2013
DOI: 10.1038/ng.2606
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Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture

Abstract: Approaches exploiting extremes of the trait distribution may reveal novel loci for common traits, but it is unknown whether such loci are generalizable to the general population. In a genome-wide search for loci associated with upper vs. lower 5th percentiles of body mass index, height and waist-hip ratio, as well as clinical classes of obesity including up to 263,407 European individuals, we identified four new loci (IGFBP4, H6PD, RSRC1, PPP2R2A) influencing height detected in the tails and seven new loci (HN… Show more

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Cited by 565 publications
(459 citation statements)
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“…1 At the same time, studies with expanding cohorts continue to uncover variants associated with a variety of traits and diseases, indicating moderate to high heritability. [2][3][4][5] Therefore, while genetic variation alone is insufficient for accurate disease or trait prediction, it is reasonable to expect that a summary score of all trait-relevant variants can meaningfully quantify the heritable component that underlies variation in complex traits or disease risks. Furthermore, this genetic score complements conventional non-genetic risk factors, and integration of genetic and non-genetic risk factors may lead to more accurate health assessment.…”
Section: Introductionmentioning
confidence: 99%
“…1 At the same time, studies with expanding cohorts continue to uncover variants associated with a variety of traits and diseases, indicating moderate to high heritability. [2][3][4][5] Therefore, while genetic variation alone is insufficient for accurate disease or trait prediction, it is reasonable to expect that a summary score of all trait-relevant variants can meaningfully quantify the heritable component that underlies variation in complex traits or disease risks. Furthermore, this genetic score complements conventional non-genetic risk factors, and integration of genetic and non-genetic risk factors may lead to more accurate health assessment.…”
Section: Introductionmentioning
confidence: 99%
“…For 61 cardio-metabolic traits with publically available GWAS summary results 14,16,17,[38][39][40][41][42][43][44][45][46][47][48] (Table S5), we applied the summarydata-based Mendelian randomization (SMR) method to propose relevant genes at the GWAS loci. 49 We performed a transcriptome-wide association for 61 cardio-metabolic traits and 24,383 probe sets with cis-eQTLs (<1 Mb) and focused on GWAS loci (p < 5 3 10 À8 ).…”
Section: Cis-eqtls At Gwas Locimentioning
confidence: 99%
“…We excluded one known locus, in GCKR, owing to its known pleotropic effect on multiple human complex traits [14]. In addition, we constructed a GRS for circulating BCAA levels with and without the PPMK1 single nucleotide polymorphism (SNP) rs9637599 and performed analysis considering the association of rs9637599 with BMI (p = 0.035) [15] and obesity class I (p = 0.00042) [16] in the publicly available GWAS summary data.…”
Section: Measurement Of Insulin Resistancementioning
confidence: 99%