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The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.
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Evolución Molecular , Redes Reguladoras de Genes/genética , Genética de Población , Sitios de Carácter Cuantitativo/genética , Adaptación Fisiológica/genética , Variación Genética/genética , Humanos , Herencia Multifactorial/genética , Selección Genética/genéticaRESUMEN
MOTIVATION: Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. AVAILABILITY AND IMPLEMENTATION: PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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MicroARNs , Proteínas Reguladoras de la Apoptosis/genética , Biología Computacional , Regulación de la Expresión Génica , Redes Reguladoras de Genes , MicroARNs/genética , ARN Mensajero , RNA-SeqRESUMEN
BACKGROUND: Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. METHODS: We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. RESULTS: Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be 'cores' of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. CONCLUSIONS: This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.
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Genes Supresores de Tumor , Predisposición Genética a la Enfermedad/genética , Neoplasias/genética , Neoplasias/inmunología , Oncogenes/genética , Polimorfismo de Nucleótido Simple , Humanos , Sitios de Carácter CuantitativoRESUMEN
Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.
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Estudio de Asociación del Genoma Completo/métodos , Especificidad de Órganos/genética , Sitios de Carácter Cuantitativo/genética , Expresión Génica/genética , Regulación de la Expresión Génica/genética , Redes Reguladoras de Genes/genética , Predisposición Genética a la Enfermedad/genética , Variación Genética , Genotipo , Humanos , Fenotipo , Polimorfismo de Nucleótido Simple/genética , Sitios de Carácter Cuantitativo/fisiología , Transcriptoma/genéticaRESUMEN
Human genes governing innate immunity provide a valuable tool for the study of the selective pressure imposed by microorganisms on host genomes. A comprehensive, genome-wide study of how selective constraints and adaptations have driven the evolution of innate immunity genes is missing. Using full-genome sequence variation from the 1000 Genomes Project, we first show that innate immunity genes have globally evolved under stronger purifying selection than the remainder of protein-coding genes. We identify a gene set under the strongest selective constraints, mutations in which are likely to predispose individuals to life-threatening disease, as illustrated by STAT1 and TRAF3. We then evaluate the occurrence of local adaptation and detect 57 high-scoring signals of positive selection at innate immunity genes, variation in which has been associated with susceptibility to common infectious or autoimmune diseases. Furthermore, we show that most adaptations targeting coding variation have occurred in the last 6,000-13,000 years, the period at which populations shifted from hunting and gathering to farming. Finally, we show that innate immunity genes present higher Neandertal introgression than the remainder of the coding genome. Notably, among the genes presenting the highest Neandertal ancestry, we find the TLR6-TLR1-TLR10 cluster, which also contains functional adaptive variation in Europeans. This study identifies highly constrained genes that fulfill essential, non-redundant functions in host survival and reveals others that are more permissive to change-containing variation acquired from archaic hominins or adaptive variants in specific populations-improving our understanding of the relative biological importance of innate immunity pathways in natural conditions.
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Inmunidad Innata/genética , Hombre de Neandertal/inmunología , Adaptación Fisiológica/genética , Animales , Estudio de Asociación del Genoma Completo , Humanos , Hombre de Neandertal/genética , Polimorfismo de Nucleótido Simple , Selección GenéticaRESUMEN
BACKGROUND: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis. RESULTS: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .
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Bases de Datos Genéticas , Especificidad de Órganos/genética , Análisis de Secuencia de ARN/métodos , Análisis de Secuencia de ARN/normas , Perfilación de la Expresión Génica , Regulación de la Expresión Génica , Humanos , Anotación de Secuencia Molecular , Análisis de Componente Principal , Control de Calidad , Estándares de Referencia , Tamaño de la Muestra , Programas InformáticosRESUMEN
BACKGROUND: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. RESULTS: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. CONCLUSIONS: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.
