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1.
Nat Med ; 25(6): 911-919, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31160820

RESUMO

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.


Assuntos
Doenças Raras/genética , Ceramidase Ácida/genética , Estudos de Casos e Controles , Criança , Pré-Escolar , Estudos de Coortes , Feminino , Variação Genética , Humanos , Masculino , Modelos Genéticos , Mutação , Oxirredutases atuantes sobre Doadores de Grupo CH-CH/genética , Canais de Potássio/genética , RNA/sangue , RNA/genética , Processamento de RNA/genética , Doenças Raras/sangue , Análise de Sequência de RNA , Sequenciamento Completo do Exoma
2.
Nat Genet ; 51(6): 1067, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31068672

RESUMO

In the version of this article initially published, in Supplementary Data 5, the logFC, FC, P value and adjusted P value for advanced AMD versus control (DE 4/1) without age correction did not correspond to the correct gene IDs. The errors have been corrected in the HTML version of the article.

3.
Genome Biol ; 20(1): 94, 2019 05 16.
Artigo em Inglês | MEDLINE | ID: mdl-31097038

RESUMO

Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders.


Assuntos
Redes Reguladoras de Genes , Técnicas Genéticas , Artefatos , Humanos
4.
Genet Epidemiol ; 43(6): 596-608, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-30950127

RESUMO

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

5.
Nat Genet ; 51(4): 606-610, 2019 04.
Artigo em Inglês | MEDLINE | ID: mdl-30742112

RESUMO

Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD)1-3. We generated transcriptional profiles of postmortem retinas from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at more than 9 million common SNPs for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets4,5. Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.


Assuntos
Predisposição Genética para Doença/genética , Degeneração Macular/genética , Locos de Características Quantitativas/genética , Transcriptoma/genética , Estudos de Casos e Controles , Perfilação da Expressão Gênica/métodos , Regulação da Expressão Gênica/genética , Estudo de Associação Genômica Ampla/métodos , Genótipo , Humanos , Fenótipo , Polimorfismo de Nucleotídeo Único/genética , Retina/fisiopatologia
6.
F1000Res ; 7: 1860, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30613398

RESUMO

Sequence similarity among distinct genomic regions can lead to errors in alignment of short reads from next-generation sequencing. While this is well known, the downstream consequences of misalignment have not been fully characterized.  We assessed the potential for incorrect alignment of RNA-sequencing reads to cause false positives in both gene expression quantitative trait locus (eQTL) and co-expression analyses. Trans-eQTLs identified from human RNA-sequencing studies appeared to be particularly affected by this phenomenon, even when only uniquely aligned reads are considered. Over 75\% of trans-eQTLs using a standard pipeline occurred between regions of sequence similarity and therefore could be due to alignment errors. Further, associations due to mapping errors are likely to misleadingly replicate between studies. To help address this problem, we quantified the potential for "cross-mapping'' to occur between every pair of annotated genes in the human genome. Such cross-mapping data can be used to filter or flag potential false positives in both trans-eQTL and co-expression analyses. Such filtering substantially alters the detection of significant associations and can have an impact on the assessment of false discovery rate, functional enrichment, and replication for RNA-sequencing association studies.


Assuntos
Regulação da Expressão Gênica , Locos de Características Quantitativas/genética , Alinhamento de Sequência , Análise de Sequência de RNA/métodos , Reações Falso-Positivas , Genótipo , Humanos , Polimorfismo de Nucleotídeo Único/genética
7.
Am J Epidemiol ; 186(7): 771-777, 2017 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-28978191

RESUMO

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.


Assuntos
Doença/etiologia , Interação Gene-Ambiente , Estudo de Associação Genômica Ampla/métodos , Animais , Doença/genética , Genômica , Humanos , Modelos Animais , Análise de Sequência de RNA
8.
Genome Res ; 27(11): 1843-1858, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-29021288

RESUMO

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.


