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Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits


We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h2) and heritability explained by the common SNPs (hg2) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h2 accounted for by common SNPs to be 58% of h2 for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.


Vyšlo v časopise: Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits. PLoS Genet 8(3): e32767. doi:10.1371/journal.pgen.1002637
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1002637

Souhrn

We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h2) and heritability explained by the common SNPs (hg2) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h2 accounted for by common SNPs to be 58% of h2 for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations.


Zdroje

1. PermuttMAWassonJCoxN 2005 Genetic epidemiology of diabetes. J Clin Invest 115 1431 1439

2. VisscherPMHillWGWrayNR 2008 Heritability in the genomics era–concepts and misconceptions. Nat Rev Genet 9 255 266

3. GibsonG 2010 Hints of hidden heritability in GWAS. Nat Genet 42 558 560

4. MaherB 2008 Personal genomes: The case of the missing heritability. Nature 456 18 21

5. PearsonTAManolioTA 2008 How to interpret a genome-wide association study. JAMA 299 1335 1344

6. YangJBenyaminBMcEvoyBPGordonSHendersAK 2010 Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42 565 569

7. YangJManolioTAPasqualeLRBoerwinkleECaporasoN 2011 Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43 519 525

8. LynchMWalshB 1998 Genetics and analysis of quantitative traits Sunderland, Mass. Sinauer xvi, 980 p.

9. VisscherPMMedlandSEFerreiraMAMorleyKIZhuG 2006 Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet 2 e41 doi:10.1371/journal.pgen.0020041

10. CoadySAJaquishCEFabsitzRRLarsonMGCupplesLA 2002 Genetic variability of adult body mass index: a longitudinal assessment in framingham families. Obes Res 10 675 681

11. TangWHongYProvinceMARichSSHopkinsPN 2006 Familial clustering for features of the metabolic syndrome. Diabetes Care 29 631

12. DupuisJLangenbergCProkopenkoISaxenaRSoranzoN 2010 New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42 105 116

13. Lango AllenHEstradaKLettreGBerndtSIWeedonMN 2010 Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467 832 838

14. LevyDEhretGBRiceKVerwoertGCLaunerLJ 2009 Genome-wide association study of blood pressure and hypertension. Nat Genet 41 677 687

15. SpeliotesEKWillerCJBerndtSIMondaKLThorleifssonG 2010 Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42 937 948

16. TeslovichTMMusunuruKSmithAVEdmondsonACStylianouIM 2010 Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466 707 713

17. DearyIJYangJDaviesGHarrisSETenesaA 2012 Genetic contributions to stability and change in intelligence from childhood to old age. Nature

18. LeeSHWrayNRGoddardMEVisscherPM 2011 Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88 294 305

19. CzernichowSKengneAPStamatakisEHamerMBattyGD 2011 Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?: evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. Obes Rev 12 680 687

20. RiceTProvinceMPerusseLBouchardCRaoDC 1994 Cross-trait familial resemblance for body fat and blood pressure: familial correlations in the Quebec Family Study. Am J Hum Genet 55 1019 1029

21. PerusseLRiceTDespresJPBergeronJProvinceMA 1997 Familial resemblance of plasma lipids, lipoproteins and postheparin lipoprotein and hepatic lipases in the HERITAGE Family Study. Arterioscler Thromb Vasc Biol 17 3263 3269

22. FraynKN 2010 Metabolic Regulation: A Human Perspective (Frayn, Metabolic Regulation) Wiley-Blackwell 384

23. MartinLJNorthKEDyerTBlangeroJComuzzieAG 2003 Phenotypic, genetic, and genome-wide structure in the metabolic syndrome. BMC Genet 4 Suppl 1 S95

24. CheverudJM 1988 A comparison of genetic and phenotypic correlations. Evolution 958 968

25. CheverudJM 1982 Relationships among ontogenetic, static, and evolutionary allometry. Am J Phys Anthropol 59 139 149

26. RoffDA 1995 The estimation of genetic correlations from phenotypic correlations: a test of Cheverud's conjecture. Heredity 74 481 490

27. LanderES 2011 Initial impact of the sequencing of the human genome. Nature 470 187 197

28. GovindarajuDRCupplesLAKannelWBO'DonnellCJAtwoodLD 2008 Genetics of the Framingham Heart Study population. Adv Genet 62 33 65

29. PurcellSNealeBTodd-BrownKThomasLFerreiraMA 2007 PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81 559 575

30. PriceALHelgasonAThorleifssonGMcCarrollSAKongA 2011 Single-tissue and cross-tissue heritability of gene expression via identity-by-descent in related or unrelated individuals. PLoS Genet 7 e1001317 doi:10.1371/journal.pgen.1001317

31. YangJLeeSHGoddardMEVisscherPM 2010 GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2012 Číslo 3
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