Incorporating Genotype Uncertainty into Mark–Recapture-Type Models for Estimating Abundance Using DNA Samples

Janine Wright, Richard Barker, Matthew Schofield, Alain Frantz, Andrea Byrom, Dianne Gleeson

    Research output: Contribution to journalArticle

    58 Citations (Scopus)

    Abstract

    Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger (Meles meles).
    Original languageEnglish
    Pages (from-to)833-840
    Number of pages8
    JournalBiometrics
    Volume65
    DOIs
    Publication statusPublished - 2009

    Fingerprint

    Mark-recapture
    Genotype
    Uncertainty
    DNA
    uncertainty
    genotype
    Mustelidae
    Drop out
    Information Storage and Retrieval
    Homozygote
    Locus
    Population Density
    Animals
    sampling
    Research Design
    Data Augmentation
    Alleles
    Model
    dropouts
    animal identification

    Cite this

    Wright, Janine ; Barker, Richard ; Schofield, Matthew ; Frantz, Alain ; Byrom, Andrea ; Gleeson, Dianne. / Incorporating Genotype Uncertainty into Mark–Recapture-Type Models for Estimating Abundance Using DNA Samples. In: Biometrics. 2009 ; Vol. 65. pp. 833-840.
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    abstract = "Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger (Meles meles).",
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    Incorporating Genotype Uncertainty into Mark–Recapture-Type Models for Estimating Abundance Using DNA Samples. / Wright, Janine; Barker, Richard; Schofield, Matthew; Frantz, Alain; Byrom, Andrea; Gleeson, Dianne.

    In: Biometrics, Vol. 65, 2009, p. 833-840.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Incorporating Genotype Uncertainty into Mark–Recapture-Type Models for Estimating Abundance Using DNA Samples

    AU - Wright, Janine

    AU - Barker, Richard

    AU - Schofield, Matthew

    AU - Frantz, Alain

    AU - Byrom, Andrea

    AU - Gleeson, Dianne

    PY - 2009

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    N2 - Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger (Meles meles).

    AB - Sampling DNA noninvasively has advantages for identifying animals for uses such as mark–recapture modeling that require unique identification of animals in samples. Although it is possible to generate large amounts of data from noninvasive sources of DNA, a challenge is overcoming genotyping errors that can lead to incorrect identification of individuals. A major source of error is allelic dropout, which is failure of DNA amplification at one or more loci. This has the effect of heterozygous individuals being scored as homozygotes at those loci as only one allele is detected. If errors go undetected and the genotypes are naively used in mark–recapture models, significant overestimates of population size can occur. To avoid this it is common to reject low-quality samples but this may lead to the elimination of large amounts of data. It is preferable to retain these low-quality samples as they still contain usable information in the form of partial genotypes. Rather than trying to minimize error or discarding error-prone samples we model dropout in our analysis. We describe a method based on data augmentation that allows us to model data from samples that include uncertain genotypes. Application is illustrated using data from the European badger (Meles meles).

    KW - Allelic dropout

    KW - Bayesian inference

    KW - Mark recapture

    KW - Markov chain Monte Carlo

    KW - Microsatellite

    KW - Noninvasive genetic sampling

    KW - Population estimation.

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