Assuntos
Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes , Processamento de RNA , Análise de Sequência de RNA/métodos , Teorema de Bayes , Bases de Dados Genéticas , Regulação da Expressão Gênica , Técnicas de Genotipagem , Humanos , Especificidade de Órgãos , Polimorfismo de Nucleotídeo Único
9.
Nature ; 550(7675): 239-243, 2017 10 11.
Artigo em Inglês | MEDLINE | ID: mdl-29022581

RESUMO

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.


Assuntos
Perfilação da Expressão Gênica , Variação Genética/genética , Especificidade de Órgãos/genética , Teorema de Bayes , Feminino , Genoma Humano/genética , Genômica , Genótipo , Humanos , Masculino , Modelos Genéticos , Análise de Sequência de RNA
10.
Nature ; 550(7675): 204-213, 2017 10 11.
Artigo em Inglês | MEDLINE | ID: mdl-29022597

RESUMO

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.


Assuntos
Perfilação da Expressão Gênica , Regulação da Expressão Gênica/genética , Variação Genética , Especificidade de Órgãos/genética , Alelos , Cromossomos Humanos/genética , Doença/genética , Feminino , Genoma Humano/genética , Genótipo , Humanos , Masculino , Locos de Características Quantitativas/genética
11.
Bioinformatics ; 33(24): 3895-3901, 2017 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-28961785

RESUMO

Motivation: Interpreting genetic variation in noncoding regions of the genome is an important challenge for personal genome analysis. One mechanism by which noncoding single nucleotide variants (SNVs) influence downstream phenotypes is through the regulation of gene expression. Methods to predict whether or not individual SNVs are likely to regulate gene expression would aid interpretation of variants of unknown significance identified in whole-genome sequencing studies. Results: We developed FIRE (Functional Inference of Regulators of Expression), a tool to score both noncoding and coding SNVs based on their potential to regulate the expression levels of nearby genes. FIRE consists of 23 random forests trained to recognize SNVs in cis-expression quantitative trait loci (cis-eQTLs) using a set of 92 genomic annotations as predictive features. FIRE scores discriminate cis-eQTL SNVs from non-eQTL SNVs in the training set with a cross-validated area under the receiver operating characteristic curve (AUC) of 0.807, and discriminate cis-eQTL SNVs shared across six populations of different ancestry from non-eQTL SNVs with an AUC of 0.939. FIRE scores are also predictive of cis-eQTL SNVs across a variety of tissue types. Availability and implementation: FIRE scores for genome-wide SNVs in hg19/GRCh37 are available for download at https://sites.google.com/site/fireregulatoryvariation/. Contact: nilah@stanford.edu. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Regulação da Expressão Gênica , Variação Genética , Software , Genômica , Humanos , Locos de Características Quantitativas
12.
BMC Cancer ; 17(1): 447, 2017 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-28651527

RESUMO

BACKGROUND: Epithelial to mesenchymal transition (EMT) is the process by which stationary epithelial cells transdifferentiate to mesenchymal cells with increased motility. EMT is integral in early stages of development and wound healing. Studies have shown that EMT could be a critical early event in tumor metastasis that is involved in acquisition of migratory and invasive properties in multiple carcinomas. METHODS: In this study, we used 15 published gene expression microarray datasets from Gene Expression Omnibus (GEO) that represent 12 cell lines from 6 cancer types across 95 observations (45 unique samples and 50 replicates) with different modes of induction of EMT or the reverse transition, mesenchymal to epithelial transition (MET). We integrated multiple gene expression datasets while considering study differences, batch effects, and noise in gene expression measurements. A universal differential EMT gene list was obtained by normalizing and correcting the data using four approaches, computing differential expression from each, and identifying a consensus ranking. We confirmed our discovery of novel EMT genes at mRNA and protein levels in an in vitro EMT model of prostate cancer - PC3 epi, EMT and Taxol resistant cell lines. We validate our discovery of C1orf116 as a novel EMT regulator by siRNA knockdown of C1orf116 in PC3 epithelial cells. RESULTS: Among differentially expressed genes, we found known epithelial and mesenchymal marker genes such as CDH1 and ZEB1. Additionally, we discovered genes known in a subset of carcinomas that were unknown in prostate cancer. This included epithelial specific LSR and S100A14 and mesenchymal specific DPYSL3. Furthermore, we also discovered novel EMT genes including a poorly-characterized gene C1orf116. We show that decreased expression of C1orf116 is associated with poor prognosis in lung and prostate cancer patients. We demonstrate that knockdown of C1orf116 expression induced expression of mesenchymal genes in epithelial prostate cancer cell line PC3-epi cells, suggesting it as a candidate driver of the epithelial phenotype. CONCLUSIONS: This comprehensive approach of statistical analysis and functional validation identified global expression patterns in EMT and candidate regulatory genes, thereby both extending current knowledge and identifying novel drivers of EMT.


