Biology subjects

Lozano, R.

Publications and source records attributed to Lozano, R..

16 recordsLinked to original sources

bioRxiv2026

DNA Methylation and Proteomic Profiling of Postmortem Brain Tissue Reveals Epigenetic Dysregulation and Neuroinflammatory in Fragile X-associated Tremor/Ataxia Syndrome (FXTAS)

BackgroundFragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. MethodsDNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by |{Delta}{beta}| > 0.10 and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. ResultsWe identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs, mean {Delta}{beta} = -0.143), an oxidative stress gene also implicated in Parkinsons disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs (mean {Delta}{beta} = +0.210). A cluster of DMP-associated genes with established roles in innate immune and NF-{kappa}B signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. ConclusionsThis integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-{kappa}B signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-{kappa}B-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

genomics↗

bioRxiv2024

A deep dive into the FALSIFLORA contribution to tomato reproductive development through a comprehensive allelic series analysis

Plants undergo continuous growth and development thanks to meristems, specialized groups of pluripotent stem cells that remain in their undifferentiated state throughout the plants life cycle. Meristem transition from the vegetative to the reproductive phase heavily influences plant reproductive success and agricultural productivity. In tomato (Solanum lycopersicum L.), FALSIFLORA (FA), the orthologue of the Arabidopsis LEAFY gene, promotes floral transition by specifying floral meristem identity and regulating the expression of genes responsible for floral organ identity and development. In this study, the allelic series of FA has been expanded by combining the screening of a tomato EMS mutant collection with overexpression, gene silencing and CRISPR/Cas9 genome editing approaches, aimed to deepen the understanding of the functional role of FA during tomato reproductive development. The phenotypic and molecular characterization of the FA allelic series revealed the multifaceted role of FA acting in both early and late stages of floral ontogeny. Thus, in addition to promoting floral transition and specifying floral meristem identity, FA also plays a role in inflorescence meristem maturation and termination, thereby regulating the inflorescence architecture. Furthermore, FA exerts regulatory control over the expression of the tomato homologs of AGAMOUS (TOMATO AGAMOUS1, TAG1) and WUSCHEL (SlWUS), underscoring its function in promoting carpel development and suppressing floral stem cell activity, thereby establishing floral determinacy. In conclusion, our findings demonstrate the potential of employing mutant allelic series as powerful tools for elucidating gene functions and deciphering the intricate molecular basis underlying biological processes. Plain Language SummaryOur research aims to gain a deeper understanding of the role of FALSIFLORA in tomato reproductive development. The FALSIFLORA allelic spectrum was expanded through mutant screening, along with overexpression, silencing, and CRISPR/Cas9 editing approaches. Our findings reveal that FALSIFLORA has a dual function in early and late floral development stages. In addition to promoting floral transition and specifying floral meristem identity, FA regulates the expression of tomato genes homologous to WUSCHEL and AGAMOUS, which play an essential role in stem cell regulation and carpel development. This study highlights the value of allelic series in elucidating gene functions and intricate biological mechanisms.

plant biology↗

bioRxiv2024

The Pivotal Involvement of the Respiratory burst oxidase G (SlRbohG) Gene in H2O2 Production Under Stress for Proper Na+ Homeostasis Regulation in Tomato

Regulation of sodium homeostasis is crucial for plant response to salinity conditions. Here we report on the genetic and physiological characterization of two tomato allelic mutants, sodium gatherer1-2 (sga1-2), which exhibit pronounced chlorosis and hyperhydration under salt stress. Mapping-by-sequencing revealed that mutant phenotype resulted from mutations in the SlRbohG gene, and CRISPR/Cas9 knockouts of this gene gave phenocopies of the sga1-2 mutants. Physiological analyses showed that sga1-2 salt hypersensitivity is linked to an increase of Na+ and water transport from roots to shoots, which explains their extreme chlorosis and hyperhydration under salinity conditions. At the molecular level, SlPIP2;12 gene, an aquaporin down-regulated in the WT under salt stress, was overexpressed in the sga1-2 mutants, which could enhance water transport to the shoot. Also, sga1-2 mutants exhibited a significant reduction in the expression of key sodium transporters, thus modifying the normal distribution of Na+ in tomato plant tissues. Furthermore, treatment of WT plants with the NADPH oxidase inhibitor DPI prevented H2O2 production in response to salinity, resulting in elevated Na+ accumulation in the shoot and reduced expression of the SlHKT1;2 gene in root. Altogether, our results show that SlRbohG plays a central role in salt tolerance through ROS-mediated signaling. HIGHLIGHTLoss of function of tomato SlRbohG gene leads hypersensibility to salt stress due to increased Na+ and water transport from root to shoot.

plant biology↗

bioRxiv2021

Tomato CRABS CLAW paralogues interact with chromatin remodelling factors to mediate carpel development and floral determinacy

