BLUP Research Articles

Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments

An integration of field-based phenotypic and genomic data can potentially increase the genetic gain in wheat breeding for complex traits such as grain and biomass yield. To validate this hypothesis in empirical field experiments, we compared the prediction accuracy between multi-kernel physiological and genomic best linear unbiased prediction (BLUP) model to a single-kernel physiological or genomic BLUP model for grain yield (GY) using a soft wheat population that was evaluated in four environments. The physiological data including canopy temperature (CT), SPAD chlorophyll content (SPAD), membrane thermostability (MT), rate of senescence (RS), stay green trait (SGT), and NDVI values were collected at four environments (2016, 2017, and 2018 at Citra, FL; 2017 at Quincy, FL). Using a genotyping-by-sequencing (GBS) approach, a total of 19,353 SNPs were generated and used to estimate prediction model accuracy. Prediction accuracies of grain yield evaluated in four environments improved when physiological traits and/or interaction effects (genotype × environment or physiology × environment) were included in the model compared to models with only genomic data. The proposed multi-kernel models that combined physiological and genomic data showed 35 to 169% increase in prediction accuracy compared to models with only genomic data included when heading date was used as a covariate. In general, higher response to selection was captured by the model combing effects of physiological and genotype × environment interaction compared to other models. The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context.

On relations between BLUPs under two transformed linear random-effects models

A general linear random-effects model with that includes both fixed and random effects and its two transformed models and are considered without making any restrictions on correlation of random effects and any full rank assumptions. Predictors of joint unknown parameter vectors under the transformed models and have different algebraic expressions and different properties in the contexts of the two transformed models. In this situation, establishing results on relations and making comparisons in between predictors under the two models are the main focuses. We first investigate relationships of best linear unbiased predictors (BLUPs) of general linear functions of fixed and random effects under the models and and construct several equalities for the BLUPs. Then, the comparison problem of covariance matrices of BLUPs under the models is considered. We derive from matrix rank and inertia formulas the necessary and sufficient conditions for variety of equalities and inequalities of covariance matrices’ comparisons under the models and

Multitrait differential response in a diverse potato panel under contrasting water regimes

AbstractWater stress is the most significant environmental stress in agriculture worldwide. Potato (Solanum tuberosum L.) is a drought‐susceptible crop. Water stress tolerance is a complex trait of increasing importance in potato. The objective of this work was to evaluate the differential response strategies associated with water stress tolerance in a highly diverse potato panel. Multienvironment trials were assessed, both in field and in glasshouse with contrasting water regimes. The yield, dry matter, and frying ability were measured in the field assays, whereas proline concentration (PrC), relative water content (RWC), and water consumption (WC) were measured in the glasshouse assays. A mixed linear model and ANOVA were performed. The best linear unbiased predictors (BLUPs) of every genotype for each measured variable were estimated. Pearson correlation coefficient (r) and principal component analysis (PCA) assessed the association among traits. The genotypes were ranked by their BLUPs values. The high diversity potato panel showed variability both in the response to water stress and within each water regime. In general, Solanum tuberosum spp. andigena genotypes were associated with the lowest PrC and higher yield, WC, and RWC. By using BLUPs, a panel of genotypes with good performance under water deficit was defined. This paper provides knowledge useful to design breeding strategies to water stress tolerance and new physiological research lines. The utilization of the elite genotypes identified in this study as progenitors constitute a starting point for breeding programs oriented to develop water‐stress‐tolerant varieties.

Genome-wide association studies and QTL mapping uncover the genetic architecture of ear tip-barrenness in maize.

