Biology subjects

Garcia, A. A. F.

Publications and source records attributed to Garcia, A. A. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Long-term realized genetic gain and population dynamics under genomic selection in Brazilian cassava germplasm

Genomic selection has become an important strategy in cassava breeding, enabling faster selection cycles and sustained genetic progress. Despite its widespread adoption, long-term evaluations integrating predictive performance, realized genetic gain, and genetic diversity remain scarce, particularly in clonally propagated crops. We present a comprehensive assessment of genomic selection outcomes in the Brazilian cassava breeding program across four recurrent selection cycles (C0 to C3) implemented between 2011 and 2024, using historical phenotypic and genomic data from 210 multi-environment trials. Predictive ability of genomic best linear unbiased prediction models ranged from low to moderate, depending on the traits genetic architecture and heritability. Prediction accuracies were highest in early cycles (C0 and C1) and showed modest declines in later cycles (C2 and C3). Root yield, shoot yield, plant height, starch content, and dry matter content exhibited stable predictive performance across cycles, with a gradual reduction in RMSE, indicating improved model calibration as training populations expanded. Regression analyses of genomic estimated breeding values revealed significant realized genetic gains for most yield-related traits. In contrast, dry matter content and starch content exhibited small, non-significant negative trends, consistent with known unfavorable genetic correlations with yield. Targeted reductions in plant architecture scores reflected deliberate selection for ideotypes suited to mechanized production systems. At the same time, analyses of genetic diversity revealed a slight decrease in observed heterozygosity, with higher values in the most advanced selection cycle. These results provide an integrated framework for monitoring predictive performance, realized genetic gain, and population genetic dynamics under long-term genomic selection. Collectively, they offer valuable insights into balancing short-term genetic improvement with long-term sustainability and support the development of strategies to optimize selection decisions, breeding planning, and population management in Brazilian cassava breeding programs.

genetics↗

bioRxiv2026

Enhancing predictive accuracy of yield traits in cassava through multi-trait genomic prediction

Multi-trait genomic prediction offers a practical route to improve selection for costly, complex traits in clonally propagated crops such as cassava. In a Brazilian breeding panel of 1,078 cassava clones genotyped with 25,923 SNPs and phenotyped for six agronomic traits, we compared single-trait (ST) and multi-trait (MT) GBLUP models. Stage-wise mixed models produced BLUEs that fed into ST and MT-GBLUP. We tested five cross-validation schemes that mimic breeder realities: ST baseline (CV1); naive all-traits MT prediction for unphenotyped candidates (CV2); MT prediction using auxiliary trait phenotypes in the test set (CV3); and two sparse-phenotyping regimes with missingness by trait (CV4) or by clone (CV5) at 25%, 50%, and 75% levels. The main results were that, under the ST baseline (CV1), predictive ability ranged from 0.50 for DMC and 0.45 for FRY down to 0.13 for Le.Dis. A naive full MT model (CV2) performed approximately on par with ST-GBLUP. In contrast, MT designs (CV3) that included informative auxiliary traits, such as shoot yield and combinations with plant vigor and leaf disease severity, yielded small gains for DMC with predictive ability of approximately 0.51 (+2%), while FRY predictive ability increased to approximately 0.65 (+44%), accompanied by RMSE reductions for FRY up to approximately 13.5% (e.g. RMSE approximately 6.2). Sparse-phenotyping simulations (CV4/CV5) demonstrated that MT models sustain or even improve predictive ability under realistic missing-data regimes {approx} 0.62{lambda}0.65). Selection concordance between MT and ST top-10% sets was generally high (>0.80), and MT configurations produced measurable improvements in expected selection response and genetic gain per cycle for several target traits. These results indicate that strategically implemented MT-GBLUP, using a small set of biologically and operationally informative auxiliary traits and optimized sparse phenotyping, can materially increase predictive accuracy and selection efficiency for economically critical cassava traits while reducing phenotyping burden.

