Evidence map›Paper›PMID 32691445›Full record

ArticleGenetic epidemiology2020

A principal component approach to improve association testing with polygenic risk scores.

Brandon J Coombes, Alexander Ploner, Sarah E Bergen, Joanna M Biernacka

Open access · greenAbstract read
In one paragraph

Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 2 pooled it
9.9field-weighted citation impact, top 1% of its field
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

68 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.

  1. Pooled it
  2. Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021
    Pooled it
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  7. Association Between the Genetic Risk for Attention-Deficit/Hyperactivity Disorder and Cognitive Function in Older Age: The MYHAT Population-Based Study.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2026
    Article
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8 more citing papers are in PubMed but not listed here.

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

4 authors at 3 institutions in 2 countries.

Brandon J CoombesDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.ORCID 0000-0003-4322-5923
Alexander PlonerDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-5042-8326
Sarah E BergenDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0002-5888-0034
Joanna M BiernackaDepartment of Health Sciences Research, Mayo Clinic, Rochester, Minnesota.ORCID 0000-0001-9350-4440
Karolinska Institutet · SEMayo Clinic · USMayo Clinic in Florida · US

Funding

Pharmacogenomics of Acamprosate Treatment Outcome (Supplement)U01AA027487 · NIAAA · MAYO CLINIC ROCHESTER · PI BIERNACKA, JOANNA M, KARPYAK, VICTOR M. · 2018 to 2023
$3.7M
2/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disordersR01MH121924 · NIMH · MAYO CLINIC ROCHESTER · PI BIERNACKA, JOANNA M · 2019 to 2024
$2.0M
NIAAA NIH HHS U01 AA027487NIMH NIH HHS R01 MH121924
6 · The paper itself

Abstract

Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS-PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS-PCA approach with a permutation test and an a priori selected p-value threshold. The PRS-PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

Indexed as

Bipolar DisorderGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansModels, GeneticMultifactorial InheritancePhenotypePolymorphism, Single NucleotidePrincipal Component AnalysisPsychotic DisordersRisk Factorspermutationpolygenic risk scoresprincipal component analysisweighting

Identifiers

PMID32691445
PMCPMC7722089
OpenAlexW3044605697

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.