Restricted Maximum Likelihood Approach Research Articles

Quantifying production, processing and post-slaughter effects on pork eating quality using random effects meta-regression.

Random effects meta-regression techniques, analyzed using a restricted maximum likelihood (REML) approach, was used to determine the influence of various factors that may be experienced or imposed on pigs, carcases and pork on pork eating quality attributes and shear force of the M. longissimus dorsi (loin). This was done to inform the development of a pathway based eating quality system for pork. Estimated means of explanatory variables were obtained for those pathway factors where sufficient published studies met the criteria for inclusion in the analysis. Due to a lack of data for interactions between factors investigated, only single factors were included as fixed terms in the REML models. This analysis identified that moisture infusion (P < 0.001), ageing for more than 2 d post-slaughter (P = 0.006) and tenderstretching (P = 0.006) each resulted in significant improvements in tenderness. Cooking loins to an endpoint temperature of ≥ 80°C negatively impacted both tenderness (P = 0.022) and juiciness (P < 0.001) scores compared with 70 to 74°C. It was not possible to develop algorithms to reliably estimate the effects of multiple factors on pork eating quality attributes to a cuts-based level due to limited studies reporting data for treatment interactions.

Estimation of genetic parameters for growth trait of turbot using Bayesian and REML approaches

Bayesian and restricted maximum likelihood (REML) approaches were used to estimate the genetic parameters in a cultured turbot Scophthalmus maximus stock. The data set consisted of harvest body weight from 2 462 progenies (17 months old) from 28 families that were produced through artificial insemination using 39 parent fish. An animal model was applied to partition each weight value into a fixed effect, an additive genetic effect, and a residual effect. The average body weight of each family, which was measured at 110 days post-hatching, was considered as a covariate. For Bayesian analysis, heritability and breeding values were estimated using both the posterior mean and mode from the joint posterior conditional distribution. The results revealed that for additive genetic variance, the posterior mean estimate (σ a 2 =9 320) was highest but with the smallest residual variance, REML estimates (σ a 2 =8 088) came second and the posterior mode estimate (σ a 2 =7 849) was lowest. The corresponding three heritability estimates followed the same trend as additive genetic variance and they were all high. The Pearson correlations between each pair of the three estimates of breeding values were all high, particularly that between the posterior mean and REML estimates (0.996 9). These results reveal that the differences between Bayesian and REML methods in terms of estimation of heritability and breeding values were small. This study provides another feasible method of genetic parameter estimation in selective breeding programs of turbot.

What influences the home range size of free-roaming domestic dogs?

In many regions of the world domestic dogs are free roaming and live in close relationship with humans. These free-roaming domestic dogs (FRDD) can cause public health problems such as dog bites and transmission of infectious diseases. To effectively control diseases transmitted by FRDD, knowledge on the dogs' behaviour is required. To identify predictors of home range (HR) size, we collected global positioning system data from 135 FRDD living in eight Aboriginal and Torres Strait Islander communities in Northern Australia. The core HR size ranged from 0·17 to 2·33 ha and the extended HR size from 0·86 to 40·46 ha. Using a linear mixed effect model with a Restricted Maximum Likelihood approach, the dog's sex and reproductive status were identified as predictors of roaming. Non-castrated males had the largest HRs, followed by neutered females. Also, FRDDs were found to roam further during the pre- than the post-wet season. These findings have implications for infectious disease spread. Identification of risk groups for disease spread within a population allows for more targeted disease response and surveillance. Further investigation of predictors of roaming in other FRDD populations worldwide would increase the external validity of such studies.