genetics↗

bioRxiv2026

Uncovering genomic regions controlling root quality traits in Cassava (Manihot esculenta Crantz) using different GWAS models

Cassava is a major staple crop in tropical regions, and improving its root nutritional quality, particularly carotenoid and dry matter content (DMC), remains a central breeding goal. To elucidate the genetic basis of these traits by locating genomic regions associated with them, we analyzed 3,043 cassava clones from the Brazilian Agricultural Research Corporation (Embrapa) breeding program, phenotyped across 188 multi-environment trials conducted from 2011 to 2022 in Brazil. All clones were genotyped using Genotyping-by-Sequencing (27,045 Single Nucleotide Polymorphism - SNPs) and Diversity Arrays Technology - DArTseq (25,923 SNPs). Trait values were estimated using a two-stage mixed model to obtain deregressed BLUPs (Best Linear Unbiased Predictions), and genome-wide association analyses were performed using both the Mixed Linear Model (MLM) and Multi-Locus Mixed Model (MLMM). We detected six significant SNPs consistently associated with carotenoid content and DMC after Bonferroni correction. These SNPs mapped to six candidate genes involved in pathways relevant to root physiology, including Abscisic Acid ABA-related signaling, hydrolase activity affecting carotenoid conversion, fatty-acid biosynthesis within plastids, cell-wall remodeling, and glycolytic energy metabolism. The loci jointly explained 75.56 % of the phenotypic variance for carotenoids and 76.23 % for DMC, with individual SNP effects ranging from [~]17 % to [~]42 % PVE (Proportion of Variance Explained). Broad-sense heritability was H2 = 0.78 for carotenoids and H{superscript 2} = 0.34 for DMC, confirming substantial genetic control and suitability for molecular breeding. Haplotype analyses revealed four superior haplotypes for carotenoids and one key haplotype for DMC, each showing significantly higher trait values compared with other allelic combinations. These haplotypes represent promising targets for marker-assisted selection and genomic selection, with direct applicability for accelerating genetic gain in elite breeding populations. The results provide actionable genomic resources for breeding programs aiming to develop biofortified and high-root quality cultivars and establish a foundation for future multi-omics and functional validation studies.

genetics↗

bioRxiv2026

Unraveling candidate genomic regions responsible for delayed post-harvest deterioration in Cassava (Manihot esculenta Crantz)

Post-harvest physiological deterioration (PPD) represents a significant challenge of cassava production and commercialization. This multifaceted biological process involves a series of mechanisms, including enzymatic stress responses, alterations in gene expression, protein synthesis, accumulation of secondary metabolites, and ultimately, programmed cell death. These changes render the storage roots unpalatable and unmarketable. Therefore, unraveling the genetic architecture of PPD and exploring the interactions of associated genes during its early and late stages is essential for the crop production. We used modern genetic resources to unravel the genetic basis of PPD, based on a genome-wide association study (GWAS), utilizing a combination of different models, including BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway), SUPER (Settlement of MLM Under Progressively Exclusive Relationship), and MLMM (Multi-locus mixed models). The phenotyping dataset spanned five years and included evaluations from 42 different trials, evaluating the Embrapa (Brazilian Agricultural Research Corporation) germplasm along with a population derivative from a genomic selection cycle. We utilized a genotype dataset comprising 26,000 high-quality SNPs (single nucleotide polymorphisms). Our findings indicated four significant genetic variants located on chromosomes 2, 5, and 13, which together explain 35.83 % of the phenotypic variation. These variants are associated with genes that are closely linked to the pathways activated during the early and late symptoms of PPD. The identification of these three key genes provides valuable insights into the genetic architecture of PPD and lays a strong foundation for molecular breeding, supporting the efforts to identify cassava genotypes with enhanced PPD tolerance, the identified genomic regions may be incorporated into genomic selection models, thereby enhancing marker-assisted selection (MAS) and improving breeding strategies for long shelf life and high-quality agronomic cassava cultivars for the cassava community.

