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      Clark (2023) and the Persistence of Hereditarian Fallacies

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          Abstract

          Clark (2023) considers the similarity in socioeconomic status between relatives, drawing on records spanning four centuries in England. The paper adapts a classic quantitative genetics model in order to argue the fit of the model to the data suggests that: (1) variation in socioeconomic status is largely determined by additive genetic variation; (2) contemporary English people “remain correlated in outcomes with their lineage relatives in exactly the same way as in preindustrial England”; and (3) social mobility has remained static over this time period due to strong assortative mating on a “social genotype.” These conclusions are based on a misconstrual of model parameters, which conflates genetic and non-genetic transmission (e.g. of wealth) within families. As we show, there is strong confounding of genetic and non-genetic sources of similarity in these data. Inconsistent with claims (2) and (3), we show that familial correlations in status are variable—generally decreasing—through the time period analyzed. Lastly, we find that statistical artifacts substantially bias estimates of familial correlations in the paper. Overall, Clark (2023) provides no information about the relative contribution of genetic and non-genetic factors to social status.

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          Efficient Bayesian mixed model analysis increases association power in large cohorts

          Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts, and may not optimize power. All existing methods require time cost O(MN2) (where N = #samples and M = #SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here, we present a far more efficient mixed model association method, BOLT-LMM, which requires only a small number of O(MN)-time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to nine quantitative traits in 23,294 samples from the Women’s Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for GWAS in large cohorts.
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            Pseudoreplication and the Design of Ecological Field Experiments

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              Variable prediction accuracy of polygenic scores within an ancestry group

              Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                03 November 2023
                : 2023.11.01.565061
                Affiliations
                [1 ]Department of Botany, University of Wyoming, Laramie, WY, USA
                [2 ]Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
                [3 ]Department of Population Health, University of Texas at Austin, Austin, TX, USA
                [4 ]Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA
                Author notes
                [*]

                these authors contributed equally

                Author information
                http://orcid.org/0000-0002-2583-2503
                http://orcid.org/0000-0002-1363-872X
                http://orcid.org/0000-0002-3655-748X
                Article
                10.1101/2023.11.01.565061
                10635045
                37961599
                eef256ca-1edf-4423-8227-33c543b571eb

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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