Genetic analysis of frost hardiness traits in tuber-bearingSolanum species

The inheritance of frost hardiness and cold acclimation potential traits was studied in three segregating populations derived from a cross betweenSolanum commersonii Dun. PI 243503 (cmm) andSolanum cardiophyllum Lindl., PI 184762 (cph), two parental genotypes with contrasting frost hardiness and cold acclimation potential. The levels of frost hardiness and cold acclimation potential were expressed as the LT₅₀, the temperature at which 50% of the cells in leaf discs were killed, as measured by the ion leakage method, following a controlled freeze test There was considerable variation in both frost hardiness and cold acclimation potential in all three segregating populations (F₁ F₁ xcmm, and F₁ xcph). Frost hardiness and cold acclimation potential were not correlated, suggesting that these two traits are under independent genetic control. The analysis of generation means indicated that the variation for both traits could be best explained by an additive-dominance model, with additive gene effects the most important Broad-sense heritability was 0.73 and 0.74 in the F₁ population, for frost hardiness and cold acclimation potential, respectively, and was 0.85 for either trait in the F₁ xcmm population, indicating that these two traits are highly inheritable. Our results suggest that it should be possible to incorporate the frost hardiness and cold acclimation traits from S.commersonii into cultivated potato species.     

Standard errors of heritabilities for forage breeding nurseries

Estimators for standard errors of heritability for two typical and complex forage breeding models are illustrated with estimates of variance and covariance components from least-squares and restricted maximum likelihood methods. The first experiment (Model 1) is a factorial design (two grazing management factors) with two layers of split plots (populations and grazing periods) and with plants within populations nested beneath the first split. The second model is for a sites pooling of randomized complete block experiments, with plants nested inside plots and with a split in time. One definition of heritability was applied to the plant habit character for both models in a red clover germplasm. Heritability estimates were statistically the same for the two methods for both models.     

On latent-variable model misspecification in structural measurement error models for binary response

Summary .  We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.     

A Clinically Orientated Approach for Combining Discordant and Concordant Sib Pairs

The use of extreme discordant sib pairs (EDSP) or extreme concordant sib pairs (ECSP) has recently been proposed to increase power for mapping quantitative traits in humans (RISCH and ZHANG, 1995, 1996). In this paper we propose a test statistic to jointly analyze EDSP and ECSP based on a clinical sampling procedure. This test statistic does not fulfill any optimality criteria. However, this approach is useful for quantitative traits of clinical significance for which EDSP are rare and/or expensive to ascertain. We show how sample size calculations can be adjusted for recombination using single markers, multipoint analysis, incompletely polymorphic markers and varying proportions of ECSP. If the true genetic model is unknown, the combined approach appears to be more robust than sampling based on only EDSP or only ECSP. We discuss how to find the optimal proportion of EDSP and ECSP to be included in an analysis under power considerations.     

The beta-binomial model for host specificity among organisms in trophic interactions

In this paper, we present a new stochastic model where the host specificity among organisms in trophic interactions in a community, say parasite-host interactions, is estimated by a beta-binomial model. The expected proportion of the host species in a community that a given parasite species is utilizing is modeled as a realization from an inhomogeneous Poisson process, where the rate of this process is assumed to be proportional to a beta probability distribution. The observed number of host species utilized by the parasites is then binomially distributed with the number of trials equaling the number of different host species in the sample. When the degree of polyphagy is estimated by the parameters of the beta-binomial model, quantities like community host specificity and the expected total number of parasite species that will utilize the host species in the community can be predicted as functions of the number of host species available. The predictions can then be applied in analysis of, e.g., symbiotic interactions among organisms, local species richness, and community structure.     

Fixed‐effect variance and the estimation of repeatabilities and heritabilities: issues and solutions

Linear mixed‐effects models are frequently used for estimating quantitative genetic parameters, including the heritability, as well as the repeatability, of traits. Heritability acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change, whereas repeatability informs us about the individual consistency of phenotypic traits. As quantities of biological interest, it is important that the denominator, the phenotypic variance in both cases, reflects the amount of phenotypic variance in the relevant ecological setting. The current practice of quantifying heritabilities and repeatabilities from mixed‐effects models frequently deprives their denominator of variance explained by fixed effects (often leading to upward bias of heritabilities and repeatabilities), and it has been suggested to omit fixed effects when estimating heritabilities in particular. We advocate an alternative option of fitting models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of the phenotypic variance. The approach is easily implemented and allows optimizing the estimation of phenotypic variance, for example by the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We address the estimation and interpretation of heritabilities in situations in which potential covariates are themselves heritable traits of the organism. Furthermore, we discuss complications that arise in generalized and nonlinear mixed models with fixed effects. In these cases, the variance parameters on the data scale depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.     

