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      High-dimensional phenotyping to define the genetic basis of cellular morphology

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          Abstract

          The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combine genomic sequencing and high-content imaging approaches on iPSCs from 297 unique donors to investigate the relationship between genetic variants and cellular morphology to map what we term cell morphological quantitative trait loci (cmQTLs). We identify novel associations between rare protein altering variants in WASF2, TSPAN15, and PRLR with several morphological traits related to cell shape, nucleic granularity, and mitochondrial distribution. Knockdown of these genes by CRISPRi confirms their role in cell morphology. Analysis of common variants yields one significant association and nominate over 300 variants with suggestive evidence (P < 10 −6) of association with one or more morphology traits. We then use these data to make predictions about sample size requirements for increasing discovery in cellular genetic studies. We conclude that, similar to molecular phenotypes, morphological profiling can yield insight about the function of genes and variants.

          Abstract

          Characterizing how genetic variation impacts cell morphology can provide an important links between disease association and cellular function. Here the authors identified the morphological impacts of genomic variants by generating high-throughput morphological profiling and whole genome sequencing data on iPSCs from 297 donors.

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          Most cited references62

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          The mutational constraint spectrum quantified from variation in 141,456 humans

          Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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            Second-generation PLINK: rising to the challenge of larger and richer datasets

            PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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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

                Contributors
                shsingh@broadinstitute.org
                rnehme@broadinstitute.org
                soumya@broadinstitute.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                6 January 2024
                6 January 2024
                2024
                : 15
                : 347
                Affiliations
                [1 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, ( https://ror.org/05a0ya142) Cambridge, MA USA
                [2 ]Department of Stem Cell and Regenerative Biology, Harvard University, ( https://ror.org/03vek6s52) Cambridge, MA USA
                [3 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Centre for Gene Therapy and Regenerative Medicine, King’s College, ; London, UK
                [4 ]Center for Data Sciences, Brigham and Women’s Hospital and Harvard Medical School, ( https://ror.org/04b6nzv94) Boston, MA USA
                [5 ]Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, ( https://ror.org/04b6nzv94) Boston, MA USA
                [6 ]Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, ( https://ror.org/04b6nzv94) Boston, MA USA
                [7 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, ( https://ror.org/05a0ya142) Cambridge, MA USA
                [8 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                [9 ]Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY USA
                [10 ]Imaging Platform, Broad Institute of MIT and Harvard, ( https://ror.org/05a0ya142) Cambridge, MA USA
                [11 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, ( https://ror.org/002pd6e78) Boston, MA USA
                [12 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [13 ]Halıcıoğlu Data Science Institute, University of California, ( https://ror.org/05t99sp05) La Jolla, CA USA
                [14 ]Department of Medicine, University of California, ( https://ror.org/05t99sp05) La Jolla, CA USA
                [15 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Genetics, , Harvard Medical School, ; Boston, MA USA
                [16 ]GRID grid.5379.8, ISNI 0000000121662407, Centre for Genetics and Genomics Versus Arthritis, , Manchester Academic Health Science Centre, University of Manchester, ; Manchester, UK
                Author information
                http://orcid.org/0000-0002-9032-8207
                http://orcid.org/0000-0002-2347-8985
                http://orcid.org/0000-0001-9640-9318
                http://orcid.org/0000-0002-0503-9348
                http://orcid.org/0000-0002-6437-2458
                http://orcid.org/0000-0002-5975-2851
                http://orcid.org/0000-0003-4436-8467
                http://orcid.org/0000-0002-0867-8364
                http://orcid.org/0000-0002-6954-8184
                http://orcid.org/0000-0003-2730-9668
                http://orcid.org/0000-0003-1555-8261
                http://orcid.org/0000-0003-3150-3025
                http://orcid.org/0000-0001-7215-3311
                http://orcid.org/0000-0002-1901-8265
                Article
                44045
                10.1038/s41467-023-44045-w
                10771466
                38184653
                cca3e502-f777-475c-a644-9e0ad9ca7808
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 February 2023
                : 28 November 2023
                Funding
                Funded by: Broad Institute of MIT and Harvard Variant to functon(V2F) Initiative
                Funded by: FundRef https://doi.org/10.13039/100000923, Silicon Valley Community Foundation (SVCF);
                Award ID: 2020-225720
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: GM122547
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000025, U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH);
                Award ID: U01 MH115727
                Award Recipient :
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                © Springer Nature Limited 2024

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                functional genomics,induced pluripotent stem cells
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                functional genomics, induced pluripotent stem cells

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