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      Integrative genomic analysis of blood pressure and related phenotypes in rats

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          ABSTRACT

          Despite remarkable progress made in human genome-wide association studies, there remains a substantial gap between statistical evidence for genetic associations and functional comprehension of the underlying mechanisms governing these associations. As a means of bridging this gap, we performed genomic analysis of blood pressure (BP) and related phenotypes in spontaneously hypertensive rats (SHR) and their substrain, stroke-prone SHR (SHRSP), both of which are unique genetic models of severe hypertension and cardiovascular complications. By integrating whole-genome sequencing, transcriptome profiling, genome-wide linkage scans (maximum n=1415), fine congenic mapping (maximum n=8704), pharmacological intervention and comparative analysis with transcriptome-wide association study (TWAS) datasets, we searched causal genes and causal pathways for the tested traits. The overall results validated the polygenic architecture of elevated BP compared with a non-hypertensive control strain, Wistar Kyoto rats (WKY); e.g. inter-strain BP differences between SHRSP and WKY could be largely explained by an aggregate of BP changes in seven SHRSP-derived consomic strains. We identified 26 potential target genes, including rat homologs of human TWAS loci, for the tested traits. In this study, we re-discovered 18 genes that had previously been determined to contribute to hypertension or cardiovascular phenotypes. Notably, five of these genes belong to the kallikrein–kinin/renin–angiotensin systems (KKS/RAS), in which the most prominent differential expression between hypertensive and non-hypertensive alleles could be detected in rat Klk1 paralogs. In combination with a pharmacological intervention, we provide in vivo experimental evidence supporting the presence of key disease pathways, such as KKS/RAS, in a rat polygenic hypertension model.

          Abstract

          Summary: Genome sequencing, transcriptome profiling and genetic mapping, with utilization of human transcriptome-wide association study data, identified 26 potential target genes that regulate blood pressure and related phenotypes in an animal model of hypertension.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              Genetic effects on gene expression across human tissues

              (2017)
              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.
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                Author and article information

                Journal
                Dis Model Mech
                Dis Model Mech
                DMM
                dmm
                Disease Models & Mechanisms
                The Company of Biologists Ltd
                1754-8403
                1754-8411
                1 May 2021
                19 May 2021
                19 May 2021
                : 14
                : 5
                : dmm048090
                Affiliations
                [1 ]Department of Gene Diagnostics and Therapeutics, Research Institute , National Center for Global Health and Medicine , Tokyo 162-8655, Japan
                [2 ]Department of Functional Pathology, Shimane University Faculty of Medicine, Izumo 693-0021, Japan
                [3 ]Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo , Tokyo 113-0033, Japan
                [4 ]Department of Metabolism and Biochemistry, Kyorin University Faculty of Medicine, Tokyo 181-8611, Japan
                Author notes
                [* ]These authors contributed equally to this work
                []Author for correspondence ( nokato@ 123456ri.ncgm.go.jp )

                Handling Editor: Elaine R. Mardis

                Author information
                http://orcid.org/0000-0002-2433-8415
                Article
                DMM048090
                10.1242/dmm.048090
                8188887
                34010951
                19d0419c-e8ed-4b85-bb20-5e9e0366f292
                © 2021. Published by The Company of Biologists Ltd

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 26 October 2020
                : 23 March 2021
                Funding
                Funded by: National Center for Global Health and Medicine, http://dx.doi.org/10.13039/100012319;
                Award ID: 23S302
                Funded by: Japan Society for the Promotion of Science, http://dx.doi.org/10.13039/501100001691;
                Award ID: 26290067
                Funded by: Health and Labor Sciences;
                Award ID: H21-004
                Categories
                Rat as a Disease Model
                Research Article

                Molecular medicine
                gene,locus,transcriptome,blood pressure,multifactorial disease,genome-wide association studies

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