Evidence map›Paper›PMID 32779269›Full record

ArticleGenetic epidemiology2020

Polygenic modelling of treatment effect heterogeneity.

Zhi Ming Xu, Stephen Burgess

Open access · hybridAbstract 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Mendelian randomization.Nature reviews. Methods primers · 2022
    Article
  4. Review
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

2 authors at 1 institution in 4 countries.

Zhi Ming XuMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID 0000-0001-5021-8823
Stephen BurgessMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.ORCID 0000-0001-5365-8760
University of Cambridge · GB

Funding

Department of HealthMedical Research Council MC_UU_00002/7Wellcome Trust 204623Wellcome Trust 204623/Z/16/Z
6 · The paper itself

Abstract

Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment-mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene-gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low-density lipoprotein cholesterol.

Indexed as

Genetic HeterogeneityBiological Specimen BanksCholesterol, LDLComputer SimulationEpistasis, GeneticFemaleGenetic VariationHumansMaleMendelian Randomization AnalysisMiddle AgedMultifactorial InheritanceRisk FactorsUnited KingdomCholesterol, LDLcausal inferenceeffect heterogeneityinstrumental variablemendelian randomizationpolygenic modelling

Identifiers

PMID32779269
PMCPMC7612928
OpenAlexW3048576450

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.