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Computation of the inverse additive relationship matrix for autopolyploid and multiple-ploidy populations

Citation

Hamilton, MG and Kerr, R, Computation of the inverse additive relationship matrix for autopolyploid and multiple-ploidy populations, Theoretical and Applied Genetics, 131, (4) pp. 851-860. ISSN 0040-5752 (2018) [Refereed Article]


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DOI: doi:10.1007/s00122-017-3041-y

Abstract

Many important agronomic, horticultural, ornamental, forestry, and aquaculture species are autopolyploids. However, the adoption of restricted maximum likelihood (REML), for estimating co/variance components, and best linear unbiased prediction (BLUP), for predicting breeding values, has been hampered in autopolyploid breeding by the absence of an appropriate means of generating the inverse additive relationship matrix (A (-1) ). This paper defines rules to generate the A (-1) of autopolyploid populations comprised of individuals of the same or different ploidy-levels, including populations exhibiting (1) odd-numbered ploidy levels (e.g. triploids), (2) sex-based differences in the probability that gametic genes are identical by descent and (3) somatic chromosome doubling. Inbreeding, due to double reduction, in autopolyploid founders in the absence of mating among relatives is also accounted for. A previously defined approach is modified, whereby rules are initially defined to build an inverse matrix of kinship coefficients (K (-1) ), which is then used to generate A (-1) . An R package (polyAinv; to implement these rules has been developed and examples of analyses provided. The adoption of REML and BLUP methods made possible by these new rules has the potential to provide further insights into the quantitative genetic architecture of autopolyploid and multiple-ploidy populations, improve estimates of breeding values, and increase genetic gains made through recurrent selection.

Item Details

Item Type:Refereed Article
Keywords:numerator relationship, matrix, polyploidy
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Forestry sciences
Research Field:Tree improvement (incl. selection and breeding)
Objective Division:Plant Production and Plant Primary Products
Objective Group:Forestry
Objective Field:Hardwood plantations
UTAS Author:Hamilton, MG (Dr Matthew Hamilton)
UTAS Author:Kerr, R (Dr Richard Kerr)
ID Code:152488
Year Published:2018
Web of Science® Times Cited:4
Deposited By:Plant Science
Deposited On:2022-08-19
Last Modified:2022-08-19
Downloads:0

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