Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping

Mollinari, Marcelo; Olukolu, Bode A.; Pereira, Guilherme da Silva; Khan, Awais; Gemenet, Dorcus C.; Yencho, G. Craig; Zeng, Zhao‐Bang

doi:10.1534/g3.119.400620

Cited by 90 publications

(107 citation statements)

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“…The comparison of models with(out) QTL and use of markers per se or haplotypes was carried out using 30,684 SNPs from the same genotyping platform and data set, filtered and processed as described by Mollinari et al (2020) , which were used to develop a 2,708.4 cM phased genetic linkage map for sweetpotato, and subsequent QTL analyses ( Pereira et al 2019; Gemenet et al 2019 ). Sweetpotato BLUEs are provided in Online Resource 5 while potato BLUEs are provided as Online Resource 6 .…”

Section: Resultsmentioning

confidence: 99%

“…By taking advantage of the fully phased integrated linkage map from BT ( Mollinari et al 2020 ), we tested the predictive ability from QTL-informed models. Towards this end, we used the same cross-validation scheme as above, where 80%:20% random samples were used as training and testing populations, respectively, replicated 1,000 times.…”

Section: Methodsmentioning

confidence: 99%

“…However, breeders of auto-polyploid, clonally propagated crops like potato and sweetpotato, which are normally heterogenous and heterozygous, have to ask themselves additional questions and identify trade-off points that enhance the success of GS-assisted breeding ( Slater et al 2016; Endelman et al 2018 ). Potato and sweetpotato present a wide range of meiotic configurations and linkage phases ( Mollinari et al 2020 ). In addition to causing complex gene action effects, allelic and configuration diversity have consequences on genotyping and genotype data quality, which consequently affects downstream analysis for quantitative-genetic parameters required to make high quality breeding decisions.…”

Section: Introductionmentioning

confidence: 99%

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Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

Gemenet

Lindqvist‐Kreuze

Olukolu

et al. 2020

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The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: i) do polyploid crop breeders need to invest more for additional sequencing depth? ii) how many markers are required to make selection decisions? iii) does considering non-additive genetic effects improve predictive ability (PA)? iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction, hence it is possible to get this number at the current sequencing depth from most service providers. We also show that considering dosage information and additive-effects only models had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.Key messagePolypoid crop breeders do not need more investment for sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

Gemenet

Lindqvist‐Kreuze

Olukolu

et al. 2020

Preprint

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“…Genotyping of the mapping population was done using the GBSpoly protocol optimized for sweetpotato and described by Wadl et al (2018). Variant calling and dosage assigning for the hexaploid data was carried out as described in Pereira et al (2019) and Mollinari et al (2019). QTL mapping was carried out based on the phased genetic linkage map (Mollinari et al, 2019) developed using the MAPpoly program (Mollinari and Garcia, 2019) optimized for polyploids.…”

Section: Results Of the Qc Experimentsmentioning

confidence: 99%

“…Variant calling and dosage assigning for the hexaploid data was carried out as described in Pereira et al (2019) and Mollinari et al (2019). QTL mapping was carried out based on the phased genetic linkage map (Mollinari et al, 2019) developed using the MAPpoly program (Mollinari and Garcia, 2019) optimized for polyploids. The genetic linkage map is available interactively at https://gt4sp-genetic-map.shinyapps.io/bt_map/.…”

Section: Results Of the Qc Experimentsmentioning

confidence: 99%

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

Gemenet¹,

Boeck²,

Pereira

et al. 2019

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Experimental error, especially through genotype misclassification and pedigree errors, negatively affects breeding decisions by creating ‘noise’ that compounds the genetic signals for selection. Unlike genotype-by-environment interactions, for which different methods have been proposed to address, the effect of ‘noise’ due to pedigree errors and misclassification has not received much attention in most crops. We used two case studies in sweetpotato, based on data from the International Potato Center’s breeding program to estimate the level of phenotype misclassification and pedigree error and to demonstrate the consequences of such errors when combining phenotypes with the respective genotypes. In the first case study, 27.7% phenotype misclassification was observed when moving genotypes from a diversity panel throughin-vitro, screenhouse and field trialing. Additionally, 22.7% pedigree error was observed from misclassification between and within families. The second case study involving multi-environment testing of a full-sib population and quantitative trait loci (QTL) mapping showed reduced genetic correlations among pairs of environments in mega-environments with higher phenotype misclassification errors when compared to the mega-environments with lower phenotype misclassification errors. Additionally, no QTL could be identified in the low genetic correlation mega-environments. Simulation analysis indicated that phenotype misclassification was more detrimental to QTL detection when compared to missingness in data. The current information is important to inform current and future breeding activities involving genomic-assisted breeding decisions in sweetpotato, and to facilitate putting in place improved workflows that minimize phenotype misclassification and pedigree errors.

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Comparison of different selection methods in the seedling stage of sugarcane breeding

et al. 2020

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Family selection is an important procedure to be considered in the early stage of sugarcane (Saccharum spp.) breeding. Different approaches are available, but few comparative studies are performed in practice. The aim of this study was to evaluate the potential genetic gain when different selection strategies at early sugarcane breeding stages are considered. Two experiments involving the first and second selection stages of the Sugarcane Breeding Program of RIDESA/UFSCar were performed. In the first stage, three selection methods based on the concept of selection between and within families were applied to predict the highest genetic gain, that is, BLUPi: simultaneously contemplates family and individual information for selection; BLUPis: promotes the dynamic allocation of individuals to be selected in each family; BLUP AUS : identifies high potential families and establishes differentiated selection intensities; additionally, mass and random selection methods were also performed. In the second stage, the selected clones were evaluated to compare the realized genetic gain. In the first stage, BLUP AUS had the highest predicted gain from selection (P GS; 12.7%) in tonnes of Pol per hectare (TPH). The BLUPis was highly correlated with BLUP AUS and was efficient. Moreover, BLUPi proved to be economically impracticable since phenotypic evaluations must be performed at the individual level. Family selection via BLUP AUS was equivalent to mass selection probably due to the low coefficient of genetic variation (CV g ≤ 15) among the families. However, the family selection strategy provides extra information for breeders that cannot be ignored; the possibility of studying the combining ability of genotypes for identifying promising parents for future cross combinations.

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Unraveling the Hexaploid Sweetpotato Inheritance Using Ultra-Dense Multilocus Mapping

Cited by 90 publications

References 58 publications

Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

Comparison of different selection methods in the seedling stage of sugarcane breeding

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