Ear tip-barrenness (ETB) phenotype threatens crop yield, because it reduces the kernel number per ear. The genetic basis of ETB in maize remains largely unknown. Herein, a genome-wide association study (GWAS) and quantitative trait loci (QTL) mapping were jointly applied to identify the significant genetic loci interrelated with ETB. Six significant SNPs were detected at a stringent P-value threshold (1.95 × 10-6 ). Additionally, four environment-stable SNPs were co-detected across a single environment and best linear unbiased prediction (BLUP) model at a less stringent P-value threshold (1 × 10-4 ). The above 10 SNPs were closely linked to 6 candidate genes, which mainly involved seed development, photosynthesis and ethylene response. Moreover, the ratio of superior allele at each significant SNP ranged from 0 to 83.33% in 30 investigated maize elite lines. QTL mapping identified 14 QTL with phenotypic variation explained (PVE) ranging from 3.64 to 7.09%, of which one QTL (qETB2-1) was repeatedly identified in two environments. Combined analysis of GWAS and QTL mapping showed that one SNP (PZE-102175229, chromosome 2: 217 66 Mb) was located in the QTL (qETB2-2, chromosome 2: 215 90-217 82 Mb). Eighteen gene models situated in the linkage disequilibrium (LD) region of the co-localized SNP were further used to evaluate their correlation with ETB by candidate gene association analysis. Two superior haplotypes and two superior alleles were detected among 74 lines for Zm00001d007195, Zm00001d007197 and Zm00001d007201. These results provide more information for clarifying the molecular mechanism of ETB and for speeding up the genetic improvement of maize varieties.

Genomic Prediction Accuracy Using Haplotypes Defined by Size and Hierarchical Clustering Based on Linkage Disequilibrium.

Genomic prediction is an effective way to estimate the genomic breeding values from genetic information based on statistical methods such as best linear unbiased prediction (BLUP). The used of haplotype, clusters of linked single nucleotide polymorphism (SNP) as markers instead of individual SNPs can improve the accuracy of genomic prediction. Since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with a cluster of markers is higher compared to an individual marker. To make haplotypes efficient in genomic prediction, finding optimal ways to define haplotypes is essential. In this study, 770K or 50K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 3,498 cattle. Using SNP chip data, haplotype was defined in three different ways based on 1) the number of SNPs included, 2) length of haplotypes (bp), and 3) agglomerative hierarchical clustering based on LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; 5, 10, 20 or 50 SNPs on average per haplotype. A linear mixed model using haplotype to calculated the covariance matrix was applied for testing the prediction accuracy of each haplotype size. Also, conventional SNP-based linear mixed model was tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight (CWT), eye muscle area (EMA) and backfat thickness (BFT) were used as the phenotypes. This study reveals that using haplotypes generally showed increased accuracy compared to conventional SNP-based model for CWT and EMA, but found to be small or no increase in accuracy for BFT. LD clustering-based haplotypes specifically the five SNPs size showed the highest prediction accuracy for CWT and EMA. Meanwhile, the highest accuracy was obtained when length-based haplotypes with five SNPs were used for BFT. The maximum gain in accuracy was 1.3% from cross-validation and 4.6% from forward validation for EMA, suggesting that genomic prediction accuracy can be increased by using haplotypes. However, the improvement from using haplotypes may depend on the trait of interest. In addition, when the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles, thereby reducing computational costs. Therefore, finding optimal ways to define haplotypes and using the haplotype alleles as markers can improve the accuracy of genomic prediction.

Visual Field Outcomes in the Tube Versus Trabeculectomy Study

To describe visual field (VF) outcomes in the Tube Versus Trabeculectomy (TVT) Study. Cohort analysis of patients in a multicenter randomized clinical trial. A total of 122 eyes of 122 patients, with 61 eyes in both the tube shunt and trabeculectomy groups. The TVT Study is a multicenter randomized clinical trial comparing the safety and efficacy of tube shunt surgery (350-mm2 Baerveldt implant) and trabeculectomy with mitomycin C (MMC) (0.4 mg/ml for 4 minutes) in patients with previous cataract or glaucoma surgery. Enrolled patients underwent perimetry at baseline and annual follow-up visits. The VFs were included if the false-positive rate was ≤20% and false-negative rate was ≤35%. The VFs were excluded if visual acuity <20/400 or loss of ≥2 Snellen lines from baseline was attributed to an etiology other than glaucoma. Longitudinal linear mixed-effects models with best linear unbiased predictions (BLUPs) were applied to estimate rates of change in mean deviation (MD) for each treatment group. Rate of MD change during follow-up period. A total of 436 reliable VFs were analyzed, with an average of 3.6 VFs per eye. Baseline MD was-13.07 ± 8.4 decibels (dB) in the tube shunt group and-13.18 ± 8.2 dB in the trabeculectomy group (P= 0.99). The rate of change in MD was-0.60 dB/year in the tube group and-0.38 dB/year in the trabeculectomy group (P= 0.34). The 95% confidence intervals for the rates of MD change were-0.77 to-0.44 dB/year in the tube group and-0.56 to-0.20 dB/year in the trabeculectomy group. No significant difference in MD slope was seen when patients were categorized by percentage of visits with intraocular pressure (IOP) <18 mmHg or by average IOP. Univariable and multivariable risk factor analyses identified history of diabetes, elevated IOP, and worse MD as baseline factors associated with more rapid VF loss. Slow rates of VF loss were observed after randomized surgical treatment in the TVT Study, but no significant difference in the rate of VF loss was seen after tube shunt implantation and trabeculectomy with MMC. Patients with diabetes, higher IOP, and more severe VF loss at baseline were at higher risk for VF progression.

Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens.

Simple SummaryWith the rapid development of the poultry industry, outbreaks of certain avian diseases such as avian influenza, Newcastle disease, and salmonellosis can cause severe loss to local economies. An effective method to avoid this problem may be developing resistant lines on the basis of immune response traits. In our study, when the number of genotyped reference animals was small, the traditional pedigree-based method (BLUP) was more efficient than the genetic linear unbiased prediction of genomes (GBLUP) method in predicting the genetic parameters of sheep red blood cell antibody titer (SRBC), the ratio of heterophils to lymphocytes (H/L), the spleen immune index (SII) and the spleen weight at 100 d (SW) in Beijing oil chickens.Poultry diseases pose a large threat to poultry production. Selection to improve immune traits is a feasible way to prevent and control avian diseases. The objective of this study was to investigate the efficiency of estimation of genetic parameters for antibody response to avian influenza virus (Ab-AIV), antibody response to Newcastle disease virus (Ab-NDV), sheep red blood cell antibody titer (SRBC), the ratio of heterophils to lymphocytes (H/L), immunoglobulin G (IgG), the spleen immune index (SII), thymus immune index (TII), thymus weight at 100 d (TW) and the spleen weight at 100 d (SW) in Beijing oil chickens, by using the best linear unbiased prediction (BLUP) method and genomic best linear unbiased prediction (GBLUP) method. The phenotypic data used in the two methods were the same and were from 519 individuals. With the BLUP model, Ab-AIV, Ab-NDV, SRBC, H/L, IgG, TII, and TW had low heritability ranging from 0.000 to 0.281, whereas SII and SW had high heritability of 0.631 and 0.573. With the GBLUP model, all individuals were genotyped with Illumina 60K SNP chips, and Ab-AIV, Ab-NDV, SRBC, H/L and IgG had low heritability ranging from 0.000 to 0.266, whereas SII, TII, TW and SW had moderate heritability ranging from 0.300 to 0.472. We compared the prediction accuracy obtained from BLUP and GBLUP through 50 time 5-fold cross-validation (CV), and the results indicated that BLUP provided a slightly higher accuracy of prediction than GBLUP in this population.

Genome-wide association mapping for dominance effects in female fertility using real and simulated data from Danish Holstein cattle

Exploring dominance variance and loci contributing to dominance variation is important to understand the genetic architecture behind quantitative traits. The objectives of this study were i) to estimate dominance variances, ii) to detect quantitative trait loci (QTL) with dominant effects, and iii) to evaluate the power and the precision of identifying loci with dominance effect through post-hoc simulations, with applications for female fertility in Danish Holstein cattle. The female fertility records analyzed were number of inseminations (NINS), days from calving to first insemination (ICF), and days from the first to last insemination (IFL), covering both abilities to recycle and to get pregnant in the female reproductive cycle. There were 3,040 heifers and 4,483 cows with both female fertility records and Illumina BovineSNP50 BeadChip genotypes (35,391 single nucleotide polymorphisms (SNP) after quality control). Genomic best linear unbiased prediction (BLUP) models were used to estimate additive and dominance genetic variances. Linear mixed models were used for association analyses. A post-hoc simulation study was performed using genotyped heifers’ data. In heifers, estimates of dominance genetic variances for female fertility traits were larger than additive genetic variances, but had large standard errors. The variance components for fertility traits in cows could not be estimated due to non-convergence of the statistical model. In total, five QTL located on chromosomes 9, 11 (2 QTL), 19, and 28 were identified and all of them showed both additive and dominance genetic effects. Among them, the SNP rs29018921 on chromosome 9 is close to a previously identified QTL in Nordic Holstein for interval between first and last insemination. This SNP is located in the 3’ untranslated region of gene peptidylprolyl isomerase like 4 (PPIL4), which was shown to be associated with milk production traits in US Holstein cattle but not known for fertility-related functions. Simulations indicated that the current sample size had limited power to detect QTL with dominance effects for female fertility probably due to low QTL variance. More females need to be genotyped to achieve reliable mapping of QTL with dominance effects for female fertility.

