Evidence map›Paper›PMID 36152628›Full record

ArticleAmerican journal of human genetics2022

ExPRSweb: An online repository with polygenic risk scores for common health-related exposures.

Ying Ma, Snehal Patil, Xiang Zhou, Bhramar Mukherjee, Lars G Fritsche

Abstract read
In one paragraph

Article in American journal of human genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  17. Exploring the genetics of lithium response in bipolar disorders.International journal of bipolar disorders · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ying MaDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
Snehal PatilDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
Xiang ZhouDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.
Bhramar MukherjeeDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA.
Lars G FritscheDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA; University of Michigan Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: larsf@umich.edu.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCI NIH HHS P30 CA046592
6 · The paper itself

Abstract

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyHumansLipidsMultifactorial InheritanceRisk FactorsLipidscomplex diseasescomplex traitsEHRelectronic health recordsexposuresGWASPGSpolygenic risk scorespolygenic scoresPRS

Identifiers

PMID36152628
PMCPMC9606385

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.