Biology subjects

Kulakow, P.

Publications and source records attributed to Kulakow, P..

10 recordsLinked to original sources

bioRxiv2022

Validation of SNP markers for marker-assisted selection of genotypes with increased carotenoid and dry matter contents in cassava

Provitamin A biofortification and increased dry matter content are important breeding targets in cassava improvement programs worldwide. Biofortified varieties contribute to the alleviation of provitamin A deficiency, a leading cause of preventable blindness in developing countries. Dry matter content is a major component of dry yield and thus underlies overall variety performance and acceptability by growers, processors, and consumers. SNP markers linked to these traits have recently been discovered through several genome-wide association studies but have not been deployed for marker-assisted selection (MAS). Assessment of marker performance in diverse genetic backgrounds is an important step towards their deployment for routine MAS. In the present study, seven previously identified markers for these traits were converted to a robust set of uniplex allele-specific PCR assays and validated in two independent pre-breeding and breeding populations. These assays were efficient in discriminating marker genotypic classes and had an average call rate greater than 98%. A high correlation was observed between the predicted and observed carotenoid content as inferred by root yellowness intensity in the breeding (r = 0.92) and pre-breeding (r = 0.95) populations. On the other hand, dry matter content-markers had moderately low predictive accuracy in both populations (r < 0.40) due to the more quantitative nature of the trait. This work confirmed marker effectiveness in multiple backgrounds, therefore, further strengthening its value in cassava biofortification to ensure nutritional security as well as dry matter content productivity. Our study provides a framework to guide future marker development, thus leading to more routine use of markers in MAS in cassava improvement programs.

genomics↗

bioRxiv2021

Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. We were able to predict all genetic variances and most covariances with non-zero accuracy. We also tested a directional dominance model and found significant inbreeding depression for most traits and a boost in total merit accuracy for root yield. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with excellent breeding values) and varieties (progeny with top performance). Author SummaryBreeders typically use truncation selection and invest significant resources evaluating progeny to find exceptional genotypes. We extended genetic variance and trait covariance prediction to include dominance and predicting the multivariate selection index variance. We enable mate selection based on potential to produce future parents (progeny with excellent breeding values) and/or varieties (progeny with top performance). Using cross-validation, we demonstrate that genetic variances and covariances can be predicted with non-zero accuracy in cassava, a staple tropical root crop.

genetics↗

bioRxiv2020

Silver thiosulfate and Benzyladenine in combination with pruning additively feminizes cassava flowers and modulates transcriptome

Cassava, a tropical storage-root crop, is a major source of food security for millions in the tropics. Cassava breeding however is hindered by the poor development of flowers and female flowers in particular, since flower development is strongly skewed towards male flowers. Our objectives were to test plant growth regulator and pruning treatments for their effectiveness in field conditions in improving flower production and fruit set in cassava. Pruning the fork type branches that arise at the shoot apex immediately below newly formed inflorescences stimulated inflorescence and floral development. The anti-ethylene silver thiosulfate (STS) also increased flower abundance. Both pruning and STS increased flower numbers without influencing sex ratios. In contrast, the cytokinin benzyladenine (BA) feminized flowers without increasing flower abundance. Combining pruning and STS treatments led to an additive increase in flower abundance; with the addition of BA, over 80% of flowers were females. This three-way treatment combination of pruning+STS+BA also led to an increase in fruit development. Transcriptomic analysis of gene expression in tissues of the apical region and developing inflorescence revealed that the enhancement of female flower development by STS+BA was accompanied by the downregulation in of several genes associated with repression of flowering, including Tempranillo 1 (TEM1), GA receptor GID1b, and ABA signaling genes ABI1 and PP2CA. We conclude that treatments with pruning, STS and BA create widespread changes on the network of hormone signaling and regulatory factors beyond ethylene and cytokinin.

plant biology↗

bioRxiv2020

Genome-wide association analysis reveals new insights into the genetic architecture of defensive, agro-morphological and quality-related traits in cassava

Cassava (Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5,310 clones developed at the International Institute of Tropical Agriculture - Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type). We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.

