Regional Heritability Mapping provides insights into Dry matter (DM) Content in African white and yellow cassava populations

Open Access ArticleThe HarvestPlus program for cassava (Manihot esculenta Crantz) fortifies cassava with beta-carotene by breeding for carotene-rich tubers (yellow cassava). However, a negative correlation between yellowness and dry matter (DM) content has been identified. Here, we investigated the genetic control of DM in white and yellow cassava subpopulations. We used regional heritability mapping (RHM) to associate DM to genomic segments in both subpopulations. Significant segments were subjected to candidate gene analysis and we attempted to validate candidates using prediction accuracies. The RHM procedure was validated using a simulation approach. The RHM revealed significant hits for white cassava on chromosomes 1, 4, 5, 10, 17 and 18 while hits for the yellow were on chromosome 1. Candidate gene analysis revealed genes in the carbohydrate biosynthesis pathway including the plant serine-threonine protein kinases (SnRKs), UDP-glycosyltransferases, UDP-sugar transporters, invertases, pectinases, and regulons. Validation using 1252 unique identifiers from the SnRK gene family genome-wide recovered 50% of the predictive accuracy of whole genome SNPs for DM while validation using 53 likely (extracted from literature) genes from significant segments recovered 32%. Genes including an acid invertase, a neutral/alkaline invertase and a glucose-6-phosphate isomerase were validated based on an a priori list for the cassava starch pathway and also a fructose-biphosphate aldolase from the calvin cycle pathway. The power of the RHM procedure was estimated at 47 percent when the causal QTL generated 10% of the phenotypic variance with sample size of 451. Cassava DM genetics is complex. RHM may be useful for complex traits

Accuracies of univariate and multivariate genomic prediction models in African cassava

Open Access Journal; Published online:15 March 2017Background: Genomic selection (GS) promises to accelerate genetic gain in plant breeding programs especially for crop species such as cassava that have long breeding cycles. Practically, to implement GS in cassava breeding, it is necessary to evaluate different GS models and to develop suitable models for an optimized breeding pipeline. In this paper, we compared (1) prediction accuracies from a single-trait (uT) and a multi-trait (MT) mixed model for a singleenvironment genetic evaluation (Scenario 1), and (2) accuracies from a compound symmetric multi-environment model (uE) parameterized as a univariate multi-kernel model to a multivariate (ME) multi-environment mixed model that accounts for genotype-by-environment interaction for multi-environment genetic evaluation (Scenario 2). For these analyses, we used 16 years of public cassava breeding data for six target cassava traits and a fivefold cross-validation scheme with 10-repeat cycles to assess model prediction accuracies. Results: In Scenario 1, the MT models had higher prediction accuracies than the uT models for all traits and locations analyzed, which amounted to on average a 40% improved prediction accuracy. For Scenario 2, we observed that the ME model had on average (across all locations and traits) a 12% improved prediction accuracy compared to the uE model. Conclusions: We recommend the use of multivariate mixed models (MT and ME) for cassava genetic evaluation. These models may be useful for other plant species

The burden of Hepatitis B and C virus infections in patients with sickle cell anaemia in Jos – Nigeria

Background: Sickle cell anaemia (SCA) is an important public health problem in Nigeria associated with frequent blood transfusion. Patients with this disease are at increased risk of contracting hepatitis B and C virus through blood transfusions.Objective: The study aimed at determining the burden of hepatitis B and C virus infections in patients with sickle cell anaemia and the role of blood transfusion in these infections acquisition in Jos.Materials and methods: This was an observational cross-sectional study conducted on patients with SCA attending the Haematology Out-patient Clinic of Jos University Teaching Hospital,between November 2014 and August 2015.Consenting patients with SCA had their blood screened for anti- HBV and HCV antibodies using fourth generation Elisa techniques after completing a questionnaire.Results: One hundred and eleven patients with SCA participated. Antibodies to Hepatitis B virus (HBV) was detected in 21 (18.9%) while that to Hepatitis C virus (HCV) was found in 16 (14.7%). Seventy six (68.5%) of the participants had history of blood transfusion while 35 (31.5%) were never transfused. 22.4% and 15.8% of those transfused were positive for HBV and HCV antibodies respectively. There was no significant difference in the proportion of those positive for anti HBV or HCV with respect to their transfusion status (p=0.24 and 0.81 respectively).Conclusion: The proportion of our patients with SCA that were anti HBV and anti-HCV positive was high. Blood transfusion did not significantly influence their positive status. Strategies aimed at controlling these viral infections in these patients and the general population should be enforced after public awareness campaigns and advocacy are instituted.Keywords: Sickle Cell Anaemia, burden, Hepatitis B Virus, Hepatitis CVirus, infection, Blood Transfusio

Genetic correlation, genome-wide association and genomic prediction of portable NIRS predicted carotenoids in cassava roots

Open Access Journal; Published online: 04 Dec 2019Random forests (RF) was used to correlate spectral responses to known wet chemistry carotenoid concentrations including total carotenoid content (TCC), all-trans β-carotene (ATBC), violaxanthin (VIO), lutein (LUT), 15-cis beta-carotene (15CBC), 13-cis beta-carotene (13CBC), alpha-carotene (AC), 9-cis beta-carotene (9CBC), and phytoene (PHY) from laboratory analysis of 173 cassava root samples in Columbia. The cross-validated correlations between the actual and estimated carotenoid values using RF ranged from 0.62 in PHY to 0.97 in ATBC. The developed models were used to evaluate the carotenoids of 594 cassava clones with spectral information collected across three locations in a national breeding program (NRCRI, Umudike), Nigeria. Both populations contained cassava clones characterized as white and yellow. The NRCRI evaluated phenotypes were used to assess the genetic correlations, conduct genome-wide association studies (GWAS), and genomic predictions. Estimates of genetic correlation showed various levels of the relationship among the carotenoids. The associations between TCC and the individual carotenoids were all significant (P 0.75, except in LUT and PHY where r < 0.3). The GWAS revealed significant genomic regions on chromosomes 1, 2, 4, 13, 14, and 15 associated with variation in at least one of the carotenoids. One of the identified candidate genes, phytoene synthase (PSY) has been widely reported for variation in TCC in cassava. On average, genomic prediction accuracies from the single-trait genomic best linear unbiased prediction (GBLUP) and RF as well as from a multiple-trait GBLUP model ranged from ∼0.2 in LUT and PHY to 0.52 in TCC. The multiple-trait GBLUP model gave slightly higher accuracies than the single trait GBLUP and RF models. This study is one of the initial attempts in understanding the genetic basis of individual carotenoids and demonstrates the usefulness of NIRS in cassava improvement

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.Bill & Melinda Gates FoundationUKaidCGIAR Research Program on Roots, Tubers and BananasPeer Revie