Literature DB >> 22156577

Genetic associations with lipoprotein subfractions provide information on their biological nature.

Ann-Kristin Petersen1, Klaus Stark, Muntaser D Musameh, Christopher P Nelson, Werner Römisch-Margl, Werner Kremer, Johannes Raffler, Susanne Krug, Thomas Skurk, Manuela J Rist, Hannelore Daniel, Hans Hauner, Jerzy Adamski, Maciej Tomaszewski, Angela Döring, Annette Peters, H-Erich Wichmann, Bernhard M Kaess, Hans Robert Kalbitzer, Fritz Huber, Volker Pfahlert, Nilesh J Samani, Florian Kronenberg, Hans Dieplinger, Thomas Illig, Christian Hengstenberg, Karsten Suhre, Christian Gieger, Gabi Kastenmüller.   

Abstract

Adverse levels of lipoproteins are highly heritable and constitute risk factors for cardiovascular outcomes. Hitherto, genome-wide association studies revealed 95 lipid-associated loci. However, due to the small effect sizes of these associations large sample numbers (>100 000 samples) were needed. Here we show that analyzing more refined lipid phenotypes, namely lipoprotein subfractions, can increase the number of significantly associated loci compared with bulk high-density lipoprotein and low-density lipoprotein analysis in a study with identical sample numbers. Moreover, lipoprotein subfractions provide novel insight into the human lipid metabolism. We measured 15 lipoprotein subfractions (L1-L15) in 1791 samples using (1)H-NMR (nuclear magnetic resonance) spectroscopy. Using cluster analyses, we quantified inter-relationships among lipoprotein subfractions. Additionally, we analyzed associations with subfractions at known lipid loci. We identified five distinct groups of subfractions: one (L1) was only marginally captured by serum lipids and therefore extends our knowledge of lipoprotein biochemistry. During a lipid-tolerance test, L1 lost its special position. In the association analysis, we found that eight loci (LIPC, CETP, PLTP, FADS1-2-3, SORT1, GCKR, APOB, APOA1) were associated with the subfractions, whereas only four loci (CETP, SORT1, GCKR, APOA1) were associated with serum lipids. For LIPC, we observed a 10-fold increase in the variance explained by our regression models. In conclusion, NMR-based fine mapping of lipoprotein subfractions provides novel information on their biological nature and strengthens the associations with genetic loci. Future clinical studies are now needed to investigate their biomedical relevance.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 22156577     DOI: 10.1093/hmg/ddr580

Source DB:  PubMed          Journal:  Hum Mol Genet        ISSN: 0964-6906            Impact factor:   6.150


  18 in total

1.  Differential Genetic Effects on Statin-Induced Changes Across Low-Density Lipoprotein-Related Measures.

Authors:  Audrey Y Chu; Franco Giulianini; Bryan J Barratt; Bo Ding; Fredrik Nyberg; Samia Mora; Paul M Ridker; Daniel I Chasman
Journal:  Circ Cardiovasc Genet       Date:  2015-08-13

Review 2.  HDL in CKD-The Devil Is in the Detail.

Authors:  Florian Kronenberg
Journal:  J Am Soc Nephrol       Date:  2018-02-22       Impact factor: 10.121

3.  Nutritional Supplementation with Essential Amino Acids and Phytosterols May Reduce Risk for Metabolic Syndrome and Cardiovascular Disease in Overweight Individuals with Mild Hyperlipidemia.

Authors:  Robert H Coker; Nicolaas E Deutz; Scott Schutzler; Marjorie Beggs; Sharon Miller; Robert R Wolfe; Jeanne Wei
Journal:  J Endocrinol Diabetes Obes       Date:  2015-04-15

4.  Genome-wide association study indicates variants associated with insulin signaling and inflammation mediate lipoprotein responses to fenofibrate.

Authors:  Alexis C Frazier-Wood; Stella Aslibekyan; Ingrid B Borecki; Paul N Hopkins; Chao-Qiang Lai; Jose M Ordovas; Robert J Straka; Hemant K Tiwari; Donna K Arnett
Journal:  Pharmacogenet Genomics       Date:  2012-10       Impact factor: 2.089

5.  Genome-wide association studies with metabolomics.

Authors:  Jerzy Adamski
Journal:  Genome Med       Date:  2012-04-30       Impact factor: 11.117

6.  Genetics of non-conventional lipoprotein fractions.

Authors:  Alexis C Frazier-Wood
Journal:  Curr Genet Med Rep       Date:  2015-08-29

7.  Genetic determinants of metabolism in health and disease: from biochemical genetics to genome-wide associations.

Authors:  Steven L Robinette; Elaine Holmes; Jeremy K Nicholson; Marc E Dumas
Journal:  Genome Med       Date:  2012-04-30       Impact factor: 11.117

8.  GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm.

Authors:  Leonardo Bottolo; Marc Chadeau-Hyam; David I Hastie; Tanja Zeller; Benoit Liquet; Paul Newcombe; Loic Yengo; Philipp S Wild; Arne Schillert; Andreas Ziegler; Sune F Nielsen; Adam S Butterworth; Weang Kee Ho; Raphaële Castagné; Thomas Munzel; David Tregouet; Mario Falchi; François Cambien; Børge G Nordestgaard; Fredéric Fumeron; Anne Tybjærg-Hansen; Philippe Froguel; John Danesh; Enrico Petretto; Stefan Blankenberg; Laurence Tiret; Sylvia Richardson
Journal:  PLoS Genet       Date:  2013-08-08       Impact factor: 5.917

9.  Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma.

Authors:  Johannes Raffler; Werner Römisch-Margl; Ann-Kristin Petersen; Philipp Pagel; Florian Blöchl; Christian Hengstenberg; Thomas Illig; Christa Meisinger; Klaus Stark; H-Erich Wichmann; Jerzy Adamski; Christian Gieger; Gabi Kastenmüller; Karsten Suhre
Journal:  Genome Med       Date:  2013-02-15       Impact factor: 11.117

10.  Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits.

Authors:  Ann-Kristin Petersen; Sonja Zeilinger; Gabi Kastenmüller; Werner Römisch-Margl; Markus Brugger; Annette Peters; Christine Meisinger; Konstantin Strauch; Christian Hengstenberg; Philipp Pagel; Fritz Huber; Robert P Mohney; Harald Grallert; Thomas Illig; Jerzy Adamski; Melanie Waldenberger; Christian Gieger; Karsten Suhre
Journal:  Hum Mol Genet       Date:  2013-09-06       Impact factor: 6.150

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.