Analysis of Brugada syndrome loci reveals that fine-mapping clustered GWAS hits enhances the annotation of disease-relevant variants.
Analysis of Brugada syndrome loci reveals that fine-mapping clustered GWAS hits enhances the annotation of disease-relevant variants.
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DOI：
10.1016/j.xcrm.2021.100250
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发表时间：
2021-04-20
期刊：
影响因子：
--
通讯作者：
Pagans S
中科院分区：
文献类型：
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作者：
Pinsach-Abuin ML;Del Olmo B;Pérez-Agustin A;Mates J;Allegue C;Iglesias A;Ma Q;Merkurjev D;Konovalov S;Zhang J;Sheikh F;Telenti A;Brugada J;Brugada R;Gymrek M;di Iulio J;Garcia-Bassets I;Pagans S
全基因组关联研究（GWAS）有助于识别携带影响人类性状和疾病的常见单核苷酸变异（SNV）的基因座。GWAS命中出现在集群中，但重点往往是每个性状或疾病相关位点中最重要的命中。其余的命中代表连锁不平衡（LD）中的SNV，并且被认为是冗余的，因此经常被边缘地报告或利用。在这里，我们询问的价值，整合一套完整的GWAS命中位点反复与心脏传导性状和心律失常，SCN 5A-SCN 10A。我们的分析揭示了5种常见的7-SNV单倍型（Hap 1 -5），其中2种组合与危及生命的Hap-Brugada综合征相关（风险Hap 1/1和保护Hap 2/3基因型）。Hap 1和Hap 2共享3个SNV;因此，该分析表明，假设聚集的GWAS命中之间存在冗余可能导致混淆疾病风险关联，并支持在单倍型组成的背景下解构GWAS数据的需要。单倍型区组分析将SCN 5A和SCN 10A周围的顺式调节区分开单倍型多样性限制为增强子区中的5种常见7-SNV组合Hap 1/1和Hap 2/3分别是Brugada综合征风险和保护基因型，Hap 1/1和Hap 2/3基因型分别与SCN 10A表达反相关。GWAS领域倾向于关注每个疾病相关基因座中最显著的命中，尽管其独特的关联不是功能的代理，并且其伴随有其他重要的群集信号。Pinsach-Abuin et al.表明解构GWAS命中簇增强了疾病相关注释。
Genome-wide association studies (GWASs) are instrumental in identifying loci harboring common single-nucleotide variants (SNVs) that affect human traits and diseases. GWAS hits emerge in clusters, but the focus is often on the most significant hit in each trait- or disease-associated locus. The remaining hits represent SNVs in linkage disequilibrium (LD) and are considered redundant and thus frequently marginally reported or exploited. Here, we interrogate the value of integrating the full set of GWAS hits in a locus repeatedly associated with cardiac conduction traits and arrhythmia, SCN5A-SCN10A. Our analysis reveals 5 common 7-SNV haplotypes (Hap1–5) with 2 combinations associated with life-threatening arrhythmia—Brugada syndrome (the risk Hap1/1 and protective Hap2/3 genotypes). Hap1 and Hap2 share 3 SNVs; thus, this analysis suggests that assuming redundancy among clustered GWAS hits can lead to confounding disease-risk associations and supports the need to deconstruct GWAS data in the context of haplotype composition. Haplotype block analysis separates cis-regulatory regions around SCN5A and SCN10A Haplotype diversity limits to 5 common 7-SNV combinations in the enhancer region Hap1/1 and Hap2/3 are Brugada syndrome risk and protective genotypes, respectively Hap1/1 and Hap2/3 genotypes are anti-correlated with SCN10A expression The GWAS field tends to focus on the most significant hit in each disease-associated locus, despite that its distinct association is not a proxy of functionality and it is accompanied by other significant clustered signals. Focusing on Brugada syndrome, Pinsach-Abuin et al. show that deconstructing GWAS hit clusters enhances disease-relevant annotations.
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影响因子：
5.5
作者：
Burashnikov, Elena;Pfeiffer, Ryan;Barajas-Martinez, Hector;Delpon, Eva;Hu, Dan;Desai, Mayurika;Borggrefe, Martin;Haeissaguerre, Michel;Kanter, Ronald;Pollevick, Guido D.;Guerchicoff, Alejandra;Laino, Ruben;Marieb, Mark;Nademanee, Koonlawee;Nam, Gi-Byoung;Robles, Roberto;Schimpf, Rainer;Stapleton, Dwight D.;Viskin, Sami;Winters, Stephen;Wolpert, Christian;Zimmern, Samuel;Veltmann, Christian;Antzelevitch, Charles
通讯作者：
Antzelevitch, Charles
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：
14.9
作者：
Buniello, Annalisa;MacArthur, Jacqueline A. L.;Parkinson, Helen
通讯作者：
Parkinson, Helen
影响因子：
9.8
作者：
Browning, Brian L.;Zhou, Ying;Browning, Sharon R.
通讯作者：
Browning, Sharon R.
影响因子：
37.8
作者：
Antzelevitch, Charles;Pollevick, Guido D.;Wolpert, Christian
通讯作者：
Wolpert, Christian