Biology subjects

Mancuso, N.

Publications and source records attributed to Mancuso, N..

23 records · Page 2Linked to original sources

bioRxiv2018

Genetic control of gene expression and splicing in the developing human brain

Most genetic risk for human diseases lies within non-coding regions of the genome, which is predicted to regulate gene expression, often in a tissue and stage specific manner. This has motivated building of extensive eQTL resources to understand how human allelic variation affects gene expression and splicing throughout the body, focusing primarily on adult tissue. Given the importance of regulatory pathways during brain development, we characterize the genetic control of the developing human cerebral cortical transcriptome, including expression and splicing, in 201 mid-gestational human brains, to understand how common allelic variation affects gene regulation during development. We leverage expression and splice quantitative trait loci to identify genes and isoforms relevant to neuropsychiatric disorders and brain volume. These findings demonstrate genetic mechanisms by which early developmental events have a striking and widespread influence on adult anatomical and behavioral phenotypes, as well as the evolution of the human cerebral cortex.\n\nHighlightsO_LIGenome wide map of human fetal brain eQTLs and sQTLs provides a new view of genetic control of expression and splicing.\nC_LIO_LIThere is substantial contrast between genetic control of transcript regulation in mature versus developing brain.\nC_LIO_LIWe identify novel regulatory regions specific to fetal brain development.\nC_LIO_LIIntegration of eQTLs and GWAS reveals specific relationships between expression and disease risk for neuropsychiatric diseases and relevant human brain phenotypes.\nC_LI

neuroscience↗

bioRxiv2018

Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here, we integrate the largest PrCa GWAS (N=142,392) with gene expression measured in 45 tissues (N=4,458), including normal and tumor prostate, to perform a multi-tissue transcriptomewide association study (TWAS) for PrCa. We identify 235 genes at 87 independent 1Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2Mb. 24 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at pre-defined level; this reduced the list of 235 associations to 120 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

genetics↗

bioRxiv2018

Phenotype-specific enrichment of Mendelian disorder genes near GWAS regions across 62 complex traits

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we quantify the overlap of genes identified through large-scale genome-wide association studies (GWAS) for 62 complex traits and diseases with genes known to cause 20 broad categories of Mendelian disorders. We identify a significant enrichment of phenotypically-matched Mendelian disorder genes in GWAS gene sets. Further, we observe elevated GWAS effect sizes near phenotypically-matched Mendelian disorder genes. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of phenotypically-matched Mendelian disorder genes. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by noncoding variants in complex traits, and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

genetics↗

bioRxiv2017

Probabilistic fine-mapping of transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) using predicted expression have identified thousands of genes whose locally-regulated expression is associated to complex traits and diseases. In this work, we show that linkage disequilibrium (LD) among SNPs induce significant gene-trait associations at non-causal genes as a function of the overlap between eQTL weights used in expression prediction. We introduce a probabilistic framework that models the induced correlation among TWAS signals to assign a probability for every gene in the risk region to explain the observed association signal while controlling for pleiotropic SNP effects and unmeasured causal expression. Importantly, our approach remains accurate when expression data for causal genes are not available in the causal tissue by leveraging expression prediction from other tissues. Our approach yields credible-sets of genes containing the causal gene at a nominal confidence level (e.g., 90%) that can be used to prioritize and select genes for functional assays. We illustrate our approach using an integrative analysis of lipids traits where our approach prioritizes genes with strong evidence for causality.

genetics↗

bioRxiv2016

Local genetic correlation gives insights into the shared genetic architecture of complex traits

Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions contribute to the genome-wide genetic correlation is currently lacking. Here, we introduce{rho} -HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach only requires GWAS summary data and makes no distributional assumption on the causal variant effects sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 35 complex traits, and identified 27 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 7 genomic regions that contribute to the genetic correlation of 12 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we leverage the distribution of local genetic correlations across the genome to assign putative direction of causality for 15 pairs of traits.

genetics↗