Benchmarker: An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms

Rebecca S Fine; Tune H Pers; Tiffany Amariuta; Soumya Raychaudhuri; Joel N Hirschhorn

doi:10.1016/j.ajhg.2019.03.027

Benchmarker: An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms

Am J Hum Genet. 2019 Jun 6;104(6):1025-1039. doi: 10.1016/j.ajhg.2019.03.027. Epub 2019 May 2.

Authors

Rebecca S Fine¹, Tune H Pers², Tiffany Amariuta³, Soumya Raychaudhuri⁴, Joel N Hirschhorn⁵

Affiliations

¹ Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Ph.D. Program in Biological and Biomedical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
² The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark; Department of Epidemiology Research, Statens Serum Institut, 2300 Copenhagen, Denmark.
³ Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Ph.D. Program in Bioinformatics and Integrative Genomics, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
⁴ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester M13 9PL, UK.
⁵ Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA. Electronic address: [email protected].

Abstract

Genome-wide association studies (GWASs) are valuable for understanding human biology, but associated loci typically contain multiple associated variants and genes. Thus, algorithms that prioritize likely causal genes and variants for a given phenotype can provide biological interpretations of association data. However, a critical, currently missing capability is to objectively compare performance of such algorithms. Typical comparisons rely on "gold standard" genes harboring causal coding variants, but such gold standards may be biased and incomplete. To address this issue, we developed Benchmarker, an unbiased, data-driven benchmarking method that compares performance of similarity-based prioritization strategies to each other (and to random chance) by leave-one-chromosome-out cross-validation with stratified linkage disequilibrium (LD) score regression. We first applied Benchmarker to 20 well-powered GWASs and compared gene prioritization based on strategies employing three different data sources, including annotated gene sets and gene expression; genes prioritized based on gene sets had higher per-SNP heritability than those prioritized based on gene expression. Additionally, in a direct comparison of three methods, DEPICT and MAGMA outperformed NetWAS. We also evaluated combinations of methods; our results indicated that combining data sources and algorithms can help prioritize higher-quality genes for follow-up. Benchmarker provides an unbiased approach to evaluate any similarity-based method that provides genome-wide prioritization of genes, variants, or gene sets and can determine the best such method for any particular GWAS. Our method addresses an important unmet need for rigorous tool assessment and can assist in mapping genetic associations to causal function.

Keywords: complex traits; gene prioritization; gene sets; genome-wide association studies; heritability; methods; pathways; polygenic traits; statistical genetics; variant prioritization.

Publication types

Evaluation Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Benchmarking
Chromosome Mapping
Genetic Loci*
Genome-Wide Association Study / methods*
Humans
Linkage Disequilibrium*
Phenotype
Polymorphism, Single Nucleotide*

Abstract

Publication types

MeSH terms

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