Assuntos
Biomarcadores Tumorais/genética , Transição Epitelial-Mesenquimal/genética , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Neoplasias/genética , Humanos , Prognóstico
13.
Nat Methods ; 14(7): 699-702, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28530654

RESUMO

Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on associations between environmental variables and allele-specific expression. Combining whole-blood RNA-seq with extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared with only four using standard GxE interaction testing. EAGLE provides new opportunities for researchers to identify GxE interactions using functional genomic data.


Assuntos
Alelos , Epigênese Genética , Regulação da Expressão Gênica , Variação Genética , Adulto , Estudos de Coortes , Feminino , Humanos , Masculino , Modelos Genéticos , Locos de Características Quantitativas
14.
Nat Genet ; 49(5): 700-707, 2017 May.
Artigo em Inglês | MEDLINE | ID: mdl-28394350

RESUMO

Genetic studies of complex traits have mainly identified associations with noncoding variants. To further determine the contribution of regulatory variation, we combined whole-genome and transcriptome data for 624 individuals from Sardinia to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 new QTLs. We identified high-frequency QTLs and found evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.


Assuntos
Perfilação da Expressão Gênica/métodos , Variação Genética , Estudo de Associação Genômica Ampla/métodos , Locos de Características Quantitativas/genética , Processamento Alternativo , Mapeamento Cromossômico , Saúde da Família , Feminino , Predisposição Genética para Doença/genética , Genética Populacional , Genótipo , Humanos , Itália , Masculino , Polimorfismo de Nucleotídeo Único , Sítio de Iniciação de Transcrição
15.
Nat Genet ; 49(5): 692-699, 2017 May.
Artigo em Inglês | MEDLINE | ID: mdl-28369037

RESUMO

Structural variants (SVs) are an important source of human genetic diversity, but their contribution to traits, disease and gene regulation remains unclear. We mapped cis expression quantitative trait loci (eQTLs) in 13 tissues via joint analysis of SVs, single-nucleotide variants (SNVs) and short insertion/deletion (indel) variants from deep whole-genome sequencing (WGS). We estimated that SVs are causal at 3.5-6.8% of eQTLs-a substantially higher fraction than prior estimates-and that expression-altering SVs have larger effect sizes than do SNVs and indels. We identified 789 putative causal SVs predicted to directly alter gene expression: most (88.3%) were noncoding variants enriched at enhancers and other regulatory elements, and 52 were linked to genome-wide association study loci. We observed a notable abundance of rare high-impact SVs associated with aberrant expression of nearby genes. These results suggest that comprehensive WGS-based SV analyses will increase the power of common- and rare-variant association studies.


Assuntos
Regulação da Expressão Gênica , Variação Genética , Genoma Humano/genética , Locos de Características Quantitativas/genética , Análise de Sequência de DNA/métodos , Algoritmos , Mapeamento Cromossômico , Estudo de Associação Genômica Ampla/métodos , Humanos , Mutação INDEL , Modelos Lineares , Polimorfismo de Nucleotídeo Único
16.
Nat Genet ; 48(9): 995-1002, 2016 09.
Artigo em Inglês | MEDLINE | ID: mdl-27479906

