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Maximum likelihood parentage assignment using quantitative genotypes


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.

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Fig. 1: Inputs and parameters required to compute quantitative genotypes (bold borders) from intensity-based assays.
Fig. 2: Inputs and parameters required to undertake maximum likelihood parentage assignment using quantitative genotypes.
Fig. 3: Overlapping histograms of Δ LOD values for assignments to 9600 simulated unknown parents of pools.
Fig. 4: Overlapping histograms of Δ LOD values for assignments to 9600 simulated unknown parents of pools.

Data availability

An R package (R Core Team 2020) entitled ‘SNPpools’, available at, was developed to implement and validate with simulations the quantitative ML method.


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This work was supported by the CSIRO Agriculture and Food project ‘Genomics platforms to assist applied aquaculture breeding’ (AgSIP53). John Henshall shared his R scripts relating to quantitative analysis of low-density SNP data for parentage assignment and estimation of family contributions to pooled samples—code from these scripts was not used in the SNPpools package or for simulations but was used to further the author’s understanding of the methods presented in Henshall et al. (2014). Harry King, Peter Kube, James Kijas, Klara Verbyla, Sonja Dominik shared their insights into the potential application of SNP pooling in selective breeding programmes. James Kijas assisted with comments on draft versions of the manuscript. The CGIAR Research Program on Fish Agrifood Systems (FISH), led by WorldFish and supported by contributors to the CGIAR Trust Fund, financially supported completion of the manuscript subsequent to the author’s departure from CSIRO.

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Correspondence to Matthew Gray Hamilton.

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Associate editor: Jinliang Wang

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Hamilton, M.G. Maximum likelihood parentage assignment using quantitative genotypes. Heredity 126, 884–895 (2021).

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