A theoretical model for quantitatively inherited traits influenced by nuclear-cytoplasmic interactions

Characterization for Drought Tolerance and Physiological Efficiency in Novel Cytoplasmic Male Sterile Sources of Sunflower (Helianthus annuus L.)

Sunflower is sensitive to drought, and furthermore, sunflower hybrids display limited cytoplasmic diversity. In addition, the wild cytoplasmic sources of sunflower are not well explored for their potential to introduce drought tolerance into newly developed hybrids. Therefore here, we carried out a Line × Tester-based genetic study using 19 sunflower genotypes representing, 13 cytoplasmic male sterile (CMS) lines from wild and conventional sources, 2 maintainer lines, and 4 restorer lines. The CMS and maintainer lines were crossed with restorer lines to develop sixty F1 hybrids. The parents and their hybrids were evaluated under two water regimes, normal irrigation and drought stress (i.e., withholding water). A total of twelve important plant descriptors were studied over a period of two years and the significant differences between parents and hybrids are reported here. More specifically, hybrid lines were higher in average values for all the descriptors. The contribution of female parent was more prominent in the expression of traits in hybrids as compared to male parents. The CMS sources varied significantly regarding seed yield per plant and other physiological traits. Proline content in the leaves of all the genotypes was three times higher in the water stress regime. Accession CMS-PKU-2A was identified as the best general combiner for leaf area and specific leaf weight., whereas CMS-234A was the best general combiner for biological yield and photosynthetic efficiency under both conditions. The cross combinations CMS-ARG-2A × RCR-8297, CMS-234A × P124R, and CMS-38A × P124R were found significant for biological yield, seed yield and oil content under both environments. Overall, this study provides useful information about the cytoplasmic effects on important sunflower traits and drought stress tolerance.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic models for seed traits of plants

Two Genetic models (an embryo model and an endosperm model) were proposed for analyzing genetic effects of nuclear genes, cytoplasmic genes, maternal genes, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment interaction for quantitative traits of plant seed. In these models, the NCI effects were partitioned into direct additive and dominance NCI components. Mixed linear model approaches were employed for statistical analysis. For both balanced and unbalanced diallel cross designs, Monte Carlo simulations were conducted to evaluate unbiasedness and precision of estimated variance components of these models. The results showed that the proposed methods work well. Random genetic effects were predicted with an adjusted unbiased prediction method. Seed traits (protein content and oil content) of Upland cotton (Gossypium hirsutum L.) were analyzed as worked examples to demonstrate the use of the models.

Analysis of genetic effects of nuclear-cytoplasmic interaction on quantitative traits: genetic model for diploid plants

A genetic model was proposed for simultaneously analyzing genetic effects of nuclear, cytoplasm, and nuclear-cytoplasmic interaction (NCI) as well as their genotype by environment (GE) interaction for quantitative traits of diploid plants. In the model, the NCI effects were further partitioned into additive and dominance nuclear-cytoplasmic interaction components. Mixed linear model approaches were used for statistical analysis. On the basis of diallel cross designs, Monte Carlo simulations showed that the genetic model was robust for estimating variance components under several situations without specific effects. Random genetic effects were predicted by an adjusted unbiased prediction (AUP) method. Data on four quantitative traits (boll number, lint percentage, fiber length, and micronaire) in Upland cotton (Gossypium hirsutum L.) were analyzed as a worked example to show the effectiveness of the model.

Mitochondrial genetic effects on latent class variables associated with susceptibility to alcoholism

We report the results of statistical genetic analyses of data from the Collaborative Study on the Genetics of Alcoholism prepared for the Genetic Analysis Workshop 14 to detect and characterize maternally inherited mitochondrial genetic effects on variation in latent class psychiatric/behavioral variables employed in the diagnosis of alcoholism. Using published extensions to variance decomposition methods for statistical genetic analysis of continuous and discrete traits we: 1) estimated the proportion of the variance in each trait due to the effects of mitochondrial DNA (mtDNA), 2) tested for pleiotropy, both mitochondrial genetic and residual additive genetic, between trait pairs, and 3) evaluated whether the simultaneous estimation of mitochondrial genetic effects on these traits improves our ability to detect and localize quantitative trait loci (QTL) in the nuclear genome. After correction for multiple testing, we find significant (p < 0.009) mitochondrial genetic contributions to the variance for two latent class variables. Although we do detect significant residual additive genetic correlations between the two traits, there is no evidence of a residual mitochondrial genetic correlation between them. Evidence for autosomal QTL for these traits is improved when linkage screens are conditioned on significant mitochondrial genetic effects. We conclude that mitochondrial genes may contribute to variation in some latent class psychiatric/behavioral variables associated with alcoholism.

