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• W3133593485 abstract "The cost of parentage assignment precludes its application in many selective breeding programmes and molecular ecology studies, and/or limits the circumstances or number of individuals to which it is applied. Pooling samples from more than one individual, and using appropriate genetic markers and algorithms to determine parental contributions to pools, is one means of reducing the cost of parentage assignment. This paper describes and validates a novel maximum likelihood (ML) parentage-assignment method, that can be used to accurately assign parentage to pooled samples of multiple individuals—previously published ML methods are applicable to samples of single individuals only—using low-density single nucleotide polymorphism (SNP) ‘quantitative’ (also referred to as ‘continuous’) genotype data. It is demonstrated with simulated data that, when applied to pools, this ‘quantitative maximum likelihood’ method assigns parentage with greater accuracy than established maximum likelihood parentage-assignment approaches, which rely on accurate discrete genotype calls; exclusion methods; and estimating parental contributions to pools by solving the weighted least squares problem. Quantitative maximum likelihood can be applied to pools generated using either a ‘pooling-for-individual-parentage-assignment’ approach, whereby each individual in a pool is tagged or traceable and from a known and mutually exclusive set of possible parents; or a ‘pooling-by-phenotype’ approach, whereby individuals of the same, or similar, phenotype/s are pooled. Although computationally intensive when applied to large pools, quantitative maximum likelihood has the potential to substantially reduce the cost of parentage assignment, even if applied to pools comprised of few individuals." @default.
• W3133593485 created "2021-03-15" @default.
• W3133593485 creator A5075912912 @default.
• W3133593485 date "2021-03-10" @default.
• W3133593485 modified "2023-10-12" @default.
• W3133593485 title "Maximum likelihood parentage assignment using quantitative genotypes" @default.
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• W3133593485 doi "https://doi.org/10.1038/s41437-021-00421-0" @default.
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