Expression of nuclear-cytoplasmic interactions on quantitatively inherited traits from interspecific matings of oat (Avena sativa L. and A. sterilis L.)

From a biological perspective, cytoplasmic effects are probably due to interactions of nuclear and cytoplasmic factors. For example, an enzyme involved in photosynthesis, ribulose-1,5-biscarboxylase, is encoded by both cytoplasmic and nuclear DNA. In this study, the phenomena of cytoplasmic and nuclear-cytoplasmic interaction effects on quantitatively inherited traits of oats (Avena sativa L. and A. sterilis L.) were investigated;Genetic models were fit to generation means of isopopulations from four matings involving A. sterilis accessions and the Cornbelt oat (A. sativa) variety \u27CI 9170\u27. One mating had an isopopulation that exhibited nuclear-cytoplasmic heterosis. The models accurately described generation means in three matings and included additive nuclear and cytoplasmic interaction effects. It appeared that assumptions that simplified the algebra of the genetic models caused a lack of fit in one mating, and that alternative models should be developed. Based upon the best fitting model for each mating, predicted graing yield of advanced backcross generations was calculated. Actual grain yields of advanced backcross populations did not agree with the predicted values;The failure of the genetic models to describe generation means from all four matings prompted the development of an alternative model based upon molecular studies of cytoplasmic genetics. Unlike the nuclear genome, the cytoplasmic genome (a) consists of single, circular, duplex molecules of DNA, (b) is transmitted solely through the maternal parent in oats, and (c) exhibits little or no variability within cytoplasmic lines of descent. These biological features were incorporated into a quantitative genetic model which describes the genotypic value of an individual. The model was used to derive theoretical components of genetic variability in random mating population. Estimation of some variance components from the model is possible with a reciprocal mating design;Twelve F(,2)-derived lines of 76 cytoplasmic isopopulations were evaluated for seven traits. All traits exhibited significant nuclear-cytoplasmic interactions, but none exhibited consistent cytoplasmic effects. Nuclear-cytoplasmic heterosis was detected for each trait in 5 to 20% of the isopopulations with A. sterilis cytoplasm

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction

An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods can be large. Thus, efficient methods for identifying combinations of factor levels that produce most accurate GPs is needed. Herein, we employ response surface methods (RSMs) to find the experimental conditions that produce the most accurate GPs. We illustrate RSM with an example of simulated doubled haploid populations and identify the combination of factors that maximize the difference between prediction accuracies of best linear unbiased prediction (BLUP) and support vector machine (SVM) GP methods. The greatest impact on the response is due to the genetic architecture of the population, heritability of the trait, and the sample size. When epistasis is responsible for all of the genotypic variance and heritability is equal to one and the sample size of the training population is large, the advantage of using the SVM method vs. the BLUP method is greatest. However, except for values close to the maximum, most of the response surface shows little difference between the methods. We also determined that the conditions resulting in the greatest prediction accuracy for BLUP occurred when genetic architecture consists solely of additive effects, and heritability is equal to one

Parametric and Nonparametric Statistical Methods for Genomic Selection of Traits with Additive and Epistatic Genetic Architectures

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cp. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE

Genetic variation and quantitative trait loci associated with developmental stability and the environmental correlation between traits in maize

11 páginas, 4 figuras, 5 tablas.There is limited experimental information about the genetic basis of micro-environmental variance (VE) (developmental stability) and environmental correlations. This study, by using a population of maize recombinant inbred lines (RIL) and simple sequence repeat (SSR) polymorphic markers, aims at the following: firstly, to quantify the genetic component of the VE or developmental stability for four traits in maize and the environmental correlation between these traits, and secondly, to identify quantitative trait loci (QTLs) that influence these quantities. We found that, when estimating variances and correlations and testing their homogeneity, estimates and tests are needed that are not highly dependent on normality assumptions. There was significant variation among the RILs in VE and in the environmental correlation for some of the traits, implying genetic heterogeneity in the VE and environmental correlations. The genetic coefficient of variation of the environmental variance (GCVVE) was estimated to be 20%, which is lower than estimates obtained for other species. A few genomic regions involved in the stability of one trait or two traits were detected, and these did not have an important influence on the mean of the trait. One region that could be associated with the environmental correlations between traits was also detected.The National Plan for Research and Development of Spain (project code AGL2006-13140) is acknowledged for financial support. B. Ordas acknowledges a contract from the Spanish National Research Council (I3P Program).Peer reviewe

Accuracy and Training Population Design for Genomic Selection on Quantitative Traits in Elite North American Oats

Genomic selection (GS) is a method to estimate the breeding values of individuals by using markers throughout the genome. We evaluated the accuracies of GS using data from five traits on 446 oat (Avena sativa L.) lines genotyped with 1005 Diversity Array Technology (DArT) markers and two GS methods (ridge regression–best linear unbiased prediction [RR-BLUP] and BayesCπ) under various training designs. Our objectives were to (i) determine accuracy under increasing marker density and training population size, (ii) assess accuracies when data is divided over time, and (iii) examine accuracy in the presence of population structure. Accuracy increased as the number of markers and training size become larger. Including older lines in the training population increased or maintained accuracy, indicating that older generations retained information useful for predicting validation populations. The presence of population structure affected accuracy: when training and validation subpopulations were closely related accuracy was greater than when they were distantly related, implying that linkage disequilibrium (LD) relationships changed across subpopulations. Across many scenarios involving large training populations, the accuracy of BayesCπ and RR-BLUP did not differ. This empirical study provided evidence regarding the application of GS to hasten the delivery of cultivars through the use of inexpensive and abundant molecular markers available to the public sector