{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:07:31Z","timestamp":1774422451144,"version":"3.50.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T00:00:00Z","timestamp":1611619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS1713120"],"award-info":[{"award-number":["DMS1713120"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS1902903"],"award-info":[{"award-number":["DMS1902903"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["3P30AG021342-16S2"],"award-info":[{"award-number":["3P30AG021342-16S2"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM134005"],"award-info":[{"award-number":["R01GM134005"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01GM122078"],"award-info":[{"award-number":["1R01GM122078"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UL1TR000427"],"award-info":[{"award-number":["UL1TR000427"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Genetic correlation is the correlation of phenotypic effects by genetic variants across the genome on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlation based on data collected from genome-wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications due to the imprecision of LD obtained from reference panels. Our findings offer guidance on how to choose appropriate methods for genetic correlation estimation in post-GWAS analysis.<\/jats:p>","DOI":"10.1093\/bib\/bbaa442","type":"journal-article","created":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T15:10:47Z","timestamp":1609341047000},"source":"Crossref","is-referenced-by-count":43,"title":["Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics"],"prefix":"10.1093","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9851-7870","authenticated-orcid":false,"given":"Yiliang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA"}]},{"given":"Youshu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6120-5278","authenticated-orcid":false,"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA"}]},{"given":"Yixuan","family":"Ye","sequence":"additional","affiliation":[{"name":"Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4514-0969","authenticated-orcid":false,"given":"Qiongshi","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA"},{"name":"Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA"},{"name":"Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI 53706, USA"}]},{"given":"Hongyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA"},{"name":"Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA"},{"name":"Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"issue":"6395","key":"2021090813432386900_ref1","article-title":"Analysis of shared heritability in common disorders of the brain","volume":"360","author":"Anttila","year":"2018","journal-title":"Science"},{"issue":"1","key":"2021090813432386900_ref2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ajhg.2017.06.005","article-title":"10 years of GWAS discovery: biology, function, and translation","volume":"101","author":"Visscher","year":"2017","journal-title":"Am J Hum 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