ArticleGenetic epidemiology2018
Adjustment for covariates using summary statistics of genome-wide association studies.
Article in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Approximate conditional phenotype analysis based on genome wide association summary statistics.Scientific reports · 2021Pooled it
- UK BioCoin: swift trait-specific summary statistics regression for UK Biobank.Nature communications · 2026Article
- Identification of multiple novel genetic mechanisms that regulate chilling tolerance in Arabidopsis.Frontiers in plant science · 2022Article
- Whole Exome-Sequencing of Pooled Genomic DNA Samples to Detect Quantitative Trait Loci in Esotropia and Exotropia of Strabismus in Japanese.Life (Basel, Switzerland) · 2021Article
- The promise and deceit of genomic selection component analyses.Proceedings. Biological sciences · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of Type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the Genetic Investigation of Anthropometric Traits consortium.
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Registered trials
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