Evidence map›Paper›PMID 37491308›Full record

SynthesisNature communications2023

Overcoming attenuation bias in regressions using polygenic indices.

Hans van Kippersluis, Pietro Biroli, Rita Dias Pereira, Titus J Galama, Stephanie von Hinke, S Fleur W Meddens, Dilnoza Muslimova, Eric A W Slob, Ronald de Vlaming, Cornelius A Rietveld

Abstract readMeta-Analysis
In one paragraph

Synthesis in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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

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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

10 authors.

Hans van KippersluisErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands. hvankippersluis@ese.eur.nl.ORCID 0000-0003-3297-5059
Pietro BiroliDepartment of Economics, University of Bologna, Bologna, Italy.ORCID 0000-0002-5033-4688
Rita Dias PereiraErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Titus J GalamaErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-1036-396X
Stephanie von HinkeErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-8272-076X
S Fleur W MeddensErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
Dilnoza MuslimovaErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0003-4917-1663
Eric A W SlobErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0002-1644-0735
Ronald de VlamingTinbergen Institute, Amsterdam, The Netherlands.ORCID 0000-0001-6416-6067
Cornelius A RietveldErasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.ORCID 0000-0003-4053-1861

Funding

Genotyping the Understanding America Study to generate novel opportunities for research on cognitive functioning and dementiaR01AG079554 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FAUL, JESSICA, GALAMA, TITUS JOHANNES · 2023 to 2025
$7.6M
Genetic Differences in the Causal Effect of Education Quantity and Quality on Cognitive Functioning and Dementia Diagnosis Later in LifeR01AG078522 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Silvia Helena Barcellos · 2022 to 2026
$3.9M
Identifying gene-by-environment interplay in health behaviorR56AG058726 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GALAMA, TITUS JOHANNES · 2020 to 2020
$842k
Department of Health BRC-1215-20014NIA NIH HHS R01 AG078522NIA NIH HHS R01 AG079554NIA NIH HHS R56 AG058726
6 · The paper itself

Abstract

Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.

Indexed as

Educational StatusHumans

Identifiers

PMID37491308
PMCPMC10368647

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.