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Search Results: 1 - 10 of 172686 matches for " Barbara E. Stranger "
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The genetics of regulatory variation in the human genome
Barbara E Stranger, Emmanouil T Dermitzakis
Human Genomics , 2005, DOI: 10.1186/1479-7364-2-2-126
Abstract:
From DNA to RNA to disease and back: The 'central dogma' of regulatory disease variation
Barbara E Stranger, Emmanouil T Dermitzakis
Human Genomics , 2006, DOI: 10.1186/1479-7364-2-6-383
Abstract:
Large-Scale Population Study of Human Cell Lines Indicates that Dosage Compensation Is Virtually Complete
Colette M Johnston,Frances L Lovell,Daniel A Leongamornlert,Barbara E Stranger,Emmanouil T Dermitzakis,Mark T Ross
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.0040009
Abstract: 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.
Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations
Alexandra C. Nica,Stephen B. Montgomery,Antigone S. Dimas,Barbara E. Stranger,Claude Beazley,Inês Barroso,Emmanouil T. Dermitzakis
PLOS Genetics , 2010, DOI: 10.1371/journal.pgen.1000895
Abstract: 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.
Cross-phenotype meta-analysis reveals large-scale trans-eQTLs mediating patterns of transcriptional co-regulation
Boel Brynedal,Towfique Raj,Barbara E Stranger,Robert Bjornson,Benjamin M Neale,Benjamin F Voight,Chris Cotsapas
Quantitative Biology , 2014,
Abstract: Genetic variation affecting gene regulation is a central driver of phenotypic differences between individuals and can be used to uncover how biological processes are organized in a cell. Although detecting cis-eQTLs is now routine, trans-eQTLs have proven more challenging to find due to the modest variance explained and the multiple tests burden of testing millions of SNPs for association to thousands of transcripts. Here, we successfully map trans-eQTLs with the complementary approach of looking for SNPs associated to the expression of multiple genes simultaneously. We find 732 trans- eQTLs that replicate across two continental populations; each trans-eQTL controls large groups of target transcripts (regulons), which are part of interacting networks controlled by transcription factors. We are thus able to uncover co-regulated gene sets and begin describing the cell circuitry of gene regulation.
Modifier Effects between Regulatory and Protein-Coding Variation
Antigone S. Dimas,Barbara E. Stranger,Claude Beazley,Robert D. Finn,Catherine E. Ingle,Matthew S. Forrest,Matthew E. Ritchie,Panos Deloukas,Simon Tavaré,Emmanouil T. Dermitzakis
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.1000244
Abstract: 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.
Fast-evolving noncoding sequences in the human genome
Christine P Bird, Barbara E Stranger, Maureen Liu, Daryl J Thomas, Catherine E Ingle, Claude Beazley, Webb Miller, Matthew E Hurles, Emmanouil T Dermitzakis
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-6-r118
Abstract: 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 FSTvalues 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.Our analysis suggests that ANC sequences have until very recently played a role in human evolution, potentially through lineage-specific changes in gene regulation.The manner in which the expression of genes is regulated defines and determines many of the cellular and developmental processes in an organism. It has been hypothesized that variation in gene regulation is responsible for much of the phenotypic diversity within and between species [1]. In particular, it was proposed a few decades ago that the phenotypic divergence between human and chimpanzees is largely due to changes in gene regulation rather than changes in the protein-coding sequences of genes [2]. Although it has been long recognized that regulatory sequences play an important role in genome function, the fine structure and evolutionary patterns of such sequences are not well understood [3], mainly because such sequences have a much more complex functional code and appear not to be restricted to particular sequence motifs. One of the most powerful approaches with which to identify regula
Genome-Wide Associations of Gene Expression Variation in Humans
Barbara E Stranger equal contributor,Matthew S Forrest equal contributor,Andrew G Clark,Mark J Minichiello,Samuel Deutsch,Robert Lyle,Sarah Hunt,Brenda Kahl,Stylianos E Antonarakis,Simon Tavaré,Panagiotis Deloukas ,Emmanouil T Dermitzakis
PLOS Genetics , 2005, DOI: 10.1371/journal.pgen.0010078
Abstract: 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.
Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization
John C Marioni, Natalie P Thorne, Armand Valsesia, Tomas Fitzgerald, Richard Redon, Heike Fiegler, T Daniel Andrews, Barbara E Stranger, Andrew G Lynch, Emmanouil T Dermitzakis, Nigel P Carter, Simon Tavaré, Matthew E Hurles
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-10-r228
Abstract: 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.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.Copy number variation (CNV) throughout the human genome has recently been the focus of much interest and array comparative genomic hybridization (aCGH) technology has been instrumental in identifying regions of the genome where CNVs occur. It is believed that such variation may explain the presence and development of adverse phenotypes ranging from HIV-1 infection to Alzheimer's and Parkinson's disease [1]. To analyze the experimental aCGH data and to identify the location of CNVs, specific statistical tools for normalization and CNV calling (segmentation) are undergoing continual refinement and development.There are many algorithms [2,3] for segmenting aCGH data into classes with differing numbers of copies. The vast majority of these algorithms identify CNVs by identifying outlier regions within a single genome (cross-gen
Patterns of Cis Regulatory Variation in Diverse Human Populations
Barbara E. Stranger equal contributor,Stephen B. Montgomery equal contributor,Antigone S. Dimas equal contributor,Leopold Parts,Oliver Stegle,Catherine E. Ingle,Magda Sekowska,George Davey Smith,David Evans,Maria Gutierrez-Arcelus,Alkes Price,Towfique Raj,James Nisbett,Alexandra C. Nica,Claude Beazley,Richard Durbin,Panos Deloukas,Emmanouil T. Dermitzakis
PLOS Genetics , 2012, DOI: 10.1371/journal.pgen.1002639
Abstract: The genetic basis of gene expression variation has long been studied with the aim to understand the landscape of regulatory variants, but also more recently to assist in the interpretation and elucidation of disease signals. To date, many studies have looked in specific tissues and population-based samples, but there has been limited assessment of the degree of inter-population variability in regulatory variation. We analyzed genome-wide gene expression in lymphoblastoid cell lines from a total of 726 individuals from 8 global populations from the HapMap3 project and correlated gene expression levels with HapMap3 SNPs located in cis to the genes. We describe the influence of ancestry on gene expression levels within and between these diverse human populations and uncover a non-negligible impact on global patterns of gene expression. We further dissect the specific functional pathways differentiated between populations. We also identify 5,691 expression quantitative trait loci (eQTLs) after controlling for both non-genetic factors and population admixture and observe that half of the cis-eQTLs are replicated in one or more of the populations. We highlight patterns of eQTL-sharing between populations, which are partially determined by population genetic relatedness, and discover significant sharing of eQTL effects between Asians, European-admixed, and African subpopulations. Specifically, we observe that both the effect size and the direction of effect for eQTLs are highly conserved across populations. We observe an increasing proximity of eQTLs toward the transcription start site as sharing of eQTLs among populations increases, highlighting that variants close to TSS have stronger effects and therefore are more likely to be detected across a wider panel of populations. Together these results offer a unique picture and resource of the degree of differentiation among human populations in functional regulatory variation and provide an estimate for the transferability of complex trait variants across populations.
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