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

Hamilton, Matthew; Kerr, Richard J.

doi:10.1007/s00122-017-3041-y

Cited by 8 publications

(12 citation statements)

References 21 publications

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“…Genomic prediction has also been advocated as a strategy to accelerate genetic gain in autopolyploid breeding (Cellon et al 2018; Endelman et al 2018; Hamilton and Kerr 2018; Slater et al 2016). One of the topics currently under investigation is how to optimize marker set selection for use in genomic prediction (Ma et al 2016; Sousa et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Quantifying the Power and Precision of QTL Analysis in Autopolyploids Under Bivalent and Multivalent Genetic Models

Bourke

Hackett

Voorrips

et al. 2019

G3 Genes|Genomes|Genetics

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New genotyping technologies, offering the possibility of high genetic resolution at low cost, have helped fuel a surge in interest in the genetic analysis of polyploid species. Nevertheless, autopolyploid species present extra challenges not encountered in diploids and allopolyploids, such as polysomic inheritance or double reduction. Here we investigate the power and precision of quantitative trait locus (QTL) analysis in outcrossing autopolyploids, comparing the results of a model that assumes random bivalent chromosomal pairing during meiosis to one that also allows for multivalents and double reduction. Through a series of simulation studies we found that marginal gains in QTL detection power are achieved using the double reduction model when multivalent pairing occurs. However, when exploring the effect of variable genotypic information across parental homologs, we found that both QTL detection power and precision require high and uniform genotypic information contents. This effect far outweighed considerations regarding bivalent or multivalent pairing (and double reduction) during meiosis. We propose that autopolyploid QTL studies be accompanied by both marker coverage information and per-homolog genotypic information coefficients (GIC). Application of these methods to an autotetraploid potato ( Solanum tuberosum L.) mapping population confirmed our ability to locate and dissect QTL in highly heterozygous outcrossing autotetraploid populations.

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Section: Discussionmentioning

confidence: 99%

Quantifying the Power and Precision of QTL Analysis in Autopolyploids Under Bivalent and Multivalent Genetic Models

Bourke

Hackett

Voorrips

et al. 2019

G3 Genes|Genomes|Genetics

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“…Thus, we have been able to provide a practical solution for these two important problems in fruit cross-breeding. Recently, Hamilton and Kerr [ 14 ] reported an efficient computational method and an R package (“polyAinv”) for the inverse additive relationship matrix—which is essential for the BLUP method—for multiple-ploidy populations. By using their method, the BLUP method and our proposed segregation prediction method can be applied not only to diploid fruit crops, but also to multiple ploidy fruit crops including species of economic importance such as Japanese persimmons, grapes, and so on.…”

Section: Discussionmentioning

confidence: 99%

Predicting segregation of multiple fruit-quality traits by using accumulated phenotypic records in citrus breeding

et al. 2018

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In the breeding of citrus (Citrus spp.), suitable fruit quality is essential for consumer acceptance of new cultivars. To identify parental combinations producing F1 progeny with fruit-quality traits exceeding certain selection criteria, we developed a simple and practical method for predicting multiple-trait segregation in an F1 progeny population. This method uses breeding values of parental genotypes and an additive genetic (co)variance matrix calculated by the best linear unbiased prediction method to construct a model for trait segregation in F1 progeny. To confirm the validity of our proposed method, we calculated the breeding values and additive genetic (co)variances based on phenotypic records on nine fruit-quality traits in 2122 genotypes, and constructed a trait segregation model. Subsequently, we applied the trait segregation model to all pairs of the 2122 genotypes (i.e., 2,252,503 combinations), and predicted the most promising combinations and evaluated their probabilities of producing superior genotypes exceeding the nine fruit-quality traits of satsuma mandarin (Citrus unshiu Marcow.) or ‘Shiranuhi’ (‘Kiyomi’ × ‘Nakano No. 3’ ponkan), two popular citrus cultivars in Japan. We consider these results to be useful not only for selecting good parental combinations for fruit quality or other important traits but also for determining the scale of breeding programs required to achieve specific breeding goals.

