Biology subjects

Pinto, D.

Publications and source records attributed to Pinto, D..

4 recordsLinked to original sources

bioRxiv2018

Prioritizing risk genes for neurodevelopmental disorders using pathway information

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.

genomics↗

bioRxiv2018

mTADA: a framework for analyzing de novo mutations in multiple traits

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. In addition, approaches that consider multiple traits can aid in the characterization of shared genetic etiology among those traits. In recent years, parent-offspring trio studies have reported an enrichment of de novo mutations (DNMs) in neuropsychiatric disorders. The analysis of DNM data in the context of neuropsychiatric disorders has implicated multiple putatively causal genes, and a number of reported genes are shared across disorders. However, a joint analysis method designed to integrate de novo mutation data from multiple studies has yet to be implemented. We here introduce multi pi e-trait TAD A (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. mTADA uses two single-trait analysis data sets to estimate the proportion of overlapping risk genes, and reports genes shared between and specific to the relevant disorders. We applied mTADA to >13,000 trios for six disorders: schizophrenia (SCZ), autism spectrum disorder (ASD), developmental disorders (DD), intellectual disability (ID), epilepsy (EPI), and congenital heart disease (CHD). We report the proportion of overlapping risk genes and the specific risk genes shared for each pair of disorders. A total of 153 genes were found to be shared in at least one pair of disorders. The largest percentages of shared risk genes were observed for pairs of DD, ID, ASD, and CHD (>20%) whereas SCZ, CHD, and EPI did not show strong overlaps In risk gene set between them. Furthermore, mTADA identified additional SCZ, EPI and CHD risk genes through integration with DD de novo mutation data. For CHD, using DD information, 31 risk genes with posterior probabilities > 0.8 were identified, and 20 of these 31 genes were not in the list of known CHD genes. We find evidence that most significant CHD risk genes are strongly expressed in prenatal stages of the human genes. Finally, we validated our findings for CHD and EPI in independent cohorts comprising 1241 CHD trios, 226 CHD singletons and 197 EPI trios. Multiple novel risk genes identified by mTADA also had de novo mutations in these independent data sets. The joint analysis method introduced here, mTADA, is able to identify risk genes shared by two traits as well as additional risk genes not found through single-trait analysis only. A number of risk genes reported by mTADA are identified only through joint analysis, specifically when ASD, DD, or ID are one of the two traits examined. This suggests that novel genes for the trait or a new trait might converge to a core gene list of the three traits.

genomics↗

bioRxiv2017

Cdk9, Spt5 and histone H2B mono-ubiquitylation cooperate to ensure antisense suppression by the Clr6-CII/Rpd3S HDAC complex

Cyclin-dependent kinase 9 (Cdk9) and histone H2B monoubiquitylation (H2Bub1) are both implicated in elongation by RNA polymerase II (RNAPII). In fission yeast, Cdk9 and H2Bub1 regulate each other through a feedback loop involving phosphorylation of the elongation factor Spt5. Conversely, genetic interactions suggest opposing functions of H2Bub1 and Cdk9 through an Spt5-independent pathway. To understand these interactions, we performed RNA-seq analysis after H2Bub1 loss, Cdk9 inhibition, or both. Either Cdk9 inhibition or H2Bub1 loss increased levels of antisense transcription initiating within coding regions of distinct subsets of genes; ablation of both pathways led to antisense derepression affecting over half the genome. Cdk9 and H2Bub1 cooperate to suppress antisense transcription by promoting function of the Clr6-CII histone deacetylase (HDAC) complex. H2Bub1 plays a second role, in opposition to Clr6-CII, to promote sense transcription in subtelomeric regions. Therefore, functional genomics revealed both collaborative and antagonistic functions of H2Bub1 and Cdk9.

molecular biology↗

bioRxiv2017

Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissecting this signal into multiple independent eQTL for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (N=467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context specific (i.e. tissue, cell type, or developmental time point specific) regulation of gene expression. Integrating the Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC conditional eQTL data reveals forty loci with strong evidence for co-localization (posterior probability >0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes and identify novel genes for schizophrenia risk, and provide specific hypotheses for their functional follow-up.

genetics↗