Robustness of the latent variable model for correlated binary data

The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992, Journal of the Royal Statistical Society, Series B 54, 3-40). Instead of modelling the binary data directly as do Liang and Zeger (1986, Biometrika 73, 13-22), the parametric marginal regression model developed by Qu et al. (1992, Biometrics 48, 1095-1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within-cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariate t-distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addtion to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficents with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.     

Heritability and correlation structure of nectar and floral morphology traits in Nicotiana alata

The heritability and genetic basis of nectar traits have been rarely studied in the field, where plants are exposed to environmental factors that could mask underlying genetic effects. Heritabilities and variance components were estimated for nectar and morphological traits of Nicotiana alata , using a partial diallel design. The main experiment was conducted in a Missouri experimental garden using a randomized block design with three plant density treatments, whereas a smaller experiment was conducted near native Brazil habitat to compare the environmental variance in traits between Missouri and Brazil. Significant heritability was detected for nectar volume and energy content, and for corolla tube length. Phenotypic correlations were significant between all traits investigated, whereas significant genetic correlations were only found between nectar volume and energy and between corolla limb width and mouth diameter. There were no significant family-by-density interactions detected in the Missouri field environment. All traits differed significantly between Missouri and Brazil environments, but significant genetic by environment (G × E) interactions between Missouri and Brazil were detected for only one trait. This study shows that nectar traits can be heritable despite considerable environmental variation.     

Effect of selection on genetic parameters of correlated traits

Summary Changes in genetic parameters of correlated traits due to the buildup of linkage (gametic phase) disequilibrium from repeated truncation selection on a single trait are studied. After several generations of selection, an equilibrium is approached where there are no further changes in genetic parameters and limiting values are reached. Formulae are derived under an infinitesimal model for these limiting values of genetic variances and covariances, heritabilities, and genetic correlations between traits directly and indirectly selected. Changes from generation zero to the limit in all these parameters become greater as heritability of the trait under direct selection increases and, to a lesser extent, as intensity of selection increases. Change in heritability of a trait under indirect selection also increases as the absolute value of the correlation between the trait under indirect and the trait under direct selection increases. The change is maximum when the initial value of heritability is close to 0.5 and insignificant when the initital value is close to zero or one. Change in the genetic correlation between the trait under direct selection and the trait under indirect selection is maximum when its initial value is close to ±0.6 and insignificant when its initial value is close to zero or ±1. Heritability of the trait indirectly selected and genetic correlation between that trait and the trait directly selected always decrease in absolute value, whereas genetic correlation between two traits indirectly selected can either decrease or increase in absolute value. It is suggested that use be made of formulae at selection equilibrium in the prediction of correlated responses after several generations of selection.     

Bayesian methods for estimating GEBVs of threshold traits

Estimation of genomic breeding values is the key step in genomic selection (GS). Manymethods have been proposed for continuous traits, but methods for threshold traits arestill scarce. Here we introduced threshold model to the framework of GS, and specifically,we extended the three Bayesian methods BayesA, BayesB and BayesCπ on the basis ofthreshold model for estimating genomic breeding values of threshold traits, and theextended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computingprocedures of the three BayesT methods using Markov Chain Monte Carlo algorithm werederived. A simulation study was performed to investigate the benefit of the presentedmethods in accuracy with the genomic estimated breeding values (GEBVs) for thresholdtraits. Factors affecting the performance of the three BayesT methods were addressed. Asexpected, the three BayesT methods generally performed better than the correspondingnormal Bayesian methods, in particular when the number of phenotypic categories was small.In the standard scenario (number of categories=2, incidence=30%,number of quantitative trait loci=50, h²=0.3), theaccuracies were improved by 30.4%, 2.4%, and 5.7% points,respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies andboth performed better than BayesTA. In conclusion, our work proved that threshold modelfits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be themethod of choice for GS of threshold traits.     

