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      Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

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          Abstract

          With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early- to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk; however, the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If the preventive heritable gene therapies were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.

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          Most cited references83

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          The Continuum of Aging and Age-Related Diseases: Common Mechanisms but Different Rates

          Geroscience, the new interdisciplinary field that aims to understand the relationship between aging and chronic age-related diseases (ARDs) and geriatric syndromes (GSs), is based on epidemiological evidence and experimental data that aging is the major risk factor for such pathologies and assumes that aging and ARDs/GSs share a common set of basic biological mechanisms. A consequence is that the primary target of medicine is to combat aging instead of any single ARD/GSs one by one, as favored by the fragmentation into hundreds of specialties and sub-specialties. If the same molecular and cellular mechanisms underpin both aging and ARDs/GSs, a major question emerges: which is the difference, if any, between aging and ARDs/GSs? The hypothesis that ARDs and GSs such as frailty can be conceptualized as accelerated aging will be discussed by analyzing in particular frailty, sarcopenia, chronic obstructive pulmonary disease, cancer, neurodegenerative diseases such as Alzheimer and Parkinson as well as Down syndrome as an example of progeroid syndrome. According to this integrated view, aging and ARDs/GSs become part of a continuum where precise boundaries do not exist and the two extremes are represented by centenarians, who largely avoided or postponed most ARDs/GSs and are characterized by decelerated aging, and patients who suffered one or more severe ARDs in their 60s, 70s, and 80s and show signs of accelerated aging, respectively. In between these two extremes, there is a continuum of intermediate trajectories representing a sort of gray area. Thus, clinically different, classical ARDs/GSs are, indeed, the result of peculiar combinations of alterations regarding the same, limited set of basic mechanisms shared with the aging process. Whether an individual will follow a trajectory of accelerated or decelerated aging will depend on his/her genetic background interacting lifelong with environmental and lifestyle factors. If ARDs and GSs are manifestations of accelerated aging, it is urgent to identify markers capable of distinguishing between biological and chronological age to identify subjects at higher risk of developing ARDs and GSs. To this aim, we propose the use of DNA methylation, N-glycans profiling, and gut microbiota composition to complement the available disease-specific markers.
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            Gene therapy clinical trials worldwide to 2017: An update

            To date, almost 2600 gene therapy clinical trials have been completed, are ongoing or have been approved worldwide. Our database brings together global information on gene therapy clinical activity from trial databases, official agency sources, published literature, conference presentations and posters kindly provided to us by individual investigators or trial sponsors. This review presents our analysis of clinical trials that, to the best of our knowledge, have been or are being performed worldwide. As of our November 2017 update, we have entries on 2597 trials undertaken in 38 countries. We have analysed the geographical distribution of trials, the disease indications (or other reasons) for trials, the proportions to which different vector types are used, and the genes that have been transferred. Details of the analyses presented, and our searchable database are available via The Journal of Gene Medicine Gene Therapy Clinical Trials Worldwide website at: http://www.wiley.co.uk/genmed/clinical. We also provide an overview of the progress being made in gene therapy clinical trials around the world, and discuss key trends since the previous review, namely the use of chimeric antigen receptor T cells for the treatment of cancer and advancements in genome editing technologies, which have the potential to transform the field moving forward.
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              Estimating and interpreting F ST: The impact of rare variants

              In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F ST as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F ST from available data. Considering this confusion, wide variation in published estimates of F ST for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F ST from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F ST that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F ST for a single SNP, (2) combining estimates of F ST across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F ST estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                10 October 2019
                October 2019
                : 20
                : 20
                : 5013
                Affiliations
                [1 ]Centre for Computational Evolution, University of Auckland, Auckland 1010, New Zealand; roli573@ 123456aucklanduni.ac.nz
                [2 ]Department of Computer Science, University of Auckland, Auckland 1010, New Zealand
                Author information
                https://orcid.org/0000-0003-1893-199X
                Article
                ijms-20-05013
                10.3390/ijms20205013
                6834143
                31658652
                f529c044-a513-4980-bf22-1c270e736611
                © 2019 by the author.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 September 2019
                : 08 October 2019
                Categories
                Article

                Molecular biology
                polygenic risk,polymorphism,heritability,polygenic disease,simulation,gene therapy,gene editing,stratification,lifetime risk,admixture

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