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“…In practice, breeding values are usually estimated using restricted maximum likelihood (REML) to solve mixed model equations, requiring the generation of an inverse additive relationship matrix A -1 , also called the numerator relationship matrix. The form of A -1 depends on, among other things, whether the inheritance is polysomic or disomic, and whether double reduction occurs ( Kerr et al, 2012 ; Amadeu et al, 2016 ; Hamilton and Kerr, 2017 ). The R package AGHmatrix was developed in order to compute the appropriate A matrix for autotetraploids with a known pedigree ( Amadeu et al, 2016 ), using theory developed in ( Kerr et al, 2012 ).…”

Section: Quantitative Trait Analysis and Genomic Selectionmentioning

confidence: 99%

“…In applying their approach to an autotetraploid blueberry ( Vaccinium corymbosum L.) population, the authors determined the A matrix under various levels of double reduction, afterwards selecting the model which maximized the likelihood of the data ( Amadeu et al, 2016 ). More recently, an alternative R package polyAinv was released which computes A -1 as well as the kinship matrix K and the inbreeding coefficients F ( Hamilton and Kerr, 2017 ). polyAinv claims to be applicable to any ploidy level (rather than just autotetraploids) and can accommodate sex-based differences in IBD probabilities ( Hamilton and Kerr, 2017 ).…”

Section: Quantitative Trait Analysis and Genomic Selectionmentioning

confidence: 99%

“…More recently, an alternative R package polyAinv was released which computes A -1 as well as the kinship matrix K and the inbreeding coefficients F ( Hamilton and Kerr, 2017 ). polyAinv claims to be applicable to any ploidy level (rather than just autotetraploids) and can accommodate sex-based differences in IBD probabilities ( Hamilton and Kerr, 2017 ). Like AGHmatrix, it also incorporates double reduction in its calculations.…”

Section: Quantitative Trait Analysis and Genomic Selectionmentioning

confidence: 99%

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Tools for Genetic Studies in Experimental Populations of Polyploids

et al. 2018

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Polyploid organisms carry more than two copies of each chromosome, a condition rarely tolerated in animals but which occurs relatively frequently in the plant kingdom. One of the principal challenges faced by polyploid organisms is to evolve stable meiotic mechanisms to faithfully transmit genetic information to the next generation upon which the study of inheritance is based. In this review we look at the tools available to the research community to better understand polyploid inheritance, many of which have only recently been developed. Most of these tools are intended for experimental populations (rather than natural populations), facilitating genomics-assisted crop improvement and plant breeding. This is hardly surprising given that a large proportion of domesticated plant species are polyploid. We focus on three main areas: (1) polyploid genotyping; (2) genetic and physical mapping; and (3) quantitative trait analysis and genomic selection. We also briefly review some miscellaneous topics such as the mode of inheritance and the availability of polyploid simulation software. The current polyploid analytic toolbox includes software for assigning marker genotypes (and in particular, estimating the dosage of marker alleles in the heterozygous condition), establishing chromosome-scale linkage phase among marker alleles, constructing (short-range) haplotypes, generating linkage maps, performing genome-wide association studies (GWAS) and quantitative trait locus (QTL) analyses, and simulating polyploid populations. These tools can also help elucidate the mode of inheritance (disomic, polysomic or a mixture of both as in segmental allopolyploids) or reveal whether double reduction and multivalent chromosomal pairing occur. An increasing number of polyploids (or associated diploids) are being sequenced, leading to publicly available reference genome assemblies. Much work remains in order to keep pace with developments in genomic technologies. However, such technologies also offer the promise of understanding polyploid genomes at a level which hitherto has remained elusive.

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

Cited by 8 publications

References 21 publications

Quantifying the Power and Precision of QTL Analysis in Autopolyploids Under Bivalent and Multivalent Genetic Models

Quantifying the Power and Precision of QTL Analysis in Autopolyploids Under Bivalent and Multivalent Genetic Models

Predicting segregation of multiple fruit-quality traits by using accumulated phenotypic records in citrus breeding

Tools for Genetic Studies in Experimental Populations of Polyploids

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