Does accounting for mitochondrial genetic variation improve the fit of genetic models?

We describe a simple variance component model for estimating the effect of mitochondrial DNA (mtDNA) inheritance on quantitative trait variation. The model is applied to quantitative trait Q5 in the simulated general population data from Genetic Analysis Workshop (GAW) 12. Although the mitochondrial effect on Q5 is small (5.3%) and the power of the method to detect the effect is correspondingly low, analysis over the available population replicates demonstrates that the effect of maternal relatedness can be detected and estimated accurately.

Genetic epidemiologic methods to screen for matrilineal inheritance in mitochondrial disorders

We propose a method to screen for the matrilineal inheritance in mitochondrial disorders by comparing the risk of disease in a person whose mother is affected or whose maternal grandmother or aunt or uncle is affected to the risk of disease in a person whose father is affected or whose paternal grandmother or aunt or uncle is affected using a modification of the reconstructed cohort design. Sampling of pedigrees is accomplished via probands and must not be influenced by family history. The cohort of the proband's offspring, and offspring of the proband's siblings, can be analyzed using survival analysis. Cox proportional hazards model, Bonney's [(1986) Biometrics 42:611-625] model, and Liang's [(1991) Genet Epidemiol 8:329-338] model. Mitochondrial transmission can be distinguished from X-linked transmission by examining sex-specific patterns of disease expression in matrilineally transmitted diseases. To illustrate our epidemiologic method, we apply our screening method to pedigrees of two disorders which have been proposed to have a mitochondrial DNA component to their inheritance.

Analysis of cytoplasmic and maternal effects. II. Genetic models for triploid endosperms

Genetic models for quantitative traits of triploid endosperms are proposed for the analysis of direct gene effects, cytoplasmic effects, and maternal gene effects. The maternal effect is partitioned into maternal additive and dominance components. In the full genetic model, the direct effect is partitioned into direct additive and dominance components and high-order dominance component, which are the cumulative effects of three-allele interactions. If the high-order dominance effects are of no importance, a reduced genetic model can be used. Monte Carlo simulations were conducted in this study for demonstrating unbiasedness of estimated variance and covariance components from the MINQUE (0/1) procedure, which is a minimum norm quadratic unbiased estimation (MINQUE) method setting 0 for all the prior covariances and 1 for all the prior variances. Robustness of estimating variance and covariance components for the genetic models was tested by simulations. Both full and reduced genetic models are shown to be robust for estimating variance and covariance components under several situations of no specific effects. Efficiency of predicting random genetic effects for the genetic models by the MINQUE (0/1) procedure was compared with the best linear unbiased prediction (BLUP). A worked example is given to illustrate the use of the reduced genetic model for kernel growth characteristics in corn (Zea mays L.).

Analysis of cytoplasmic and maternal effects I. A genetic model for diploid plant seeds and animals

A genetic model for modified diallel crosses is proposed for estimating variance and covariance components of cytoplasmic, maternal additive and dominance effects, as well as direct additive and dominance effects. Monte Carlo simulations were conducted to compare the efficiencies of minimum norm quadratic unbiased estimation (MINQUE) methods. For both balanced and unbalanced mating designs, MINQUE (0/1), which has 0 for all the prior covariances and 1 for all the prior variances, has similar efficiency to MINQUE(θ), which has parameter values for the prior values. Unbiased estimates of variance and covariance components and their sampling variances could be obtained with MINQUE(0/1) and jackknifing. A t-test following jackknifing is applicable to test hypotheses for zero variance and covariance components. The genetic model is robust for estimating variance and covariance components under several situations of no specific effects. A MINQUE(0/1) procedure is suggested for unbiased estimation of covariance components between two traits with equal design matrices. Methods of unbiased prediction for random genetic effects are discussed. A linear unbiased prediction (LUP) method is shown to be efficient for the genetic model. An example is given for a demonstration of estimating variance and covariance components and predicting genetic effects.

An alternative model for additive and cytoplasmic genetic and maternal effects on lactation

Results from a recent study that presented evidence suggesting a lack of cytoplasmic inheritance of lactation traits were reexamined using a model that included effects of additive genotype, cytoplasmic genotype, their interaction, and maternal influences. Under this model, equivalent estimates of heritability from daughter-dam and grand-daughter-grand-dam regression that exceed those from paternal half-sib correlation were consistent with the presence of cytoplasmic inheritance. A definitive answer to the question of cytoplasmic inheritance of lactation traits awaits a designed experiment, likely employing transfer of identical nuclei into varying cytoplasmic backgrounds.