CRABS CLAW (CRC) orthologues play a crucial role in floral meristem (FM) determinacy and gynoecium formation across angiosperms, key developmental processes for ensuring successful plant reproduction and crop production. Here, we revealed that the incomplete penetrance and variable expressivity of the carpel-inside-carpel phenotype observed in flowers of the tomato fruit iterative growth (fig) mutant is due to a lack of function of a homologue of the CRC gene, Solanum lycopersicum CRCa (SlCRCa). Likewise, a comprehensive functional analysis of SlCRCa and SlCRCb paralogues, including Arabidopsis complementation experiments, allowed us to propose that they operate as positive regulators of FM determinacy by acting in a compensatory and partially redundant manner to safeguard the proper formation of flowers and fruits. Furthermore, we provide the first evidence for the role of putative CRC orthologues as members of the chromatin remodelling complex that terminates floral stem cell activity by repressing WUSCHEL expression.

developmental biology↗

bioRxiv2021

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential to maintenance of water-use efficiency (WUE). However, little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. High-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions. Results from thermal imaging significantly correlated with photosynthetic gas-exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study (GWAS/TWAS) identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with greatest confidence, 77 were orthologs of Arabidopsis genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production. One sentence summaryRapid phenotyping of 659 accessions of Sorghum bicolor revealed heritable stomatal responses to a decrease in light. GWAS/TWAS was used to identify candidate genes influencing traits important to WUE.

plant biology↗

bioRxiv2020

Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is also studied as a feedstock for biofuel and forage. Mechanistic modelling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping for discovery of genotype to phenotype associations remain bottlenecks in efforts to understand the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were then the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. SD was correlated with plant height and biomass production. Plasticity in SD and SLA were interrelated with each other, and productivity, across wet versus dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population supported identification of associations between DNA sequence variation, or RNA transcript abundance, and trait variation. 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose orthologs in Arabidopsis have functions related to stomatal or leaf development and leaf gas exchange. These advances in methodology and knowledge will aid efforts to improve the WUE of C4 crops.

plant biology↗

bioRxiv2020

Purging of deleterious mutations during domestication in the predominant selfing crop soybean

As a predominant plant protein and oil source for both food and feed, soybean is unique in that both domesticated and wild types are predominantly selfing. Here we present a genome-wide variation map of 781 soybean accessions that include 418 domesticated (Glycine max) and 345 wild (Glycine soja) accessions and 18 of their natural hybrids. We identified 10.5 million single nucleotide polymorphisms and 5.7 million small indels that contribute to within- and between-population variations. We describe improved detection of domestication-selective sweeps and drastic reduction of overall deleterious alleles in domesticated soybean relative to wild soybean in contrast to the cost of domestication hypothesis. This resource enables the marker density of existing data sets to be increased to improve the resolution of association studies.

genomics↗

bioRxiv2019

A sorghum Practical Haplotype Graph facilitates genome-wide imputation and cost-effective genomic prediction

Successful management and utilization of increasingly large genomic datasets is essential for breeding programs to increase genetic gain and accelerate cultivar development. To help with data management and storage, we developed a sorghum Practical Haplotype Graph (PHG) pangenome database that stores all identified haplotypes and variant information for a given set of individuals. We developed two PHGs in sorghum, one with 24 individuals and another with 398 individuals, that reflect the diversity across genic regions of the sorghum genome. 24 founders of the Chibas sorghum breeding program were sequenced at low coverage (0.01x) and processed through the PHG to identify genome-wide variants. The PHG called SNPs with only 5.9% error at 0.01x coverage - only 3% lower than its accuracy when calling SNPs from 8x coverage sequence. Additionally, 207 progeny from the Chibas genomic selection (GS) training population were sequenced and processed through the PHG. Missing genotypes in the progeny were imputed from the parental haplotypes available in the PHG and used for genomic prediction. Mean prediction accuracies with PHG SNP calls range from 0.57-0.73 for different traits, and are similar to prediction accuracies obtained with genotyping-by-sequencing (GBS) or markers from sequencing targeted amplicons (rhAmpSeq). This study provides a proof of concept for using a sorghum PHG to call and impute SNPs from low-coverage sequence data and also shows that the PHG can unify genotype calls from different sequencing platforms. By reducing the amount of input sequence needed, the PHG has the potential to decrease the cost of genotyping for genomic selection, making GS more feasible and facilitating larger breeding populations that can capture maximum recombination. Our results demonstrate that the PHG is a useful research and breeding tool that can maintain variant information from a diverse group of taxa, store sequence data in a condensed but readily accessible format, unify genotypes from different genotyping methods, and provide a cost-effective option for genomic selection for any species.

genomics↗

bioRxiv2019

Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maize

Sorghum and maize share a close evolutionary history that can be explored through comparative genomics. To perform a large-scale comparison of the genomic variation between these two species, we analyzed 13 million variants identified from whole genome resequencing of 468 sorghum lines together with 25 million variants previously identified in 1,218 maize lines. Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes, and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the \"domestication cost\" hypothesis that predicts a higher deleterious burden among domesticates compared to wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy higher than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing them for removal through genome editing and breeding.

