Biology subjects

Boocock, J.

Publications and source records attributed to Boocock, J..

5 recordsLinked to original sources

bioRxiv2018

Fast genetic mapping of complex traits in C. elegans using millions of individuals in bulk

Genetic studies of complex traits in animals have been hindered by the need to generate, maintain, and phenotype large panels of recombinant lines. We developed a new method, C. elegans eXtreme Quantitative Trait Locus (ceX-QTL) mapping, that overcomes this obstacle via bulk selection on millions of unique recombinant individuals. We used ceX-QTL to map a drug resistance locus with high resolution. We also mapped differences in gene expression in live worms and discovered a regulatory feedback loop that responds to changes in HSP-90 chaperone activity. Lastly, we used ceX-QTL to map loci that influence fitness and discovered that one such locus is caused by a deletion in a highly conserved chromatin reader in the N2 reference strain. ceX-QTL is fast, powerful and cost-effective, and will accelerate the study of complex traits in animals.

genetics↗

bioRxiv2018

A non-coding genetic variant maximally associated with serum urate levels is functionally linked to HNF4A-dependent PDZK1 expression

Several dozen genetic variants associate with serum urate levels, but the precise molecular mechanisms by which they affect serum urate are unknown. Here we tested for functional linkage of the maximally-associated genetic variant rs1967017 at the PDZK1 locus to elevated PDZK1 expression.\n\nWe performed expression quantitative trait locus (eQTL) and likelihood analyses followed by gene expression assays. Zebrafish were used to determine the ability of rs1967017 to direct tissue-specific gene expression. Luciferase assays in HEK293 and HepG2 cells measured the effect of rs1967017 on transcription amplitude.\n\nPAINTOR analysis revealed rs1967017 as most likely to be causal and rs1967017 was an eQTL for PDZK1 in the intestine. The region harboring rs1967017 was capable of directly driving green fluorescent protein expression in the kidney, liver and intestine of zebrafish embryos, consistent with a conserved ability to confer tissue-specific expression. The urate-increasing T-allele of rs1967017 strengthens a binding site for the transcription factor HNF4A. siRNA depletion of HNF4A reduced endogenous PDZK1 expression in HepG2 cells. Luciferase assays showed that the T-allele of rs1967017 gains enhancer activity relative to the urate-decreasing C-allele, with T-allele enhancer activity abrogated by HNF4A depletion. HNF4A physically binds the rs1967017 region, suggesting direct transcriptional regulation of PDZK1 by HNF4A.\n\nWith other reports our data predict that the urate-raising T-allele of rs1967017 enhances HNF4A binding to the PDZK1 promoter, thereby increasing PDZK1 expression. As PDZK1 is a scaffold protein for many ion channel transporters, increased expression can be predicted to increase activity of urate transporters and alter excretion of urate.

cell biology↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗

bioRxiv2017

A Bayesian Framework for Multiple Trait Colocalization from Summary Association Statistics

MotivationMost genetic variants implicated in complex diseases by genome-wide association studies (GWAS) are non-coding, making it challenging to understand the causative genes involved in disease. Integrating external information such as quantitative trait locus (QTL) mapping of molecular traits (e.g., expression, methylation) is a powerful approach to identify the subset of GWAS signals explained by regulatory effects. In particular, expression QTLs (eQTLs) help pinpoint the responsible gene among the GWAS regions that harbor many genes, while methylation QTLs (mQTLs) help identify the epigenetic mechanisms that impact gene expression which in turn affect disease risk. In this work we propose multiple-trait-coloc (moloc), a Bayesian statistical framework that integrates GWAS summary data with multiple molecular QTL data to identify regulatory effects at GWAS risk loci.\n\nResultsWe applied moloc to schizophrenia (SCZ) and eQTL/mQTL data derived from human brain tissue and identified 52 candidate genes that influence SCZ through methylation. Our method can be applied to any GWAS and relevant functional data to help prioritize disease associated genes.\n\nAvailabilitymoloc is available for download as an R package (https://github.com/clagiamba/moloc). We also developed a web site to visualize the biological findings (icahn.mssm.edu/moloc). The browser allows searches by gene, methylation probe, and scenario of interest.\n\nContactclaudia.giambartolomei@gmail.com\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

genomics↗

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↗