晒晾烟重要农艺性状遗传分析

以8个优良的晒晾烟品种(系)为亲本,配置完全双列杂交,进而利用基于混合线性模型的统计学方法,对晾晒烟株高、叶数、节距、腰叶长、腰叶宽以及茎围6个主要农艺性状进行遗传分析。结果表明,在多数农艺性状遗传中加性效应和显性效应都起重要作用,但所占比例有所不同,腰叶长和叶数主要受加性效应影响,而株高、节距、腰叶宽和茎围性状显性效应起主要作用;6种重要农艺性状狭义遗传率由高到低分别为:腰叶长>叶数>株高>节距>腰叶宽>茎围,其中,腰叶长和叶数狭义遗传率较高,分别为0.35和0.33,适合进行早代选择。另外,估算了供试材料的遗传效应值,并对各亲本及组合的育种利用价值进行了评价。本研究为晒晾烟重要农艺性状遗传改良提供了一定的理论依据。     

Breeding Enrichment of Genetic Variation of Grain Yield and Its Attributes in Bread Wheat under Drought Stress and Well Irrigation

Drought stress (DS) is one of the most critical environmental abiotic stresses for wheat production in the arid environments. Selection of high-yielding genotypes tolerant to DS can play a significant role in mitigation the negative impacts associated with DS. In the present study, generation means analysis (GMA) was used to study the performance of two crosses under well irrigation (WI) and deficit irrigation [cross I (Line 44 × Shandweel-1) and cross II (Line 20 × Sakha 93)]. Significant differences were observed for days to heading (DH), days to maturity (DM), plant height (PH), spike length (SL), number of spikes per plant (NS/P), number of grains per spike (NG/S), thousand-grain weight (TGW), grain yield per plant (GY/P), and proline content (PC) in the six populations of the two crosses within each irrigation level. Cross II had early maturity and the highest PC, NS/P, TGW, and GY/P regardless of the irrigation level. Cross I showed positive significant relative heterosis and heterobeltiosis for GY/P under the two irrigation levels. The inheritance of characters of cross I revealed additive, dominant, and epistatic effects, which varied with trait and stress. Additive genetic effects predominated in DH, SL, and PC, while non- additive were found in DM, NS/P, NG/S, and GY/P. Narrow-sense heritability estimates (h²_n) were high for DH and PC, moderate to high for PH and SL, moderate for DM, NG/S, NS/P, and TGW, and low for GY/P. Based on different drought indices the populations BC₁, BC₂, F₁, and P₁ of cross II and BC1 of cross I were more tolerant to drought stress. Therefore, PC, TGW and DH can be used as selection indicators to improve wheat for drought tolerance in early generations and other yield components traits in late generations. The second cross (Line 20 × Sakha 93) shows promise and is of interest to a drought tolerance breeding program, where wheat breeders can use recombinant breeding strategies to construct desirable drought stress genes. Correlation and path coefficient revealed that TGW and PC were the main contributor in grain yield in both environments.     

Effects of the demographic transition on the genetic variances and covariances of human life‐history traits

The recent demographic transitions to lower mortality and fertility rates in most human societies have led to changes and even quick reversals in phenotypic selection pressures. This can only result in evolutionary change if the affected traits are heritable, but changes in environmental conditions may also lead to subsequent changes in the genetic variance and covariance (the G matrix) of traits. It currently remains unclear if there have been concomitant changes in the G matrix of life‐history traits following the demographic transition. Using 300 years of genealogical data from Finland, we found that four key life‐history traits were heritable both before and after the demographic transition. The estimated heritabilities allow a quantifiable genetic response to selection during both time periods, thus facilitating continued evolutionary change. Further, the G matrices remained largely stable but revealed a trend for an increased additive genetic variance and thus evolutionary potential of the population after the transition. Our results demonstrate the validity of predictions of evolutionary change in human populations even after the recent dramatic environmental change, and facilitate predictions of how our biology interacts with changing environments, with implications for global public health and demography.     

Methods for the study of cytoplasmic effects on quantitative traits

Summary The methods used to study cytoplasmic effects in quantitative traits often do not measure quantitative genetic parameters, while those that do are either complicated or do not take into account situations where the expression of cytoplasmic effects does not persist, but decreases in advanced generations. We present two simple models that take cytoplasmic effects and the quantitative genetic parameters into account. One of the models (A) is for cases where cytoplasmic effects remain constant through successive generations, and the second model (B) is for traits where cytoplasm-genotype interactions are present. This model also takes into account the decreasing persistence of cytoplasmic effects with advancing generations, which is often reported in the literature.     

