAS位点的机制。
The large majority of variants identified by GWAS are non-coding, motivating detailed characterization of the function of non-coding variants. Experimental methods to assess variants’ effect on gene expressions in native chromatin context via direct perturbation are low-throughput. Existing high-throughput computational predictors thus have lacked large gold standard sets of regulatory variants for training and validation. Here, we leverage a set of 14,807 putative causal eQTLs in humans obtained through statistical fine-mapping, and we use 6121 features to directly train a predictor of whether a variant modifies nearby gene expression. We call the resulting prediction the expression modifier score (EMS). We validate EMS by comparing its ability to prioritize functional variants with other major scores. We then use EMS as a prior for statistical fine-mapping of eQTLs to identify an additional 20,913 putatively causal eQTLs, and we incorporate EMS into co-localization analysis to identify 310 additional candidate genes across UK Biobank phenotypes. Finding causal variants and genes from GWAS loci results remains a challenge. Here, the authors train a model to predict if a variant affects nearby gene expression, and apply the method to identify new possible causal eQTLs and mechanisms of GWAS loci.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
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：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
7
作者：
Inoue, Fumitaka;Kircher, Martin;Shendure, Jay
通讯作者：
Shendure, Jay
影响因子：
8.8
作者：
Agarwal, Vikram;Shendure, Jay
通讯作者：
Shendure, Jay