Evaluation of Soybean Entries in the Pan-African Trials for Response to Coniothyrium glycines, the Cause of Red Leaf Blotch.

Red leaf blotch (RLB), caused by the fungus Coniothyrium glycines, is an important disease of soybean known to cause yield losses across soybean-growing regions in Africa. Fungicides are one option to manage this disease, but utilization of host resistance may be a better option suited for smallholder soybean farmers in Africa. Fifty-nine soybean entries were evaluated for RLB severity in nine field locations in Ethiopia, Kenya, Uganda, and Zambia. Disease incidence was 100% and disease severity differed (P < 0.01) among entries at eight of the nine locations. Mean severity ratings ranged from 1.4 to 3.2 based on a 0-to-5 scale, with higher disease severities recorded in Ethiopia followed by Zambia. Eight of the 59 entries were common to all nine locations and had severity ratings ranging from 1.6 to 2.9. The cultivar SC Signal had the lowest RLB severity ratings in the combined analysis over locations. Based on correlations of weather variables to RLB severity, mean rainfall from planting to 30 days before assessment date had a positive correlation (r = 0.70; P = 0.035), as did mean morning maximum wind speed (r = 0.88; P = 0.016). Other variables, such as temperature and relative humidity, did not correlate to RLB severity. This is the most comprehensive report to date on the occurrence of RLB in the region, which for the first time demonstrates an association between rainfall and wind speed with RLB severity. It also represents the first extensive report evaluating soybean genotypes for resistance against RLB in multiple environments.

Genome-wide association study identified candidate genes controlling continuous storage root formation and bulking in hexaploid sweetpotato.

BACKGROUND: Continuous storage root formation and bulking (CSRFAB) in sweetpotato is an important trait from agronomic and biological perspectives. Information about the molecular mechanisms underlying CSRFAB traits is lacking. RESULTS: Here, as a first step toward understanding the genetic basis of CSRFAB in sweetpotato, we performed a genome-wide association study (GWAS) using phenotypic data from four distinct developmental stages and 33,068 single nucleotide polymorphism (SNP) and insertion-deletion (indel) markers. Based on Bonferroni threshold (p-value < 5 × 10- 7), we identified 34 unique SNPs that were significantly associated with the complex trait of CSRFAB at 150 days after planting (DAP) and seven unique SNPs associated with discontinuous storage root formation and bulking (DCSRFAB) at 90 DAP. Importantly, most of the loci associated with these identified SNPs were located within genomic regions (using Ipomoea trifida reference genome) previously reported for quantitative trait loci (QTL) controlling similar traits. Based on these trait-associated SNPs, 12 and seven candidate genes were respectively annotated for CSRFAB and DCSRFAB traits. Congruent with the contrasting and inverse relationship between discontinuous and continuous storage root formation and bulking, a DCSRFAB-associated candidate gene regulates redox signaling, involved in auxin-mediated lateral root formation, while CSRFAB is enriched for genes controlling growth and senescence. CONCLUSION: Candidate genes identified in this study have potential roles in cell wall remodeling, plant growth, senescence, stress, root development and redox signaling. These findings provide valuable insights into understanding the functional networks to develop strategies for sweetpotato yield improvement. The markers as well as candidate genes identified in this pioneering research for CSRFAB provide important genomic resources for sweetpotato and other root crops.

Recombination fraction and genetic linkage among key disease resistance genes (Co-42 /Phg-2 and Co-5/"P.ult") in common bean.

Anthracnose (Colletotrichum lindemuthianum), Angular leaf spot (Pseudocercospora griseola) and Pythium root rot are important pathogens affecting common bean production in the tropics. A promising strategy to manage these diseases consists of combining several resistance (R) genes into one cultivar. The aim of the study was to determine genetic linkage between gene pairs, Co-42 /Phg-2, on bean-chromosome Pv08 and Co-5/"P.ult" on-chromosome Pv07, to increase the efficiency of dual selection of resistance genes for major bean diseases, with molecular markers. The level of recombination was determined by tracking molecular markers for both BC3F6 and F2 generations. Recombination fraction r, among gene pairs, the likelihood of linkage, L(r), and logarithm of odds (LOD) scores were computed using the statistical relationship of likelihood which assumes a binomial distribution. The SCAR marker pair SAB3/PYAA19 for the gene pair Co-5/"P.ult" exhibited moderate linkage (r = 32 cM with a high LOD score of 9.2) for BC3F6 population, but relatively stronger linkage for the F2 population (r = 21 cM with a high LOD score of 18.7). However, the linkage among SCAR marker pair SH18/SN02, for the gene pair Co-42 /Phg-2 was incomplete for BC3F6 population (r = 47 cM with a low LOD score of 0.16) as well as F2 population (r = 44 cM with a low LOD score of 0.7). Generally, the weak or incomplete genetic linkage between marker pairs studied showed that all the four genes mentioned earlier have to be tagged with a corresponding linked marker during selection. The approaches used in this study will contribute to two loci linkage mapping techniques in segregating plant populations.

