The resolution of the genetics of gene expression

Understanding the influence of genetics on the molecular mechanisms underpinning human phenotypic diversity is fundamental to being able to predict health outcomes and treat disease. To interrogate the role of genetics on cellular state and function, gene expression has been extensively used. Past and present studies have highlighted important patterns of heritability, population differentiation and tissue-specificity in gene expression. Current and future studies are taking advantage of systems biology-based approaches and advances in sequencing technology: new methodology aims to translate regulatory networks to enrich pathways responsible for disease etiology and 2nd generation sequencing now offers single-molecular resolution of the transcriptome providing unprecedented information on the structural and genetic characteristics of gene expression. Such advances are leading to a future where rich cellular phenotypes will facilitate understanding of the transmission of genetic effect from the gene to organis

Rare and Common Regulatory Variation in Population-Scale Sequenced Human Genomes

Population-scale genome sequencing allows the characterization of functional effects of a broad spectrum of genetic variants underlying human phenotypic variation. Here, we investigate the influence of rare and common genetic variants on gene expression patterns, using variants identified from sequencing data from the 1000 genomes project in an African and European population sample and gene expression data from lymphoblastoid cell lines. We detect comparable numbers of expression quantitative trait loci (eQTLs) when compared to genotypes obtained from HapMap 3, but as many as 80% of the top expression quantitative trait variants (eQTVs) discovered from 1000 genomes data are novel. The properties of the newly discovered variants suggest that mapping common causal regulatory variants is challenging even with full resequencing data; however, we observe significant enrichment of regulatory effects in splice-site and nonsense variants. Using RNA sequencing data, we show that 46.2% of nonsynonymous variants are differentially expressed in at least one individual in our sample, creating widespread potential for interactions between functional protein-coding and regulatory variants. We also use allele-specific expression to identify putative rare causal regulatory variants. Furthermore, we demonstrate that outlier expression values can be due to rare variant effects, and we approximate the number of such effects harboured in an individual by effect size. Our results demonstrate that integration of genomic and RNA sequencing analyses allows for the joint assessment of genome sequence and genome function

Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation [1] and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome–wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript

A Survey of Genomic Properties for the Detection of Regulatory Polymorphisms

Advances in the computational identification of functional noncoding polymorphisms will aid in cataloging novel determinants of health and identifying genetic variants that explain human evolution. To date, however, the development and evaluation of such techniques has been limited by the availability of known regulatory polymorphisms. We have attempted to address this by assembling, from the literature, a computationally tractable set of regulatory polymorphisms within the ORegAnno database (http://www.oreganno.org). We have further used 104 regulatory single-nucleotide polymorphisms from this set and 951 polymorphisms of unknown function, from 2-kb and 152-bp noncoding upstream regions of genes, to investigate the discriminatory potential of 23 properties related to gene regulation and population genetics. Among the most important properties detected in this region are distance to transcription start site, local repetitive content, sequence conservation, minor and derived allele frequencies, and presence of a CpG island. We further used the entire set of properties to evaluate their collective performance in detecting regulatory polymorphisms. Using a 10-fold cross-validation approach, we were able to achieve a sensitivity and specificity of 0.82 and 0.71, respectively, and we show that this performance is strongly influenced by the distance to the transcription start site

Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies

Summary: Genevar (GENe Expression VARiation) is a database and Java tool designed to integrate multiple datasets, and provides analysis and visualization of associations between sequence variation and gene expression. Genevar allows researchers to investigate expression quantitative trait loci (eQTL) associations within a gene locus of interest in real time. The database and application can be installed on a standard computer in database mode and, in addition, on a server to share discoveries among affiliations or the broader community over the Internet via web services protocols. Availability: http://www.sanger.ac.uk/resources/software/genevar Contact: [email protected]

Precision asteroseismology of the pulsating white dwarf GD 1212 using a two-wheel-controlled Kepler spacecraft

We present a preliminary analysis of the cool pulsating white dwarf GD 1212, enabled by more than 11.5 days of space-based photometry obtained during an engineering test of the two-reaction-wheel-controlled Kepler spacecraft. We detect at least 19 independent pulsation modes, ranging from 828.2-1220.8 s, and at least 17 nonlinear combination frequencies of those independent pulsations. Our longest uninterrupted light curve, 9.0 days in length, evidences coherent difference frequencies at periods inaccessible from the ground, up to 14.5 hr, the longest-period signals ever detected in a pulsating white dwarf. These results mark some of the first science to come from a two-wheel-controlled Kepler spacecraft, proving the capability for unprecedented discoveries afforded by extending Kepler observations to the ecliptic.Comment: 8 pages, 4 figures, accepted for publication in The Astrophysical Journa

Text-mining assisted regulatory annotation

Text-mining technologies can be integrated with genome annotation systems, increasing the availability of annotated cis-regulatory data