scholarly journals Somatically Mutated Genes Under Positive and Negative Selection Found by Transcriptome Sequence Analysis Include Oncogene and Tumor Suppressor Candidates

2018 ◽  
Author(s):  
Casey W. Drubin ◽  
Avinash Ramu ◽  
Nicole B. Rockweiler ◽  
Donald F. Conrad
Keyword(s):  
Somatic Mutation ◽  
Negative Selection ◽  
Somatic Mutations ◽  
Clonal Selection ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide ◽  
Mutation Burden

AbstractIntroductionOncogenic somatic mutations confer proliferative advantage and undergo positive clonal selection. We developed software and applied new analytical approaches to identify: (1) somatic mutations in diverse tissues, (2) somatically mutated genes under positive and negative selection, (3) post-transcriptional modifications in the mitochondrial transcriptome, and (4) inherited germline alleles predisposing people to higher somatic mutation burden or higher levels of post-transcriptional modification.MethodsTranscriptome sequence data (Genotype Tissue Expression project) for 7051 tissue samples from 549 postmortem donors and representing 44 tissue types were used. Germline mutations were inferred from whole-exome DNA sequencing and SNP arrays. DNA somatic mutations were inferred from variant allele frequencies (VAF) in RNA-seq data. Post-transcriptional modifications were inferred from Polymorphism Information Content (PIC) at the p9 sites of mitochondrial tRNA sequences. Positive and negative clonal selection was evaluated using a nonsynonomous/synonomous mutation rate (dN/dS) model. Genome-wide association studies (GWAS) were assessed with mitochondrial PIC for post-transcriptional modification level, or using the total number of somatic mutations observed per donor for somatic mutation burden.ResultsOur dN/dS model identified 78 genes under negative selection for somatic mutations (dN/dS < 1, padj< 0.05) and 14 under positive selection (dN/dS > 1, padj<0.05). Our GWAS identified 2 sites associated with post-transcriptional modification (1 approaching significance with p=5.99×10−8, 1 with p<5×10−8) and ∼20 sites associated with somatic mutation burden (p<5×10−8).ConclusionsTo our knowledge these are the first genome-wide association studies on normal somatic mutation burden. These studies were an attempt to increase understanding of the somatic mutation process. Our work identified somatic mutations at the global organismal level that may promote cell proliferation in a tissue-specific manner. By identifying tissue-specific mutations in actively expressed genes that appear before cancer phenotype is detected, this work also identifies gene candidates that might initiate tumorigenesis.

scholarly journals Annotation of loci from genome-wide association studies using tissue-specific quantitative interaction proteomics

2014 ◽  
Vol 11 (8) ◽  
pp. 868-874 ◽  
Author(s):  
Alicia Lundby ◽  
◽  
Elizabeth J Rossin ◽  
Annette B Steffensen ◽  
Moshe Rav Acha ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide ◽  
Interaction Proteomics

scholarly journals A Gene Co-expression Network-based Analysis of Multiple Brain Tissues Reveals Novel Genes and Molecular Pathways Underlying Major Depression

2019 ◽  
Author(s):  
Zachary F Gerring ◽  
Eric R Gamazon ◽  
Eske M Derks ◽  
Keyword(s):  
Major Depression ◽  
Whole Blood ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Risk Genes ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Modules ◽  
Brain Tissues

AbstractMajor depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of individual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages multi-tissue eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits.Author summaryAlthough genome-wide association studies have identified genetic risk variants associated with major depression, our understanding of the mechanisms through which they influence disease susceptibility remain largely unknown. Genetic risk variants are highly enriched in non-coding regions of the genome and affect gene expression. Genes are known to interact and regulate the activity of one another and form highly organized (co-expression) networks. Here, we generate tissue-specific gene co-expression networks, each containing groups of functionally related genes or “modules”, to delineate interactions between genes and thereby facilitate the identification of gene processes in major depression. We developed and applied a novel research methodology (called “eMagma”) which integrates genetic and transcriptomic information in a tissue-specific analysis and tests for their enrichment in gene co-expression modules. Using this novel approach, we identified gene modules in multiple tissues that are both enriched with major depression genetic association signals and biologically meaningful pathways. We also show gene modules are strongly preserved across brain regions, but not in whole blood, suggesting blood may not be a useful tissue surrogate for the genetic dissection of major depression. Our novel analytical framework provides fundamental insights into the functional genetics major depression and can be applied to other neuropsychiatric disorders.

scholarly journals Leveraging Gene Co-expression Patterns to Infer Trait-Relevant Tissues in Genome-wide Association Studies

2019 ◽  
Author(s):  
Lulu Shang ◽  
Jennifer A. Smith ◽  
Xiang Zhou
Keyword(s):  
Autoimmune Diseases ◽  
Single Cell ◽  
Neurological Disorders ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide ◽  
Rnaseq Data

AbstractGenome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect sizes for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. Our results also provide empirical evidence supporting one hypothesis of the omnigenic model: that trait-relevant gene co-expression networks underlie disease etiology.

scholarly journals Integrative tissue-specific functional annotations in the human genome provide novel insights on many complex traits and improve signal prioritization in genome wide association studies

2015 ◽  
Author(s):  
Qiongshi Lu ◽  
Ryan Lee Powles ◽  
Qian Wang ◽  
Beixin Julie He ◽  
Hongyu Zhao
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Functional Annotations ◽  
Functional Regions ◽  
Genome Wide ◽  
Multiple Resolutions ◽  
Artery Disease

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline.

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