Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits

Breeding programs of the species Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and robusta). The use of hybrid families and individuals has been less common. The objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits. As such, 71 conilon clones, 56 robusta clones, and 20 hybrid families were evaluated over several years for the following traits: vegetative vigor, incidence of rust and cercosporiosis, fruit ripening time, fruit size, plant height, canopy diameter, and yield per plant. Components of variance and genetic parameters were estimated via residual maximum likelihood (REML) and genotypic values were predicted via best linear unbiased prediction (BLUP). Genetic variability among parents (clones) and hybrid families was detected for most of the evaluated traits. The Mulamba-Rank index suggests potential gains up to 17% for the genotypic aggregate of traits in the hybrid population. An intrapopulation recurrent selection within the hybrid population would be the best breeding strategy because the genetic variability, narrow and broad senses heritabilities and selective accuracies for important traits were maximized in the crossed population. Besides, such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and this maximizes the genetic gain for unit of time.

D-Optimal Split-plot Designs With Random Whole Plot Factor and Fixed Sub-plot Factor

This study examines design preference in Completely Randomized (CR) split-plot experiments involving random whole plot factor effect and fixed sub-plot factor effect. Many previous works on optimally designing split-plot experiments assumed only factors with fixed levels. However, the cases where interests are on random factors have received little attention. These problems have similarities with optimal design of experiments for fixed parameters of non-linear models because the solution rely on the unknown parameters.  Design Space (DS) containing exhaustive list of balanced designs for a fixed sample size were compared for optimality using the product of determinants of derived information matrices of the Maximum Likelihood (ML) estimators equivalent to random and fixed effect in the model. Different magnitudes of components of variance configurations where variances of factor effects are larger than variances of error term were empirically used for the comparisons. The results revealed that the D-optimal designs are those with whole plot factor levels greater than replicates within each level of whole plot.

Reliability of multi-modal MRI-derived brain phenotypes for multi-site assessment of neuropsychiatric complications of SARS-CoV-2 infection

Background: Magnetic resonance imaging (MRI) of the brain could be a key diagnostic and research tool for understanding the neuropsychiatric complications of COVID-19. For maximum impact, multi-modal MRI protocols will be needed to measure the effects of SARS-CoV2 infection on the brain by diverse potentially pathogenic mechanisms, and with high reliability across multiple sites and scanner manufacturers. Methods: A multi-modal brain MRI protocol comprising sequences for T1-weighted MRI, T2-FLAIR, diffusion MRI (dMRI), resting-state functional MRI (fMRI), susceptibility-weighted imaging (swMRI) and arterial spin labelling (ASL) was defined in close approximation to prior UK Biobank (UKB) and C-MORE protocols for Siemens 3T systems. We iteratively defined a comparable set of sequences for General Electric (GE) 3T systems. To assess multi-site feasibility and between-site variability of this protocol, N=8 healthy participants were each scanned at 4 UK sites: 3 using Siemens PRISMA scanners (Cambridge, Liverpool, Oxford) and 1 using a GE scanner (Kings College London). Over 2,000 Imaging Derived Phenotypes (IDPs) measuring both data quality and regional image properties of interest were automatically estimated by customised UKB image processing pipelines. Components of variance and intra-class correlations were estimated for each IDP by linear mixed effects models and benchmarked by comparison to repeated measurements of the same IDPs from UKB participants. Results: Intra-class correlations for many IDPs indicated good-to-excellent between-site reliability. First considering only data from the Siemens sites, between-site reliability generally matched the high levels of test-retest reliability of the same IDPs estimated in repeated, within-site, within-subject scans from UK Biobank. Inclusion of the GE site resulted in good-to-excellent reliability for many IDPs, but there were significant between-site differences in mean and scaling, and reduced ICCs, for some classes of IDP, especially T1 contrast and some dMRI-derived measures. We also identified high reliability of quantitative susceptibility mapping (QSM) IDPs derived from swMRI images, multi-network ICA-based IDPs from resting-state fMRI, and olfactory bulb structure IDPs from T1, T2-FLAIR and dMRI data. Conclusion: These results give confidence that large, multi-site MRI datasets can be collected reliably at different sites across the diverse range of MRI modalities and IDPs that could be mechanistically informative in COVID brain research. We discuss limitations of the study and strategies for further harmonization of data collected from sites using scanners supplied by different manufacturers. These protocols have already been adopted for MRI assessments of post-COVID patients in the UK as part of the COVID-CNS consortium.