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Perfilación de la Expresión Génica , Redes Reguladoras de Genes , Ciclo Celular/genética , Línea Celular , Humanos , Especificidad de ÓrganosRESUMEN
Genome-wide scans for selection have identified multiple regions of the human genome as being targeted by positive selection. However, only a small proportion has been replicated across studies, and the prevalence of positive selection as a mechanism of adaptive change in humans remains controversial. Here we explore the power of two haplotype-based statistics--the integrated haplotype score (iHS) and the Derived Intraallelic Nucleotide Diversity (DIND) test--in the context of next-generation sequencing data, and evaluate their robustness to demography and other selection modes. We show that these statistics are both powerful for the detection of recent positive selection, regardless of population history, and robust to variation in coverage, with DIND being insensitive to very low coverage. We apply these statistics to whole-genome sequence data sets from the 1000 Genomes Project and Complete Genomics. We found that putative targets of selection were highly significantly enriched in genic and nonsynonymous single nucleotide polymorphisms, and that DIND was more powerful than iHS in the context of small sample sizes, low-quality genotype calling, or poor coverage. As we excluded genomic confounders and alternative selection models, such as background selection, the observed enrichment attests to the action of recent, strong positive selection. Further support to the adaptive significance of these genomic regions came from their enrichment in functional variants detected by genome-wide association studies, informing the relationship between past selection and current benign and disease-related phenotypic variation. Our results indicate that hard sweeps targeting low-frequency standing variation have played a moderate, albeit significant, role in recent human evolution.
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Genética de Población , Genoma Humano , Selección Genética , Pueblo Asiatico/genética , Población Negra/genética , Bases de Datos Genéticas , Variación Genética , Humanos , Modelos Genéticos , Polimorfismo de Nucleótido Simple , Análisis de Secuencia de ADN , Población Blanca/genéticaRESUMEN
Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.
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Expression quantitative trait locus (eQTL) analysis associates SNPs with gene expression; these relationships can be represented as a bipartite network with association strength as "edge weights" between SNPs and genes. However, most eQTL networks use binary edge weights based on thresholded FDR estimates: definitions that influence reproducibility and downstream analyses. We constructed twenty-nine tissue-specific eQTL networks using GTEx data and evaluated a comprehensive set of network specifications based on false discovery rates, test statistics, and p values, focusing on the degree centrality-a metric of an SNP or gene node's potential network influence. We found a thresholded Benjamini-Hochberg q value weighted by the Z-statistic balances metric reproducibility and computational efficiency. Our estimated gene degrees positively correlate with gene degrees in gene regulatory networks, demonstrating that these networks are complementary in understanding regulation. Gene degrees also correlate with genetic diversity, and heritability analyses show that highly connected nodes are enriched for tissue-relevant traits.
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Redes Reguladoras de Genes , Sitios de Carácter Cuantitativo , Sitios de Carácter Cuantitativo/genética , Reproducibilidad de los Resultados , Redes Reguladoras de Genes/genética , Fenotipo , GenómicaRESUMEN
Enhancers are key players in the spatio-temporal coordination of gene expression during numerous crucial processes, including tissue differentiation across development. Characterizing the transcription factors (TFs) and genes they connect, and the molecular functions underpinned is important to better characterize developmental processes. In plants, the recent molecular characterization of enhancers revealed their capacity to activate the expression of several target genes. Nevertheless, identifying these target genes at a genome-wide level is challenging, particularly for large-genome species, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to plant regulatory networks remains poorly understood. Here, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage (V2-IST) and husks (bracts) at flowering. Using systems biology, we integrate genomic, epigenomic, and transcriptomic data to model the regulatory relationships between TFs and their potential target genes, and identify regulatory modules specific to husk and V2-IST. We show that leaves at the V2-IST stage are characterized by the response to hormones and macromolecules biogenesis and assembly, which are regulated by the BBR/BPC and AP2/ERF TF families, respectively. In contrast, husks are characterized by cell wall modification and response to abiotic stresses, which are, respectively, orchestrated by the C2C2/DOF and AP2/EREB families. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped part of the regulatory network in each tissue, and that MITEs have provided potential new TF binding sites involved in husk tissue-specificity.
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Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson's disease and Alzheimer's disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.
