Evidence map›Paper›PMID 36819922›Full record

ArticleFrontiers in genetics2022

Considering strategies for SNP selection in genetic and polygenic risk scores.

Julien St-Pierre, Xinyi Zhang, Tianyuan Lu, Lai Jiang, Xavier Loffree, Linbo Wang, Sahir Bhatnagar, Celia M T Greenwood, CANSSI team on Improving Robust High-Dimensional Causal Inference and Prediction Modelling

Abstract read
In one paragraph

Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. 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

9 authors.

Julien St-PierreDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Xinyi ZhangDepartment of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
Tianyuan LuLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada.
Lai JiangLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada.
Xavier LoffreeLady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada.
Linbo WangDepartment of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
Sahir BhatnagarDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Celia M T GreenwoodDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
CANSSI team on Improving Robust High-Dimensional Causal Inference and Prediction Modelling

Funding

Risk Factors for Onset and Persistence of TMDU01DE017018 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DIATCHENKO, LUDA, FILLINGIM, ROGER B · 2005 to 2016
$35.9M
WISCONSIN LONGITUDINAL STUDY--SIBLING INTERVIEWS (1)R01AG009775 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI HERD, PAMELA · 1991 to 2012
$23.4M
Vulvar Vestibulitis syndrome (VVS)P01NS045685 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZOLNOUN, DENNIZ A · 2004 to 2014
$12.5M
Aging Together: Brothers and Sisters of the Wisconsin Longitudinal StudyR01AG033285 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI HERD, PAMELA, WARREN, JOHN ROBERT · 2009 to 2013
$11.8M
A Longitudinal Resource for Genetic Research in Behavioral & Health SciencesR01AG041868 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI FREESE, JEREMY J, HAUSER, ROBERT M. · 2013 to 2017
$3.0M
NHLBI NIH HHS HHSN268201200008CNHLBI NIH HHS HHSN268201200008INIA NIH HHS R01 AG009775NIA NIH HHS R01 AG033285NIA NIH HHS R01 AG041868NIDCR NIH HHS U01 DE017018NINDS NIH HHS P01 NS045685
6 · The paper itself

Abstract

Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.

Indexed as

feature selectionhigh-dimensional datainstrumental variable methodsmeasurement errormendelian randomizationpolygenic risk scoresregularized models

Identifiers

PMID36819922
PMCPMC9930898

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.