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GWAS supports understanding of polygenic cancer inheritance, where many low-penetrance variants collectively shape heritable susceptibility. Beyond robust genotype–phenotype signals, integrating GWAS with functional genomics and advanced statistical genetics enables functional characterization via molecular QTL mapping and transcriptome-wide association studies. Aggregating genome-wide polygenic signals including subthreshold effects helps estimate genetic correlations and improve clinical risk prediction using polygenic risk scores; the review summarizes GWAS rationale, updates in downstream methods, and cancer applications.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-genome-wide-association-studies-to-better-understand-the-etiology-of-cancers-review-article/332966/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leveraging-genome-wide-association-studies-to-better-understand-the-etiology-of-cancers-review-article/332966.png","ImageObject",300,407,{"name":92,"@type":93},"Aurelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What does a genome-wide association study (GWAS) do in cancer research?","Question",{"text":113,"@type":114},"GWAS statistically assesses the association between genetic variants across the whole genome and a phenotype of interest in cancer. It detects genotype–phenotype signals by comparing allele frequencies among many individuals.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How do GWAS results help explain heritable susceptibility to cancer?",{"text":118,"@type":114},"GWAS enables elucidation of polygenic inheritance, where numerous low-penetrance variants collectively contribute to a substantial portion of heritable cancer risk.",{"name":120,"@type":111,"acceptedAnswer":121},"What downstream analyses are highlighted for deeper insights beyond GWAS signals?",{"text":122,"@type":114},"The review emphasizes combining GWAS data with functional genomic datasets and statistical genetic methods. It specifically mentions molecular QTL mapping, transcriptome-wide association studies, and polygenic signal aggregation for genetic correlation estimation and clinical risk prediction.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},332966,1790009063,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},1099514068365,"https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1794309523707217728","Received: 23 June 2024  \nRevised: 21 October 2024  \nAccepted: 5 November 2024  \nDOI: 10. 1111/cas.16402  \nR E V I E W A RTI C LE  \nLeveraging genome-wide association studies to better understand the etiology of cancers  \nKyuto Sonehara1,2,3  | Yukinori Okada1,2,3,4,5  \n1Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan 2Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan 3 Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan  \n4Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan 5Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan  \nCorrespondence  \nKyuto Sonehara, Department of Genome Informatics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.  \nEmail: [qsonehara@m.u-tokyo.ac.jp](qsonehara@m.u-tokyo.ac.jp)  \nFunding information  \nMoonshot Research and Development Program, Grant/Award Number:  \nJPMJMS2021 and JPMJMS2024; Takeda Science Foundation; Center for Advanced Modality and DDS (CAMaD), Osaka University; Japan Agency for Medical Research and Development, Grant/Award Number: JP223fa627002, JP223fa627010, JP22ek0109594, JP22ek0410075, JP22km0405211, JP22km0405217, JP233fa627011 and JP23zf0127008; Institute for Open and Transdisciplinary Research Initiatives, Osaka University; Bioinformatics Initiative of Osaka University Graduate School of Medicine; Center for Infectious Disease Education and Research (CiDER), Osaka University; Japan Society for the Promotion of Science, Grant/Award Number: 22H00476 and 23K14451  \nAbstract  \nGenome-wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome-wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low-penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype–phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome-wide association studies. Furthermore, aggregating genome-wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypesand helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS-derived downstream analyses, and demonstrate their applications to GWAS of cancers.  \nK E Y WO R D S  \neQTL, genetic correlation, genome-wide association study, polygenic risk score, transcriptomewide association study  \nAbbreviations: eQTL, expression QTL; GCT, germ cell tumor; GWAS, genome-wide association study; IGCT, intracranial GCT; LD, linkage disequilibrium; miRNA, microRNA; molQTL, molecular QTL; pQTL, protein QTL; PRS, polygenic risk score; QTL, quantitative trait loci; SNP, single nucleotide polymorphism; TCGA, The Cancer Genome Atlas; TGCT, testicular GCT; TWAS, transcriptome-wide association study.  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2024 The Author(s) . Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.  \n1 | INTRODUCTION  \nCancer has heritable components, as evidence","cbCaigr6zLWvdoMe","https://ap.wps.com/l/cbCaigr6zLWvdoMe","pdf",1006256,"English","# Introduction\n# Abstract\n# Keywords and Abbreviations\n# Review Focus: GWAS Rationale, Downstream Updates, and Applications","[{\"question\":\"What does a genome-wide association study (GWAS) do in cancer research?\",\"answer\":\"GWAS statistically assesses the association between genetic variants across the whole genome and a phenotype of interest in cancer. It detects genotype–phenotype signals by comparing allele frequencies among many individuals.\"},{\"question\":\"How do GWAS results help explain heritable susceptibility to cancer?\",\"answer\":\"GWAS enables elucidation of polygenic inheritance, where numerous low-penetrance variants collectively contribute to a substantial portion of heritable cancer risk.\"},{\"question\":\"What downstream analyses are highlighted for deeper insights beyond GWAS signals?\",\"answer\":\"The review emphasizes combining GWAS data with functional genomic datasets and statistical genetic methods. It specifically mentions molecular QTL mapping, transcriptome-wide association studies, and polygenic signal aggregation for genetic correlation estimation and clinical risk prediction.\"}]","Leveraging genome-wide association studies to better understand the etiology of cancers - Review article | PDF",1790000189,23]