title: Improved detection of disease-associated variation by sex-specific characterization and prediction of genes required for fertility.
abstract: Despite its great potential, high-throughput functional genomic data are rarely integrated and applied to characterizing the genomic basis of fertility. We obtained and reprocessed over 30 functional genomics datasets from human and mouse germ cells to perform genome-wide prediction of genes underlying various reproductive phenotypes in both species. Genes involved in male fertility are easier to predict than their female analogs. Of the multiple genomic data types examined, protein-protein interactions are by far the most informative for gene prediction, followed by gene expression, and then epigenetic marks. As an application of our predictions, we show that copy number variants (CNVs) disrupting predicted fertility genes are more strongly associated with gonadal dysfunction in male and female case-control cohorts when compared to all gene-disrupting CNVs (OR = 1.64, p < 1.64 × 10(-8) vs. OR = 1.25, p < 4 × 10(-6)). Using gender-specific fertility gene annotations further increased the observed associations (OR = 2.31, p < 2.2 × 10(-16)). We provide our gene predictions as a resource with this article.
authors.affiliation: Departments of Genetics, and Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: N R Y
authors.initials: NR
authors.lastname: Ho
authors.affiliation: Departments of Genetics, and Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: N
authors.initials: N
authors.lastname: Huang
authors.affiliation: Departments of Genetics, and Pathology & Immunology, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: D F
authors.initials: DF
authors.lastname: Conrad
mesh_terms: D000818:Animals; D016022:Case-Control Studies; D056915:DNA Copy Number Variations; D030541:Databases, Genetic; D016002:Discriminant Analysis; D044127:Epigenesis, Genetic; D005260:Female; D005298:Fertility / Q000235:genetics*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D007247:Infertility, Female / Q000235:genetics* / Q000503:physiopathology; D007248:Infertility, Male / Q000235:genetics* / Q000503:physiopathology; D016014:Linear Models; D008297:Male; D051379:Mice; D008957:Models, Genetic; D016017:Odds Ratio; D010641:Phenotype; D011237:Predictive Value of Tests; D060066:Protein Interaction Maps; D012307:Risk Factors
chemical_list: D005819:Genetic Markers
keywords: fertility genes; machine learning; ovary; systems biology; testis