Forecasting with unbalanced panel data

AbstractThis paper derives the best linear unbiased prediction (BLUP) for an unbalanced panel data model. Starting with a simple error component regression model with unbalanced panel data and random effects, it generalizes the BLUP derived by Taub (Journal of Econometrics, 1979, 10, 103–108) to unbalanced panels. Next it derives the BLUP for an unequally spaced panel data model with serial correlation of the AR(1) type in the remainder disturbances considered by Baltagi and Wu (Econometric Theory, 1999, 15, 814–823). This in turn extends the BLUP for a panel data model with AR(1) type remainder disturbances derived by Baltagi and Li (Journal of Forecasting, 1992, 11, 561–567) from the balanced to the unequally spaced panel data case. The derivations are easily implemented and reduce to tractable expressions using an extension of the Fuller and Battese (Journal of Econometrics, 1974, 2, 67–78) transformation from the balanced to the unbalanced panel data case.

Imputed spatial data: Cautions arising from response and covariate imputation measurement error

When data for observations are missing, scientists often remove those observations from an analysis, or replace them with imputations, with few other options available to these analysts. Confining an analysis to those observations with complete data can waste resources expended for, especially, those observations with near-complete data. Selectively retaining variables with complete data for observations with incomplete data can compromise mathematical properties of data analyses (e.g., covariance matrices constructed with pairwise deletion having negative eigenvalues). Unless missing data observations are a random subsample of a given sample, entirely removing these observations can result in biased statistical results. Imputations, such as those furnished by kriging, are best linear unbiased predictors (BLUPs), which can be conditional expectations. As such, they are smoothed values that may be viewed as representing attribute values with measurement error, and hence estimated attribute variance based upon them tends to be biased downward. Because, for example, regression coefficients use variance estimates in their calculations, this downward bias can propagate through a data analysis and its statistical inferences. This paper compares spatial regression analyses between complete datasets and the same datasets in which data values are suppressed and then these missing data are imputed, investigating the presence of such imputations in a response variable as well as in covariates. This study employs the following three imputation methods: kriging, spatial autoregression, and Moran eigenvector spatial filtering. Its emphasis is on predictive modeling as well as spatial data quality and uncertainty.

THREE CANDIDATE GENES AND ITS ASSOCIATION WITH QUANTITATIVE VARIATION OF EGG PRODUCTION TRAITES OF LOCAL QUAIL BY USING PCR-RFLP

The aim of study was to investigate the potential candidate gene SEMA3E, TLX and GH and their association with the economic traits, which is becoming increasingly important in poultry breeding program. A total of 72 genomic DNA samples from three female local lines of quail including (desert, brown and white) were collected to determine the association of genes with reproduction trait using PCR-RFLP Technique. According to growth performance the Best Linear Unbiased Prediction (BLUP) value in selected females for high (H) and low (L) production traits was ranged from -9.2173 to 0.3827. This value is used to estimate body weight at first egg (BWFE), age at first egg (AFE), weight at first egg (WFE), Egg number per Bird (ENPH), Average egg weight (EWTA), and hen day (HD) in three lines. The results indicate that there was a wide intra specific SEMA3E, TLX and GH variability among these local quails which identifying twelve differences genotypes. The identified genotypes for all genes had a significantly (P&lt;0.05) affected on the reproduction trait during first 150 days. The quail’s genotype AAABAA always exhibited the largest body weight at first egg in desert H line, while L line of white was greater in Egg number per bird and hen day of the genotype ACCCAC. No significant associations were observed between all loci and age at first egg trait among local quails. The results indicate that there are agreements between BLUP values with PCR-RFLP results to achieve a favorable selection response in reproductive performance of local quail in Kurdistan region, Iraqi.