Genetic parameters of body weight and ascites in broilers: effect of different incidence rates of ascites syndrome

ABSTRACT1. The objectives of the current study were to investigate the effect of incidence rate (5%, 10%, 20%, 30% and 50%) of ascites syndrome on the expression of genetic characteristics for body weight at 5 weeks of age (BW5) and AS and to compare different methods of genetic parameter estimation for these traits.2. Based on stochastic simulation, a population with discrete generations was created in which random mating was used for 10 generations. Two methods of restricted maximum likelihood and Bayesian approach via Gibbs sampling were used for the estimation of genetic parameters. A bivariate model including maternal effects was used. The root mean square error for direct heritabilities was also calculated.3. The results showed that when incidence rates of ascites increased from 5% to 30%, the heritability of AS increased from 0.013 and 0.005 to 0.110 and 0.162 for linear and threshold models, respectively.4. Maternal effects were significant for both BW5 and AS. Genetic correlations were decreased by increasing incidence rates of ascites in the population from 0.678 and 0.587 at 5% level of ascites to 0.393 and −0.260 at 50% occurrence for linear and threshold models, respectively.5. The RMSE of direct heritability from true values for BW5 was greater based on a linear-threshold model compared with the linear model of analysis (0.0092 vs. 0.0015). The RMSE of direct heritability from true values for AS was greater based on a linear-linear model (1.21 vs. 1.14).6. In order to rank birds for ascites incidence, it is recommended to use a threshold model because it resulted in higher heritability estimates compared with the linear model and that BW5 could be one of the main components of selection goals.

Comparisons of early growth and estimation of combining ability of parents for Strongylocentrotus intermedius and Hemicentrotus pulcherrimus sea urchin hybrid families

PDF HTML阅读 XML下载 导出引用 引用提醒 中间球海胆与马粪海胆杂交家系早期生长比较和亲本配合力估计 DOI: 作者: 作者单位: 大连海洋大学, 农业部北方海水增养殖重点实验室, 辽宁 大连 116023 作者简介: 张伟杰(1984-),男,实验师,从事棘皮动物遗传育种研究.E-mail:zhangweijie@dlou.edu.cn 通讯作者: 中图分类号: S917 基金项目: 农业部农业科研杰出人才及创新团队项目；辽宁省农业攻关及成果产业化项目（2015203003）. Comparisons of early growth and estimation of combining ability of parents for Strongylocentrotus intermedius and Hemicentrotus pulcherrimus sea urchin hybrid families Author: Affiliation: Key Laboratory of Mariculture & Stock Enhancement in the North China's Sea, Ministry of Agriculture;Dalian Ocean University, Dalian 116023, China Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为比较中间球海胆（）杂交家系间的生长差异，评估杂交家系亲本在体重上的配合力，构建了种间杂交家系共42个。采用动物模型、约束性最大似然法（REML）和最佳线性无偏预测（BLUP）等方法估计了杂交海胆250、295和340日龄体重的育种值，以及各亲本的一般配合力（GCA）和各亲本组合的特殊配合力（SCA）。采用方差分析方法，比较了体重表型值和育种值在杂交家系间的差异。结果表明，3个生长阶段杂交家系间体重的表型值和育种值差异均达到极显著水平（<0.01），体重育种值在家系间的差异大于表型值，在50%家系留种率下，育种值选择和表型值选择的家系相同率介于88.89%~94.44%，育种值选择具有0.93%~4.83%的选择效率优势；250、295和340日龄各父本的一般配合力分别介于-0.22~0.33，-0.31~0.41和-0.29~0.31，各母本的一般配合力分别介于-0.24~0.33，-0.31~0.41和-0.28~0.28，各父母本组合的特殊配合力分别介于-0.07~0.09，-0.10~0.13和-0.32~0.32，M5、M6和M17等父本的一般配合力始终较高，F9、F11和F19等母本的一般配合力始终较高，M5F9和M6F11等杂交组合的特殊配合力始终较高。 Abstract:To compare the growth differences between sea urchin hybrid families and evaluate the parents' combining ability in body weight, 42 hybrid families were produced. Using the restricted maximum likelihood and best linear unbiased prediction approaches, the breeding value of body weight at 250, 295, and 340 days of age, the general combining ability (GCA) of male and female parents and the specific combining ability (SCA) of parental combinations were evaluated. Differences in the phenotypic value and breeding value of body weight among families were compared using variance analysis. The results showed that both the phenotypic and breeding values of body weight significantly differed among families (<0.01). The breeding value of body weight showed a greater variation among families than the phenotypic value. When the proportion of the selected families was 50%, 88.89%-94.44% of the selected families were identical between the phenotypic and breeding value selection methods, and the breeding value selection method had a 0.93%-4.83% better selection efficiency. At the ages of 250, 295, and 340 days, the calculated GCAs of the male parents were -0.22-0.33, -0.31-0.41, and -0.29-0.31 whereas those of the female parents were -0.24-0.33, -0.31-0.41, and -0.28-0.28, respectively. The calculated SCAs of the parental combinations were -0.07-0.09, -0.10-0.13, and -0.32-0.32. At the ages of 250, 295, and 340 days, the male parents M5, M6, and M17 and the female parents F9, F11, and F19 always had higher GCAs than the other parents. The parental combinations M5F9 and M6F11 always had higher SCAs than the other combinations. The results provide a reference point for progeny testing and family selection in hybrid families. 参考文献 相似文献 引证文献