genetics↗

bioRxiv2025

Tests for segregation distortion in higher ploidy F1 populations

F1 populations are widely used in genetic mapping studies in agriculture, where known pedigrees enable rigorous quality control measures such as segregation distortion testing. However, conventional tests for segregation distortion are inadequate for polyploids, as they fail to account for double reduction, preferential pairing, and genotype uncertainty, leading to inflated type I error rates. Gerard et al. (2025) (doi:10.1007/s00122-025-04816-z) developed a statistical framework to address these issues in tetraploids. Here, we extend these methods to higher even ploidy levels and introduce additional strategies to mitigate the influence of outliers. Through extensive simulations, we demonstrate that our tests maintain appropriate type I error control while retaining power to detect true segregation distortion. We further validate our approach using empirical data from a hexaploid mapping population. Our methods are implemented in the segtest R package, available on the GitHub (https://github.com/dcgerard/segtest).

bioinformatics↗

bioRxiv2025

Developing a model to implement marker-assisted selection for root-knot nematode resistance in common bean

Common bean (Phaseolus vulgaris L.) is a vital crop for direct human consumption, with essential nutrients and valuable protein that provides food security in developing countries. However, its cultivation faces significant threats from Meloidogyne incognita, a root-knot nematode (RKN), resulting in considerable yield loss. Developing crop resistance remains a key strategy for mitigating nematode infections. To investigate the genetic architecture of common bean responses to RKN (specifically, race 3 of M. incognita), we performed controlled crosses between the genotypes IAC-Tybata and Branquinho with contrasting resistance. The resulting segregating population (F2) of 333 individuals was genotyped using GBS (genotyping-by-sequencing). We used a phenotyping approach, already optimized in the lab, to collect trait data for a subset of 200 F2:3 families. Evaluations of egg mass (EM), root-galling index (GI), and root dry mass (RM) were conducted 30 days after RKN inoculation under greenhouse conditions, in a completely randomized design with ten replicates. Linkage and quantitative trait loci (QTL) mapping were performed, while functional mapping of associated regions facilitated identification of candidate genes. A linkage map encompassing 954 SNPs assigned to 11 linkage groups totaling 1,687 cM formed the basis for Interval Mapping (IM), Composite Interval Mapping (CIM), and Multiple Interval Mapping (MIM), revealing four major QTLs (on Pv03, Pv05, Pv08, and Pv10) and epistasis between QTL on Pv08 and on Pv10 associated with the GI trait. No significant QTL were identified for EM and RM. The model enabled calculation of genotypic values through marker-assisted selection (MAS). The high correlation between observed and predicted values (0.72) underscores the models significance. Candidate genes previously associated with nematode resistance were also identified within the QTL interval on chromosome Pv10. Our results will be valuable for future selection of varieties resistant to this important crop disease.

genetics↗

bioRxiv2024

The genetic architecture of resistance to flubendiamide insecticides in Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)