Use of low cost near-infrared spectroscopy, to predict pasting properties of high quality cassava flour.

Determination of pasting properties of high quality cassava flour using rapid visco analyzer is expensive and time consuming. The use of mobile near infrared spectroscopy (SCiO™) is an alternative high throughput phenotyping technology for predicting pasting properties of high quality cassava flour traits. However, model development and validation are necessary to verify that reasonable expectations are established for the accuracy of a prediction model. In the context of an ongoing breeding effort, we investigated the use of an inexpensive, portable spectrometer that only records a portion (740-1070 nm) of the whole NIR spectrum to predict cassava pasting properties. Three machine-learning models, namely glmnet, lm, and gbm, implemented in the Caret package in R statistical program, were solely evaluated. Based on calibration statistics (R2, RMSE and MAE), we found that model calibrations using glmnet provided the best model for breakdown viscosity, peak viscosity and pasting temperature. The glmnet model using the first derivative, peak viscosity had calibration and validation accuracy of R2 = 0.56 and R2 = 0.51 respectively while breakdown had calibration and validation accuracy of R2 = 0.66 and R2 = 0.66 respectively. We also found out that stacking of pre-treatments with Moving Average, Savitzky Golay, First Derivative, Second derivative and Standard Normal variate using glmnet model resulted in calibration and validation accuracy of R2 = 0.65 and R2 = 0.64 respectively for pasting temperature. The developed calibration model predicted the pasting properties of HQCF with sufficient accuracy for screening purposes. Therefore, SCiO™ can be reliably deployed in screening early-generation breeding materials for pasting properties.

Genome-Wide Association Analysis for Resistance to Coniothyrium glycines Causing Red Leaf Blotch Disease in Soybean.

Soybean is a high oil and protein-rich legume with several production constraints. Globally, several fungi, viruses, nematodes, and bacteria cause significant yield losses in soybean. Coniothyrium glycines (CG), the causal pathogen for red leaf blotch disease, is the least researched and causes severe damage to soybean. The identification of resistant soybean genotypes and mapping of genomic regions associated with resistance to CG is critical for developing improved cultivars for sustainable soybean production. This study used single nucleotide polymorphism (SNP) markers generated from a Diversity Arrays Technology (DArT) platform to conduct a genome-wide association (GWAS) analysis of resistance to CG using 279 soybean genotypes grown in three environments. A total of 6395 SNPs was used to perform the GWAS applying a multilocus model Fixed and random model Circulating Probability Unification (FarmCPU) with correction of the population structure and a statistical test p-value threshold of 5%. A total of 19 significant marker-trait associations for resistance to CG were identified on chromosomes 1, 5, 6, 9, 10, 12, 13, 15, 16, 17, 19, and 20. Approximately 113 putative genes associated with significant markers for resistance to red leaf blotch disease were identified across soybean genome. Positional candidate genes associated with significant SNP loci-encoding proteins involved in plant defense responses and that could be associated with soybean defenses against CG infection were identified. The results of this study provide valuable insight for further dissection of the genetic architecture of resistance to CG in soybean. They also highlight SNP variants and genes useful for genomics-informed selection decisions in the breeding process for improving resistance traits in soybean.

Continuous Storage Root Formation and Bulking in Sweetpotato.

This study investigated the phenotypic variation of continuous storage root formation and bulking (CSRFAB) growth patterns underlying the development of sweetpotato genotypes for identification of potential varieties adapted to piecemeal harvesting for small scale farmers. The research was conducted between September 2016 and August 2017 in Uganda. Genotypes from two distinct sweetpotato genepool populations (Population Uganda A and Population Uganda B) comprising 130 genotypes, previously separated using 31 simple sequence repeat (SSR) markers were used. Measurements (4 harvest times with 4 plants each) were repeated on genotypes in a randomized complete block design with 2 replications in 2 locations for 2 seasons. We developed a scoring scale of 1 to 9 and used it to compare growth changes between consecutive harvests. Data analysis was done using residual or restricted maximum likelihood (REML). Data showed a non-linear growth pattern within and between locations, seasons, and genotypes for most traits. Some genotypes displayed early initiation and increase of bulking, while others showed late initiation. Broad sense heritability of CSRFAB was low due to large GxE interactions but higher in other traits  probably due to high genetic influence and the effectiveness of the methodology. A high level of reproducibility (89%) was observed comparing 2016B and 2017A seasons (A and B are first and second season, respectively) at the National Crops Resources Research Institute (NaCRRI), Namulonge, Uganda. Choosing CSRFAB genotypes can more than double the sweetpotato production (average maximum yield of 13.1 t/ha for discontinuous storage root formation and bulking (DSRFAB) versus 28.6 t/ha for CSRFAB, demonstrating the importance of this underresearched component of storage root yield.