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

Rebecca S Fine1, Tune H Pers2, Tiffany Amariuta3

  • 1Department 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.

American Journal of Human Genetics
|May 7, 2019
PubMed
Summary

We developed Benchmarker, an unbiased method to compare algorithms that prioritize genes from genome-wide association studies (GWASs). Benchmarker objectively evaluates gene prioritization strategies, aiding in mapping genetic associations to function.

Keywords:
complex traitsgene prioritizationgene setsgenome-wide association studiesheritabilitymethodspathwayspolygenic traitsstatistical geneticsvariant prioritization

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWASs) identify genetic variants linked to traits but often implicate multiple genes.
  • Interpreting GWAS results requires prioritizing likely causal genes and variants.
  • Objective comparison of gene prioritization algorithms is currently lacking.

Purpose of the Study:

  • To develop an unbiased, data-driven method for evaluating gene prioritization algorithms used in GWAS analysis.
  • To compare the performance of different gene prioritization strategies using diverse data sources.

Main Methods:

  • Developed Benchmarker, a novel benchmarking tool utilizing leave-one-chromosome-out cross-validation and stratified linkage disequilibrium (LD) score regression.
  • Applied Benchmarker to 20 well-powered GWAS datasets.
  • Compared gene prioritization strategies based on annotated gene sets and gene expression data.

Main Results:

  • Genes prioritized using annotated gene sets showed higher per-SNP heritability than those from gene expression data.
  • DEPICT and MAGMA demonstrated superior performance compared to NetWAS in gene prioritization.
  • Combining data sources and algorithms enhanced the prioritization of high-quality genes for further investigation.

Conclusions:

  • Benchmarker offers an unbiased approach to rigorously assess similarity-based gene prioritization methods for GWAS.
  • The findings highlight the utility of gene sets over gene expression for prioritizing GWAS loci.
  • This method aids in selecting the optimal prioritization tool for specific GWAS and facilitates mapping genetic associations to biological function.