Insecticide resistance is a major problem in food production, environmental sustainability, and human health. The cotton bollworm Helicoverpa armigera is a globally distributed crop pest affecting over 300 crop species. H. armigera has rapidly evolved insecticide resistance, making it one of the most damaging pests worldwide. Understanding the genetic basis of insecticide resistance provides insights to develop tools, such as molecular markers, that can be used to slow or prevent the evolution of resistance. We explore the genetic architecture of H. armigera resistance to a widely used insecticide, flubendiamide, using two complementary approaches: genome-wide association studies (GWAS) in wild-caught samples and quantitative trait locus (QTL) mapping in a controlled cross of susceptible and resistant laboratory strains. Both approaches identified one locus on chromosome 2, revealing two SNPs within 976 bp that can be used to monitor field resistance to flubendiamide. This was the only region identified using linkage mapping, though GWAS revealed additional sites associated with resistance. Other loci identified by GWAS in field populations contained known insecticide detoxification genes from the ATP-binding cassette family, ABCA1, ABCA3, ABCF2 and MDR1. Our findings revealed an oligogenic genetic architecture, in contrast to previous reports of monogenic resistance associated with the ryanodine receptor. This work elucidates the genetic basis of rapidly evolving insecticide resistance and will contribute to the development of effective insecticide resistance management strategies. Author summaryInsecticide resistance in agricultural pests challenges food security, environmental sustainability, and human health. The cotton bollworm Helicoverpa armigera is resistant to various insecticide classes as well as Bacillus thuringiensis toxins. Understanding the genetic basis of this resistance is crucial for developing effective insecticide resistance management (IRM) strategies. Our study investigated the genetic architecture of resistance of H. armigera to flubendiamide and identified SNPs associated with resistance. We used two approaches: association mapping in wild-derived samples and QTL mapping in a controlled backcross of susceptible and resistant laboratory strains. One specific region on chromosome 2 was found in both GWAS and QTL mapping analysis and showed significant potential as a PCR-based marker for monitoring resistance to flubendiamide in H. armigera. Additionally, the GWAS identified five more SNPs in the field populations. The candidate genes identified are primarily associated with insecticide detoxification mechanisms and calcium homeostasis. Our study advances the understanding of the complex genetic architecture of flubendiamide resistance in H. armigera, providing valuable tools for IRM programs and establishing a basis for future studies.

genetics↗

bioRxiv2023

Advances in genomic characterization of Urochloa humidicola: exploring polyploid inheritance and apomixis

Tropical forage grasses are an important food source for animal feeding, with Urochloa humidicola, also known as Koronivia grass, being one of the main pasture grasses for poorly drained soils in the tropics. However, genetic and genomic resources for this species are lacking due to its genomic complexity, including high heterozygosity, evidence of segmental allopolyploidy, and reproduction by apomixis. These complexities hinder the application of marker-assisted selection (MAS) in breeding programs. Here, we developed the highest-density linkage map currently available for the hexaploid tropical forage grass U. humidicola. This map was constructed using a biparental F1 population generated from a cross between the female parent H031 (CIAT 26146), the only known sexual genotype for the species, and the apomictic male parent H016 (BRS cv. Tupi). The linkage analysis included 4,873 single nucleotide polymorphism (SNP) markers with allele dosage information. It allowed mapping of the apospory locus and phenotype to linkage group 3, in a region syntenic with chromosome 3 of Urochloa ruziziensis and chromosome 1 of Setaria italica. We also identified hexaploid haplotypes for all individuals, assessed the meiotic configuration, and estimated the level of preferential pairing in parents during the meiotic process, which revealed the autopolyploid origin of sexual H031 in contrast to H016, which presented allopolyploid behavior in preferential pairing analysis. These results provide new information regarding the genetic organization, mode of reproduction, and allopolyploid origin of U. humidicola, potential SNPs markers associated to apomixes for MAS and resources for research on polyploids and tropical forage grasses. Key messageWe present the highest-density genetic map for the hexaploid Urochloa humidicola. SNP markers expose genetic organization, reproduction, and species origin, aiding polyploid and tropical forage research.

genomics↗

bioRxiv2022

Developing best practices for genotyping-by-sequencing analysis using linkage maps as benchmarks

BackgroundGenotyping-by-Sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by PCR duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations. ResultsWe developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset-dependent) and others produce consistent advantageous results among them (dataset-independent). ConclusionsWe set as default in the Reads2Map workflows the approaches that showed to be dataset-independent for GBS datasets according to our results. This reduces the number required of tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

bioinformatics↗

bioRxiv2022

A joint learning approach for genomic prediction in polyploid grasses

Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.