A clustering cure rate model with application to a sealant study

ABSTRACTIn this paper, the destructive negative binomial (DNB) cure rate model with a latent activation scheme [V. Cancho, D. Bandyopadhyay, F. Louzada, and B. Yiqi, The DNB cure rate model with a latent activation scheme, Statistical Methodology 13 (2013b), pp. 48–68] is extended to the case where the observations are grouped into clusters. Parameter estimation is performed based on the restricted maximum likelihood approach and on a Bayesian approach based on Dirichlet process priors. An application to a real data set related to a sealant study in a dentistry experiment is considered to illustrate the performance of the proposed model.

Estimation of genotypic correlation and heritability of apricot traits, using restricted maximum likelihood in repeated measures data

In estimation of genetic parameters in perennial tree species on the basis of analysis of variance (ANOVA), heterogeneity of years and genotype × environment interaction for data sets during the juvenility to maturity life period is ignored. Therefore, a linear mixed model based on restricted maximum likelihood (REML) approximation for modeling of covariance structure of longitudinal data can improve our ability to analyze repeated measures data. In the present research, a modeling of variance-covariance structure by mixed model based on the REML approach has been used for characteristics of 26 apricot genotypes recorded during three years. Fitting unstructured covariance (UN) models for all traits indicated a great heterogeneity of variances among repeated years and the trends of response variables in the genotypes (except for RWC) was due to imperfect correlation of subjects measured in different years. Based on the same structure, positive correlations were estimated among fruit set, potassium content, and yield of pistil in repetitive years, and most traits showed high heritability estimation. To our knowledge, this is the first report in plant that genotypic correlation and heritability and their standard errors are estimated in a repeated measures data over years using REML approximation.

Fast and Accurate Construction of Confidence Intervals for Heritability

Estimation of heritability is fundamental in genetic studies. Recently, heritability estimation using linear mixed models (LMMs) has gained popularity because these estimates can be obtained from unrelated individuals collected in genome-wide association studies. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. Existing methods for the construction of confidence intervals and estimators of SEs for REML rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals. Here, we show that the estimation of confidence intervals by state-of-the-art methods is inaccurate, especially when the true heritability is relatively low or relatively high. We further show that these inaccuracies occur in datasets including thousands of individuals. Such biases are present, for example, in estimates of heritability of gene expression in the Genotype-Tissue Expression project and of lipid profiles in the Ludwigshafen Risk and Cardiovascular Health study. We also show that often the probability that the genetic component is estimated as 0 is high even when the true heritability is bounded away from 0, emphasizing the need for accurate confidence intervals. We propose a computationally efficient method, ALBI (accurate LMM-based heritability bootstrap confidence intervals), for estimating the distribution of the heritability estimator and for constructing accurate confidence intervals. Our method can be used as an add-on to existing methods for estimating heritability and variance components, such as GCTA, FaST-LMM, GEMMA, or EMMAX.