Choosing parent tropical wheat genotypes through genetic dissimilarity based on REML/BLUP

Parent selection is a crucial step in breeding programs. In the present study, we evaluated the genetic diversity in tropical wheat genotypes using best linear unbiased predictions (BLUPs) by different grouping methods. We identified potential parents to compose a crossing block with the aim of improving wheat for the Brazilian Cerrado. A total of 41 tropical wheat genotypes were evaluated in a field experiment. The evaluated traits included days to flowering; disease symptoms of fusarium head blight, blast, and leaf rust; flag leaf height; plant height; spike mass; hectoliter weight; and grain yield. The BLUPs were predicted and, from these, the standardized average Euclidean distance was calculated. Then, UPGMA, Tocher, and principal component clusters were generated from this genotypic distance matrix. Evaluating genetic diversity based on BLUP allowed the identification of two groups of highly dissimilar genotypes with high estimated genotypic values with which to compose a partial diallel.

Multivariate best linear unbiased predictor as a tool to improve multi-trait selection in sugarcane

Abstract: The objective of this work was to evaluate the use of the multivariate best linear unbiased predictor (BLUP) method for multi-trait selection, to estimate the genetic parameters in sugarcane (Saccharum officinarum) genotypes. The experiment was carried out in a randomized complete block design with 21 sugarcane genotypes, in seven crop years, in a factorial arrangement with three replicates. The measured traits were: total yield of stems per hectare, total volume of juice per hectare, production of total soluble sugars, and stem length. The source variation in the crop years strongly contributed for the obtention of the expected values of the sum of squares, without causing distortions in the variance components and genetic variables. The measured traits showed genetic variability and allowed of efficient univariate and multivariate selections. The highest selection efficiency was obtained by using more than eight measurements, since they favored the estimates of heritability, accuracy, and repeatability. The 'IAC873396', 'Nova Iraí', 'IACSP 93-6006', and 'RB 835089' genotypes were superior as to the traits tested, regardless of the crop year. The BLUP multivariate technique for multi-trait selection is robust and allows of the increasing of the selection gains, accuracy, and reliability of predictions for sugarcane breeding.

Genome-wide association study of leaf chemistry traits in tobacco.

Leaf chemistry traits are some of the key factors influencing tobacco quality, which can be significantly reduced by lower chemical components in cured leaf. To improve tobacco quality through breeding, genetic diversity analysis, population structure analysis, and genome-wide association studies were performed in a panel of 347 tobacco germplasms and the markers associated with five leaf chemistry traits, including total sugar (TS), reducing sugar (RS), total nitrogen (TN), nicotine (NIC), and total potassium (TP) contents were identified. Four groups were classified at a genetic distance of 0.316 by genetic diversity analysis based on coefficient parameter NEI72 using a program NTSYS-pc2.10e, whereas four well-differentiated subpopulations were postulated in the 347 tobacco accessions. A total of 47 target trait-associated SNPs was detected in at least three environments as well as the best linear unbiased predictions (BLUPs) across all environments, among which two, two, four, six, and one highly suggestive associated SNPs were repeatedly detected in all environments and BLUPs for TS, RS, TN, NIC, and TP, respectively. On the basis of the phenotypic effects of the alleles corresponding to suggestive associated SNPs, five tobacco accessions harboring favorable alleles with elite phenotypic performance in leaf chemistry traits were identified. The results could facilitate quality tobacco breeding for higher leaf chemistry trait contents through molecular marker-assisted approaches.

Assessing the sensitivities of genomic selection for growth and wood quality traits in lodgepole pine using Bayesian models

