|KRAJA, ALDI - Washington University|
|BORECKI, INGRID - Washington University|
|TSAI, MICHAEL - University Of Minnesota|
|ORDOVAS, JOSE - Jean Mayer Human Nutrition Research Center On Aging At Tufts University|
|HOPKINS, PAUL - University Of Utah|
|Lai, Chao Qiang|
|FRAZIER-WOOD, ALEXIS - University Of Alabama|
|STRAKA, ROBERT - University Of Minnesota|
|HIXSON, JAMES - University Of Texas|
|PROVINCE, MICHAEL - Washington University|
|ARNETT, DONNA - University Of Alabama|
Submitted to: Lipids
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/25/2012
Publication Date: 2/1/2013
Citation: Kraja, A.T., Borecki, I.B., Tsai, M.Y., Ordovas, J.M., Hopkins, P.N., Lai, C., Frazier-Wood, A.C., Straka, R.J., Hixson, J.E., Province, M.A. 2013. Genetic analysis of 16 NMR-lipoprotein fractions in humans, the GOLDN study. Lipids. 48(2):155-165.
Interpretive Summary: Lipoproteins are spherical particles that carry lipids, particularly cholesterol and triglyceride, in the plasma. There is a well-established association between dyslipidemias, or disorders of lipoprotein metabolism, and coronary heart disease (CHD).Elevated levels of blood cholesterol, especially low-density lipoprotein cholesterol (LDL-C), and low levels of high-density lipoprotein cholesterol (HDL-C) increase risk for CHD. Moreover, it is known that some lipoprotein subfractions are more likely to harden arteries than others. Therefore examination of specific lipoprotein subclasses and their modulation by genetics, diet and therapeutic agents could contribute to an improved understanding of the dynamic interaction between, genetics, dietary factors, lipid metabolism and CHD. To this end, sixteen lipoprotein subfractions were measured on more than 1,000 subjects of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, both fasting and at 3.5 and 6 hours after a post-meal fat (PPL) challenge. Moreover, these measures were repeated before and after 3 weeks of Fenofibrate (FF) treatment. A genome-wide association study was carried out to relate genetic variation through the genome to lipoprotein subfractions and classical lipid biomarkers. Several variants of a particular DNA sequences (polymorphisms) were found associated with these lipid traits. For LDL measures, these included FAM84B, CRIPT, ACOXL, BCL2L11, PCDH10, NXPH1, and SLC24A4; for HDL measures the genetic regions included HOMER1, KIT, VSNL1, QPRT, SYNPR, NXPH1, NELL1 and RUNX3. Finally for very-low-density lipoprotein VLDL related traits, the genes and regions with the higher scores included DOK5-CBLN4-MC3R, NELL1, STXBP6, APOB, GPR133, FAM84B and NR5A2. Nevertheless, these data should be considered as preliminary. Although the GOLDN study is one of the largest in studying PPL and FF treatment effects, the relatively small samples (over 700-1,000 subjects) in association tests calls for a replication of these findings.
Technical Abstract: Sixteen nuclear magnetic resonance (NMR) spectroscopy lipoprotein measurements of more than 1,000 subjects of GOLDN study, at fasting and at 3.5 and 6 h after a postprandial fat (PPL) challenge at visits 2 and 4, before and after a 3 weeks Fenofibrate (FF) treatment, were included in 6 time-independent multivariate factor analyses. Their top 1,541 unique SNPs were assessed for association with GOLDN NMR-particles and classical lipids. Several SNPs with -log10 p > 7.3 and MAF greater than or equal to 0.10, mostly intergenic associated with NMR-single traits near genes FAM84B (8q24.21), CRIPT (2p21), ACOXL (2q13), BCL2L11 (2q13), PCDH10 (4q28.3), NXPH1 (7p22), and SLC24A4 (14q32.12) in association with NMR-LDLs; HOMER1 (5q14.2), KIT (4q11-q12), VSNL1 (2p24.3), QPRT (16p11.2), SYNPR (3p14.2), NXPH1 (7p22), NELL1 (11p15.1), and RUNX3 (1p36) with NMR-HDLs; and DOK5-CBLN4-MC3R (20q13), NELL1 (11p15.1), STXBP6 (14q12), APOB (2p24-p23), GPR133 (12q24.33), FAM84B (8q24.21) and NR5A2 (1q32.1) in association with NMR-VLDLs particles. NMR single traits associations produced 75 % of 114 significant candidates, 7 % belonged to classical lipids and 18 % overlapped, and 16 % matched for time of discovery between NMR- and classical traits. Five proxy genes, (ACOXL, FAM84B, NXPH1, STK40 and VAPA) showed pleiotropic effects. While tagged for significant associations in our study and with some extra evidence from the literature, candidates as CBNL4, FAM84B, NXPH1, SLC24A4 remain unclear for their functional relation to lipid metabolism. Although GOLDN study is one of the largest in studying PPL and FF treatment effects, the relatively small samples (over 700-1,000 subjects) in association tests appeals for a replication of such a study. Thus, further investigation is needed.