bioinformatics↗

bioRxiv2021

Allele expression biases in mixed-ploid sugarcane accessions

Allele-specific expression (ASE) represents differences in the magnitude of expression between alleles of the same gene. This is not straightforward for polyploids, especially autopolyploids, as knowledge about the dose of each allele is required for accurate estimation of ASE. This is the case for the genomically complex Saccharum species, characterized by high levels of ploidy and aneuploidy. We used a Beta-Binomial model to test for allelic imbalance in Saccharum, with adaptations for mixed-ploid organisms. The hierarchical Beta-Binomial model was used to test if allele expression followed the expectation based on genomic allele dosage. The highest frequencies of ASE occurred in sugarcane hybrids, suggesting a possible influence of interspecific hybridization in these genotypes. For all accessions, ASEGs were less frequent than those with balanced allelic expression. These genes were related to a broad range of processes, mostly associated with general metabolism, organelles, responses to stress and responses to stimuli. In addition, the frequency of ASEGs in high-level functional terms was similar among the genotypes, with a few genes associated with more specific biological processes. We hypothesize that ASE in Saccharum is largely a genotype-specific phenomenon, as a large number of ASEGs were exclusive to individual accessions.

genomics↗

bioRxiv2021

Leveraging probability concepts for genotype by environment recommendation

Statistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate offspring, and obtain highly productive genotypes for distinct environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the No-U-Turn sampler algorithm to get Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our findings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specific adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defined intensity of selection that results in a more informed decision-making process towards cultivar recommendation in multi-environment trials.

genetics↗

bioRxiv2021

Significance of linkage disequilibrium and epistasis on the genetic variances and covariance between relatives in non-inbred and inbred populations

Because no feasible theoretical model can depict the complexity of phenotype development from a genotype, the joint significance of linkage disequilibrium (LD), epistasis, and inbreeding on the genetic variances remains unclear. The objective of this investigation was to assess the impact of LD and epistasis on the genetic variances and covariances between relatives in non-inbred and inbred populations using simulated data. We provided the theoretical background and simulated grain yield assuming 400 genes in 10 chromosomes of 200 and 50 cM. We generated five populations with low to high LD levels, assuming 10 generations of random cross and selfing. The analysis of the parametric LD in the populations shows that the LD level depends mainly on the gene density. The significance of the LD level is impressive on the magnitude of the genotypic and additive variances, which is the most important component of the genotypic variance, regardless of the LD level and the degree of inbreeding. Regardless of the type of epistasis, the ratio epistatic variance/genotypic variance is proportional to the percentage of the epistatic genes. For the epistatic variances, except for duplicate epistasis and dominant and recessive epistasis, with 100% of epistatic genes, their magnitudes are much lower than the magnitude of the additive variance. The additive x additive variance is the most important epistatic variance. Our results explain why LD for genes and relationship information are key factors affecting the genomic prediction accuracy of complex traits and the efficacy of association studies.

genetics↗

bioRxiv2020

fullsibQTL: an R package for QTL mapping in biparental populations of outcrossing species

Accurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developed fullsibQTL, a novel and freely available R package to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations. fullsibQTL is an R open source package with C and R source code (GPLv3). It is multiplatform and can be installed from https://github.com/augusto-garcia/fullsibQTL.

bioinformatics↗

bioRxiv2019

High-resolution Linkage Map with Allele Dosage Allows the Identification of an Apomixis Region and Complex Traits in Guinea Grass (Megathyrsus maximus)

