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Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

ABSTRACT: Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify?>800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N?=?5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h²_total), and the proportion of heritability captured by known metabolite loci (h²_{Metabolite-hits}) for 309 lipids and 52 organic acids. Our study reveals significant differences in h²_{Metabolite-hits} among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h²_{Metabolite-hits} estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.

SUBMITTER: Hagenbeek FA

PROVIDER: S-EPMC6946682 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

Hagenbeek Fiona A FA Pool René R van Dongen Jenny J Draisma Harmen H M HHM Jan Hottenga Jouke J Willemsen Gonneke G Abdellaoui Abdel A Fedko Iryna O IO den Braber Anouk A Visser Pieter Jelle PJ de Geus Eco J C N EJCN Willems van Dijk Ko K Verhoeven Aswin A Suchiman H Eka HE Beekman Marian M Slagboom P Eline PE van Duijn Cornelia M CM Harms Amy C AC Hankemeier Thomas T Bartels Meike M Nivard Michel G MG Boomsma Dorret I DI

Nature communications 20200107 1

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabo ...[more]

PMID: 31911595

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Project description:Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.

Project description:BACKGROUND:Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. METHODS:Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. RESULTS:GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl (2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (? = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (? = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (? = 0.51, SE =0.18), and bladder and lung (? = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. CONCLUSION:Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.

Dataset Information

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

Publications

Heritability estimates for 361 blood metabolites across 40 genome-wide association studies.

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