ArticlePloS one2013
Evaluation of 41 candidate gene variants for obesity in the EPIC-Potsdam cohort by multi-locus stepwise regression.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Understanding food addiction in obesity: a genetic perspective.Journal of eating disorders · 2025Article
- Association between the rs2241883 polymorphism of the fatty acid-binding protein-1 (FABP1) gene and obesity in a population of MASHAD study cohort.Molecular genetics & genomic medicine · 2023Article
- Genetic variation in TBC1 domain family member 1 gene associates with the risk of lean NAFLDFrontiers in genetics · 2022Article
- TBC1D1 interacting proteins, VPS13A and VPS13C, regulate GLUT4 homeostasis in C2C12 myotubes.Scientific reports · 2020Article
- Regionally clusteredJournal of neurology, neurosurgery, and psychiatry · 2018Article
- Isoform-specific AMPK association with TBC1D1 is reduced by a mutation associated with severe obesity.The Biochemical journal · 2018Article
- The Extending Spectrum of NPC1-Related Human Disorders: From Niemann-Pick C1 Disease to Obesity.Endocrine reviews · 2018Review
- Neuronal Pentraxin 2 predicts medial temporal atrophy and memory decline across the Alzheimer's disease spectrum.Brain, behavior, and immunity · 2016Article
- Tbc1d1 deletion suppresses obesity in leptin-deficient mice.International journal of obesity (2005) · 2016Article
- Rab GAPs AS160 and Tbc1d1 play nonredundant roles in the regulation of glucose and energy homeostasis in mice.American journal of physiology. Endocrinology and metabolism · 2016Article
- Article
- Deletion of the Rab GAP Tbc1d1 modifies glucose, lipid, and energy homeostasis in mice.American journal of physiology. Endocrinology and metabolism · 2015Article
- SMAD3 is associated with the total burden of radiographic osteoarthritis: the Chingford study.PloS one · 2014Article
- Novel "thrifty" models of increased eating behaviour.Current psychiatry reports · 2013Review
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7 authors.
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Abstract
objectiveObesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes.
methodsIn 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean=25.9 kg/m(2), SD=4.1) or waist circumference (WC, mean=85.2 cm, SD=12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the 'best' SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test.
resultsThe strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = -0.33, SE=0.13), FTO rs9939609 (β=0.28, SE=0.13), MC4R rs17700144 (β=0.41, SE=0.15), and MC4R rs10871777 (β=0.34, SE=0.14). All these SNPs showed similar effects on waist circumference. The two 'best' six-SNP combinations for BMI (global p-value= 3.45⋅10(-6) and 6.82⋅10(-6)) showed effects ranging from -1.70 (SE=0.34) to 0.74 kg/m(2) (SE=0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10(-6) and 9.76⋅10(-6)) with an allele combination effect of -2.96 cm (SE=0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10(-4) to 1.02⋅10(-2)). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance.
conclusionMSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset.
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