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Am J Hum Genet. 2014 Sep 4;95(3):245-56. doi: 10.1016/j.ajhg.2014.08.004.

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

Author information

1
Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: xxli@stanford.edu.
2
Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
3
Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.
4
Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
5
Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
6
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
7
Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA. Electronic address: smontgom@stanford.edu.

Abstract

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 from a 17-individual, three-generation family to contrast expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) within this family to eQTLs and sQTLs within a population sample. Using this design, we found that eQTLs and sQTLs with large effects in the family were enriched with rare regulatory and splicing variants (minor allele frequency < 0.01). They were also more likely to influence essential genes and genes involved in complex disease. In addition, we tested the capacity of diverse noncoding annotation to predict the impact of rare noncoding variants. We found that distance to the transcription start site, evolutionary constraint, and epigenetic annotation were considerably more informative for predicting the impact of rare variants than for predicting the impact of common variants. These results highlight that rare noncoding variants are important contributors to individual gene-expression profiles and further demonstrate a significant capability for genomic annotation to predict the impact of rare noncoding variants.

PMID:
25192044
PMCID:
PMC4157143
DOI:
10.1016/j.ajhg.2014.08.004
[Indexed for MEDLINE]
Free PMC Article
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