Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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. RNA-Sequencing of CEPH/UTAH family 1463

ORGANISM(S): Homo sapiens

SUBMITTER: Xin Li

PROVIDER: E-GEOD-56961 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants.

Li Xin X Battle Alexis A Karczewski Konrad J KJ Karczewski Konrad J KJ Zappala Zach Z Knowles David A DA Smith Kevin S KS Kukurba Kim R KR Wu Eric E Simon Noah N Montgomery Stephen B SB

American journal of human genetics 20140901 3

Recent and rapid human population growth has led to an excess of rare genetic variants that are expected to contribute to an individual's genetic burden of disease risk. To date, much of the focus has been on rare protein-coding variants, for which potential impact can be estimated from the genetic code, but determining the impact of rare noncoding variants has been more challenging. To improve our understanding of such variants, we combined high-quality genome sequencing and RNA sequencing data ...[more]

PMID: 25192044

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Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.

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

Publications

Transcriptome sequencing of a large human family identifies the impact of rare noncoding variants.

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