Deficit of homozygosity among 1.52 million individuals and genetic causes of recessive lethality.

Genotypes causing pregnancy loss and perinatal mortality are depleted among living individuals and are therefore difficult to find. To explore genetic causes of recessive lethality, we searched for sequence variants with deficit of homozygosity among 1.52 million individuals from six European populations. In this study, we identified 25 genes harboring protein-altering sequence variants with a strong deficit of homozygosity (10% or less of predicted homozygotes). Sequence variants in 12 of the genes cause Mendelian disease under a recessive mode of inheritance, two under a dominant mode, but variants in the remaining 11 have not been reported to cause disease. Sequence variants with a strong deficit of homozygosity are over-represented among genes essential for growth of human cell lines and genes orthologous to mouse genes known to affect viability. The function of these genes gives insight into the genetics of intrauterine lethality. We also identified 1077 genes with homozygous predicted loss-of-function genotypes not previously described, bringing the total set of genes completely knocked out in humans to 4785.

Improving the diagnostic yield of exome- sequencing by predicting gene-phenotype associations using large-scale gene expression analysis.

The diagnostic yield of exome and genome sequencing remains low (8-70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases.

Exome sequencing and network analysis identifies shared mechanisms underlying spinocerebellar ataxia.

The autosomal dominant cerebellar ataxias, referred to as spinocerebellar ataxias in genetic nomenclature, are a rare group of progressive neurodegenerative disorders characterized by loss of balance and coordination. Despite the identification of numerous disease genes, a substantial number of cases still remain without a genetic diagnosis. Here, we report five novel spinocerebellar ataxia genes, FAT2, PLD3, KIF26B, EP300, and FAT1, identified through a combination of exome sequencing in genetically undiagnosed families and targeted resequencing of exome candidates in a cohort of singletons. We validated almost all genes genetically, assessed damaging effects of the gene variants in cell models and further consolidated a role for several of these genes in the aetiology of spinocerebellar ataxia through network analysis. Our work links spinocerebellar ataxia to alterations in synaptic transmission and transcription regulation, and identifies these as the main shared mechanisms underlying the genetically diverse spinocerebellar ataxia types.

Biological interpretation of genome-wide association studies using predicted gene functions.

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Gene expression analysis identifies global gene dosage sensitivity in cancer.

Many cancer-associated somatic copy number alterations (SCNAs) are known. Currently, one of the challenges is to identify the molecular downstream effects of these variants. Although several SCNAs are known to change gene expression levels, it is not clear whether each individual SCNA affects gene expression. We reanalyzed 77,840 expression profiles and observed a limited set of 'transcriptional components' that describe well-known biology, explain the vast majority of variation in gene expression and enable us to predict the biological function of genes. On correcting expression profiles for these components, we observed that the residual expression levels (in 'functional genomic mRNA' profiling) correlated strongly with copy number. DNA copy number correlated positively with expression levels for 99% of all abundantly expressed human genes, indicating global gene dosage sensitivity. By applying this method to 16,172 patient-derived tumor samples, we replicated many loci with aberrant copy numbers and identified recurrently disrupted genes in genomically unstable cancers.

Genome-wide association analyses identify variants in developmental genes associated with hypospadias.

Hypospadias is a common congenital condition in boys in which the urethra opens on the underside of the penis. We performed a genome-wide association study on 1,006 surgery-confirmed hypospadias cases and 5,486 controls from Denmark. After replication genotyping of an additional 1,972 cases and 1,812 controls from Denmark, the Netherlands and Sweden, 18 genomic regions showed independent association with P < 5 × 10(-8). Together, these loci explain 9% of the liability to developing this condition. Several of the identified regions harbor genes with key roles in embryonic development (including HOXA4, IRX5, IRX6 and EYA1). Subsequent pathway analysis with GRAIL and DEPICT provided additional insight into possible genetic mechanisms causing hypospadias.