Fast-evolving noncoding sequences in the human genome

BACKGROUND: Gene regulation is considered one of the driving forces of evolution. Although protein-coding DNA sequences and RNA genes have been subject to recent evolutionary events in the human lineage, it has been hypothesized that the large phenotypic divergence between humans and chimpanzees has been driven mainly by changes in gene regulation rather than altered protein-coding gene sequences. Comparative analysis of vertebrate genomes has revealed an abundance of evolutionarily conserved but noncoding sequences. These conserved noncoding (CNC) sequences may well harbor critical regulatory variants that have driven recent human evolution. RESULTS: Here we identify 1,356 CNC sequences that appear to have undergone dramatic human-specific changes in selective pressures, at least 15% of which have substitution rates significantly above that expected under neutrality. The 1,356 'accelerated CNC' (ANC) sequences are enriched in recent segmental duplications, suggesting a recent change in selective constraint following duplication. In addition, single nucleotide polymorphisms within ANC sequences have a significant excess of high frequency derived alleles and high F(ST)values relative to controls, indicating that acceleration and positive selection are recent in human populations. Finally, a significant number of single nucleotide polymorphisms within ANC sequences are associated with changes in gene expression. The probability of variation in an ANC sequence being associated with a gene expression phenotype is fivefold higher than variation in a control CNC sequence. CONCLUSION: Our analysis suggests that ANC sequences have until very recently played a role in human evolution, potentially through lineage-specific changes in gene regulation

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]

Association of CNVs with methylation variation.

Germline copy number variants (CNVs) and single-nucleotide polymorphisms (SNPs) form the basis of inter-individual genetic variation. Although the phenotypic effects of SNPs have been extensively investigated, the effects of CNVs is relatively less understood. To better characterize mechanisms by which CNVs affect cellular phenotype, we tested their association with variable CpG methylation in a genome-wide manner. Using paired CNV and methylation data from the 1000 genomes and HapMap projects, we identified genome-wide associations by methylation quantitative trait locus (mQTL) analysis. We found individual CNVs being associated with methylation of multiple CpGs and vice versa. CNV-associated methylation changes were correlated with gene expression. CNV-mQTLs were enriched for regulatory regions, transcription factor-binding sites (TFBSs), and were involved in long-range physical interactions with associated CpGs. Some CNV-mQTLs were associated with methylation of imprinted genes. Several CNV-mQTLs and/or associated genes were among those previously reported by genome-wide association studies (GWASs). We demonstrate that germline CNVs in the genome are associated with CpG methylation. Our findings suggest that structural variation together with methylation may affect cellular phenotype

Large-Scale Population Study of Human Cell Lines Indicates that Dosage Compensation Is Virtually Complete

X chromosome inactivation in female mammals results in dosage compensation of X-linked gene products between the sexes. In humans there is evidence that a substantial proportion of genes escape from silencing. We have carried out a large-scale analysis of gene expression in lymphoblastoid cell lines from four human populations to determine the extent to which escape from X chromosome inactivation disrupts dosage compensation. We conclude that dosage compensation is virtually complete. Overall expression from the X chromosome is only slightly higher in females and can largely be accounted for by elevated female expression of approximately 5% of X-linked genes. We suggest that the potential contribution of escape from X chromosome inactivation to phenotypic differences between the sexes is more limited than previously believed

Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx

Gene expression and its regulation can vary substantially across tissue types. In order to generate knowledge about gene expression in human tissues, the Genotype-Tissue Expression (GTEx) program has collected transcriptome data in a wide variety of tissue types from post-mortem donors. However, many tissue types are difficult to access and are not collected in every GTEx individual. Furthermore, in non-GTEx studies, the accessibility of certain tissue types greatly limits the feasibility and scale of studies of multi-tissue expression. In this work, we developed multi-tissue imputation methods to impute gene expression in uncollected or inaccessible tissues. Via simulation studies, we showed that the proposed methods outperform existing imputation methods in multi-tissue expression imputation and that incorporating imputed expression data can improve power to detect phenotype-expression correlations. By analyzing data from nine selected tissue types in the GTEx pilot project, we demonstrated that harnessing expression quantitative trait loci (eQTLs) and tissue-tissue expression-level correlations can aid imputation of transcriptome data from uncollected GTEx tissues. More importantly, we showed that by using GTEx data as a reference, one can impute expression levels in inaccessible tissues in non-GTEx expression studies

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization.

BACKGROUND: Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined. RESULTS: We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses. CONCLUSION: Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations

The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits

Modifier Effects between Regulatory and Protein-Coding Variation

Genome-wide associations have shown a lot of promise in dissecting the genetics of complex traits in humans with single variants, yet a large fraction of the genetic effects is still unaccounted for. Analyzing genetic interactions between variants (epistasis) is one of the potential ways forward. We investigated the abundance and functional impact of a specific type of epistasis, namely the interaction between regulatory and protein-coding variants. Using genotype and gene expression data from the 210 unrelated individuals of the original four HapMap populations, we have explored the combined effects of regulatory and protein-coding single nucleotide polymorphisms (SNPs). We predict that about 18% (1,502 out of 8,233 nsSNPs) of protein-coding variants are differentially expressed among individuals and demonstrate that regulatory variants can modify the functional effect of a coding variant in cis. Furthermore, we show that such interactions in cis can affect the expression of downstream targets of the gene containing the protein-coding SNP. In this way, a cis interaction between regulatory and protein-coding variants has a trans impact on gene expression. Given the abundance of both types of variants in human populations, we propose that joint consideration of regulatory and protein-coding variants may reveal additional genetic effects underlying complex traits and disease and may shed light on causes of differential penetrance of known disease variants

Genome-wide associations of gene expression variation in humans

The exploration of quantitative variation in human populations has become one of the major priorities for medical genetics. The successful identification of variants that contribute to complex traits is highly dependent on reliable assays and genetic maps. We have performed a genome-wide quantitative trait analysis of 630 genes in 60 unrelated Utah residents with ancestry from Northern and Western Europe using the publicly available phase I data of the International HapMap project. The genes are located in regions of the human genome with elevated functional annotation and disease interest including the ENCODE regions spanning 1% of the genome, Chromosome 21 and Chromosome 20q12-13.2. We apply three different methods of multiple test correction, including Bonferroni, false discovery rate, and permutations. For the 374 expressed genes, we find many regions with statistically significant association of single nucleotide polymorphisms (SNPs) with expression variation in lymphoblastoid cell lines after correcting for multiple tests. Based on our analyses, the signal proximal (cis-) to the genes of interest is more abundant and more stable than distal and trans across statistical methodologies. Our results suggest that regulatory polymorphism is widespread in the human genome and show that the 5-kb (phase I) HapMap has sufficient density to enable linkage disequilibrium mapping in humans. Such studies will significantly enhance our ability to annotate the non-coding part of the genome and interpret functional variation. In addition, we demonstrate that the HapMap cell lines themselves may serve as a useful resource for quantitative measurements at the cellular level