Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weigh...

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Bibliographic Details
Published in:Communications biology 2023-11, Vol.6 (1), p.1199-14, Article 1199
Main Authors: Moore, Amy, Marks, Jesse A., Quach, Bryan C., Guo, Yuelong, Bierut, Laura J., Gaddis, Nathan C., Hancock, Dana B., Page, Grier P., Johnson, Eric O.
Format: Article
Language:English
Subjects:
631/114/2415
631/208/205/2138
Biology
Biomedical and Life Sciences
Genome-wide association studies
Genome-Wide Association Study - methods
Genomes
Life Sciences
Phenotype
Pleiotropy
Polymorphism, Single Nucleotide
Predictive Value of Tests
Quantitative Trait Loci
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Summary:Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 17 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies. Evaluation of 17 published functional weighting methods for improving GWAS statistical power demonstrates a tradeoff between high sensitivity and high positive predictive value.
ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-023-05413-w