Changes to sperm surface carbohydrates following exposure to seminal plasma and freezing

Epididymal, ejaculated and frozen–thawed spermatozoa are exceptionally different sperm types. When placed directly into the uterus all sperm types display high fertility. However, when forced to traverse the ovine cervix (following vaginal or cervical AI), epididymal and frozen–thawed ram spermatozoa display dramatically lower fertility than fresh ejaculated spermatozoa. We suggest that changes to the outer carbohydrate-rich glycoprotein coat of spermatozoa represent a potential reason for altered cervical transit ability, as this is the primary interface with the female environment. As such, we used lectin binding to investigate modifications of the carbohydrate portion of sperm glycoconjugates in response to seminal plasma exposure and freezing. Fresh and frozen ejaculated spermatozoa and fresh epididymal spermatozoa were subjected to a swim up to remove loosely associated glycoproteins and remnants of freezing diluent. Spermatozoa were stained with fluorescein isothiocyanate (FITC) conjugated lectins (LPA, PNA, WGA and ConA), specific for the sugars of interest (sialic acid, galactose, N-acetylglucosamine andmannose, respectively) and counter-stained with propidium iodide (PI). Median FITC fluorescence of the PI negative (live) population was compared amongst treatments using a restricted maximum likelihood approach. The results provide strong evidence that both exposure to seminal plasma and freezing are processes which alter the outermost surface of spermatozoa. Seminal plasma exposure significantly increased N-acetylglucosamine (WGA; p<0.001) on the sperm membrane. Conversely, freezing led to a significant decrease in available galactose (PNA; p<0.01) and N-acetylglucosamine (WGA; p<0.001) as well as increased availability of mannose (ConA; p<0.001), typically a carbohydrate located proximal to the membrane in glycoconjugates. The observed changes lend support to the theory that while exposure to seminal plasmamay orchestrate meaningful alteration of the sperm surface, the freezing process further modifies this important region, possibly to its detriment. This is the first evidence that industry standard freezing of ram semen leads to large and significant changes in selected sperm surface carbohydrates. Further research is required to fully elucidate the consequenceof these changesandestablishpossiblemeans to prevent or repair the damage to the glycoprotein coat of ram spermatozoa. Research support by AWI and NSWSMBA Trust.

Estimates of genetic parameters for different body weights andmuscle and fat depths of Karayaka lambs

In the current paper the direct additive and maternal genetic effects on birth, weaning (at 90 days of age), and scanning (at 20 weeks of age) weights and muscle and fat depths of the ribeye area in Karayaka lambs were investigated. Analyses were carried out by the restricted maximum likelihood approach, fitting 6 animal models with various combinations of direct and maternal effects. The best model was chosen after testing for improvement in the log-likelihood values. Direct heritability (h 2 d ) for all traits decreased when maternal genetic effects were included in the models. The maternal heritability (h 2 m ) ranged from 0.15 to 0.22 for birth weight, from 0.04 to 0.14 for weaning weight, and from 0.08 to 0.16 for scanning weight. The effects of h 2 m on muscle depth and fat depth of the ribeye area were not considered due to their insignificance. The permanent environmental effect of the dam was significant for birth, weaning, and scanning weights. Moderate negative genetic correlations (r am ) between the direct and maternal genetic effects were observed, which were significant for birth (-0.179 and -0.221), weaning (-0.310 and -0.415), and scanning (-0.116 and -0.141) weights. As a result, h 2 d and h 2 m can be used as selection criteria for birth, weaning, and scanning weights in Karayaka lambs.

Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects

Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.

Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation.

