ArticleScientific reports2021
Controlling for background genetic effects using polygenic scores improves the power of genome-wide association studies.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled it
- Boosting the power of genome-wide association studies within and across ancestries by using polygenic scores.Nature genetics · 2023Pooled it
- Genome-wide analyses of an avian herpesvirus identify 10 loci associated with tumorigenicity and vaccine escape.Science advances · 2026Article
- An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation.Plants (Basel, Switzerland) · 2026Article
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder.Biological psychiatry global open science · 2025Article
- Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS.Communications biology · 2025Article
- SPANature communications · 2025Article
- A scalable variational inference approach for increased mixed-model association power.Nature genetics · 2025Article
- Adjusting for common variant polygenic scores improves yield in rare variant association analyses.Nature genetics · 2023Article
- Increased power from conditional bacterial genome-wide association identifies macrolide resistance mutations in Neisseria gonorrhoeae.Nature communications · 2020Article
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5 authors at 1 institution in 1 country.
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Abstract
Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.
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