An atlas of genetic influences on human blood metabolites

Shin, So–Youn; Fauman, Eric B.; Petersen, Ann-Kristin; Krumsiek, Jan; Santos, Rita; Huang, Jie; Arnold, Matthias; Erte, Idil; Forgetta, Vincenzo; Yang, Tsun-Po; Walter, Klaudia; Menni, Cristina; Chen, Lu; Vasquez, Louella; Valdes, Ana M.; Hyde, Craig; Wang, Vicky; Ziemek, Daniel; Roberts, Phoebe M.; Xi, Li; Grundberg, Elin; Waldenberger, Mélanie; Richards, J. Brent; Mohney, Robert P.; Milburn, Michael V.; John, Sally; Trimmer, Jeff K.; Theis, Fabian J.; Overington, John P.; Suhre, Karsten; Brosnan, M. Julia; Gieger, Christian; Kastenmüller, Gabi; Spector, Tim D.; Soranzo, Nicole

doi:10.1038/ng.2982

Cited by 1,286 publications

(1,537 citation statements)

References 48 publications

Supporting

Mentioning

1,388

Contrasting

Unclassified

Order By: Relevance

“…Lead SNPs associated with IGF‐I and IGFBP‐3 concentrations were examined in a published metabolite‐SNP association database (Suhre et al ., 2011; Shin et al ., 2014). The IGF‐I‐associated SNP rs780093 at GCKR locus was associated with glucose/mannose ratio ( P = 9.4 × 10 −143 ), and the IGFBP‐3‐associated SNP rs4234798 at SORCS2 locus was associated with caprylate (8:0)/phenylalanine ratio ( P = 7.3 × 10 −7 ).…”

Section: Resultsmentioning

confidence: 99%

“…Top SNPs associated with levels of IGF‐I and IGFBP‐3 were examined in relationship to other phenotypes using published data on serum metabolites (Suhre et al ., 2011; Shin et al ., 2014), anthropometric traits (Heid et al ., 2010; Lango Allen et al ., 2010; Speliotes et al ., 2010), bone mineral density (Estrada et al ., 2012), diabetes (Voight et al ., 2010; Morris et al ., 2012) and glycemic traits (Dupuis et al ., 2010; Saxena et al ., 2010; Soranzo et al ., 2010), coronary artery disease (Coronary Artery Disease Genetics C, 2011; Schunkert et al ., 2011; Consortium CAD and Deloukas, 2013), and survival beyond 90 years (Broer et al ., 2015). Detailed information of the published datasets used including its references is given in Table S9 (Supporting information).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Teumer¹,

Qi²,

Nethander³

et al. 2016

Aging Cell

View full text Add to dashboard Cite

SummaryThe growth hormone/insulin‐like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF‐related proteins including IGF‐I and IGF‐binding protein‐3 (IGFBP‐3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Through genomewide association study of up to 30 884 adults of European ancestry from 21 studies, we confirmed and extended the list of previously identified loci associated with circulating IGF‐I and IGFBP‐3 concentrations (IGF1, IGFBP3,GCKR,TNS3, GHSR, FOXO3, ASXL2, NUBP2/IGFALS, SORCS2, and CELSR2). Significant sex interactions, which were characterized by different genotype–phenotype associations between men and women, were found only for associations of IGFBP‐3 concentrations with SNPs at the loci IGFBP3 and SORCS2. Analyses of SNPs, gene expression, and protein levels suggested that interplay between IGFBP3 and genes within the NUBP2 locus (IGFALS and HAGH) may affect circulating IGF‐I and IGFBP‐3 concentrations. The IGF‐I‐decreasing allele of SNP rs934073, which is an eQTL of ASXL2, was associated with lower adiposity and higher likelihood of survival beyond 90 years. The known longevity‐associated variant rs2153960 (FOXO3) was observed to be a genomewide significant SNP for IGF‐I concentrations. Bioinformatics analysis suggested enrichment of putative regulatory elements among these IGF‐I‐ and IGFBP‐3‐associated loci, particularly of rs646776 at CELSR2. In conclusion, this study identified several loci associated with circulating IGF‐I and IGFBP‐3 concentrations and provides clues to the potential role of the IGF axis in mediating effects of known (FOXO3) and novel (ASXL2) longevity‐associated loci.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Teumer¹,

Qi²,

Nethander³

et al. 2016

Aging Cell

View full text Add to dashboard Cite

show abstract

“…This has been shown in a GWAS of blood metabolites and of caffeine metabolites 27, 28. Caffeine is metabolized primarily by the enzyme CYP1A2 29, but does not inhibit nicotine pharmacokinetics in‐vivo nor does it alter cigarette consumption 25, 30.…”

Section: Discussionmentioning

confidence: 91%

Does coffee consumption impact on heaviness of smoking?

