Dataset Information

Transcriptome sequencing of a large human family identifies the impact of rare non-coding variants

ABSTRACT: We have combined high-quality genome sequencing and RNA-sequencing data within a 17-individual, three generation family. Using these data, we have contrasted cis-acting expression, allele-specific expression and splicing quantitative trait loci (collectively termed eQTLs) within the family to eQTLs discovered within a cell-type and ethnicity-matched population sample. We identified that eQTL that exhibit larger effects in the family compared to the population are enriched for rare regulatory and splicing variants and were more likely to influence essential genes. In addition, we identify several large effect-size eQTLs within the family for genes involved in complex disease. Through analysis of eQTLs in a large family we also report the utility of non-coding genome annotation to predicting the effect of rare non-coding variants. We find that a combination of distance to the transcription start site, evolutionary constraint and epigenetic annotation is considerably more informative for predicting the consequence of rare non-coding variants than for common variants. In summary, through transcriptome analyses within a large family we are able to identify the contribution of rare non-coding variants to expression phenotypes and further demonstrate the predictive potential of diverse non-coding genome annotation for interpretation of the impact of rare non-coding variants.

ORGANISM(S): Homo sapiens

PROVIDER: GSE56961 | GEO | 2014/09/01

SECONDARY ACCESSION(S): PRJNA245078

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Genome-wide association studies (GWAS) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium (LD) makes it challenging to discern causal variants. Computational finemapping prioritized thousands of credible variants, ~98% of which lie within poorly characterized non-coding regions. To functionally validate their regulatory effects, we performed a massively parallel reporter assay (MPRA) on 5,173 finemapped schizophrenia GWAS variants in primary human neural progenitors (HNPs). We identified 440 variants with allelic regulatory effects (MPRA-positive variants), with 72% of GWAS loci containing at least one MPRA-positive variant. Transcription factor binding had modest predictive power for predicting the allelic activity of MPRA-positive variants, while GWAS association, finemap posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit eQTL signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. MPRA-positive variants differed from eQTLs, as they were more frequently located in distal neuronal enhancers. Therefore, we leveraged neuronal 3D chromatin architecture to identify 273 genes that physically interact with MPRA-positive variants. These genes annotated by chromatin interactome displayed higher mutational constraints and regulatory complexity than genes annotated by eQTLs, recapitulating a recent finding that eQTL- and GWAS-detected variants map to genes with different properties. Finally, we propose a model in which allelic activity of multiple variants within a GWAS locus can be aggregated to predict gene expression by taking chromatin contact frequency and accessibility into account. In conclusion, we demonstrate that MPRA can effectively identify functional regulatory variants and delineate previously unknown regulatory principles of schizophrenia.

Dataset Information

Transcriptome sequencing of a large human family identifies the impact of rare non-coding variants

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets