DEVELOPMENT AND APPLICATION OF A MASS-ESTIMATION METHOD FOR WEDDELL SEALS

Traditional methods of acquiring mass data limit the ability to collect large samples from across populations of some pinnipeds, or to sample without great disturbance to the animals. In order to collect substantial samples of mass data from the Weddell seal (Leptonychotes weddellii) population in Erebus Bay, Antarctica, we developed the equipment and methods for estimating the mass of Weddell seals using digital photographs. Resulting regression models predict the mass of adult female seals to within ±13.8% of estimated mass, and ±25.9% of estimated mass for pups. We show the protocols developed are repeatable and efficient enough to be applied to a large number of animals in a relatively short period of time and may be useful for studies of other marine mammals. We caution that prediction intervals exist around mass estimates and must be accounted for when estimates are applied to biological questions. In a limited application of the method, differences in mass transfer between experienced and inexperienced maternal females and their pups were detected when prediction error variance around mass estimates was explicitly included. Similar mass‐estimation methods may therefore be useful in consideration of biological questions requiring large samples of mass previously unattainable.     

Responses of alloplasmic (cytoplasm=Triticum timopheevii) and euplasmic wheats (Triticum aestivum) to photoperiod and vernalization

Summary Heritability estimated from sire family variance components, ignoring dams, pools conventional paternal and maternal half sib estimates, in a way which is biased upward, and sub-optimal for minimizing the sampling variance. Standard error of a sire family estimate will be smaller than that of the equivalent paternal half sib estimate, but not as small as that of an estimate obtained by optimal pooling of paternal and maternal half sib estimates. If only additive genetic variance components are significant, the bias may be removed by use of a computed average genetic relationship for sire families, in place of a nominal R = 0.25. Average genetic relationship may be computed from mean and variance of dam family size within sire families. If dominance, epistatic, or maternal components are significant, this simple correction is not appropriate. In situations likely to be encountered in large domestic species such as sheep and cattle (dam family size small and uniform) bias will be negligible. The method could be useful where cost of dam identification is a limiting factor.     

Genomic best linear unbiased prediction method including imprinting effects for genomic evaluation

Background

Genomic best linear unbiased prediction (GBLUP) is a statistical method used to predict breeding values using single nucleotide polymorphisms for selection in animal and plant breeding. Genetic effects are often modeled as additively acting marker allele effects. However, the actual mode of biological action can differ from this assumption. Many livestock traits exhibit genomic imprinting, which may substantially contribute to the total genetic variation of quantitative traits. Here, we present two statistical models of GBLUP including imprinting effects (GBLUP-I) on the basis of genotypic values (GBLUP-I1) and gametic values (GBLUP-I2). The performance of these models for the estimation of variance components and prediction of genetic values across a range of genetic variations was evaluated in simulations.

Results

Estimates of total genetic variances and residual variances with GBLUP-I1 and GBLUP-I2 were close to the true values and the regression coefficients of total genetic values on their estimates were close to 1. Accuracies of estimated total genetic values in both GBLUP-I methods increased with increasing degree of imprinting and broad-sense heritability. When the imprinting variances were equal to 1.4% to 6.0% of the phenotypic variances, the accuracies of estimated total genetic values with GBLUP-I1 exceeded those with GBLUP by 1.4% to 7.8%. In comparison with GBLUP-I1, the superiority of GBLUP-I2 over GBLUP depended strongly on degree of imprinting and difference in genetic values between paternal and maternal alleles. When paternal and maternal alleles were predicted (phasing accuracy was equal to 0.979), accuracies of the estimated total genetic values in GBLUP-I1 and GBLUP-I2 were 1.7% and 1.2% lower than when paternal and maternal alleles were known.

Conclusions

This simulation study shows that GBLUP-I1 and GBLUP-I2 can accurately estimate total genetic variance and perform well for the prediction of total genetic values. GBLUP-I1 is preferred for genomic evaluation, while GBLUP-I2 is preferred when the imprinting effects are large, and the genetic effects differ substantially between sexes.     

Inheritance of an oviposition behavior by an egg parasitoid

A quantitative genetic study revealed genetic and environmental sources of variance in percentage parasitism of European corn borer egg masses and secondary sex ratios by Trichogramma nubilale. Full and half-sib groups of T. nubilale were obtained from a nested mating design, which permitted the partitioning of the variance of T. nubilale parasitism of European corn borer egg masses into additive genetic variance, maternal/dominant variance and environmental variance. A mother-daughter regression of the percentage of an egg mass parasitized allowed a determination of the direction of a potential response to selection in the event of maternal effects. No or very little additive genetic effects were associated with the percentage of eggs within a mass parasitized and secondary sex ratios, but a significant amount of the variance for both traits had a maternal and/or dominant genetic source. The relationship between mothers and daughters in egg mass parasitism was positive, and 55.4% of the progeny of a given mother had behaviors that resemble their mother. Most of the variance had an environmental and/or unknown genetic source implying potentially high phenotypic plasticity associated with all these traits. The presence of maternal effects and phenotypic plasticity could have multiple and complex effects on progeny characters and potential responses to selection.     

棉花苗期棉蚜的二项式抽样设计及其精度分析

基于棉花苗期棉蚜（Ａｐｈｉｓ ｇｏｓｓｙｐｉｉ）的２４组调查数据,利用每样方虫口不超过数阈值Ｔ（为０,１,２,…,９,１０,１５,２０,３０）头蚜虫的植株比例（ＰＴ）与种群密度（ｍ,头／株）的关系,拟合经验关系式１ｎ（ｍ）＝α＋ｂ１ｎ〔－１ｎ（ＰＴ）〕设计二项式抽样。通过对不同数阈值Ｔ的决定系数（ｒ＾２）、估计方差（Ｖａｒ（ｍ））和抽样精度（ｄ估计）等进行综合分析,结果表明该蚜虫在数阈值Ｔ为１５时     

Generalized Linear Mixed Models for Binary Data: Are Matching Results from Penalized Quasi-Likelihood and Numerical Integration Less Biased?

Background

Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation.

Methods

We performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias.

Results

Overall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the dataset

Conclusions

Given how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible.This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results.     

Prediction of breeding values and selection responses with genetic heterogeneity of environmental variance

There is empirical evidence that genotypes differ not only in mean, but also in environmental variance of the traits they affect. Genetic heterogeneity of environmental variance may indicate genetic differences in environmental sensitivity. The aim of this study was to develop a general framework for prediction of breeding values and selection responses in mean and environmental variance with genetic heterogeneity of environmental variance. Both means and environmental variances were treated as heritable traits. Breeding values and selection responses were predicted with little bias using linear, quadratic, and cubic regression on individual phenotype or using linear regression on the mean and within-family variance of a group of relatives. A measure of heritability was proposed for environmental variance to standardize results in the literature and to facilitate comparisons to "conventional" traits. Genetic heterogeneity of environmental variance can be considered as a trait with a low heritability. Although a large amount of information is necessary to accurately estimate breeding values for environmental variance, response in environmental variance can be substantial, even with mass selection. The methods developed allow use of the well-known selection index framework to evaluate breeding strategies and effects of natural selection that simultaneously change the mean and the variance.     

Consequences of paternally inherited effects on the genetic evaluation of maternal effects

Background

Mixed models are commonly used for the estimation of variance components and genetic evaluation of livestock populations. Some evaluation models include two types of additive genetic effects, direct and maternal. Estimates of variance components obtained with models that account for maternal effects have been the subject of a long-standing controversy about strong negative estimates of the covariance between direct and maternal effects. Genomic imprinting is known to be in some cases statistically confounded with maternal effects. In this study, we analysed the consequences of ignoring paternally inherited effects on the partitioning of genetic variance.

Results

We showed that the existence of paternal parent-of-origin effects can bias the estimation of variance components when maternal effects are included in the evaluation model. Specifically, we demonstrated that adding a constraint on the genetic parameters of a maternal model resulted in correlations between relatives that were the same as those obtained with a model that fits only paternally inherited effects for most pairs of individuals, as in livestock pedigrees. The main consequence is an upward bias in the estimates of the direct and maternal additive genetic variances and a downward bias in the direct-maternal genetic covariance. This was confirmed by a simulation study that investigated five scenarios, with the trait affected by (1) only additive genetic effects, (2) only paternally inherited effects, (3) additive genetic and paternally inherited effects, (4) direct and maternal additive genetic effects and (5) direct and maternal additive genetic plus paternally inherited effects. For each scenario, the existence of a paternally inherited effect not accounted for by the estimation model resulted in a partitioning of the genetic variance according to the predicted pattern. In addition, a model comparison test confirmed that direct and maternal additive models and paternally inherited models provided an equivalent fit.

Conclusions

Ignoring paternally inherited effects in the maternal models for genetic evaluation can lead to a specific pattern of bias in variance component estimates, which may account for the unexpectedly strong negative direct-maternal genetic correlations that are typically reported in the literature.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0141-5) contains supplementary material, which is available to authorized users.     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

Bias when predicting crude protein,dry matter digestibility and voluntary intake of tropical grasses by near-infrared reflectance

A study was made of the bias (non-random error) of regressions for predicting crude protein (CP) content, dry matter digestibility (DMD) in vivo and voluntary intake (VI) of forage from its near-infrared reflectance (NIRR) properties measured at 19 wavelengths. The regression was derived from 97 samples including six tropical grasses, leaf and stem fractions, pelleted and chopped forms and regrowths varying from 28 to 150 days.Bias associated with differences in feed type was proved to exist in regression equations for predicting nutritive value when it was shown that the residual errors of these regressions were significantly reduced by including the combined effects of grass species, fraction, form and age. Measures of the bias which might be expected were calculated for two contrasting situations. The internal measure of bias (IMB) was ‘estimated’ as a possible average bias of a feed type when predicting nutritive value for samples of similar types to those used when deriving the calibration equation, while the extrapolation measure of bias (EMB) was ‘estimated’ as that associated with prediction of the nutritive value for feed types not included in the calibration equation.Crude protein was estimated with a residual standard deviation (RSD) of ± 0.77% when regressed against the 19 reflectance values, but when information on grass species, parts and regrowth was also included in the regression the RSD was reduced to ± 0.46%. The maximum difference in the IMB between grass species was 0.80% crude protein, while for EMB it was 3.35% crude protein. When separated leaf and stem were considered the differences were larger.Dry matter digestibility was estimated with an RSD of ± 2.7% which decreased to ± 1.8% when grass species, age, form and study were taken into account. Grass species had only a small effect on IMB but the maximum difference for EMB between species reached 5.6%. The EMB indicated that the largest differences between deviations were associated with age of regrowth but this effect was absent with the IMB.Voluntary intake was estimated with an RSD of ± 7.2 g/kg W^0.75/day, which decreased to ± 2.5 g/kg W^0.75/day when grass species, form, part and age were taken into account. The largest bias was for pelleted and chopped forage with mean differences of 14.5 for IMB and 28.8 g/kg W^0.75/day for EMB, respectively.It was concluded that when nutritive value is predicted by NIRR the estimates may often be biased according to the grass species, plant part or physical form of the sample tested and that the pattern of bias may be complicated. In order to obtain accurate predictions from NIRR full account must be taken of this bias. This might be achieved by correcting the nutritive value estimates made from an NIRR regression, based on a large number of feed types, by addition of the bias associated with the feed type being estimated.     

Bias in the prediction of genetic gain due to mass and half-sib selection in random mating populations

The prediction of gains from selection allows the comparison of breeding methods and selection strategies, although these estimates may be biased. The objective of this study was to investigate the extent of such bias in predicting genetic gain. For this, we simulated 10 cycles of a hypothetical breeding program that involved seven traits, three population classes, three experimental conditions and two breeding methods (mass and half-sib selection). Each combination of trait, population, heritability, method and cycle was repeated 10 times. The predicted gains were biased, even when the genetic parameters were estimated without error. Gain from selection in both genders is twice the gain from selection in a single gender only in the absence of dominance. The use of genotypic variance or broad sense heritability in the predictions represented an additional source of bias. Predictions based on additive variance and narrow sense heritability were equivalent, as were predictions based on genotypic variance and broad sense heritability. The predictions based on mass and family selection were suitable for comparing selection strategies, whereas those based on selection within progenies showed the largest bias and lower association with the realized gain.     

