Dataset Information

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers.

ABSTRACT:

Background

Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.

Methods

We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.

Results

As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS-metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.

Conclusions

We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.

Funding

This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit.

SUBMITTER: Fang S

PROVIDER: S-EPMC9553209 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers.

Fang Si S Holmes Michael V MV Gaunt Tom R TR Davey Smith George G Richardson Tom G TG

eLife 20221011

<h4>Background</h4>Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.<h4>Methods</h4>We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.< ...[more]

PMID: 36219204

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Project description:BackgroundMetabolic biomarkers are reported to be associated with the risk of lung cancer (LC). However, the observed associations from epidemiological studies are either inconsistent or inconclusive.MethodsThe genetic summary data of high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), triglyceride (TG), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) and those of the LC and its histological subtypes were retrieved from previous GWASs. We performed two-sample Mendelian randomization (MR) and multivariable MR analyses to examine the associations between genetically predicted metabolic biomarkers and LC in East Asians and Europeans.ResultsIn East Asians, the inverse-variance weighted (IVW) method suggests that LDL (odds ratio [OR] = 0.799, 95% CI 0.712-0.897), TC (OR = 0.713, 95% CI 0.638-0.797), and TG (OR = 0.702, 95% CI 0.613-0.804) were significantly associated with LC after correction for multiple testing. For the remaining three biomarkers, we did not detect significant association with LC by any MR method. Multivariable MR (MVMR) analysis yielded an OR of 0.958 (95% CI 0.748-1.172) for HDL, 0.839 (95% CI 0.738-0.931) for LDL, 0.942 (95% CI 0.742-1.133) for TC, 1.161 (95% CI 1.070-1.252) for TG, 1.079 (95% CI 0.851-1.219) for FPG, and 1.101 (95% CI 0.922-1.191) for HbA1c. In Europeans, the univariate MR analyses did not detect significant association between exposures and outcomes. However, in MVMR analysis integrating circulating lipids and lifestyle risk factors (smoking, alcohol drinking, and body mass index), we found that TG was positively associated with LC in Europeans (OR = 1.660, 95% CI 1.060-2.260). Subgroup and sensitivity analysis yielded similar results to the main analyses.ConclusionsOur study provides genetic evidence that circulating levels of LDL was negatively associated with LC in East Asians, whereas TG was positively associated with LC in both populations.

Project description:BackgroundGenetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases.MethodsHere, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.ResultsStructural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter-intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene-environment correlations; and (c) adjusting an exposure-outcome association for a polygenic score can increase rather than decrease bias.ConclusionsStrikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

Dataset Information

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers.

Background

Methods

Results

Conclusions

Funding

Publications

Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers.

Similar Datasets

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