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Regulación de la Expresión Génica , Redes Reguladoras de Genes , Especificidad de Órganos/genética , Caracteres Sexuales , Cromosomas Humanos X/genética , Proteínas de Unión al ADN/genética , Proteínas de Unión al ADN/metabolismo , Femenino , Humanos , Masculino , Factores de Transcripción/genética , Factores de Transcripción/metabolismoRESUMEN
BACKGROUND: MicroRNAs (MiRs) play an important role in the pathogenesis of chronic inflammatory diseases. This study is the first to investigate miR expression profiles in purified CD4+ T lymphocytes and CD14+ monocytes from patients with axial spondyloarthritis (axSpA) using a high-throughput qPCR approach. METHODS: A total of 81 axSpA patients fulfilling the 2009 ASAS classification criteria, and 55 controls were recruited from October 2014 to July 2017. CD14+ monocytes and CD4+ T lymphocytes were isolated from peripheral blood mononuclear cells. MiR expression was investigated by qPCR using the Exiqon Human MiRnome panel I analyzing 372 miRNAs. Differentially expressed miRNAs identified in the discovery cohort were validated in the replication cohort. RESULTS: We found a major difference in miR expression patterns between T lymphocytes and monocytes regardless of the patient or control status. Comparing disease-specific differentially expressed miRs, 13 miRs were found consistently deregulated in CD14+ cells in both cohorts with miR-361-3p, miR-223-3p, miR-484, and miR-16-5p being the most differentially expressed. In CD4+ T cells, 11 miRs were differentially expressed between patients and controls with miR-16-1-3p, miR-28-5p, miR-199a-5p, and miR-126-3p were the most strongly upregulated miRs among patients. These miRs are involved in disease relevant pathways such as inflammation, intestinal permeability or bone formation. Mir-146a-5p levels correlated inversely with the degree of inflammation in axSpA patients. CONCLUSIONS: We demonstrate a consistent deregulation of miRs in both monocytes and CD4+ T cells from axSpA patients, which could contribute to the pathophysiology of the disease with potential interest from a therapeutic perspective.
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Linfocitos T CD4-Positivos/metabolismo , Perfilación de la Expresión Génica/métodos , MicroARNs/genética , Monocitos/metabolismo , Espondiloartritis/genética , Adulto , Antirreumáticos/farmacología , Linfocitos T CD4-Positivos/citología , Linfocitos T CD4-Positivos/efectos de los fármacos , Células Cultivadas , Estudios de Cohortes , Femenino , Humanos , Masculino , Persona de Mediana Edad , Monocitos/citología , Monocitos/efectos de los fármacos , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Columna Vertebral/efectos de los fármacos , Columna Vertebral/metabolismo , Columna Vertebral/patología , Espondiloartritis/tratamiento farmacológico , Espondiloartritis/metabolismoRESUMEN
Genotype-to-phenotype association studies typically use macroscopic physiological measurements or molecular readouts as quantitative traits. There are comparatively few suitable quantitative traits available between cell and tissue length scales, a limitation that hinders our ability to identify variants affecting phenotype at many clinically informative levels. Here we show that quantitative image features, automatically extracted from histopathological imaging data, can be used for image quantitative trait loci (iQTLs) mapping and variant discovery. Using thyroid pathology images, clinical metadata, and genomics data from the Genotype-Tissue Expression (GTEx) project, we establish and validate a quantitative imaging biomarker for immune cell infiltration. A total of 100,215 variants were selected for iQTL profiling and tested for genotype-phenotype associations with our quantitative imaging biomarker. Significant associations were found in HDAC9 and TXNDC5. We validated the TXNDC5 association using GTEx cis-expression QTL data and an independent hypothyroidism dataset from the Electronic Medical Records and Genomics network.
