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

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  • Shashaank Vattikuti
  • Juen Guo
  • Carson C Chow

Abstract

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. Author Summary: The narrow-sense heritability of a trait such as body-mass index is a measure of the variability of the trait between people that is accounted for by their additive genetic differences. Knowledge of these genetic differences provides insight into biological mechanisms and hence treatments for diseases. Genome-wide association studies (GWAS) survey a large set of genetic markers common to the population. They have identified several single markers that are associated with traits and diseases. However, these markers do not seem to account for all of the known narrow-sense heritability. Here we used a recently developed model to quantify the genetic information contained in GWAS for single traits and shared between traits. We specifically investigated metabolic syndrome traits that are associated with type 2 diabetes and heart disease, and we found that for the majority of these traits much of the previously unaccounted for heritability is contained within common markers surveyed in GWAS. We also computed the genetic correlation between traits, which is a measure of the genetic components shared by traits. We found that the genetic correlation between these traits could be predicted from their phenotypic correlation.

Suggested Citation

  • Shashaank Vattikuti & Juen Guo & Carson C Chow, 2012. "Heritability and Genetic Correlations Explained by Common SNPs for Metabolic Syndrome Traits," PLOS Genetics, Public Library of Science, vol. 8(3), pages 1-8, March.
    • Handle: RePEc:plo:pgen00:1002637
    DOI: 10.1371/journal.pgen.1002637
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    References listed on IDEAS

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    1. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
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    1. Young Jin Kim & Sanghoon Moon & Mi Yeong Hwang & Sohee Han & Hye-Mi Jang & Jinhwa Kong & Dong Mun Shin & Kyungheon Yoon & Sung Min Kim & Jong-Eun Lee & Anubha Mahajan & Hyun-Young Park & Mark I. McCar, 2022. "The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Guanghao Qi & Nilanjan Chatterjee, 2018. "Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits," PLOS Genetics, Public Library of Science, vol. 14(10), pages 1-21, October.
    3. Yuan-Cheng Chen & Chao Xu & Ji-Gang Zhang & Chun-Ping Zeng & Xia-Fang Wang & Rou Zhou & Xu Lin & Zeng-Xin Ao & Jun-Min Lu & Jie Shen & Hong-Wen Deng, 2018. "Multivariate analysis of genomics data to identify potential pleiotropic genes for type 2 diabetes, obesity and dyslipidemia using Meta-CCA and gene-based approach," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-16, August.
    4. Zhen Qiao & Julia Sidorenko & Joana A. Revez & Angli Xue & Xueling Lu & Katri Pärna & Harold Snieder & Peter M. Visscher & Naomi R. Wray & Loic Yengo, 2023. "Estimation and implications of the genetic architecture of fasting and non-fasting blood glucose," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Boran Gao & Can Yang & Jin Liu & Xiang Zhou, 2021. "Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies," PLOS Genetics, Public Library of Science, vol. 17(1), pages 1-25, January.
    6. Matthew Reimherr & Dan Nicolae, 2016. "Estimating Variance Components in Functional Linear Models With Applications to Genetic Heritability," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 407-422, March.
    7. Nora Franceschini & Cara L Carty & Yingchang Lu & Ran Tao & Yun Ju Sung & Ani Manichaikul & Jeff Haessler & Myriam Fornage & Karen Schwander & Niha Zubair & Stephanie Bien & Lucia A Hindorff & Xiuqing, 2016. "Variant Discovery and Fine Mapping of Genetic Loci Associated with Blood Pressure Traits in Hispanics and African Americans," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.

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