## Supplementary Material for: Computational Analysis of Missense Variants of G Protein-Coupled Receptors Involved in the Neuroendocrine Regulation of Reproduction

2015-06-18T00:00:00Z (GMT) by
<b><i>Introduction:</i></b> Many missense variants in G protein-coupled receptors (GPCRs) involved in the neuroendocrine regulation of reproduction have been identified by phenotype-driven or large-scale exome sequencing. Computational functional prediction analysis is commonly performed to evaluate their impact on receptor function. <b><i>Methods:</i></b> To assess the performance and outcome of functional prediction analyses for these GPCRs, we performed a statistical analysis of the prediction performance of SIFT and PolyPhen-2 for variants with documented biological function as well as variants retrieved from Ensembl. We obtained missense variants with documented biological function testing from patients with reproductive disorders from a comprehensive literature search. Missense variants from individuals with known reproductive disorders were retrieved from the Human Gene Mutation Database. Missense variants from the general population were retrieved from the Ensembl genome database. <b><i>Results:</i></b> The accuracies of SIFT and PolyPhen-2 were 83 and 85%, respectively. The performance of both prediction tools was greater in predicting loss-of-function variants (SIFT: 92%; PolyPhen-2: 95%) than in predicting variants that did not affect function (SIFT: 54%; PolyPhen-2: 57%). Concordance between SIFT and PolyPhen-2 did not improve accuracy. Surprisingly, approximately half of the variants retrieved from Ensembl were predicted as loss-of-function variants by SIFT (47%) and PolyPhen-2 (54%). <b><i>Conclusion:</i></b> Our findings provide new guidance for interpreting the results and limitations of computational functional prediction analyses for GPCRs and will help to determine which variants require biological function testing. In addition, our findings raise important questions regarding the link between genotype and phenotype in the general population.

CC BY 4.0