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      Transcriptome-wide association study and eQTL colocalization identify potentially causal genes responsible for human bone mineral density GWAS associations

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          Abstract

          Genome-wide association studies (GWASs) for bone mineral density (BMD) in humans have identified over 1100 associations to date. However, identifying causal genes implicated by such studies has been challenging. Recent advances in the development of transcriptome reference datasets and computational approaches such as transcriptome-wide association studies (TWASs) and expression quantitative trait loci (eQTL) colocalization have proven to be informative in identifying putatively causal genes underlying GWAS associations. Here, we used TWAS/eQTL colocalization in conjunction with transcriptomic data from the Genotype-Tissue Expression (GTEx) project to identify potentially causal genes for the largest BMD GWAS performed to date. Using this approach, we identified 512 genes as significant using both TWAS and eQTL colocalization. This set of genes was enriched for regulators of BMD and members of bone relevant biological processes. To investigate the significance of our findings, we selected PPP6R3, the gene with the strongest support from our analysis which was not previously implicated in the regulation of BMD, for further investigation. We observed that Ppp6r3 deletion in mice decreased BMD. In this work, we provide an updated resource of putatively causal BMD genes and demonstrate that PPP6R3 is a putatively causal BMD GWAS gene. These data increase our understanding of the genetics of BMD and provide further evidence for the utility of combined TWAS/colocalization approaches in untangling the genetics of complex traits.

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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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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              The Genotype-Tissue Expression (GTEx) project.

              Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
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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
                23 November 2022
                2022
                : 11
                : e77285
                Affiliations
                [1 ] Center for Public Health Genomics, School of Medicine, University of Virginia ( https://ror.org/0153tk833) Charlottesville United States
                [2 ] Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia ( https://ror.org/0153tk833) Charlottesville United States
                [3 ] Department of Radiation Oncology, University of Virginia ( https://ror.org/0153tk833) Charlottesville United States
                [4 ] Department of Microbiology, Immunology, and Cancer Biology, School of Medicine, University of Virginia ( https://ror.org/0153tk833) Charlottesville United States
                [5 ] Department of Orthopedics, Anschutz Medical Campus, University of Colorado ( https://ror.org/03wmf1y16) Aurora United States
                [6 ] Department of Public Health Sciences, School of Medicine, University of Virginia ( https://ror.org/0153tk833) Charlottesville United States
                [7 ] Department of Mechanical Engineering, Boston University ( https://ror.org/05qwgg493) Boston United States
                [8 ] Department of Mechanical Engineering, University of Colorado Boulder ( https://ror.org/02ttsq026) Boulder United States
                [9 ] Department of Orthopaedic Surgery, Boston University Medical Center ( https://ror.org/05qwgg493) Boston United States
                Baylor College of Medicine ( https://ror.org/02pttbw34) United States
                Icahn School of Medicine at Mount Sinai ( https://ror.org/04a9tmd77) United States
                Baylor College of Medicine ( https://ror.org/02pttbw34) United States
                Baylor College of Medicine ( https://ror.org/02pttbw34) United States
                University of Bristol ( https://ror.org/0524sp257) United Kingdom
                Author information
                https://orcid.org/0000-0001-9816-8044
                https://orcid.org/0000-0002-6748-4711
                Article
                77285
                10.7554/eLife.77285
                9683789
                36416764
                cb0c0340-79c7-4666-abed-271cd96150cf
                © 2022, Al-Barghouthi 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
                : 24 January 2022
                : 16 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000069, National Institute of Arthritis and Musculoskeletal and Skin Diseases;
                Award ID: AR071657
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000097, National Center for Research Resources;
                Award ID: S10RR021072
                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
                Computational and Systems Biology
                Genetics and Genomics
                Custom metadata
                Systems genetics approaches identify potentially causal bone mineral density genes.

                Life sciences
                twas,gwas,osteoporosis,eqtl,human,mouse
                Life sciences
                twas, gwas, osteoporosis, eqtl, human, mouse

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