Forage grasses are mainly used in animal feed to fatten cattle and dairy herds. Among tropical forage crops that reproduce by seeds, guinea grass (Megathyrsus maximus) is considered one of the most productive. This species has several genomic complexities, such as autotetraploidy and apomixis, due to the process of domestication. Consequently, approaches that relate phenotypic and genotypic data are incipient. In this context, we built a linkage map with allele dosage and generated novel information about the genetic architecture of traits that are important for the breeding of M. maximus. From a full-sib progeny, a linkage map containing 858 single nucleotide polymorphism (SNP) markers with allele dosage information expected for an autotetraploid was obtained. The high genetic variability of the progeny allowed us to map ten quantitative trait loci (QTLs) related to agronomic traits, such as regrowth capacity and total dry matter, and 36 QTLs related to nutritional quality, which were distributed among all homology groups (HGs). Various overlapping regions associated with the quantitative traits suggested QTL hotspots. In addition, we were able to map one locus that controls apospory (apo-locus) in HG II. A total of 55 different gene families involved in cellular metabolism and plant growth were identified from markers adjacent to the QTLs and apomixis locus by using the Panicum virgatum genome as a reference in comparisons with the genomes of Arabidopsis thaliana and Oryza sativa. Our results provide a better understanding of the genetic basis of reproduction by apomixis and traits important for breeding programs that considerably influence animal productivity as well as the quality of meat and milk.

plant biology↗

bioRxiv2019

Improving yield and fruit quality traits in sweet passion fruit: evidence for genotype by environment interaction and cross-compatibility in selected genotypes

Breeding for yield and fruit quality traits in passion fruits is complex due to the polygenic nature of these traits and the existence of genetic correlations among them. Therefore, studies focused on crop management practices and breeding using modern quantitative genetic approaches are still needed, especially for Passiflora alata, an understudied crop, popular known as the sweet passion fruit. It is assumed to be a self-incompatible species and is highly appreciated for its typical aroma and flavor characteristics. With the aim of estimating the genetic and phenotypic parameters related to fruit traits, our group has already investigated a sweet passion fruit progeny consisting of 100 individuals from which 30 genotypes were selected. In this study, we reevaluated these superior genotypes in three environmental conditions. The results of the multi-environment trial analysis indicate that the genotypes do not behave consistently across these environments, and this was taken into account when selecting genotypes. Pairwise genetic correlations among the fruit traits were evaluated, and different genotype rankings obtained depending on the trait and environment, providing further evidence of genotype by environment interaction. Finally, we used a multiplicative selection index to select 20% of genotypes for weight of pulp and against thickness and weight of the fruit skin. The response to selection was positive for all traits except soluble solids, and the superior (six) genotypes were ranked. The consensus is that open-pollinated populations can be used as commercial varieties in crop species that are sensitive to inbreeding depression or within breeding programs that are not well developed. For these reasons, we performed a complete diallel cross for evaluating the capability of the selected genotypes to be fertilized and set fruits. Most of the crosses were compatible, resulting in over 50% fruit set. It is worth noting that all genotypes produce fruits if used as females, which is essential to guarantee yields in commercial orchards.

plant biology↗

bioRxiv2019

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench] lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In 5-fold cross-validation, prediction accuracies ranged from 0.48 (PBN) to 0.51 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.74 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

genetics↗

bioRxiv2018

Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models

Modern SNP genotyping technologies allow to measure the relative abundance of different alleles for a given locus and consequently to estimate their allele dosage, opening a new road for genetic studies in autopolyploids. Despite advances in genetic linkage analysis in autotetraploids, there is a lack of statistical models to perform linkage analysis in organisms with higher ploidy levels. In this paper, we present a statistical method to estimate recombination fractions and infer linkage phases in full-sib populations of autopolyploid species with even ploidy levels in a sequence of SNP markers using hidden Markov models. Our method uses efficient two-point procedures to reduce the search space for the best linkage phase configuration and reestimate the final parameters using the maximum-likelihood of the Markov chain. To evaluate the method, and demonstrate its properties, we rely on simulations of autotetraploid, autohexaploid and autooctaploid populations and on a real tetraploid potato data set. The results demonstrate the reliability of our approach, including situations with complex linkage phase scenarios in hexaploid and octaploid populations.\n\nAuthor summaryIn this paper, we present a complete multilocus solution based on hidden Markov models to estimate recombination fractions and infer the linkage phase configuration in full-sib mapping populations with even ploidy levels under random chromosome segregation. We also present an efficient pairwise loci analysis to be used in cases were the multilocus analysis becomes compute-intensive.

genetics↗