日本大阪大学Yukinori Okada等研究人员合作绘制出220种人类表型的交叉群体遗传关联图谱。相关论文于2021年9月30日在线发表在《自然—遗传学》杂志上。

为了扩大非欧洲人群的遗传关联图谱，研究人员在日本生物银行（n=179,000）进行了220项深表型全基因组关联研究（疾病、生物标志物和药物使用），方法是结合过去的病史和电子医疗记录的文本挖掘。对英国生物库和FinnGen（总数=628,000）进行的元分析确定了约5,000个新的基因座，提高了人类性状基因组图谱的分辨率。该图谱阐明了以主要组织相容性复合体基因座为代表的多态性景观，并且研究人员进行了HLA精细测绘。

最后，研究人员对表征范围内的汇总统计矩阵进行了统计分解，并确定了潜在的遗传成分，从而明确指出了相关的变体和目前不同人群疾病分类的生物机制。分解后的成分使类似疾病（如过敏性疾病）的遗传信息亚类型化成为可能。这项研究为通过遗传学对人类疾病进行无假设的重新调查提供了一条潜在的途径。

据悉，目前的全基因组关联研究还没有捕捉到人群和表型范围的足够多样性。

附：英文原文

Title: A cross-population atlas of genetic associations for 220 human phenotypes

Author: Sakaue, Saori, Kanai, Masahiro, Tanigawa, Yosuke, Karjalainen, Juha, Kurki, Mitja, Koshiba, Seizo, Narita, Akira, Konuma, Takahiro, Yamamoto, Kenichi, Akiyama, Masato, Ishigaki, Kazuyoshi, Suzuki, Akari, Suzuki, Ken, Obara, Wataru, Yamaji, Ken, Takahashi, Kazuhisa, Asai, Satoshi, Takahashi, Yasuo, Suzuki, Takao, Shinozaki, Nobuaki, Yamaguchi, Hiroki, Minami, Shiro, Murayama, Shigeo, Yoshimori, Kozo, Nagayama, Satoshi, Obata, Daisuke, Higashiyama, Masahiko, Masumoto, Akihide, Koretsune, Yukihiro, Ito, Kaoru, Terao, Chikashi, Yamauchi, Toshimasa, Komuro, Issei, Kadowaki, Takashi, Tamiya, Gen, Yamamoto, Masayuki, Nakamura, Yusuke, Kubo, Michiaki, Murakami, Yoshinori, Yamamoto, Kazuhiko, Kamatani, Yoichiro, Palotie, Aarno, Rivas, Manuel A., Daly, Mark J., Matsuda, Koichi, Okada, Yukinori

Issue&Volume: 2021-09-30

Abstract: Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n=179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (ntotal=628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.

DOI: 10.1038/s41588-021-00931-x

Source: https://www.nature.com/articles/s41588-021-00931-x