Key messageA novel reparametrization-based INLA approach as a fast alternative to MCMC for the Bayesian estimation of genetic parameters in multivariate animal model is presented. Multi-trait genetic parameter estimation is a relevant topic in animal and plant breeding programs because multi-trait analysis can take into account the genetic correlation between different traits and that significantly improves the accuracy of the genetic parameter estimates. Generally, multi-trait analysis is computationally demanding and requires initial estimates of genetic and residual correlations among the traits, while those are difficult to obtain. In this study, we illustrate how to reparametrize covariance matrices of a multivariate animal model/animal models using modified Cholesky decompositions. This reparametrization-based approach is used in the Integrated Nested Laplace Approximation (INLA) methodology to estimate genetic parameters of multivariate animal model. Immediate benefits are: (1) to avoid difficulties of finding good starting values for analysis which can be a problem, for example in Restricted Maximum Likelihood (REML); (2) Bayesian estimation of (co)variance components using INLA is faster to execute than using Markov Chain Monte Carlo (MCMC) especially when realized relationship matrices are dense. The slight drawback is that priors for covariance matrices are assigned for elements of the Cholesky factor but not directly to the covariance matrix elements as in MCMC. Additionally, we illustrate the concordance of the INLA results with the traditional methods like MCMC and REML approaches. We also present results obtained from simulated data sets with replicates and field data in rice.

Promotion Time Cure Rate Model with Bivariate Random Effects

In this article, we consider the inclusion of random effects in both the survival function for at-risk subjects and the cure probability assuming a bivariate normal distribution for those effects in each cluster. For parameter estimation, we implemented the restricted maximum likelihood (REML) approach. We consider Weibull and Piecewise Exponential distributions to model the survival function for non-cured individuals. Simulation studies are performed, and based on a real database we evaluate the performance of our proposed model. Effect of different follow-up times and the effect of considering independent random effects instead of bivariate random effects are also studied.

TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks

Abstract. We introduce topological restricted maximum likelihood (TopREML) as a method to predict runoff signatures in ungauged basins. The approach is based on the use of linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is taken into account by using water balance considerations to constrain the covariance structure of runoff and to account for the stronger spatial correlation between flow-connected basins. The restricted maximum likelihood (REML) framework generates the best linear unbiased predictor (BLUP) of both the predicted variable and the associated prediction uncertainty, even when incorporating observable covariates into the model. The method was successfully tested in cross-validation analyses on mean streamflow and runoff frequency in Nepal (sparsely gauged) and Austria (densely gauged), where it matched the performance of comparable methods in the prediction of the considered runoff signature, while significantly outperforming them in the prediction of the associated modeling uncertainty. The ability of TopREML to combine deterministic and stochastic information to generate BLUPs of the prediction variable and its uncertainty makes it a particularly versatile method that can readily be applied in both densely gauged basins, where it takes advantage of spatial covariance information, and data-scarce regions, where it can rely on covariates, which are increasingly observable via remote-sensing technology.

A practical two‐step approach for mixed model‐based kriging, with an application to the prediction of soil organic carbon concentration

SummarySoil scientists often use prediction models to obtain values at unsampled locations. The spatial variation in the soil is best captured by using the empirical best linear unbiased predictor (EBLUP) based on a restricted maximum likelihood (REML) approach that efficiently exploits available data on both mean trends and correlation structures. We proposed a practical two‐step implementation of the REML approach for model‐based kriging, exemplified by predicting soil organic carbon (SOC) concentrations in mineral soils in Estonia from the large‐scale digital soil map information and a previously established prediction model. The prediction model was a linear mixed model with soil type, physical clay content (particle size &lt; 0.01 mm) and A‐horizon thickness as fixed effects and site, transect, plot, year, year‐transect random intercepts and site‐specific random slopes for clay content. We used only the site‐specific intercept EBLUPs for estimating spatial correlation parameters as they described most of the variation in the random effects (86.8%). Fitting an exponential correlation model to these EBLUPs resulted in an estimated range of 10.5 km and the estimated proportion of the variance from the nugget effect was 0.23. The results of a simulation study showed a downwards bias that decreased with sample size. The results were validated through an external dataset, resulting in root mean square errors (RMSE) of 1.06 and 1.07% for the two‐step approach for kriging and the model with only fixed effects (no kriging), respectively. These results indicate that using the two‐step approach for kriging may improve prediction.

