title: Consistency between cross-sectional and longitudinal SNP: blood lipid associations.
abstract: Various studies have linked different genetic single nucleotide polymorphisms (SNPs) to different blood lipids (BL), but whether these "connections" were identified using cross-sectional or longitudinal (i.e., changes over time) designs has received little attention. Cross-sectional and longitudinal assessments of BL [total, high-, low-density lipoprotein cholesterol (TC, HDL, LDL), triglycerides (TG)] and non-genetic factors (body mass index, smoking, alcohol intake) were measured for 2,002 Geneva, Switzerland, adults during 1999-2008 (two measurements, median 6 years apart), and 20 SNPs in 13 BL metabolism-related genes. Fixed and mixed effects repeated measures linear regression models, respectively, were employed to identify cross-sectional and longitudinal SNP:BL associations among the 1,516 (76%) study participants who reported not being treated for hypercholesterolemia at either measurement time. One-third more (12 vs. 9) longitudinal than cross-sectional associations were found [Bonferroni-adjusted two-tailed p < 0.00125 (=0.05/2)/20) for each of the four ensembles of 20 SNP:individual BL associations tested under the two study designs]. There was moderate consistency between the cross-sectional and longitudinal findings, with eight SNP:BL associations consistently identified across both study designs: [APOE.2 and APOE.4 (rs7412 and rs429358)]:TC; HL/LIPC (rs2070895):HDL; [APOB (rs1367117), APOE.2 and APOE.4 (rs7412 and rs429358)]:LDL; [APOA5 (rs2072560) and APOC III (rs5128)]:TG. The results suggest that cross-sectional studies, which include most genome-wide association studies (GWAS), can assess the large majority of SNP:BL associations. In the present analysis, which was much less powered than a GWAS, the cross-sectional study was around 2/3 (67%) as efficient as the longitudinal study.
authors.affiliation: Department of Community Medicine and Primary Care, University Hospitals of Geneva, University of Geneva, Geneva, Switzerland. michael.c.costanza@uvm.edu
authors.forename: Michael C
authors.initials: MC
authors.lastname: Costanza
authors.forename: Sigrid
authors.initials: S
authors.lastname: Beer-Borst
authors.forename: Richard W
authors.initials: RW
authors.lastname: James
authors.forename: Jean-Michel
authors.initials: JM
authors.lastname: Gaspoz
authors.forename: Alfredo
authors.initials: A
authors.lastname: Morabia
mesh_terms: D000328:Adult; D000368:Aged; D002784:Cholesterol / Q000097:blood*; D003430:Cross-Sectional Studies; D005260:Female; D005819:Genetic Markers; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D008969:Molecular Sequence Data; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D013557:Switzerland
chemical_list: D005819:Genetic Markers; D002784:Cholesterol