genomics↗

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↗

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↗

bioRxiv2018

RNA polymerase mapping in plants identifies enhancers enriched in causal variants

Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have been reported to be absent in plants based on a study of Arabidopsis thaliana. Here, our PRO-Seq analysis in cassava (Manihot esculenta) identified peaks of transcriptionally-engaged RNA polymerase II (Pol2) at both 5 and 3 ends of genes, consistent with paused or slowly-moving Pol2, and divergent transcription at potential intragenic enhancers. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many enhancer candidates. These sites show distinct patterns of methylation and nucleotide variation based on genomic evolutionary rate profiling characteristic of active enhancers. Maize GRO-Seq data showed RNA polymerase occupancy at promoters and enhancers consistent with cassava but not Arabidopsis. Furthermore, putative enhancers in maize identified by dREG significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation. We show that SNPs within these divergently transcribed intergenic regions predict significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, suggesting a functional importance of these sites on cassava. The findings shed new light on plant transcription regulation and its impact on development and plasticity.

genomics↗

bioRxiv2018

Leveraging mutational burden for complex trait prediction in sorghum

Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. Though selection keeps deleterious mutations rare, their complete removal from the genome is impeded due to lack of recombination, drift, and their coupling with favorable loci. To study how deleterious mutations impact agronomic phenotypes, we identified putative deleterious mutations among ~5.5M segregating variants of 229 diverse sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that about 33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups; the caudatum shows higher mutation burden while the guinea has lower burden. Across racial groups, the mutation burden correlated negatively with biomass, plant height, Specific Leaf Area (SLA), and tissue starch content, suggesting deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (5.2%). There is no advantage in total heritable variance for SLA and starch. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, our results suggest that including putatively deleterious variants in models do not significantly improve breeding accuracy because of extensive linkage. However, knowledge of deleterious variants could be leveraged for sorghum breeding through genome editing.

genetics↗

bioRxiv2017

Leveraging Transcriptomics Data for Genomic Prediction Models in Cassava

BackgroundGenomic prediction models were, in principle, developed to include all the available marker information; with this approach, these models have shown in various crops moderate to high predictive accuracies. Previous studies in cassava have demonstrated that, even with relatively small training populations and low-density GBS markers, prediction models are feasible for genomic selection. In the present study, we prioritized SNPs in close proximity to genome regions with biological importance for a given trait. We used a number of strategies to select variants that were then included in single and multiple kernel GBLUP models. Specifically, our sources of information were transcriptomics, GWAS, and immunity-related genes, with the ultimate goal to increase predictive accuracies for Cassava Brown Streak Disease (CBSD) severity.\n\nResultsWe used single and multi-kernel GBLUP models with markers imputed to whole genome sequence level to accommodate various sources of biological information; fitting more than one kinship matrix allowed for differential weighting of the individual marker relationships. We applied these GBLUP approaches to CBSD phenotypes (i.e., root infection and leaf severity three and six months after planting) in a Ugandan Breeding Population (n = 955). Three means of exploiting an established RNAseq experiment of CBSD-infected cassava plants were used. Compared to the biology-agnostic GBLUP model, the accuracy of the informed multi-kernel models increased the prediction accuracy only marginally (1.78% to 2.52%).\n\nConclusionsOur results show that markers imputed to whole genome sequence level do not provide enhanced prediction accuracies compared to using standard GBS marker data in cassava. The use of transcriptomics data and other sources of biological information resulted in prediction accuracies that were nominally superior to those obtained from traditional prediction models.

genomics↗

bioRxiv2017

Genome-wide association mapping and genomic prediction unravels CBSD resistance in a Manihot esculenta breeding population

Cassava (Manihot esculenta Crantz), a key carbohydrate dietary source for millions of people in Africa, faces severe yield loses due to two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD). The completion of the cassava genome sequence and the whole genome marker profiling of clones from African breeding programs (www.nextgencassava.org) provides cassava breeders the opportunity to deploy additional breeding strategies and develop superior varieties with both farmer and industry preferred traits. Here the identification of genomic segments associated with resistance to CBSD foliar symptoms and root necrosis as measured in two breeding panels at different growth stages and locations is reported. Using genome-wide association mapping and genomic prediction models we describe the genetic architecture for CBSD severity and identify loci strongly associated on chromosomes 4 and 11. Moreover, the significantly associated region on chromosome 4 colocalises with a Manihot glaziovii introgression segment and the significant SNP markers on chromosome 11 are situated within a cluster of nucleotide-binding site leucine-rich repeat (NBS-LRR) genes previously described in cassava. Overall, predictive accuracy values found in this study varied between CBSD severity traits and across GS models with Random Forest and RKHS showing the highest predictive accuracies for foliar and root CBSD severity scores.

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↗