rs11642015 - FTO

Magnitude 2.2 · 8 studies on file

Reported associations

• A cross-population atlas of genetic associations for 220 human phenotypes. - Nature genetics (2021) · 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, 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 · PubMed 34594039

  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 (n = 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-wid

• Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease. - Diabetes (2021) · Martin S, Cule M, Basty N, Tyrrell J, Beaumont RN, Wood AR, Frayling TM, Sorokin E, Whitcher B, Liu Y, Bell JD, Thomas EL, Yaghootkar H · PubMed 33980691

  To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with "adverse" metabolic effects (UFA) and the other with, paradoxically, "favorable" metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We det

• Leveraging Polygenic Functional Enrichment to Improve GWAS Power. - American journal of human genetics (2019) · Kichaev G, Bhatia G, Loh PR, Gazal S, Burch K, Freund MK, Schoech A, Pasaniuc B, Price AL · PubMed 30595370

  Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attaine

• Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. - Nature genetics (2019) · Jansen PR, Watanabe K, Stringer S, Skene N, Bryois J, Hammerschlag AR, de Leeuw CA, Benjamins JS, Muñoz-Manchado AB, Nagel M, Savage JE, Tiemeier H, White T, Tung JY, Hinds DA, Vacic V, Wang X, Sullivan PF, van der Sluis S, Polderman TJC, Smit AB, Hjerling-Leffler J, Van Someren EJW, Posthuma D · PubMed 30804565

  Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric tr

• Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. - Nature genetics (2019) · Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, Iwata N, Ikegawa S, Hirata M, Matsuda K, Kubo M, Okada Y, Kamatani Y · PubMed 29403010

  Clinical measurements can be viewed as useful intermediate phenotypes to promote understanding of complex human diseases. To acquire comprehensive insights into the underlying genetics, here we conducted a genome-wide association study (GWAS) of 58 quantitative traits in 162,255 Japanese individuals. Overall, we identified 1,407 trait-associated loci (P < 5.0 × 10 ), 679 of which were novel. By incorporating 32 additional GWAS results for complex diseases and traits in Japanese individuals, we further highlighted pleiotropy, genetic correlations, and cell-type specificity across quantitative traits and diseases, which substantially expands the current understanding of the associated genetics and biology. This study identified both shared polygenic effects and cell-type specificity

• A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. - American journal of human genetics (2018) · Sung YJ, Winkler TW, de las Fuentes L, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, Lu Y, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Li C, Feitosa MF, Kilpeläinen TO, Richard MA, Noordam R, Aslibekyan S, Aschard H, Bartz TM, Dorajoo R, Liu Y, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Warren HR, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, Horimoto ARVR, Hsu FC, Jackson AU, Kähönen M, Kasturiratne A, Kühnel B, Leander K, Lee WJ, Lin KH, 'an Luan J, McKenzie CA, Meian H, Nelson CP, Rauramaa R, Schupf N, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking D, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cabrera CP, Cade B, Caizheng Y, Campbell A, Canouil M, Chakravarti A, Chauhan G, Christensen K, Cocca M, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Debette S, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Fisher VA, Forouhi NG, Franco OH, Friedlander Y, Gao H, Gigante B, Graff M, Gu CC, Gu D, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng CK, Hirata M, Hofman A, Howard BV, Hunt S, Irvin MR, Jia Y, Joehanes R, Justice AE, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh WP, Koistinen HA, Komulainen P, Kooperberg C, Krieger JE, Kubo M, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Lim SH, Lin S, Liu CT, Liu J, Liu J, Liu K, Liu Y, Loh M, Lohman KK, Long J, Louie T, Mägi R, Mahajan A, Meitinger T, Metspalu A, Milani L, Momozawa Y, Morris AP, Mosley TH, Munson P, Murray AD, Nalls MA, Nasri U, Norris JM, North K, Ogunniyi A, Padmanabhan S, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Seshadri S, Sever P, Sitlani CM, Smith JA, Snieder H, Starr JM, Strauch K, Tang H, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, Waldenberger M, Wang L, Wang YX, Wei WB, Williams C, Wilson G, Wojczynski MK, Yao J, Yuan JM, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Chen YI, de Faire U, Deary IJ, Esko T, Farrall M, Forrester T, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Hung YJ, Jonas JB, Kato N, Kooner JS, Laakso M, Lehtimäki T, Liang KW, Magnusson PKE, Newman AB, Oldehinkel AJ, Pereira AC, Redline S, Rettig R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Wu T, Zheng W, Kamatani Y, Laurie CC, Bouchard C, Cooper RS, Evans MK, Gudnason V, Kardia SLR, Kritchevsky SB, Levy D, O'Connell JR, Psaty BM, van Dam RM, Sims M, Arnett DK, Mook-Kanamori DO, Kelly TN, Fox ER, Hayward C, Fornage M, Rotimi CN, Province MA, van Duijn CM, Tai ES, Wong TY, Loos RJF, Reiner AP, Rotter JI, Zhu X, Bierut LJ, Gauderman WJ, Caulfield MJ, Elliott P, Rice K, Munroe PB, Morrison AC, Cupples LA, Rao DC, Chasman DI · PubMed 29455858

  Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10 ) in stage 1 and formally replicate

• Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. - Nature genetics (2017) · Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, Iwata N, Ikegawa S, Hirata M, Matsuda K, Iwasaki M, Yamaji T, Sawada N, Hachiya T, Tanno K, Shimizu A, Hozawa A, Minegishi N, Tsugane S, Yamamoto M, Kubo M, Kamatani Y · PubMed 28892062

  Obesity is a risk factor for a wide variety of health problems. In a genome-wide association study (GWAS) of body mass index (BMI) in Japanese people (n = 173,430), we found 85 loci significantly associated with obesity (P < 5.0 × 10 ), of which 51 were previously unknown. We conducted trans-ancestral meta-analyses by integrating these results with the results from a GWAS of Europeans and identified 61 additional new loci. In total, this study identifies 112 novel loci, doubling the number of previously known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory marks, we found enrichment of variants in CD19 cells. We also found significant genetic correlations between BMI and lymphocyte count (P = 6.46 × 10 , r = 0.18) and between BMI and multiple com

• Genome-wide analysis of heart failure yields insights into disease heterogeneity and enables prognostic prediction in the Japanese population - Unknown journal (n.d.) · Unknown authors · PubMed 41184235

  ABSTRACT: To understand the genetic basis of heart failure (HF) in the Japanese population, we performed genome-wide association studies (GWASs) comprising 16,251 all-cause HF cases, 4254 HF with reduced ejection fraction (HFrEF) cases, 7154 HF with preserved ejection fraction cases, and 11,122 non-ischemic HF cases among 213,828 individuals and identified five novel loci. A subsequent cross-ancestry meta-analysis and multi-trait analysis of the GWAS data identified 19 novel loci in total, with 31 out of the 76 genome-wide significant loci associated with HFrEF despite its smaller sample size. Among these susceptibility loci, a common non-coding variant in TTN (rs1484116) was associated with reduced cardiac function and worse long-term mortality. We leveraged the HF meta-GWASs along with c

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