Evaluation of salt stress of Iranian wheat germplasm under field conditions

Salinity is one of the most serious problems of crop production worldwide. In this research, a set of different wheat landraces with high diversity collected throughout Iran, advanced lines in breeding programs, and some well-known tolerant and sensitive cultivars were used to estimate genetic parameters of agronomic traits by using the restricted maximum likelihood (REML) approach. The results showed that several genotypes such as BUMI8, Salt18, BUMI1, Check cultivar and Salt25 had no reduction in grain yield under saline conditions compared with normal conditions. Minimum reductions in grain yield were related to BUMI6, Roshan and Shahpasand genotypes. High broad-sense heritability for most traits showed that they could be used to select and improve the salt tolerance of wheat germplasm in breeding programs. Grain yield had high broad-sense heritability under normal (H2b = 0.61) and saline (H2b = 0.55) conditions. Traits related to height and number of spikes per plant showed a positive correlation with grain yield. Time to heading and to maturity showed negative correlation with grain yield. Number of kernels per spike, kernel weight per spike, number of spikes per plant and spike weight showed positive correlations with grain yield under saline conditions. A significantly negative correlation was also seen between grain yield and days to heading or days to maturity under saline conditions. High variation and moderate to high heritabilities of yield and yield components in normal and salt-stressed field conditions for Iranian bread wheat germplasm are promising to enhance the narrow genetic pool of salinity tolerance.

Estimation of genetic parameters for body measurements and their association with yearling liveweight in the Makuie sheep breed

The main objective of this study was to estimate the genetic parameters for body measurement and yearling live bodyweight traits in the Makuie sheep breed. The data set consisted of 2 144 lambs from 122 sires and 984 dams recorded in 21 years from 1989 to 2009 at the Makuie Sheep Breeding and Raising Station (MSBS). The traits that were studied were height at withers, height at rump, body length, heart girth, leg circumference and yearling weight. The analyses were carried out based on an animal model with the restricted maximum likelihood (REML) approach using a derivate-free (DF) algorithm. Six animal models were fitted, differentiated by including or excluding maternal effects, and with and without covariance between maternal and direct genetic effects. Ignoring the maternal effects, the direct heritability was estimated as 0.20, 0.24, 0.10, 0.14, 0.02 and 0.36 for height at wither, height at rump, body length, heart girth, leg circumference and yearling weight, respectively. The estimates were slightly higher when maternal effects, genetic or environmental, were ignored in the analyses. Using bivariate analysis, the estimates for additive genetic correlations ranged from 0.56 to 0.81 among the studied traits. A log-likelihood ratio test (LRT) was used to select the most appropriate univariate model for each trait. Based on LRT, the direct additive genetic and maternal permanent environmental effects were regarded as an important source of variation of the studied traits. The estimates of genetic parameters can be considered a basis for calculating selection indices for body measurements, as well as revealing their association with yearling bodyweight traits.Keywords: (Co) variance components; direct and maternal effects; heritability

A Simplification and Implementation of Random-effects Meta-analyses Based on the Exact Distribution of Cochran’s Q

The random-effects (RE) model is the standard choice for meta-analysis in the presence of heterogeneity, and the standard RE method is the DerSimonian and Laird (DSL) approach, where the degree of heterogeneity is estimated using a moment-estimator. The DSL approach does not take into account the variability of the estimated heterogeneity variance in the estimation of Cochran's Q. Biggerstaff and Jackson derived the exact cumulative distribution function (CDF) of Q to account for the variability of τ^². The first objective is to show that the explicit numerical computation of the density function of Cochran's Q is not required. The second objective is to develop an R package with the possibility to easily calculate the classical RE method and the new exact RE method. The novel approach was validated in extensive simulation studies. The different approaches used in the simulation studies, including the exact weights RE meta-analysis, the I² and τ² estimates together with their confidence intervals were implemented in the R package metaxa. The comparison with the classical DSL method showed that the exact weights RE meta-analysis kept the nominal type I error level better and that it had greater power in case of many small studies and a single large study. The Hedges RE approach had inflated type I error levels. Another advantage of the exact weights RE meta-analysis is that an exact confidence interval for τ² is readily available. The exact weights RE approach had greater power in case of few studies, while the restricted maximum likelihood (REML) approach was superior in case of a large number of studies. Differences between the exact weights RE meta-analysis and the DSL approach were observed in the re-analysis of real data sets. Application of the exact weights RE meta-analysis, REML, and the DSL approach to real data sets showed that conclusions between these methods differed. The simplification does not require the calculation of the density of Cochran's Q, but only the calculation of the cumulative distribution function, while the previous approach required the computation of both the density and the cumulative distribution function. It thus reduces computation time, improves numerical stability, and reduces the approximation error in meta-analysis. The different approaches, including the exact weights RE meta-analysis, the I² and τ² estimates together with their confidence intervals are available in the R package metaxa, which can be used in applications.