Managing the risk of comparing estimated breeding values across flocks or herds through connectedness: a review and application

Comparing predicted breeding values (BV) among animals in different management units (e.g. flocks, herds) is challenging if units have different genetic means. Unbiased estimates of differences in BV may be obtained by assigning base animals to genetic groups according to their unit of origin, but units must be connected to estimate group effects. If many small groups exist, error of BV prediction may be increased. Alternatively, genetic groups can be excluded from the statistical model, which may bias BV predictions. If adequate genetic connections exist among units, bias is reduced. Several measures of connectedness have been proposed, but their relationships to potential bias in BV predictions are not well defined. This study compares alternative strategies to connect small units and assesses the ability of different connectedness statistics to quantify potential bias in BV prediction. Connections established using common sires across units were most effective in reducing bias. The coefficient of determination of the mean difference in predicted BV was a perfect indicator of potential bias remaining when comparing individuals in separate units. However, this measure is difficult to calculate; correlated measures such as prediction errors of differences in unit means and correlations among prediction errors are suggested as practical alternatives.     

Kriging with nonparametric variance function estimation

A method for fitting regression models to data that exhibit spatial correlation and heteroskedasticity is proposed. It is well known that ignoring a nonconstant variance does not bias least-squares estimates of regression parameters; thus, data analysts are easily lead to the false belief that moderate heteroskedasticity can generally be ignored. Unfortunately, ignoring nonconstant variance when fitting variograms can seriously bias estimated correlation functions. By modeling heteroskedasticity and standardizing by estimated standard deviations, our approach eliminates this bias in the correlations. A combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model. The approach is demonstrated on a large data set of predicted nitrogen runoff from agricultural lands in the Midwest and Northern Plains regions of the U.S.A. For this data set, the model comprises three main components: (1) the mean function, which includes farming practice variables, local soil and climate characteristics, and the nitrogen application treatment, is assumed to be linear in the parameters and is fitted by generalized least squares; (2) the variance function, which contains a local and a spatial component whose shapes are left unspecified, is estimated by local linear regression; and (3) the spatial correlation function is estimated by fitting a parametric variogram model to the standardized residuals, with the standardization adjusting the variogram for the presence of heteroskedasticity. The fitting of these three components is iterated until convergence. The model provides an improved fit to the data compared with a previous model that ignored the heteroskedasticity and the spatial correlation.     

Inter- and intra-researcher variation in measurement of morphometrics on Steller sea lions (<Emphasis Type="Italic">Eumetopias jubatus</Emphasis>)

Substantial effort has been made to increase the accuracy of photogrammetric measurement techniques, especially on marine mammals. However, accuracy of the physical measurements is rarely considered when calculating the photogrammetric accuracy. We examined inter- and intra-researcher variation in the measurement of physical morphometrics of Steller sea lions (Eumetopias jubatus) to assess their potential effects on photogrammetric accuracy and body mass predictions based on photogrammetrically derived morphometrics. Significant differences were found inter-researcher in median standard length and axillary girth and intra-researcher when measuring axillary girth of the same animals. Photogrammetrically derived estimates of these parameters are comparable to physical measurements when taking into account the inherent inter- and intra-researcher variation.     

Maternal effects on early juvenile traits inIris pumila (Iridaceae)

The variation in the maternal effects on a number of the earlyIris pumila seedling traits expressed under high and low light levels in a growthroom was studied. A reciprocal crossing design was used for the establishment of 20 full-sib families. Each family consisted of two maternal groups differing only in genotype used as a seed parent. Maternal variation was estimated from the variance between maternal groups within sibships. Two parameters for maternal variation were computed: the maternal effects coefficient m² and the coefficient of maternal variation CV_M. Maternal effects significantly accounted for the phenotypic variation in seed mass and most of the early seedling traits. Light conditions prevailing during the seedling development markedly influenced overall structure of maternal correlations. Implications of maternal correlations on the integration of juvenile phenotype have also been discussed.     

Prediction of body composition in habitually active middle-aged men.

In 45 physically active men (ages 35-67 yr) who underwent hydrostatic weighing to determine body composition, multiple regression equations were developed for the prediction of body density (D), lean body weight (LBW), fat body weight (FBW), and % fat using selected anthropometric measurements. The prediction accuracy for these parameters using several previously generated anthropometric regression equations was also determined. With equations developed from the present data a substantially higher correlation was obtained between measured and predicted LBW (r = 0.95) than between measured and predicted D (r = 0.85), FBW (r = 0.88), or % fat (r = 0.84). When previously developed equations were applied to the present sample, correlations between measured and predicted values were considerably lower (4-42%) than in the original studies; this reduction was least in the case of LBW. Analysis of previous data indicated that in selected populations total body weight can account for a relatively large fraction of the variance in LBW. LBW may be estimated quite accurately (r greater than or equal to 0.90) in physically active men with one of several regression equations which include total body weight as an independent variable.     

