Woolf et als GWAS by subtraction is not useful for cross-generational Mendelian randomization studies

Clicks: 66
ID: 282675
2023
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #462 of 803 articles by views in arXiv

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Mendelian randomization (MR) is an epidemiological method that can be used to strengthen causal inference regarding the relationship between a modifiable environmental exposure and a medically relevant trait and to estimate the magnitude of this relationship1. Recently, there has been considerable interest in using MR to examine potential causal relationships between parental phenotypes and outcomes amongst their offspring. In a recent issue of BMC Research Notes, Woolf et al (2023) present a new method, GWAS by subtraction, to derive genome-wide summary statistics for paternal smoking and other paternal phenotypes with the goal that these estimates can then be used in downstream (including two sample) MR studies. Whilst a potentially useful goal, Woolf et al. (2023) focus on the wrong parameter of interest for useful genome-wide association studies (GWAS) and downstream cross-generational MR studies, and the estimator that they derive is neither efficient nor appropriate for such use.
Reference Key
moen2023woolf Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors David M Evans; George Davey Smith; Gunn-Helen Moen
Journal arXiv
Year 2023
DOI
DOI not found
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.