Combining Ability for Quantitative Traits Under Normal And Heat Stress Conditions in Maize (Zea Mays L.)

Investigation was carried out to ascertain the genetic architecture for heat tolerance and yield components from diallel crosses in maize (Zea mays L.). The combining ability in both the normal and heat stress conditions revealed highly significant mean squares due to general combining ability (GCA) and specific combining ability (SCA) in both the direct and reciprocal crosses for all the characters except for anthesis-silking interval in normal condition of the reciprocal crosses. Estimate of components of variance for 13 characters revealed higher SCA variance than that of GCA and reciprocal crosses for all the characters. CML 411 was good general combiner for grain yield in both the conditions, whereas, CML 306 and CML 307 were good general combiners in heat stress condition, and CML 164, CML 304 and CML 305 were average general combiners in normal condition. On the basis of high yield, high SCA and at least high GCA of seed parent, the CML 411*CML 305 and CML 411*CML 307 were identified as promising hybrids for normal and heat stress conditions, respectively. Bangladesh J. Bot. 50(3): 659-669, 2021 (September)

A Comparison of Geosampling and Random Walk Methods for Household Sample Selection in Uttar Pradesh, India

Our research evaluates an innovative sampling technique for household surveys called “geosampling” which leverages recent advances in geographic information systems, computer vision algorithms, and satellite imagery. We compare geosampling to the random walk method. We conducted two surveys in Uttar Pradesh, India: one using geosampling (1,026 completes) and another using random walk (939 completes). We compare the two sampling techniques along three dimensions: (a) performance indicators—response rates and contact attempts; (b) sample composition; and (c) components of variance. We help researchers understand the survey contexts for which geosampling and random walk are best suited.

Gauge repeatability and reproducibility study of airborne sound isolation measurements

A gauge repeatability and reproducibility study (GRR) uses analysis of variations (ANOVA) on an appropriately designed experiment to separate and quantify the components of the overall uncertainty. The authors have previously presented results of GRR studies of the measurement of airborne and impact insulation of floor-ceiling and demising wall assemblies in several apartment buildings, in which the uncertainty in the measurement method and the variability of the nominally-identical assemblies were compared. The results of two additional GRR studies on measurements of airborne noise isolation of wood stud demising walls are presented. The first study, like previous studies, evaluates the components of variance attributable to operator, repeatability, and part. The second study uses a fixed operator and part, and evaluates the variance due to loudspeaker type, position, and level on the measured noise reduction. The measurement standard (ASTM E336) gives limited guidance on the type and location of the loudspeaker used on the source side, and this study can inform whether changes in the standard with regards to the loudspeakers could reduce the uncertainty in measurement.

Revelation of Epistasis through Triple Test Cross (TTC) Analysis in Rice (Oryza sativa L.)

Background: An investigation was performed to identify epistasis, additive, dominance components of genetic variation and yield and yield variability attributing characteristics by triple test cross testing involving three testers (P1, P2 and F1) and ten rice lines.Methods: The study materials consisted of F1 seeds of three crosses, involving six parents namely, ASD16, ADT47, ASD18, CO51, TKM9 and MTU 7029. They are evaluated in randomized complete block design with three replications. Observations were reported for seven traits, namely plant height, number of tillers per plant, number of productive tillers per plant, length of panicle, number of grains per panicle, weight of 1000 grains and yield of grain per plant on five randomly selected plants per replication.Result: The segregating population of three crosses exhibited wide range of variability for most of the traits. The difference between GCV and PCV was low for most of the characters indicated less influence of environment. Among the three crosses ASD18 x CO15 recorded high percent of heritability and genetic advance for grain yield per plant. The estimate of total epistasis revealed that i type of epistasis (additive x additive) was highly significant for number of tillers per plant, number of productive tillers per plant, panicle length and 1000 grain weight. The effect of the additive (D) variance was very important for all the traits except the number of grains per panicle. Across all traits, the degree of dominance (H / D)1/ 2 was less than unity ( less than 1) suggesting, partial of dominance. Since, the pre dominance component of epistasis in autogamous crop is additive x additive (i type), it was suggested that the selection may be post ponded to later generation until all the non-additive components of variance has been mitigated to additive components.