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BACKGROUND: DNA methylation is influenced by both environmental and genetic factors and is increasingly thought to affect variation in complex traits and diseases. Yet, the extent of ancestry-related differences in DNA methylation, their genetic determinants, and their respective causal impact on immune gene regulation remain elusive. RESULTS: We report extensive population differences in DNA methylation between 156 individuals of African and European descent, detected in primary monocytes that are used as a model of a major innate immunity cell type. Most of these differences (~ 70%) are driven by DNA sequence variants nearby CpG sites, which account for ~ 60% of the variance in DNA methylation. We also identify several master regulators of DNA methylation variation in trans, including a regulatory hub nearby the transcription factor-encoding CTCF gene, which contributes markedly to ancestry-related differences in DNA methylation. Furthermore, we establish that variation in DNA methylation is associated with varying gene expression levels following mostly, but not exclusively, a canonical model of negative associations, particularly in enhancer regions. Specifically, we find that DNA methylation highly correlates with transcriptional activity of 811 and 230 genes, at the basal state and upon immune stimulation, respectively. Finally, using a Bayesian approach, we estimate causal mediation effects of DNA methylation on gene expression in ~ 20% of the studied cases, indicating that DNA methylation can play an active role in immune gene regulation. CONCLUSION: Using a system-level approach, our study reveals substantial ancestry-related differences in DNA methylation and provides evidence for their causal impact on immune gene regulation.
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Población Negra/genética , Metilación de ADN , Regulación de la Expresión Génica , Inmunidad Innata , Población Blanca/genética , Adulto , Epigénesis Genética , Humanos , Masculino , Monocitos , Sitios de Carácter CuantitativoRESUMEN
Genetic, epigenetic, and environmental factors contribute to the multifactorial disorder progressive supranuclear palsy (PSP). Here, we study epigenetic changes by genome-wide analysis of DNA from postmortem tissue of forebrains of patients and controls and detect significant (P < 0.05) methylation differences at 717 CpG sites in PSP vs. controls. Four-hundred fifty-one of these sites are associated with protein-coding genes. While differential methylation only affects a few sites in most genes, DLX1 is hypermethylated at multiple sites. Expression of an antisense transcript of DLX1, DLX1AS, is reduced in PSP brains. The amount of DLX1 protein is increased in gray matter of PSP forebrains. Pathway analysis suggests that DLX1 influences MAPT-encoded Tau protein. In a cell system, overexpression of DLX1 results in downregulation of MAPT while overexpression of DLX1AS causes upregulation of MAPT. Our observations suggest that altered DLX1 methylation and expression contribute to pathogenesis of PSP by influencing MAPT.
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Metilación de ADN/genética , Epigénesis Genética/genética , Proteínas de Homeodominio/metabolismo , Parálisis Supranuclear Progresiva/genética , Parálisis Supranuclear Progresiva/patología , Factores de Transcripción/metabolismo , Anciano , Anciano de 80 o más Años , Femenino , Proteínas de Homeodominio/genética , Humanos , Masculino , Factores de Transcripción/genética , Proteínas tau/genética , Proteínas tau/metabolismoRESUMEN
Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.
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Redes Reguladoras de Genes , Activación Transcripcional , Genoma Humano , Humanos , Especificidad de Órganos , Mapas de Interacción de Proteínas , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , TranscriptomaRESUMEN
In this paper, we examined whether meditation practice influences the epigenetic clock, a strong and reproducible biomarker of biological aging, which is accelerated by cumulative lifetime stress and with age-related chronic diseases. Using the Illumina 450K array platform, we analyzed the DNA methylome from blood cells of long-term meditators and meditation-naïve controls to estimate their Intrinsic Epigenetic Age Acceleration (IEAA), using Horvath's calculator. IEAA was similar in both groups. However, controls showed a different IEAA trajectory with aging than meditators: older controls (age≥52) had significantly higher IEAAs compared with younger controls (age <52), while meditators were protected from this epigenetic aging effect. Notably, in the meditation group, we found a significant negative correlation between IEAA and the number of years of regular meditation practice. From our results, we hypothesize that the cumulative effects of a regular meditation practice may, in the long-term, help to slow the epigenetic clock and could represent a useful preventive strategy for age-related chronic diseases. Longitudinal randomized controlled trials in larger cohorts are warranted to confirm and further characterize these findings.