Large-scale genome-wide association studies that incor-5porate deep phenotype and cross-population analyses could enhance disease classification, IMS researchers show.Completion of the Human Genome Project in 2003 paved the way for numerous genome-wide association stud-ies (GWAS), which aim to find links between genetic vari-ants and visible traits. Many are probing the genetic basis of disease, which could improve a myriad of clinical practices, including how medical conditions are characterised.While historically based on symptoms and affected or-gans, disease categories are regularly being updated with in-sights derived from emerging diagnostic technologies such as imaging and biomarker assays. GWAS could help further refine these classifications and offer novel criteria by which to group new diseases or difficult-to-categorise syndromes.However, despite their wide adoption, GWAS lack com-prehensiveness. Most studies have been conducted in Eu-ropeans, for example, leaving large gaps in our knowledge of Asian genomes. To expand the atlas of genetic associa-tions, a study highlighted on the cover of the October 2021 issue of Nature Genetics and led by Yukinori Okada, Team Leader of the Laboratory for Systems Genetics at IMS, in-troduced two key features to the analysis.“The first is deep phenotyping, which means exploring many phenotypes. The second is conducting trans-biobank GWAS,” Okada explained.First, the researchers trawled through data from Bio-Bank Japan, a repository of biological and clinical data on patients with 47 target diseases, to extract over 200 features, including information on biomarkers and drug use, for analysis. These included 108 phenotypes on which GWAS has never before been conducted in East Asian populations.Next, the team performed cross-population meta-anal-yses to compare corresponding traits between Japanese and Europeans based on data from UK Biobank and FinnGen repositories. They showed that the two populations share many common genetic variants, which, while expected, was only able to be confirmed thanks to the large-scale na-ture of their cross-biobank study, Okada noted.In addition to extracting disease-related variants, the anal-ysis can also be useful for redefining or reclassifying disease. To show this, the researchers used a mathematical model to extract latent components characteristic of a two-dimension-al matrix of phenotypes and their associated variants.“We interpreted the biological meaning of these compo-nents by projecting many bioresources,” Okada explained, such as biomarker and metabolome GWAS data.When a set of allergic diseases were categorised based on these components, the groupings reflected existing clas-sifications, suggesting that genetics-driven hypotheses can complement current disease categorisation strategies.This ambitious study, according to Okada, was made possible by the efforts of co-first authors Saori Sakaue, whose ideas drove the project, and Masahiro Kanai, whose computing prowess has enable the team to share their find-ings in an open source web resource called BioBank Japan PheWeb (https://pheweb.jp/).In the future, the team hope to integrate GWAS with data from other omics and single-cell analyses to continue to expand the landscape of associations between genetics and disease. Figure: Trans-biobank genome-wide association study (GWAS) meta-analysis of over 200 human phenotypesMore than 200 phenotypes were extracted for analysis based on patient medical history, electronic medical records and data on biomarkers and drug use available through BioBank Japan. Subsequent cross-population meta-analysis of Japanese data with those from Euro-peans in UK Biobank and FinnGen repositories revealed many shared genetic variants.The GWAS summary statistics are publicly available at https://pheweb.jp/Original paper:Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, Narita A, Konuma T, Yamamoto K, Akiyama M, Ishigaki K, Suzuki A, Suzuki K, Obara W, Yamaji K, Takahashi K, Asai S, Takahashi Y, Suzuki T, Shinozaki N, Yamaguchi H, Minami S, Murayama S, Yoshimori K, Nagayama S, Obata D, Higashiyama M, Masumoto A, Koretsune Y; FinnGen, Ito K, Terao C, Yamauchi T, Komuro I, Kadowaki T, Tamiya G, Yamamoto M, Nakamura Y, Kubo M, Murakami Y, Yamamoto K, Kamatani Y, Palotie A, Rivas MA, Daly MJ, Matsuda K, Okada Y. A cross-population atlas of genetic associations for 220 human phe-notypes. Nat Genet 53, 1415-1424 (2021)Yukinori OkadaExpanding the genetic atlas of disease
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