Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.
Whole-exome sequence analysis of anthropometric traits illustrates challenges in identifying effects of rare genetic variants.
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DOI：
10.1016/j.xhgg.2022.100163
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发表时间：
2023-01-12
期刊：
影响因子：
--
通讯作者：
Liu, Ching-Ti
中科院分区：
文献类型：
--
作者：
Young, Kristin L.;Fisher, Virginia;Deng, Xuan;Brody, Jennifer A.;Graff, Misa;Lim, Elise;Lin, Bridget M.;Xu, Hanfei;Amin, Najaf;An, Ping;Aslibekyan, Stella;Fohner, Alison E.;Hidalgo, Bertha;Lenzini, Petra;Kraaij, Robert;Medina-Gomez, Carolina;Prokic, Ivana;Rivadeneira, Fernando;Sitlani, Colleen;Tao, Ran;van Rooij, Jeroen;Zhang, Di;Broome, Jai G.;Buth, Erin J.;Heavner, Benjamin D.;Jain, Deepti;Smith, Albert, V;Barnes, Kathleen;Boorgula, Meher Preethi;Chavan, Sameer;Darbar, Dawood;De Andrade, Mariza;Guo, Xiuqing;Haessler, Jeffrey;Irvin, Marguerite R.;Kalyani, Rita R.;Kardia, Sharon L. R.;Kooperberg, Charles;Kim, Wonji;Mathias, Rasika A.;McDonald, Merry-Lynn;Mitchell, Braxton D.;Peyser, Patricia A.;Regan, Elizabeth A.;Redline, Susan;Reiner, Alexander P.;Rich, Stephen S.;Rotter, Jerome I.;Smith, Jennifer A.;Weiss, Scott;Wiggins, Kerri L.;Yanek, Lisa R.;Arnett, Donna;Heard-Costa, Nancy L.;Leal, Suzanne;Lin, Danyu;McKnight, Barbara;Province, Michael;van Duijn, Cornelia M.;North, Kari E.;Cupples, L. Adrienne;Liu, Ching-Ti
人体测量特征，测量身体大小和形状，是心脏代谢疾病的高度遗传和重要的临床风险因素。这些性状在全基因组关联研究（GWAS）中得到了广泛的研究，鉴定了数百个全基因组显著位点。我们对身高、体重指数（BMI）和腰臀比（WHR）的遗传学进行了全外显子组序列分析。我们在多达22，004名个体中对全外显子序列变异与身高，BMI和WHR的单变异和基于基因的关联进行了荟萃分析，并评估了来自Trans-Omics for Precision Medicine（TOPMed）的10个独立队列的多达16，418名个体的重复研究结果。我们确定了四个性状与单核苷酸变异（SNV;两个身高和两个BMI）的关联，并复制了LECT 2基因与身高的关联。我们的表达数量性状基因座（eQTL）的分析在以前报道的GWAS基因座牵连CEP 63和RFT 1作为潜在的功能基因已知的高度基因座。我们进一步评估了SNV的富集，SNV是与我们的三个性状相关的基因座内的单基因或综合征变体。这导致了高度的显著富集结果，而我们观察到所有SNV都没有Bonferroni校正的显著性。在我们的发现数据集中，样本量为20，000个全外显子组序列，我们的发现证明了基因组测序在遗传关联研究中的重要性，但它们也说明了识别罕见遗传变异影响的挑战。人体测量特征是心脏代谢疾病的重要临床危险因素。对22，004名个体的身高、BMI和WHR进行的外显子序列分析确定了四种与身高和BMI的新关联，并复制了LECT 2与身高的关联。GWAS基因座的eQTL分析表明CEP 63和RFT 1可能是高度的功能基因。
Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants. Anthropometric traits are significant clinical risk factors for cardiometabolic disorders. Exome sequence analysis of height, BMI, and WHR in 22,004 individuals identified four novel associations with height and BMI and replicated the LECT2-height association. eQTL analysis of GWAS loci implicated CEP63 and RFT1 as potential functional genes for height.
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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
腰围比与心脏代谢性状，2型糖尿病和冠状动脉疾病的遗传关联。
DOI：
10.1001/jama.2016.21042
发表时间：
2017-02-14
期刊：
JAMA
影响因子：
--
作者：
Emdin CA;Khera AV;Natarajan P;Klarin D;Zekavat SM;Hsiao AJ;Kathiresan S
通讯作者：
Kathiresan S
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
10.6
作者：
Amin, Najaf;Belonogova, Nadezhda M.;van Duijn, Cornelia M.
通讯作者：
van Duijn, Cornelia M.
影响因子：
--
作者：
Liu, Ching-Ti;Young, Kristin L.;Cupples, L. Adrienne
通讯作者：
Cupples, L. Adrienne