genetics↗

bioRxiv2019

Improving root characterisation for genomic prediction in cassava

Cassava is widely cultivated due to its high drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots within and between genotypes poses significant constraints on harvesting and post-harvest processing. Routine assessment of storage root size and shape in breeding plots relies on visual scores.\n\nHere, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program for root shape and size-related traits using image analysis of storage root photographs taken in the field.\n\nIn our study, using univariate genome-wide association analysis, we detected for most shape and size related traits, significant QTL regions located on chromosomes 1 and 12. The QTL region on chromosome 12 has been previously associated, using IITA breeding populations, to cassava mosaic disease (CMD) resistance.\n\nBecause the uniformity in size and shape of cassava roots is an important breeding goal, we calculated the standard deviation of individual root measurements per clone. The use of standard deviation measurements allowed the identification of new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Using genomic prediction cross validation, the accuracies of root size and shape-related traits were lower than those previously reported for dry matter content (DM) and cassava mosaic virus resistance (CMD). Predictive accuracies of the mean values of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies in the C1 population. This study aimed to evaluate the feasibility of the image phenotyping protocol and to assess the use of genome-wide analyses for size and shape image-extracted traits. The methodology described here and the results obtained in this study are promising and open up the opportunity to apply high-throughput methods in cassava.

genomics↗

bioRxiv2019

Introgressed Manihot glaziovii Alleles in Modern Cassava Germplasm Benefit Important Traits and Are Under Balancing Selection

1.Introgression of alleles from wild relatives has often been adaptive, usually for disease resistance, in plant breeding. However, the significance of historical hybridization events in modern breeding is often not clear. Cassava (Manihot esculenta) is among the most important staple foods in the world, sustaining hundreds of millions of people in the tropics, especially in sub-Saharan Africa. Widespread genotyping makes cassava a model for clonally-propagated root and tuber crops in the developing world and provides an opportunity to study the modern benefits and consequences of historical introgression. We detected large introgressed M. glaziovii genome-segments in a collection of 2742 modern cassava landraces and elite germplasm, the legacy of 1930s era breeding to combat epidemics disease. African landraces and improved varieties were on average 3.8% (max 13.6%) introgressed. Introgressions accounted for significant (mean 20%, max 56%) portion of the heritability of tested traits M. glaziovii alleles on the distal 10Mb of chr. 1 increased dry matter and root number. On chr. 4, introgressed alleles in a 20Mb region improved harvest index and brown streak disease tolerance. Three cycles of selection initially doubled the introgression frequency on chr. 1. Later stage variety trials selectively excluded homozygotes which indicates a heterozygous advantage. We show that maintaining large recombination-suppressed introgressions in the heterozygous state allows the accumulation of deleterious mutations. We conclude that targeted recombination of introgression segments would therefore increase the efficiency of cassava breeding by allowing simultaneous fixation of beneficial alleles and purging of genetic load.\n\nSignificance StatementCrosses to wild relatives have often been adaptive for crop breeding, but their modern importance is usually poorly understood. Cassava (Manihot esculenta) is an important staple crop, feeding hundreds of millions in the developing world, and is a model for vegetatively-propagated non-inbred crops. In the 1930s, crossing to M. glaziovii averted mosaic disease epidemic in Africa. We reveal that large genome segments, the legacy of those crosses, benefit a number of traits including yield in modern cassava and are consistently favored during selection. Elite cultivars are almost exclusively heterozygous for wild alleles; homozygotes are rejected during early stage trials, suggesting inbreeding depression. More recombination around beneficial wild alleles will allow purging of genetic load and increase genetic gain in cassava.

genetics↗

bioRxiv2017

Accuracies Of Univariate And Multivariate Genomic Prediction Models In African Cassava.

List of abbreviations\n\nAbstractO_ST_ABSBackgroundC_ST_ABSGenomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for long cycle crops like cassava. To practically implement GS in cassava breeding, it is useful to evaluate different GS models and to develop suitable models for an optimized breeding pipeline.\n\nMethodsWe compared prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for single environment genetic evaluation (Scenario 1) while for multi-environment evaluation accounting for genotype-by-environment interaction (Scenario 2) we compared accuracies from a univariate (uE) and a multivariate (ME) multi-environment mixed model. We used sixteen years of data for six target cassava traits for these analyses. All models for Scenario 1 and Scenario 2 were based on the one-step approach. A 5-fold cross validation scheme with 10-repeat cycles were used to assess model prediction accuracies.\n\nResultsIn Scenario 1, the MT models had higher prediction accuracies than the uT models for most traits and locations analyzed amounting to 32 percent better prediction accuracy on average. However for Scenario 2, we observed that the ME model had on average (across all locations and traits) 12 percent better predictive power than the uE model.\n\nConclusionWe recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species.

