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      Functional dynamic genetic effects on gene regulation are specific to particular cell types and environmental conditions

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          Abstract

          Genetic effects on gene expression and splicing can be modulated by cellular and environmental factors; yet interactions between genotypes, cell type, and treatment have not been comprehensively studied together. We used an induced pluripotent stem cell system to study multiple cell types derived from the same individuals and exposed them to a large panel of treatments. Cellular responses involved different genes and pathways for gene expression and splicing and were highly variable across contexts. For thousands of genes, we identified variable allelic expression across contexts and characterized different types of gene-environment interactions, many of which are associated with complex traits. Promoter functional and evolutionary features distinguished genes with elevated allelic imbalance mean and variance. On average, half of the genes with dynamic regulatory interactions were missed by large eQTL mapping studies, indicating the importance of exploring multiple treatments to reveal previously unrecognized regulatory loci that may be important for disease.

          eLife digest

          The activity of the genes in a cell depends on the type of cell they are in, the interactions with other genes, the environment and genetics. Active genes produce a greater number of mRNA molecules, which act as messenger molecules to instruct the cell to produce proteins. The amount of mRNA molecules in cells can be measured to assess the levels of gene activity. Genes produce mRNAs through a process called transcription, and the collection of all the mRNA molecules in a cell is called the transcriptome.

          Cells obtained from human samples can be grown in the lab under different conditions, and this can be used to transform them into different types of cells. These cells can then be exposed to different treatments – such as specific chemicals – to understand how the environment affects them. Cells derived from different people may respond differently to the same treatment based on their unique genetics. Exposing different types of cells from many people to different treatments can help explain how genetics, the environment and cell type affect gene activity.

          Findley et al. grew three different types of cells from six different people in the lab. The cells were exposed to 28 different treatments, which reflect different environmental changes. Studying all these different factors together allowed Findley et al. to understand how genetics, cell type and environment affect the activity of over 53,000 genes. Around half of the effects due to an interaction between genetics and the environment and had not been seen in other larger studies of the transcriptome. Many of these newly observed changes are in genes that have connections to different diseases, including heart disease.

          The results of Findley et al. provide evidence indicating to which extent lifestyle and the environment can interact with an individual’s genetic makeup to impact gene activity and long-term health. The more researchers can understand these factors, the more useful they can be in helping to predict, detect and treat illnesses. The findings also show how genes and the environment interact, which may be relevant to understanding disease development. There is more work to be done to understand a wider range of environmental factors across more cell types. It will also be important to establish how this work on cells grown in the lab translates to human health.

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          Most cited references128

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Gene Ontology: tool for the unification of biology

              Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                14 May 2021
                2021
                : 10
                : e67077
                Affiliations
                [1 ]Center for Molecular Medicine and Genetics, Wayne State University DetroitUnited States
                [2 ]Department of Human Genetics, University of Chicago ChicagoUnited States
                [3 ]Department of Medicine, University of Chicago ChicagoUnited States
                [4 ]Department of Computer Science, UCLA Los AngelesUnited States
                [5 ]Department of Human Genetics, UCLA Los AngelesUnited States
                [6 ]Department of Computational Medicine, UCLA Los AngelesUnited States
                [7 ]Department of Biostatistics, University of Michigan Ann ArborUnited States
                [8 ]Center for Individualized and Genomic Medicine Research, Henry Ford Hospital DetroitUnited States
                [9 ]Department of Obstetrics and Gynecology, Wayne State University DetroitUnited States
                University of Michigan United States
                University of Michigan United States
                University of Michigan United States
                University of Tartu Estonia
                Author notes
                [†]

                Department of Biochemistry and Molecular Biology, University of Texas Medical Branch at Galveston, Galveston, United States.

                Author information
                https://orcid.org/0000-0001-9922-3076
                https://orcid.org/0000-0003-1485-320X
                https://orcid.org/0000-0002-1262-2275
                https://orcid.org/0000-0001-8284-8926
                https://orcid.org/0000-0001-8252-9052
                Article
                67077
                10.7554/eLife.67077
                8248987
                33988505
                43b0b77f-60d0-4a3d-97ed-6c2a4107f742
                © 2021, Findley et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 February 2021
                : 13 May 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R01GM109215
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R35GM131726
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: F30GM131580
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R35GM125055
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100008982, National Science Foundation;
                Award ID: III-1705121
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Chromosomes and Gene Expression
                Genetics and Genomics
                Custom metadata
                Investigation of dynamic gene regulation across cell types and environments reveals new GxE regulatory loci that are important for disease.

                Life sciences
                allele specific expression,gxe,gene regulation,cardiovascular disease,ipsc,regulatory variants,human

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