Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Gleb Kichaev; Gaurav Bhatia; Po-Ru Loh; Steven Gazal; Kathryn Burch; Malika K Freund; Armin Schoech; Bogdan Pasaniuc; Alkes L Price

doi:10.1016/j.ajhg.2018.11.008

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Am J Hum Genet. 2019 Jan 3;104(1):65-75. doi: 10.1016/j.ajhg.2018.11.008. Epub 2018 Dec 27.

Authors

Gleb Kichaev¹, Gaurav Bhatia², Po-Ru Loh³, Steven Gazal³, Kathryn Burch⁴, Malika K Freund⁵, Armin Schoech³, Bogdan Pasaniuc⁶, Alkes L Price⁷

Affiliations

¹ Interdepartamental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA. Electronic address: gkichaev@ucla.edu.
² Department of Epidemiology. Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
³ Department of Epidemiology. Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA.
⁴ Interdepartamental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.
⁵ Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.
⁶ Interdepartamental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, CA 90095, USA; Department Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.
⁷ Department of Epidemiology. Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA. Electronic address: aprice@hsph.harvard.edu.

Abstract

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

Keywords: GWAS; complex traits; functional genomics.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Calibration
Databases, Genetic
Datasets as Topic
False Positive Reactions
Genome-Wide Association Study*
Humans
Multifactorial Inheritance / genetics*
Polymorphism, Single Nucleotide / genetics*
Probability
Reproducibility of Results
United Kingdom

Abstract

Publication types

MeSH terms

Grants and funding