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      The influence of evolutionary history on human health and disease

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          Abstract

          Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

          Abstract

          Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.

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          Most cited references217

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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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            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 plant immune system.

              Many plant-associated microbes are pathogens that impair plant growth and reproduction. Plants respond to infection using a two-branched innate immune system. The first branch recognizes and responds to molecules common to many classes of microbes, including non-pathogens. The second responds to pathogen virulence factors, either directly or through their effects on host targets. These plant immune systems, and the pathogen molecules to which they respond, provide extraordinary insights into molecular recognition, cell biology and evolution across biological kingdoms. A detailed understanding of plant immune function will underpin crop improvement for food, fibre and biofuels production.
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                Author and article information

                Contributors
                tony.capra@ucsf.edu
                Journal
                Nat Rev Genet
                Nat Rev Genet
                Nature Reviews. Genetics
                Nature Publishing Group UK (London )
                1471-0056
                1471-0064
                6 January 2021
                : 1-15
                Affiliations
                [1 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Department of Biomedical Informatics, , Vanderbilt University School of Medicine, ; Nashville, TN USA
                [2 ]GRID grid.252890.4, ISNI 0000 0001 2111 2894, Department of Computer Science, , Baylor University, ; Waco, TX USA
                [3 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Vanderbilt Genetics Institute, , Vanderbilt University, ; Nashville, TN USA
                [4 ]Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN USA
                [5 ]GRID grid.152326.1, ISNI 0000 0001 2264 7217, Department of Biological Sciences, , Vanderbilt University, ; Nashville, TN USA
                [6 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Bakar Computational Health Sciences Institute and Department of Epidemiology and Biostatistics, , University of California, ; San Francisco, CA USA
                Author information
                http://orcid.org/0000-0002-5485-1041
                http://orcid.org/0000-0003-0068-6703
                http://orcid.org/0000-0002-7248-6551
                http://orcid.org/0000-0001-9743-1795
                Article
                305
                10.1038/s41576-020-00305-9
                7787134
                33408383
                0a8da3e3-2c2b-48ff-b398-d0beeafa9371
                © Springer Nature Limited 2021

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 26 October 2020
                Categories
                Review Article

                evolutionary genetics,medical genetics,genetic variation

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