genetics↗

bioRxiv2017

Prospects for genomic selection in cassava breeding

Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) reduces selection cycle times by the prediction of breeding value for selection of unevaluated lines based on genome-wide marker data. GS has been implemented at three breeding programs in sub-Saharan Africa. Initial studies provided promising estimates of predictive abilities in single populations using standard prediction models and scenarios. In the present study we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: (1) cross-validation within each population, (2) cross-population prediction and (3) cross-generation prediction. We also evaluated the impact of increasing training population size by phenotyping progenies selected either at random or using a genetic algorithm. Cross-validation results were mostly consistent across breeding programs, with non-additive models like RKHS predicting an average of 10% more accurately. Accuracy was generally associated with heritability. Cross-population prediction accuracy was generally low (mean 0.18 across traits and models) but prediction of cassava mosaic disease severity increased up to 57% in one Nigerian population, when combining data from another related population. Accuracy across-generation was poorer than within (cross-validation) as expected, but indicated that accuracy should be sufficient for rapid-cycling GS on several traits. Selection of prediction model made some difference across generations, but increasing training population (TP) size was more important. In some cases, using a genetic algorithm, selecting one third of progeny could achieve accuracy equivalent to phenotyping all progeny. Based on the datasets analyzed in this study, it was apparent that the size of a training population (TP) has a significant impact on prediction accuracy for most traits. We are still in the early stages of GS in this crop, but results are promising, at least for some traits. The TPs need to continue to grow and quality phenotyping is more critical than ever. General guidelines for successful GS are emerging. Phenotyping can be done on fewer individuals, cleverly selected, making for trials that are more focused on the quality of the data collected.\n\nAbbreviations

genetics↗

bioRxiv2017

Resistance genes in global crop breeding networks

Resistance genes are a major tool for managing crop diseases. The crop breeder networks that exchange resistance genes and deploy them in elite varieties help to determine the global landscape of resistance and epidemics, and comprise an important system for maintaining food security. These networks function as complex adaptive systems, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. We evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat, which illustrate a range of public and private configurations. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Principles for maintaining system resilience can be applied to global resistance gene deployment. For example, both diversity and redundancy in the roles played by individual crop breeding groups (public versus private, global versus local) may support societal goals for crop production. Another principle is management of connectivity, where enhanced connectivity among crop breeders may benefit global resistance gene deployment, but increase risks to the durability of resistance genes without effective policies regarding deployment.

genetics↗

bioRxiv2017

Genome-wide association mapping of correlated traits in cassava: dry matter and total carotenoid content

Cassava (Manihot esculenta (L.) Crantz) is a starchy root crop cultivated in the tropics for fresh consumption and commercial processing. Dry matter content and micronutrient density, particularly of provitamin A - traits that are negatively correlated - are among the primary selection objectives in cassava breeding. This study aimed at identifying genetic markers associated with these traits and uncovering the potential underlying cause of their negative correlation - whether linkage and/or pleiotropy. A genome-wide association mapping using 672 clones genotyped at 72,279 SNP loci was carried out. Root yellowness was used indirectly to assess variation in carotenoid content. Two major loci for root yellowness was identified on chromosome 1 at positions 24.1 and 30.5 Mbp. A single locus for dry matter content that co-located with the 24.1 Mbp peak for carotenoid content was identified. Haplotypes at these loci explained a large proportion of the phenotypic variability. Evidence of mega-base-scale linkage disequilibrium around the major loci of the two traits and detection of the major dry matter locus in independent analysis for the white- and yellow-root subpopulations suggests that physical linkage rather that pleiotropy is more likely to be the cause of the negative correlation between the target traits. Moreover, candidate genes for carotenoid (phytoene synthase) and starch biosynthesis (UDP-glucose pyrophosphorylase and sucrose synthase) occurred in the vicinity of the identified locus at 24.1 Mbp. These findings elucidate on the genetic architecture of carotenoids and dry matter in cassava and provides an opportunity to accelerate genetic improvement of these traits.\n\nCORE IDEASO_LICassava, a starchy root crop, is a major source of dietary calories in the tropics.\nC_LIO_LIMost varieties consumed are poor in micronutrients, including pro-vitamin A.\nC_LIO_LIThese two traits are governed by few major loci on chromosome one.\nC_LIO_LIGenetic linkage, rather than pleiotropy, is the most likely cause of their negative correlation.\nC_LI

genetics↗