RESUMO

In each individual, a highly diverse T cell receptor (TCR) repertoire interacts with peptides presented by major histocompatibility complex (MHC) molecules. Despite extensive research, it remains controversial whether germline-encoded TCR-MHC contacts promote TCR-MHC specificity and, if so, whether differences exist in TCR V gene compatibilities with different MHC alleles. We applied expression quantitative trait locus (eQTL) mapping to test for associations between genetic variation and TCR V gene usage in a large human cohort. We report strong trans associations between variation in the MHC locus and TCR V gene usage. Fine-mapping of the association signals identifies specific amino acids from MHC genes that bias V gene usage, many of which contact or are spatially proximal to the TCR or peptide in the TCR-peptide-MHC complex. Hence, these MHC variants, several of which are linked to autoimmune diseases, can directly affect TCR-MHC interaction. These results provide the first examples of trans-QTL effects mediated by protein-protein interactions and are consistent with intrinsic TCR-MHC specificity.


Assuntos
Variação Genética/genética , Região Variável de Imunoglobulina/metabolismo , Complexo Principal de Histocompatibilidade/fisiologia , Locos de Características Quantitativas , Receptores de Antígenos de Linfócitos T/metabolismo , Estudos de Coortes , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Região Variável de Imunoglobulina/genética , Receptores de Antígenos de Linfócitos T/genética
17.
Genome Res ; 26(6): 768-77, 2016 06.
Artigo em Inglês | MEDLINE | ID: mdl-27197214

RESUMO

The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.


Assuntos
Cromossomos Humanos X/genética , Transcriptoma , Feminino , Perfilação da Expressão Gênica , Regulação da Expressão Gênica , Predisposição Genética para Doença , Genoma Humano , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Caracteres Sexuais
18.
Am J Hum Genet ; 98(1): 216-24, 2016 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-26749306

RESUMO

Methods for multiple-testing correction in local expression quantitative trait locus (cis-eQTL) studies are a trade-off between statistical power and computational efficiency. Bonferroni correction, though computationally trivial, is overly conservative and fails to account for linkage disequilibrium between variants. Permutation-based methods are more powerful, though computationally far more intensive. We present an alternative correction method called eigenMT, which runs over 500 times faster than permutations and has adjusted p values that closely approximate empirical ones. To achieve this speed while also maintaining the accuracy of permutation-based methods, we estimate the effective number of independent variants tested for association with a particular gene, termed Meff, by using the eigenvalue decomposition of the genotype correlation matrix. We employ a regularized estimator of the correlation matrix to ensure Meff is robust and yields adjusted p values that closely approximate p values from permutations. Finally, using a common genotype matrix, we show that eigenMT can be applied with even greater efficiency to studies across tissues or conditions. Our method provides a simpler, more efficient approach to multiple-testing correction than existing methods and fits within existing pipelines for eQTL discovery.


Assuntos
Desequilíbrio de Ligação , Locos de Características Quantitativas , Humanos
19.
PLoS Comput Biol ; 11(5): e1004220, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25970446

RESUMO

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.


Assuntos
Algoritmos , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Modelos Genéticos , Sequência de Bases , Humanos , Especificidade de Órgãos/genética
20.
Science ; 347(6222): 664-7, 2015 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-25657249

RESUMO

The phenotypic consequences of expression quantitative trait loci (eQTLs) are presumably due to their effects on protein expression levels. Yet the impact of genetic variation, including eQTLs, on protein levels remains poorly understood. To address this, we mapped genetic variants that are associated with eQTLs, ribosome occupancy (rQTLs), or protein abundance (pQTLs). We found that most QTLs are associated with transcript expression levels, with consequent effects on ribosome and protein levels. However, eQTLs tend to have significantly reduced effect sizes on protein levels, which suggests that their potential impact on downstream phenotypes is often attenuated or buffered. Additionally, we identified a class of cis QTLs that affect protein abundance with little or no effect on messenger RNA or ribosome levels, which suggests that they may arise from differences in posttranslational regulation.


Assuntos
Regulação da Expressão Gênica , Variação Genética , Biossíntese de Proteínas/genética , Locos de Características Quantitativas , RNA Mensageiro/genética , Transcrição Genética , Região 3'-Flanqueadora , Região 5'-Flanqueadora , Linhagem Celular , Éxons , Humanos , Fenótipo , Ribossomos/metabolismo
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