近红外漫反射光谱法快速测定药用植物淫羊藿总黄酮含量

利用一种基于近红外漫反射光谱技术的淫羊藿总黄酮快速定量分析方法, 测定了淫羊藿属(Epimedium)7个种中总黄酮的含量。研究结果表明, 近红外光谱经二阶导数处理、主成分分析聚类及加权多元离散校正处理后, 采用改进最小二乘法回归得到的定标模型的预测效果最佳; 定标样品集预测方程的定标决定系数、交互验证标准差及交互验证相关系数分别为0.985 5、0.023 0和0.044 3; 检验样品集方程的定标决定系数、检验标准差及偏差分别为0.965 1、0.018和0.006, 达到了快速测定淫羊藿总黄酮含量的要求。该方法为淫羊藿总黄酮的快速定量分析提供了新的依据。     

GENETIC AND MATERNAL INFLUENCES ON BRAIN AND BODY SIZE IN RANDOMBRED HOUSE MICE

Genetic and environmental (chiefly maternal) variance and covariance components were estimated for brain and body size in randombred house mice of three different ages (one, three, and five months). Heritabilities estimated from regressions of offspring on their five-month-old male parents were fairly low over all three ages, averaging about 0.2 for brain size and about 0.3 for body size. Heritability estimates from female parents were higher, however, presumably because of the influence of maternal-environmental components of variance. The total maternal impact was estimated from full-sib analyses and, for the more reliable three- and five-month ages, averaged 23% for brain size and 20% for body size. Phenotypic, genetic, and environmental correlations and regressions of brain and body size also were calculated by parent-offspring and sib-correlation techniques, the phenotypic correlations generally decreasing from about 0.4 at one month to 0.2 at three and five months. Genetic correlations of brain and body size estimated from covariances of offspring on male parents were negative whereas those from female parents were positive in sign, and this as well as positive maternal correlations was taken as evidence of the influence of maternal sources of covariance for these traits. It was concluded that, in addition to direct genetic effects, indirect genetic sources of variance and covariance mediated through the prenatal maternal environment are quite important in the determination of brain size and its association with body size.     

Application of spectrofluorometry to the prediction of PHB concentrations in a fed-batch process

On-line estimation of biopolymer production during fermentation would be a useful adjunct to the development of strategies for process control and optimization. This study examined the applicability of spectrofluorometry, along with other on-line measurements, for the prediction of poly-ß-hydroxybutyric acid (PHB) concentrations in a high-cell density fed-batch fermentation of Ralstonia eutropha. Models previously used for modelling PHB evolution with time are not sufficiently accurate for situations where transient intermediate accumulations or PHB degradation occur. Thus, the mass balance in the model was modified to account for these situations. An estimation algorithm was developed that is based on a hybrid model consisting of a dynamic mass balance of PHB where the main reaction coefficient was regressed with respect to spectrofluorometric data. The regression between the kinetic parameter and the spectrofluorometric data was accomplished using partial least squares (PLS) regression to avoid high sensitivity to noise expected from highly correlated data, such as the spectrofluorometric readings. The model accounts for dynamics of intermediates and in this way allows the prediction of dynamic behaviour in PHB concentrations that cannot be predicted with other reported mathematical models.     

Outlining folding nuclei in globular proteins

Our theoretical approach for prediction of folding/unfolding nuclei in three-dimensional protein structures is based on a search for free energy saddle points on networks of protein unfolding pathways. Under some approximations, this search is performed rapidly by dynamic programming and results in prediction of Phi values, which can be compared with those found experimentally. In this study, we optimize some details of the model (specifically, hydrogen atoms are taken into account in addition to heavy atoms), and compare the theoretically obtained and experimental Phi values (which characterize involvement of residues in folding nuclei) for all 17 proteins, where Phi values are now known for many residues. We show that the model provides good Phi value predictions for proteins whose structures have been determined by X-ray analysis (the average correlation coefficient is 0.65), with a more limited success for proteins whose structures have been determined by NMR techniques only (the average correlation coefficient is 0.34), and that the transition state free energies computed from the same model are in a good anticorrelation with logarithms of experimentally measured folding rates at mid-transition (the correlation coefficient is -0.73).