Exome sequencing for bipolar disorder points to roles of de novo loss-of-function and protein-altering mutations.
M KataokaN MatobaT SawadaA-A KazunoM IshiwataK FujiiK MatsuoA TakataTadafumi KatoPublished in: Molecular psychiatry (2016)
Although numerous genetic studies have been conducted for bipolar disorder (BD), its genetic architecture remains elusive. Here we perform, to the best of our knowledge, the first trio-based exome sequencing study for BD to investigate potential roles of de novo mutations in the disease etiology. We identified 71 de novo point mutations and one de novo copy-number mutation in 79 BD probands. Among the genes hit by de novo loss-of-function (LOF; nonsense, splice site or frameshift) or protein-altering (LOF, missense and inframe indel) mutations, we found significant enrichment of genes highly intolerant (first percentile of intolerant genes assessed by Residual Variation Intolerance Score) to protein-altering variants in general population, an observation that is also reported in autism and schizophrenia. When we performed a joint analysis using the data of schizoaffective disorder in published studies, we found global enrichment of de novo LOF and protein-altering mutations in the combined group of bipolar I and schizoaffective disorders. Considering relationship between de novo mutations and clinical phenotypes, we observed significantly earlier disease onset among the BD probands with de novo protein-altering mutations when compared with non-carriers. Gene ontology enrichment analysis of genes hit by de novo protein-altering mutations in bipolar I and schizoaffective disorders did not identify any significant enrichment. These results of exploratory analyses collectively point to the roles of de novo LOF and protein-altering mutations in the etiology of bipolar disorder and warrant further large-scale studies.
Keyphrases
- bipolar disorder
- copy number
- genome wide
- major depressive disorder
- mitochondrial dna
- protein protein
- amino acid
- dna methylation
- binding protein
- healthcare
- randomized controlled trial
- genome wide identification
- systematic review
- machine learning
- risk assessment
- artificial intelligence
- climate change
- big data
- transcription factor
- genome wide analysis
- case control