Evaluation of gene-obesity interaction effects on cholesterol levels: a genetic predisposition score on HDL-cholesterol is modified by obesity.
OBJECTIVE
Effect modification by obesity or obesity-related phenotypes (e.g. physical activity and diet) was observed in some candidate gene studies on lipids. We aimed to evaluate gene-obesity interaction effects on HDL (HDL-C), LDL (LDL-C) and total cholesterol (TC) levels using genetic predisposition scores.


METHODS
We derived imputed genotypes for 104 SNPs in 84 lipid-associated loci in the population-based studies KORA F3 (n = 1406) and KORA F4 (n = 1515). We inferred specific unbiased weights for each SNP from linear regression estimates in KORA F4. Weighted genotypic predisposition SNP-scores were then calculated for each lipid trait in KORA F3. Interaction terms of SNP-scores with each of the obesity parameters (BMI, waist-hip-ratio, waist circumference) were included in age- and sex-adjusted linear regression models on HDL-C, LDL-C and TC.


RESULTS
All three SNP-scores were shown to be highly associated with their respective lipid levels (p = 1.11 × 10(-47) for HDL-C, p = 3.25 × 10(-19) for LDL-C and 1.53 × 10(-13) for TC). BMI, WHR and waist circumference modified the effect of the weighted HDL-C SNP-score on HDL-C significantly (all interaction p-values < 0.0062). No interaction term was significant for LDL-C or TC. Accounting for gene-obesity interaction substantially increased the proportion of HDL-C variance explained by 3-3.5%.


CONCLUSIONS
Our investigation revealed a significant interaction effect between obesity parameters and a SNP-score on HDL-C: the combined effect of HDL-C-increasing alleles on HDL-C is attenuated with increasing levels of obesity-relevant parameters. Future studies aiming to detect new genetic variants or to model genetic predictions of HDL-C levels should take obesity or obesity associated parameters into account.
Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Schoepfstr. 41, A-6020 Innsbruck, Austria.
Claudia
C
Lamina
Lukas
L
Forer
Sebastian
S
Schönherr
Barbara
B
Kollerits
Janina S
JS
Ried
Christian
C
Gieger
Annette
A
Peters
H-Erich
HE
Wichmann
Florian
F
Kronenberg
D000368:Aged; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood*; D008078:Cholesterol, LDL / Q000097:blood; D005260:Female; D059647:Gene-Environment Interaction; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D005858:Germany / Q000453:epidemiology; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D009765:Obesity / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology / Q000235:genetics*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D055105:Waist Circumference / Q000235:genetics; D049629:Waist-Hip Ratio
D015415:Biomarkers; D008076:Cholesterol, HDL; D008078:Cholesterol, LDL