Quantitative analysis of correlations among flower traits in Gerbera hybrida,Compositae

Summary A Mean Correlation Response (MCR) model was developed to estimate the relative effectiveness of direct selection when other traits also respond to the selection. A measure of the relative effects of mean correlated response and direct response (R) and a measure of the relative efficiency of direct selection (IE) were applied to a genetic correlation matrix of 38 traits. These were measurements of inflorescence, receptacle and involucre, scape, disk florets, ray florets, and trans florets in the Davis population of Gerbera hybrida, Compositae. Generally, traits with high heritability had high direct and mean correlated response; these were often traits measuring disk and trans florets. Traits with low heritability had low direct and mean correlated response; these were often traits measuring the inflorescence. Traits of the inflorescence had the lowest efficiency of direct to mean correlated response.     

The genetic architecture of behavioral traits in a spider

The existence of consistent individual differences in behavior has been shown in a number of species, and several studies have found observable sex differences in these behaviors, yet their evolutionary implications remain unclear. Understanding the evolutionary dynamics of behavioral traits requires knowledge of their genetic architectures and whether this architecture differs between the sexes. We conducted a quantitative genetic study in a sexually size‐dimorphic spider, Larinioides sclopetarius, which exhibits sex differences in adult lifestyles. We observed pedigreed spiders for aggression, activity, exploration, and boldness and used animal models to disentangle genetic and environmental influences on these behaviors. We detected trends toward (i) higher additive genetic variances in aggression, activity, and exploration in males than females, and (ii) difference in variances due to common environment/maternal effects, permanent environment and residual variance in aggression and activity with the first two variances being higher in males for both behaviors. We found no sex differences in the amount of genetic and environmental variance in boldness. The mean heritability estimates of aggression, activity, exploration, and boldness range from 0.039 to 0.222 with no sizeable differences between females and males. We note that the credible intervals of the estimates are large, implying a high degree of uncertainty, which disallow a robust conclusion of sex differences in the quantitative genetic estimates. However, the observed estimates suggest that sex differences in the quantitative genetic architecture of the behaviors cannot be ruled out. Notably, the present study suggests that genetic underpinnings of behaviors may differ between sexes and it thus underscores the importance of taking sex differences into account in quantitative genetic studies.     

Multivariate analysis for various quantitative traits in wheat advanced lines

Genetic diversity play key role in the germplasm improvement which is directly correlated with the crop production. Various statistical techniques have been used to study diversity among different genotypes. Among these techniques multivariate is most frequently used one for the genetic association of genotypes. In the present study a total of 64 advance lines included one check cultivar were evaluated under the field conditions of Cereal Crop Research Institute, Pirsabaq Nowshera, Pakistan during September 2017. Data were recorded for nine different parameters. Multivariate analysis divided the total 64 genotypes into four groups. The first five PCs with Eigen values > 1 contributed 86.95% of the variability amongst genotypes. Characters with maximum values in PC1 were Spikelets spike^-1 (SPPS) (0.732), spike length (SPL) (0.722) and biological yield (BY) (0.607), PC2 comprised of 100-grain weight (TGW) (0.605), grain yield (GY) (0.482) while days to heading (DH) (0.393), for PC3 major contributors were BY (0.550) and number of tillers meter square^-1 (NTPS) (0.289), the contribution of PC4 were flag leaf area (FLA) (0.716) and SPL (0.298) and the maximum values for various traits in PC5 were SPPS (0.732), SPL (0.722) and BY (0.607). From the findings of present study best performing lines can be directly recommended for general cultivation or to be used in future breeding programs.     

Hypothesis testing for the genetic background of quantitative traits

The testing of Bayesian point null hypotheses on variance component models have resulted in a tough assignment for which no clear and generally accepted method exists. In this work we present what we believe is a succeeding approach to such a task. It is based on a simple reparameterization of the model in terms of the total variance and the proportion of the additive genetic variance with respect to it, as well as on the explicit inclusion on the prior probability of a discrete component at origin. The reparameterization was used to bypass an arbitrariness related to the impropriety of uninformative priors onto unbounded variables while the discrete component was necessary to overcome the zero probability assigned to sets of null measure by the usual continuous variable models. The method was tested against computer simulations with appealing results.     

Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response

Summary .  Generalized linear mixed models (GLMMs) are widely used in the analysis of clustered data. However, the validity of likelihood-based inference in such analyses can be greatly affected by the assumed model for the random effects. We propose a diagnostic method for random-effect model misspecification in GLMMs for clustered binary response. We provide a theoretical justification of the proposed method and investigate its finite sample performance via simulation. The proposed method is applied to data from a longitudinal respiratory infection study.