A de novo paradigm for male infertility

De novo mutations are known to play a prominent role in sporadic disorders with reduced fitness. We hypothesize that de novo mutations play an important role in severe male infertility and explain a portion of the genetic causes of this understudied disorder. To test this hypothesis, we utilize trio-based exome sequencing in a cohort of 185 infertile males and their unaffected parents. Following a systematic analysis, 29 of 145 rare (MAF < 0.1%) protein-altering de novo mutations are classified as possibly causative of the male infertility phenotype. We observed a significant enrichment of loss-of-function de novo mutations in loss-of-function-intolerant genes (p-value = 1.00 × 10^-5) in infertile men compared to controls. Additionally, we detected a significant increase in predicted pathogenic de novo missense mutations affecting missense-intolerant genes (p-value = 5.01 × 10^-4) in contrast to predicted benign de novo mutations. One gene we identify, RBM5, is an essential regulator of male germ cell pre-mRNA splicing and has been previously implicated in male infertility in mice. In a follow-up study, 6 rare pathogenic missense mutations affecting this gene are observed in a cohort of 2,506 infertile patients, whilst we find no such mutations in a cohort of 5,784 fertile men (p-value = 0.03). Our results provide evidence for the role of de novo mutations in severe male infertility and point to new candidate genes affecting fertility.

Improved detection of disease-associated variation by sex-specific characterization and prediction of genes required for fertility

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.

Guidelines for investigating causality of sequence variants in human disease

The discovery of rare genetic variants is accelerating, and clear guidelines for distinguishing disease-causing sequence variants from the many potentially functional variants present in any human genome are urgently needed. Without rigorous standards we risk an acceleration of false-positive reports of causality, which would impede the translation of genomic research findings into the clinical diagnostic setting and hinder biological understanding of disease. Here we discuss the key challenges of assessing sequence variants in human disease, integrating both gene-level and variant-level support for causality. We propose guidelines for summarizing confidence in variant pathogenicity and highlight several areas that require further resource development.

Using population mixtures to optimize the utility of genomic databases: linkage disequilibrium and association study design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis - such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.