et al. 2017

View full text Add to dashboard Cite

Background and AimsCoffee consumption and cigarette smoking are strongly associated, but whether this association is causal remains unclear. We sought to: (1) determine whether coffee consumption influences cigarette smoking causally, (2) estimate the magnitude of any association and (3) explore potential mechanisms.DesignWe used Mendelian randomization (MR) analyses of observational data, using publicly available summarized data from the Tobacco and Genetics (TAG) consortium, individual‐level data from the UK Biobank and in‐vitro experiments of candidate compounds.SettingThe TAG consortium includes data from studies in several countries. The UK Biobank includes data from men and women recruited across England, Wales and Scotland.ParticipantsThe TAG consortium provided data on n ≤ 38 181 participants. The UK Biobank provided data on 8072 participants.MeasurementsIn MR analyses, the exposure was coffee consumption (cups/day) and the outcome was heaviness of smoking (cigarettes/day). In our in‐vitro experiments we assessed the effect of caffeic acid, quercetin and p‐coumaric acid on the rate of nicotine metabolism in human liver microsomes and cDNA‐expressed human CYP2A6.FindingsTwo‐sample MR analyses of TAG consortium data indicated that heavier coffee consumption might lead to reduced heaviness of smoking [beta = −1.49, 95% confidence interval (CI) = −2.88 to −0.09]. However, in‐vitro experiments found that the compounds investigated are unlikely to inhibit significantly the rate of nicotine metabolism following coffee consumption. Further MR analyses in UK Biobank found no evidence of a causal relationship between coffee consumption and heaviness of smoking (beta = 0.20, 95% CI = –1.72 to 2.12).ConclusionsAmount of coffee consumption is unlikely to have a major causal impact upon amount of cigarette smoking. If it does influence smoking, this is not likely to operate via effects of caffeic acid, quercetin or p‐coumaric acid on nicotine metabolism. The observational association between coffee consumption and cigarette smoking may be due to smoking impacting on coffee consumption or confounding.

show abstract

“…The genotype data have been imputed (using HapMap II reference panel containing ∼2.5 million autosomal single‐nucleotide polymorphisms [SNPs]) and used in several international consortia for different phenotypes 10. In this study, we performed genomewide association studies (GWAS) for all metabolites including those significantly associated with bone phenotypes 16. The software MERLIN was used to account for twin structure.…”

Section: Methodsmentioning

confidence: 99%

“…Genomic loci passing a stringent threshold for association with these metabolites ( p < 1 × 10 −10 for the sentinel SNP in a locus) as well as hip and spine GRS scores were considered for the bidirectional instrumental variable analysis 16. In the direct metabolite‐to‐BMD analysis, metabolite levels were used as endogenous variables; age, height, weight, and duration of HRT were included as exogenous variables; and allele frequency (additive model) was used as the instrument.…”

Section: Methodsmentioning

confidence: 99%

Metabolomic Pathways to Osteoporosis in Middle-Aged Women: A Genome-Metabolome-Wide Mendelian Randomization Study

Moayyeri

Cheung

Tan

et al. 2017

Journal of Bone and Mineral Research

Self Cite

View full text Add to dashboard Cite

The metabolic state of the body can be a major determinant of bone health. We used a Mendelian randomization approach to identify metabolites causally associated with bone mass to better understand the biological mechanisms of osteoporosis. We tested bone phenotypes (femoral neck, total hip, and lumbar spine bone mineral density [BMD]) for association with 280 fasting blood metabolites in 6055 women from TwinsUK cohort with genomewide genotyping scans. Causal associations between metabolites and bone phenotypes were further assessed in a bidirectional Mendelian randomization study using genetic markers/scores as instrumental variables. Significant associations were replicated in 624 participants from the Hong Kong Osteoporosis Study (HKOS). Fifteen metabolites showed direct associations with bone phenotypes after adjusting for covariates and multiple testing. Using genetic instruments, four of these metabolites were found to be causally associated with hip or spine BMD. These included androsterone sulfate, epiandrosterone sulfate, 5alpha‐androstan‐3beta17beta‐diol disulfate (encoded by CYP3A5), and 4‐androsten‐3beta17beta‐diol disulfate (encoded by SULT2A1). In the HKOS population, all four metabolites showed significant associations with hip and spine BMD in the expected directions. No causal reverse association between BMD and any of the metabolites were found. In the first metabolome‐genomewide Mendelian randomization study of human bone mineral density, we identified four novel biomarkers causally associated with BMD. Our findings reveal novel biological pathways involved in the pathogenesis of osteoporosis. © 2017 American Society for Bone and Mineral Research.

show abstract

An atlas of genetic influences on human blood metabolites

Cited by 1,286 publications

References 48 publications

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

Does coffee consumption impact on heaviness of smoking?

Metabolomic Pathways to Osteoporosis in Middle-Aged Women: A Genome-Metabolome-Wide Mendelian Randomization Study

Contact Info

Product

Resources

About