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SNP-based heritability and selection analyses: Improved models and new results

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DOI

  • Doug Speed
  • Anubhav Kaphle, Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
  • ,
  • David J Balding, UCL, University College London, University of London, Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
Complex-trait genetics has advanced dramatically through methods to estimate the heritability tagged by SNPs, both genome-wide and in genomic regions of interest such as those defined by functional annotations. The models underlying many of these analyses are inadequate, and consequently many SNP-heritability results published to date are inaccurate. Here, we review the modelling issues, both for analyses based on individual genotype data and association test statistics, highlighting the role of a low-dimensional model for the heritability of each SNP. We use state-of-art models to present updated results about how heritability is distributed with respect to functional annotations in the human genome, and how it varies with allele frequency, which can reflect purifying selection. Our results give finer detail to the picture that has emerged in recent years of complex trait heritability widely dispersed across the genome. Confounding due to population structure remains a problem that summary statistic analyses cannot reliably overcome.
OriginalsprogEngelsk
Artikelnummer2100170
TidsskriftBioEssays
Vol/bind44
Nummer5
Sider (fra-til)e2100170
ISSN0265-9247
DOI
StatusUdgivet - maj 2022

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