TY - JOUR
T1 - Benchmarker
T2 - An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms
AU - Fine, Rebecca S.
AU - Pers, Tune H.
AU - Amariuta, Tiffany
AU - Raychaudhuri, Soumya
AU - Hirschhorn, Joel N.
PY - 2019/6/6
Y1 - 2019/6/6
N2 - 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.
AB - 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.
U2 - 10.1016/j.ajhg.2019.03.027
DO - 10.1016/j.ajhg.2019.03.027
M3 - Journal article
C2 - 31056107
SN - 0002-9297
VL - 104
SP - 1025
EP - 1039
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 6
ER -