Genomic selection is an attractive option to complement the existing investments of the lodgepole pine (Pinus contorta Douglas) breeding program in British Columbia, Canada. There were two cycles of progeny testing established from 1984 to 2006 connected by full- and half-sib family structure that span a diverse range of ecosystems and climates. The relationship structure across the program is ideal for genomic selection, but it is unclear how genomic selection models will perform using a fixed content array with 51,213 single-nucleotide polymorphism (SNP) markers and different amounts of relatedness between the training and selection populations, across testing cycles of different ages, and across environments for growth and wood quality traits. Through cross-validation, we compared the sensitivity of genomic selection using two Bayesian models (Bayes B and C) with best linear unbiased prediction (BLUP) using a realized relationship matrix (GBLUP) and a pedigree (ABLUP). GEBVs from GBLUP were used to approximate the true breeding value for comparing models. Prediction accuracy was very high when there was relatedness between the training and validation populations (0.81 for Bayes C and 0.83 for GBLUP), but dropped considerably when relatedness was removed (0.27 for Bayes C and 0.29 for GBLUP). Prediction accuracy was high when predicting between test cycles and was highest when older first-cycle progeny tests were used to predict for younger trees in second-cycle tests (0.68 for Bayes C). Marker-based (Bayesian) and relationship-based (GBLUP) genomic selection models performed well and should be considered as a tool to complement the lodgepole pine breeding program in British Columbia.

Genome-wide association mapping of flooding tolerance in soybean

Flooding threatens soybean production and limits soybean yields worldwide. The most effective and economic approach to decrease loss of yield due to flooding is to develop flood-tolerant soybean cultivars. The objective of this study was to identify genetic loci and candidate genes associated with flooding tolerance. A panel of 384 soybean plant introductions (PIs) was evaluated for flooding tolerance in two consecutive years in the field. The plant foliar damage score was used to index soybean response to flooding stress. A total of 42,291 SNP markers were obtained from the Illumina Infinium SoySNP50K BeadChip database. After filtration for quality control, 31,125 SNPs were used for genome-wide association mapping utilizing four different models (regression linear model (GLM), mixed linear model (MLM), compressed mixed linear model (CMLM), and enriched compressed mixed linear model (ECMLM)). Fourteen SNPs were identified to be associated with flooding tolerance across all environments and models at a significance level of −Log10 (P) ≥ 2.5. Five SNPs were located within the coding regions of five candidate genes. Several PIs with lower best linear unbiased prediction (BLUPs) of the breeding values and a large number of favorable flood-tolerant alleles were found as new genetic sources for use in soybean breeding programs.

Genetic and non-genetic factors influencing birth weight in the Tuli cattle breed of Zimbabwe

Genetic and non-genetic factors were estimated for birth weight in the Tuli cattle breed of Zimbabwe. Performance records ( n = 1154) were obtained from the Livestock Identification Trust (LIT) for the period (2008–2014) for three farms; X ( n = 494), Z ( n = 143) and Y ( n = 517). We used the Restricted Maximum Likelihood (REML) for fitting the Sire model. The model used the farm, season, sex, year and dam age as fixed effects while the sire was fitted as a random variable. Best Linear Unbiased Prediction (BLUP) was used to predict breeding values (BVs). The mean birth weight was (30.89±0.128). Wet season had higher birth (31.62±0.27) weight than dry season (30.62±0.144); however, the difference was not statistically significant. The other factors, farm, sex, year and dam age had a significant effect (P &lt;0.05) on birth weight. Heritability and repeatability estimates were 0.44 and 0.35, respectively. Based on the results, the high heritability estimate for birth weight suggests that the trait responds well to selection. Sire breeding values ranged from -4.400 to 6.845 with the majority of sires between -2 and 2, showing that there was stabilising selection over the years to minimise both overweights and underweights.

Visual selection of Urochloa ruziziensis genotypes for green biomass yield

The breeding program of Urochloa ruziziensis evaluates many genotypes in initial phases. Evaluations through grades might make the selection less costly. The aim of this study was to verify the efficiency of visual selection for green biomass yield in relation to different selection strategies, such as mass selection by phenotypic mean, BLUP (Best Linear Unbiased Prediction) and at random. For this purpose, 2,309 regular genotypes were evaluated in an augmented block design in two cuts. The evaluators gave grades for plant vigor, and later, the plots were measured for green biomass yield. The coincidences of the selected genotypes were estimated by different selection strategies. Then, 254 clones of the genotypes selected in different strategies were evaluated in a clonal test in a triple lattice design in four cuts. The statistical analyses were performed in SAS using the Mixed procedure. The regular genotype level and clone-mean basis heritabilities were 31.16 and 62.91%, respectively, for green mass yield. The expected selection gains were 21.09% (visual), 25.43% (phenotypic mean), and 27.5% (BLUP). Moreover, the realized heritabilities for these strategies were 15.58, 11.87, and 15.86%, respectively, which might be associated with genotype by environment interaction. Therefore, the visual selection could be a useful strategy in initial phases of a U. ruziziensis breeding program because the efficiency was moderate to high in relation to phenotypic mean and BLUP.

