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TwinEQTL: ultrafast and powerful association analysis for eQTL and GWAS in twin studies.

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第一作者： Kai,Xia

第一单位： Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.;Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA.

作者： Kai,Xia ^[1] ; Andrey A,Shabalin ^[2] ; Zhaoyu,Yin ^[3] ; Wonil,Chung ^[4] ; Patrick F,Sullivan ^[5] ; Fred A,Wright ^[6] ; Martin,Styner ^[5] ; John H,Gilmore ^[5] ; Rebecca C,Santelli ^[7] ; Fei,Zou ^[8]

作者单位： Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA.;Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA. ^[1] Department of Psychiatry, University of Utah, Salt Lake City, UT 84108, USA. ^[2] Gilead Sciences, Foster City, CA 94404, USA. ^[3] School of Public Health, Harvard, Boston, MA 02115, USA. ^[4] Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA. ^[5] Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA. ^[6] Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI 48912, USA. ^[7] Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. ^[8]

关键词 GWAS Twin eQTL

主题词人类(Humans);基因频率(Gene Frequency);全基因组关联研究(Genome-Wide Association Study);线性模型(Linear Models);多态性, 单核苷酸(Polymorphism, Single Nucleotide);数量性状基因座位(Quantitative Trait Loci)

DOI 10.1093/genetics/iyac088

PMID 35689615

发布时间 2023-06-12

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