Paul T Williams1. 1. Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California, USA, ptwilliams@lbl.gov.
Abstract
BACKGROUND: The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent. METHOD: The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity. SUMMARY: Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.
BACKGROUND: The phenotypic expression of a high-density lipoprotein (HDL) genetic risk score has been shown to depend upon whether the phenotype (HDL-cholesterol) is high or low relative to its distribution in the population (quantile-dependent expressivity). This may be due to the effects of genetic mutations on HDL-metabolism being concentration dependent. METHOD: The purpose of this article is to assess whether some previously reported HDL gene-lifestyle interactions could potentially be attributable to quantile-dependent expressivity. SUMMARY: Seventy-three published examples of HDL gene-lifestyle interactions were interpreted from the perspective of quantile-dependent expressivity. These included interactive effects of diet, alcohol, physical activity, adiposity, and smoking with genetic variants associated with the ABCA1, ADH3, ANGPTL4, APOA1, APOA4, APOA5, APOC3, APOE, CETP, CLASP1, CYP7A1, GALNT2, LDLR, LHX1, LIPC, LIPG, LPL, MVK-MMAB, PLTP, PON1, PPARα, SIRT1, SNTA1,and UCP1genes. The selected examples showed larger genetic effect sizes for lifestyle conditions associated with higher vis-à-vis lower average HDL-cholesterol concentrations. This suggests these reported interactions could be the result of selecting subjects for conditions that differentiate high from low HDL-cholesterol (e.g., lean vs. overweight, active vs. sedentary, high-fat vs. high-carbohydrate diets, alcohol drinkers vs. abstainers, nonsmokers vs. smokers) producing larger versus smaller genetic effect sizes. Key Message: Quantile-dependent expressivity provides a potential explanation for some reported gene-lifestyle interactions for HDL-cholesterol. Although overall genetic heritability appears to be quantile specific, this may vary by genetic variant and environmental exposure.
Authors: M C Vohl; B Lamarche; A Pascot; G Leroux; D Prud'homme; C Bouchard; A Nadeau; J P Després Journal: Int J Obes Relat Metab Disord Date: 1999-09
Authors: Tricia Y Li; Cuilin Zhang; Folkert W Asselbergs; Lu Qi; Eric Rimm; David J Hunter; Frank B Hu Journal: Am J Clin Nutr Date: 2007-11 Impact factor: 7.045
Authors: Mireia Junyent; Laurence D Parnell; Chao-Qiang Lai; Yu-Chi Lee; Caren E Smith; Donna K Arnett; Michael Y Tsai; Edmond K Kabagambe; Robert J Straka; Michael Province; Ping An; Ingrid Borecki; José M Ordovás Journal: Am J Clin Nutr Date: 2009-07-15 Impact factor: 7.045
Authors: Kelly Volcik; Christie M Ballantyne; Henry J Pownall; A Richey Sharrett; Eric Boerwinkle Journal: J Stud Alcohol Drugs Date: 2007-07 Impact factor: 2.582
Authors: Shuang Liang; Lyn M Steffen; Brian T Steffen; Weihua Guan; Natalie L Weir; Stephen S Rich; Ani Manichaikul; Jose D Vargas; Michael Y Tsai Journal: Atherosclerosis Date: 2013-02-18 Impact factor: 5.162