Estimation of genetic parameters for test-day milk yield in Girolando cows using a random regression model

ABSTRACT The objective of this study was to estimate the components of variance and genetic parameters of test-day milk yield in first lactation Girolando cows, using a random regression model. A total of 126,892 test-day milk yield (TDMY) records of 15,351 first-parity Holstein, Gyr, and Girolando breed cows were used, obtained from the Associação Brasileira dos Criadores de Girolando. To estimate the components of (co) variance, the additive genetic functions and permanent environmental covariance were estimated by random regression in three functions: Wilmink, Legendre Polynomials (third order) and Linear spline Polynomials (three knots). The Legendre polynomial function showed better fit quality. The genetic and permanent environment variances for TDMY ranged from 2.67 to 5.14 and from 9.31 to 12.04, respectively. Heritability estimates gradually increased from the beginning (0.13) to mid-lactation (0.19). The genetic correlations between the days of the control ranged from 0.37 to 1.00. The correlations of permanent environment followed the same trend as genetic correlations. The use of Legendre polynomials via random regression model can be considered as a good tool for estimating genetic parameters for test-day milk yield records.

Vector space algebra for scaling and centering relationship matrices under non-Hardy–Weinberg equilibrium conditions

Background Scales are linear combinations of variables with coefficients that add up to zero and have a similar meaning to “contrast” in the analysis of variance. Scales are necessary in order to incorporate genomic information into relationship matrices for genomic selection. Statistical and biological parameterizations using scales under different assumptions have been proposed to construct alternative genomic relationship matrices. Except for the natural and orthogonal interactions approach (NOIA) method, current methods to construct relationship matrices assume Hardy–Weinberg equilibrium (HWE). The objective of this paper is to apply vector algebra to center and scale relationship matrices under non-HWE conditions, including orthogonalization by the Gram-Schmidt process. Theory and methods Vector space algebra provides an evaluation of current orthogonality between additive and dominance vectors of additive and dominance scales for each marker. Three alternative methods to center and scale additive and dominance relationship matrices based on the Gram-Schmidt process (GSP-A, GSP-D, and GSP-N) are proposed. GSP-A removes additive-dominance co-variation by first fitting the additive and then the dominance scales. GSP-D fits scales in the opposite order. We show that GSP-A is algebraically the same as the NOIA model. GSP-N orthonormalizes the additive and dominance scales that result from GSP-A. An example with genotype information on 32,645 single nucleotide polymorphisms from 903 Large-White × Landrace crossbred pigs is used to construct existing and newly proposed additive and dominance relationship matrices. Results An exact test for departures from HWE showed that a majority of loci were not in HWE in crossbred pigs. All methods, except the one that assumes HWE, performed well to attain an average of diagonal elements equal to one and an average of off diagonal elements equal to zero. Variance component estimation for a recorded quantitative phenotype showed that orthogonal methods (NOIA, GSP-A, GSP-N) can adjust for the additive-dominance co-variation when estimating the additive genetic variance, whereas GSP-D does it when estimating dominance components. However, different methods to orthogonalize relationship matrices resulted in different proportions of additive and dominance components of variance. Conclusions Vector space methodology can be applied to measure orthogonality between vectors of additive and dominance scales and to construct alternative orthogonal models such as GSP-A, GSP-D and an orthonormal model such as GSP-N. Under non-HWE conditions, GSP-A is algebraically the same as the previously developed NOIA model.

Use of the REML/BLUP methodology for the selection of sweet orange genotypes

The objective of this work was to select superior sweet orange (Citrus sinensis) genotypes with higher yield potential based on data from eight harvests, using the residual or restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) methodology. The experiment was carried out from 2002 to 2008 and in 2010 in the municipality of Rio Branco, in the state of Acre, Brazil. Analyzes of deviance were performed to test the significance of the components of variance according to the random effects of the used model, and parameters were estimated from individual genotypic and phenotypic variances. A selection intensity of 20% was adopted regarding genotypic selection, i.e., only the best 11 of the 55 genotypes tested were selected. The estimates of the genetic parameters show the existence of genetic variability and the selection potential of the studied sweet orange genotypes. The genotypic correlation between harvests is of low magnitude, except for the variable average fruit mass, and, as a reflex, there is a change in the ordering of the genotypes. Genotypes 5, 48, 19, 14, and 47 stand out as being the most productive, and, therefore, are the most suitable for selection purposes. Genotypes 14 and 47 show superior performance for the character set evaluated.