{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:03:14Z","timestamp":1765807394562},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2016,12,5]],"date-time":"2016-12-05T00:00:00Z","timestamp":1480896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"name":"Theme-based Research Scheme","award":["T12-402\/13N"],"award-info":[{"award-number":["T12-402\/13N"]}]},{"name":"Hong Kong Research Grant Council"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,2,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>In genome-wide association studies (GWASs) of common diseases\/traits, we often analyze multiple GWASs with the same phenotype together to discover associated genetic variants with higher power. Since it is difficult to access data with detailed individual measurements, summary-statistics-based meta-analysis methods have become popular to jointly analyze datasets from multiple GWASs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, we propose a novel summary-statistics-based joint analysis method based on controlling the joint local false discovery rate (Jlfdr). We prove that our method is the most powerful summary-statistics-based joint analysis method when controlling the false discovery rate at a certain level. In particular, the Jlfdr-based method achieves higher power than commonly used meta-analysis methods when analyzing heterogeneous datasets from multiple GWASs. Simulation experiments demonstrate the superior power of our method over meta-analysis methods. Also, our method discovers more associations than meta-analysis methods from empirical datasets of four phenotypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>The R-package is available at: http:\/\/bioinformatics.ust.hk\/Jlfdr.html.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw690","type":"journal-article","created":{"date-parts":[[2016,10,31]],"date-time":"2016-10-31T12:05:18Z","timestamp":1477915518000},"page":"500-507","source":"Crossref","is-referenced-by-count":20,"title":["Controlling the joint local false discovery rate is more powerful than meta-analysis methods in joint analysis of summary statistics from multiple genome-wide association studies"],"prefix":"10.1093","volume":"33","author":[{"given":"Wei","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China"}]},{"given":"Weichuan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China"}]}],"member":"286","published-online":{"date-parts":[[2016,12,5]]},"reference":[{"key":"2023020204413208000_btw690-B1","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1126\/science.1156409","article-title":"Genetic mapping in human disease","volume":"322","author":"Altshuler","year":"2008","journal-title":"Science"},{"key":"2023020204413208000_btw690-B2","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. 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