rs1454687
▶GWAS Catalog Trait Associations (13)
Genome-wide significant associations (p < 5×10⁻⁸) from the NHGRI-EBI GWAS Catalog.
GWAS Catalog Trait Associations (13)
Genome-wide significant associations (p < 5×10⁻⁸) from the NHGRI-EBI GWAS Catalog.
body mass index
Huang J et al. “Genomics and phenomics of body mass index reveals a complex disease network.” Nature Communications 13(1):7973 (2022)
Allele G
OR 0.02
p 9.0e-37
N 1,122,049
Large GWAS
European
Jee YH et al. “Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.” Nature Communications 16(1):4935 (2025)
Allele G
OR 0.02
p 1.0e-34
N 928,679
Large GWAS
multi-ancestry
Hawkes G et al. “Genetic evidence that high BMI in childhood has a protective effect on intermediate diabetes traits, including measures of insulin sensitivity and secretion, after accounting for BMI in adulthood.” Diabetologia 66(8):1472-1480 (2023)
Allele G
OR 0.02
p 1.0e-29
N 441,761
Large GWAS
European
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele G
OR 0.02
p 2.0e-28
N 394,642
Large GWAS
European
Harris BHL et al. “New role of fat-free mass in cancer risk linked with genetic predisposition.” Scientific Reports 14(1):7270 (2024)
Allele G
OR 0.02
p 6.0e-17
N 342,566
Large GWAS
European
body weight
Jee YH et al. “Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures.” Nature Communications 16(1):4935 (2025)
Allele C
OR 0.01
p 5.0e-23
N 928,679
Large GWAS
multi-ancestry
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele C
OR 0.02
p 2.0e-22
N 394,642
Large GWAS
European
waist circumference
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele G
OR 0.02
p 1.0e-21
N 394,642
Large GWAS
European
fat pad mass
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele G
OR 0.02
p 2.0e-21
N 394,642
Large GWAS
European
visceral adipose tissue quantity
Karlsson T et al. “Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease.” Nature Medicine 25(9):1390-1395 (2019)
Allele C
OR 0.02
p 1.0e-20
N 325,153
Large GWAS
European
body height
Richardson TG et al. “Use of genetic variation to separate the effects of early and later life adiposity on disease risk: mendelian randomisation study.” Bmj (clinical Research Ed.) 369:m1203 (2020)
Allele C
OR 0.01
p 3.0e-19
N 453,169
Large GWAS
European
base metabolic rate measurement
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele G
OR 0.01
p 7.0e-17
N 394,642
Large GWAS
European
aspartate aminotransferase measurement, low density lipoprotein triglyceride measurement, serum alanine aminotransferase amount, body fat percentage, high density lipoprotein cholesterol measurement, sex hormone-binding globulin measurement
Martin S et al. “Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease.” Diabetes 70(8):1843-1856 (2021)
Allele C
OR —
p 1.0e-14
N 389,354
Large GWAS
European
whole body water mass
Loya H et al. “A scalable variational inference approach for increased mixed-model association power.” Nature Genetics 57(2):461-468 (2025)
Allele G
OR 0.01
p 5.0e-14
N 394,642
Large GWAS
European
polyunsaturated fatty acids to total fatty acids percentage
Zoodsma M et al. “A genetic map of human metabolism across the allele frequency spectrum.” Nature Genetics 57(10):2445-2455 (2025)
Allele G
OR 0.01
p 6.0e-11
N 450,015
Large GWAS
multi-ancestry
This variant is in our database but has no known associations or PRS memberships yet.
Gene information from NCBI Gene. Variant classifications from ClinVar.
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