Adjusting for common variant polygenic scores improves yield in rare variant association analyses

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Large-scale sequencing data necessitates improved statistical power for rare variant association tests.
• Identifying associations with rare genetic variants is crucial for understanding complex traits and diseases.

Purpose of the Study:

• To investigate the utility of adjusting for common variant polygenic scores in gene-based rare variant association tests.
• To quantify the improvement in statistical power and assess potential increases in false-positive rates.

Main Methods:

• Applied gene-based rare variant association tests across 65 quantitative traits in the UK Biobank dataset.
• Incorporated adjustment for common variant polygenic scores into the association testing framework.
• Evaluated various statistical models, including efficient sparse mixed-effects models.

Main Results:

• Adjusting for common variant polygenic scores significantly improved the yield of gene-based rare variant association tests, with up to a 20% increase in power.
• The observed improvements were consistent across various statistical models, particularly benefiting sparse mixed-effects models.
• No marked increases in false-positive rates or genomic inflation were detected.

Conclusions:

• Polygenic score adjustment is an effective strategy for enhancing statistical power in rare variant association discovery.
• This approach offers a computationally efficient method to improve the detection of associations involving rare genetic variants.
• The findings have implications for genetic studies aiming to uncover the contribution of rare variants to complex traits.