Genetic control of the performance of maize hybrids using complex pedigrees and microsatellite markers

This study seeks to quantify the importance of epistatic effects on the heterosis of maize using complex pedigrees in a single model of the so-called bi-, tri-, and tetra-alleles in an analysis with and without information from microsatellite markers. To this end, 51 inbred lines were sampled from different backgrounds, obtaining 6 double cross hybrids, 14 triple cross hybrids, and 58 single cross hybrids, for a total of 78 hybrids. Seventy-nine microsatellite markers were used in the genotyping of the 51 lines. These markers were distributed throughout the 10 linkage groups in maize. This information was used to construct an information matrix on kinship. The mixed models and restricted maximum likelihood approaches were used to estimate additive, dominant and epistatic effects. It was observed that the dominant by dominant epistasis was the most important effect related to genetic control of the heterosis in maize. Also, our study demonstrated that it is possible to exploit a large amount of information when we jointly analyze simple, double, and three-way cross hybrids under the same model. Using this approach, it is possible to dissect heterosis into several components and to adopt the best crossbreeding strategy based on the importance of each component. Additionally, it was possible to verify that the use of molecular markers improves the accuracy of calculating the epistatic and dominance effects. Thus, using the current state-of-art in quantitative genetics and statistical methods the concept of crossbreeding can be expanded to frontiers that are far beyond the traditional general and specific combining ability.

Ambulatory cell phone injuries in the United States: An emerging national concern

BackgroundOver the past 15years, the use of cell phones has increased 8-fold in the United States. Cell phone use has been shown to increase crash risks for drivers, but no systematic analyses have described injuries related to ambulatory cell phone use. ObjectiveThe purpose of this study is to describe and quantitate injuries and deaths among persons using cell phones while walking. MethodsWe searched the National Electronic Injury Surveillance System (NEISS) for emergency department (ED) reports of injuries related to phone use. The cases that returned were screened initially using words that would eliminate cases unlikely to be related to cell phone use and walking, possibly linked to distraction. The resulting cases were randomized and evaluated for consistency with predetermined case definitions by two authors blinded to the dates of the incidents. Cases that were disagreed upon were evaluated in a second screening by both authors for final case determination. National ED visit rates were estimated based on NEISS sampling methods. Annual variations were analyzed using linear regression with a restricted maximum likelihood approach. ResultsOur screening process identified 5,754 possible cases that occurred between 2000 and 2011, and 310 were agreed on as cases of cell-phone-induced distraction. The majority of the patients were female (68%) and 40years of age or younger (54%). The primary mechanism of injury was a fall (72%), and most patients were treated and released from the ED (85%). No patients died from their injuries while they were in the ED. Linear modeling by year revealed a statistically significant increase in distraction injury rates over the years of study (p<0.001 for trend). ConclusionsThe number of ED visits by ambulatory persons injured while being distracted by cell phone use has been increasing. More research is needed to determine the risks associated with walking and talking on a cell phone and to develop strategies for intervention. Practical applicationsCell phone use continues to increase both at home and outdoor environments. The use of smart phones, with their more enticing features, increases the likelihood of distraction-induced injuries even more. Manufacturers should consider the addition of tools or applications on smart phones to remind users to remain alert to outside auditory stimuli that herald external hazards and to encourage them to not use these devices while engaged in other activities.