Genome-wide association study identifies 112 new loci for body mass index in the Japanese population

Akiyama, Masato; Okada, Yukinori; Kanai, Masahiro; Takahashi, Atsushi; Momozawa, Yukihide; Ikeda, Masashi; Iwata, Nakao; Ikegawa, Shiro; Hirata, Makoto; Matsuda, Koichi; Iwasaki, Motoki; Yamaji, Taiki; Sawada, Norie; Hachiya, Tsuyoshi; Tanno, Kozo; Shimizu, Atsushi; Hozawa, Atsushi; Minegishi, Naoko; Tsugane, Shoichiro; Yamamoto, Masayuki; Kubo, Michiaki; Kamatani, Yoichiro

doi:10.1038/ng.3951

Cited by 387 publications

(409 citation statements)

References 58 publications

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“…In contrast, the association between alleles for increased educational attainment 32 and increased risk taking may seem counter-intuitive but is consistent with previous findings from UK Biobank ( n ~116,000) 13 . The allele associated with waist circumference 41 was associated with increased risk taking, but the opposite was observed for BMI 39 , (although the BMI study was in a Japanese population 39 , whereas the risk-taking study was in a European study, so ethnic-specific effects (in regulation of BMI and/or risk taking) could be responsible for this discrepancy).…”

Section: Resultsmentioning

confidence: 94%

“…These risk-taking loci have previously been associated with: educational attainment 32 , SCZ 33,34 and PTSD 35 (Chr1 locus); cognitive function 36,37 , educational attainment 32,38 , adiposity 39–41 and alcohol consumption 42 ( CADM2 locus); SCZ 43,44 and ADHD 45 (Chr6 locus); and sleep duration 46 ( FOXP2 locus). Of the previously reported SNPs at these loci, 16 met the threshold for “suggestive” evidence of association with risk taking in this study (Table S18).…”

Section: Resultsmentioning

confidence: 99%

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Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression

et al. 2018

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Risk-taking behaviour is an important component of several psychiatric disorders, including attention-deficit hyperactivity disorder, schizophrenia and bipolar disorder. Previously, two genetic loci have been associated with self-reported risk taking and significant genetic overlap with psychiatric disorders was identified within a subsample of UK Biobank. Using the white British participants of the full UK Biobank cohort (n = 83,677 risk takers versus 244,662 controls) for our primary analysis, we conducted a genome-wide association study of self-reported risk-taking behaviour. In secondary analyses, we assessed sex-specific effects, trans-ethnic heterogeneity and genetic overlap with psychiatric traits. We also investigated the impact of risk-taking-associated SNPs on both gene expression and structural brain imaging. We identified 10 independent loci for risk-taking behaviour, of which eight were novel and two replicated previous findings. In addition, we found two further sex-specific risk-taking loci. There were strong positive genetic correlations between risk-taking and attention-deficit hyperactivity disorder, bipolar disorder and schizophrenia. Index genetic variants demonstrated effects generally consistent with the discovery analysis in individuals of non-British White, South Asian, African-Caribbean or mixed ethnicity. Polygenic risk scores comprising alleles associated with increased risk taking were associated with lower white matter integrity. Genotype-specific expression pattern analyses highlighted DPYSL5, CGREF1 and C15orf59 as plausible candidate genes. Overall, our findings substantially advance our understanding of the biology of risk-taking behaviour, including the possibility of sex-specific contributions, and reveal consistency across ethnicities. We further highlight several putative novel candidate genes, which may mediate these genetic effects.

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Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 99%

Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression

et al. 2018

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“…With the availability and utility of data from biobanks (e.g., UK Biobank), genetic variation to common disorders will focus particularly on quantitative traits, such as blood pressure for cardiovascular disease (Warren et al, 2017), mood for major depressive disorder (Boomsma et al, 2008), and body mass index for obesity (Akiyama et al, 2017). Lastly, the current method is applicable to studying binary phenotypes only.…”

Section: Discussionmentioning

confidence: 99%

Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia

Liu

Huang

Urbanowicz

et al. 2019

Genetic Epidemiology

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Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP. K E Y W O R D Sgenetic interaction, heterogeneity in marginal effects, meta-analysis, privacy-preserving algorithm, two-stage testing procedure *Jason H. Moore, Paul Scheet, and Yong Chen contributed equally to this work.

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“…The strategy of our screening is shown in . In previous studies, 6171 gastric cancer cases and 27 178 controls were genotyped using Illumina OmniExpressExome or OmniExpress + HumanExome BeadChip (Table ). We excluded the following samples from analysis: closely related samples, gender‐mismatched samples including lack of information, control samples with past history of any cancers, and samples from subjects whose ancestries were estimated to be distinct from East Asian populations using a principal component analysis.…”

Section: Methodsmentioning

confidence: 99%

Genome‐wide association study identifies gastric cancer susceptibility loci at 12q24.11‐12 and 20q11.21

Tanikawa

Kamatani

Toyoshima³

et al. 2018

Cancer Science

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Gastric cancer is the third leading cause of cancer mortality in Japan and worldwide. Although previous studies identify various genetic variations associated with gastric cancer, host genetic factors are largely unidentified. To identify novel gastric cancer loci in the Japanese population, herein, we carried out a large‐scale genome‐wide association study using 6171 cases and 27 178 controls followed by three replication analyses. Analysis using a total of 11 507 cases and 38 904 controls identified two novel loci on 12q24.11‐12 (rs6490061, P = 3.20 × 10−8 with an odds ratio [OR] of 0.905) and 20q11.21 (rs2376549, P = 8.11 × 10−10 with an OR of 1.109). rs6490061 is located at intron 19 of the CUX2 gene, and its expression was suppressed by Helicobacter pylori infection. rs2376549 is included within the gene cluster of DEFB families that encode antibacterial peptides. We also found a significant association of rs7849280 in the ABO gene locus on 9q34.2 (P = 2.64 × 10−13 with an OR of 1.148). CUX2 and ABO expression in gastric mucosal tissues was significantly associated with rs6490061 and rs7849280 (P = 0.0153 and 8.00 × 10−11), respectively. Our findings show the crucial roles of genetic variations in the pathogenesis of gastric cancer.

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Genome-wide association study identifies 112 new loci for body mass index in the Japanese population

Cited by 387 publications

References 58 publications

Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression

Genetics of self-reported risk-taking behaviour, trans-ethnic consistency and relevance to brain gene expression

Embracing study heterogeneity for finding genetic interactions in large‐scale research consortia

Genome‐wide association study identifies gastric cancer susceptibility loci at 12q24.11‐12 and 20q11.21

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