Intrapopulation recurrent selection in sweet sorghum for improving sugar yield

Sweet sorghum [Sorghum bicolor (L.) Moench] accumulates fermentable sugars in the stalk making this crop an important bioenergy feedstock. The objectives of this study were to implement an intrapopulation recurrent selection program to increase the sugar yield in sweet sorghum [Sorghum bicolor (L.) Moench] and to estimate the genetic progress after a recurrent selection cycle for the main agro-industrial traits of interest. The base population for cycle 0 (C0) was developed from 15 fertility restorer lines using a genetic male-sterile source to promote recombination. A total of 196 half-sib progeny from C0 were evaluated in experiments with a 14 × 14 lattice design in the 2016–2017 season. The following traits were evaluated: days to flowering (DF), plant height (PH), fresh biomass yield (FBY), total soluble solids content (TSS) and tons of Brix per hectare (TBH). Data were analyzed using the mixed model approach. Genetic variation was observed among the C0 half-sib progeny for all of the agro-industrial traits studied. The 40 best progeny were selected based on the best linear unbiased predictions (BLUPs) for TBH, which were then recombined to form the base population for cycle 1 (C1). Subsequently, 40 half-sib progeny from each cycle (C0 and C1) were randomly selected and evaluated in experiments with a 9 × 9 lattice design to estimate the genetic progress for DF, PH, FBY, TSS and TBH traits. The genetic progress was significant, with increases of 3.73% for PH, 8.04% for FBY, and 8.65% for TBH, demonstrating the efficiency of the intrapopulation recurrent selection program used.

Alternative Approaches for Assessing Cassava Brown Streak Root Necrosis to Guide Resistance Breeding and Selection.

Cassava brown streak disease (CBSD) caused by the rapidly evolving cassava brown streak viruses (CBSVs), causes immense yield losses to the cassava value chain in eastern and southern Africa. Western Africa, another region that heavily depends on cassava is under eminent threat from CBSD. Resistance breeding is the best practical solution. However, complexities associated with CBSD resistance screening i.e., variable root sampling units, limit systematic attainment of genetic progress. Accordingly, we compared efficiency of five CBSD root necrosis assessment methods to guide selection: cassava brown streak disease root incidence (CBSDRi), cassava brown streak disease root severity (CBSDRs), cassava brown streak disease root severity computed as harmonic mean (CBSD-Harmonic), proportion-based root necrosis index (CBSD-proportion), and standardized root necrosis index (CBSD-standardized). The indexes (CBSD-proportion and CBSD-standardized) correct for variable sample size. We analyzed CBSD root necrosis data of 256 clones evaluated across 12 environments. Higher and variable standard errors were associated with root severity score 1 (no CBSD root necrosis). Lowest and highest plot-based heritability were respectively registered for CBSD-standardized (0.22) and CBSD-proportion (0.71). CBSDRs was only positively correlated with CBSDRi (r = 0.92) and CBSD-Harmonic (r = 0.97). Using best linear unbiased predictions (BLUPs), we ranked the top 15 CBSD resistant clones; only one clone (UG130014) featured in all the five assessment methods; two clones (UG130006 and UG120156) featured in four (CBSD-Harmonic, CBSDRi, CBSDRs, and CBSD-standardized); and five clones (UG120180, UG120063, UG130002, UG130033, and UG120183) featured in three methods (CBSD-Harmonic, CBSDRi, and CBSDRs). Influence of sample size was also quantified by sub-setting and analyzing CBSDRs data to have plots with at least 40 or 30 roots. Data stabilization was evident in plots with 30 roots. The significant influence of root sample sizes on overall ranking of clones, justifies the use of CBSD root necrosis indexes in early selection stages i.e., seedling and/or clonal trials, that are often characterized by high variations in roots assessed per plot. It is expected that this information will provide a foundation for harmonizing and/or optimizing on-going and future CBSD resistance breeding efforts.