Journal articles on the topic 'Trait estimation'
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Fanourakis, Dimitrios, Filippos Kazakos, and Panayiotis A. Nektarios. "Allometric Individual Leaf Area Estimation in Chrysanthemum." Agronomy 11, no. 4 (April 18, 2021): 795. http://dx.doi.org/10.3390/agronomy11040795.
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A model for estimating the area of individual leaves (LA) by employing their dimensions was developed for chrysanthemum. Further hypotheses were tested: (a) LA estimation is improved by considering blade length (Lb) rather than leaf length (L), and (b) a reasonable LA estimation can be attainable by considering L in conjunction to a shape trait, which is cultivar dependent. For the model development, six cultivars were employed (1500 leaves in total), while for model validation, an independent set of nine cultivars was utilized (1125 leaves in total). Several characteristics were digitally assessed in fully expanded leaves which included petiole length, leaf L, width (W), perimeter, shape traits (aspect ratio, circularity, roundness, solidity), together with LA. LA estimation was more accurate by considering both L and W, as compared to a single dimension. A linear model, employing the product of L by W as independent variable, provided the most accurate LA estimation (R2 = 0.84). The model validation indicated a highly significant correlation between computed and measured LA (R2 = 0.88). Replacing L by Lb reasonably predicted LA (R2 = 0.832) but at some expense of accuracy. Contrary to expectation, considering L (or W) and a cultivar-specific shape trait generally led to poor LA estimations.
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Lopez, Bryan Irvine, Ju-Hwan Son, Kangseok Seo, and Dajeong Lim. "Estimation of Genetic Parameters for Reproductive Traits in Hanwoo (Korean Cattle)." Animals 9, no. 10 (September 24, 2019): 715. http://dx.doi.org/10.3390/ani9100715.
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Genetic parameters for the reproductive traits of Hanwoo cattle were estimated using data obtained from 15,355 cows in 92 herds across South Korea, which were inseminated from May 1997 to July 2016. An “average information” restricted maximum likelihood (REML) procedure that fit in single-trait and multi-trait animal models was used to estimate the variance components of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL). Results showed the low estimates of heritability for all reproductive traits from both single-trait and multi-trait models. Estimates of heritability for AFC were 0.08 and 0.10 with single-trait and multi-trait models, respectively, while the estimates of heritability using the same animal models ranged from 0.01 to 0.07, 0.01 to 0.09, and 0.10 to 0.16 for CI, DO, and GL, accordingly. While AFC showed positive genetic correlations of 0.52 and 0.46 with CI and DO, respectively, the estimates of genetic and phenotypic correlations of GL with AFC were close to zero. Moreover, phenotypic correlations of GL with CI and DO were also close to zero; however, the corresponding genetic correlations were 0.13 and –0.38 for CI and DO, respectively. These estimated variance components and genetic correlations for reproductive traits can be utilized for genetic improvement programs of Hanwoo cattle.
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Nagy, I., J. Farkas, I. Curik, G. Gorjanc, P. Gyovai, and Zs Szendrő. "Estimation of additive and dominance variance for litter size components in rabbits." Czech Journal of Animal Science 59, No. 4 (April 15, 2014): 182–89. http://dx.doi.org/10.17221/7342-cjas.
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Additive, dominance, and permanent environmental variance components were estimated for the number of kits born alive, number of kits born dead, and total number of kits born of a synthetic rabbit line (called Pannon Ka). The data file consisted of 11 582 kindling records of 2620 does collected between the years 1996–2013. The total number of animals in the pedigree files was 4012. The examined traits were evaluated using single-trait and two-trait (number of kits born alive-dead) animal models containing all or part of the following effects: additive genetic effects, permanent environmental effects, dominance effects. Heritability estimates calculated using the basic single-trait and two-trait models were 0.094 ± 0.018 and 0.090 ± 0.016 for number of kits born alive, 0.037 ± 0.010 and 0.041 ± 0.012 for number of kits born dead, and 0.117 ± 0.018 for total number of kits born, respectively. The relative significance of permanent environmental effects was 0.069 ± 0.014 and 0.069 ± 0.012 for number of kits born alive, 0.025 ± 0.011 and 0.023 ± 0.010 for number of kits born dead, and 0.060 ± 0.013 for total number of kits born, respectively. Using the extended single-trait and two-trait models, the ratios of the dominance components compared to the phenotypic variances were 0.048 ± 0.008 and 0.046 ± 0.007 for number of kits born alive, 0.068 ± 0.006 and 0.065 ± 0.006 for number of kits born dead, and 0.005 ± 0.0073 for total number of kits born, respectively. Genetic correlation coefficients between number of kits born alive and number of kits born dead were 0.401 ± 0.171 and 0.521 ± 0.182, respectively. Spearman’s rank correlations between the breeding values of the different single-trait models were close to unity in all traits (0.992–0.990). Much lower breeding value stability was found for two-trait models (0.384–0.898), especially for number of kits born dead. Results showed that the dominance components for number of kits born alive and number of kits born dead were not zero and affected the ranking of the animals (based on the breeding values).
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Cappa, Eduardo P., and Rodolfo JC Cantet. "Bayesian inference for normal multiple-trait individual-tree models with missing records via full conjugate Gibbs." Canadian Journal of Forest Research 36, no. 5 (May 1, 2006): 1276–85. http://dx.doi.org/10.1139/x06-024.
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In forest genetics, restricted maximum likelihood (REML) estimation of (co)variance components from normal multiple-trait individual-tree models is affected by the absence of observations in any trait and individual. Missing records affect the form of the distribution of REML estimates of genetics parameters, or of functions of them, and the estimating equations are computationally involved when several traits are analysed. An alternative to REML estimation is a fully Bayesian approach through Markov chain Monte Carlo. The present research describes the use of the full conjugate Gibbs algorithm proposed by Cantet et al. (R.J.C. Cantet, A.N. Birchmeier, and J.P. Steibel. 2004. Genet. Sel. Evol. 36: 4964) to estimate (co)variance components in multiple-trait individual-tree models. This algorithm converges faster to the marginal posterior densities of the parameters than regular data augmentation from multivariate normal data with missing records. An expression to calculate the deviance information criterion for the selection of linear parameters in normal multiple-trait models is also given. The developments are illustrated by means of data from different crosses of two species of Pinus.
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Zhu, Anqi, Nana Matoba, Emma P. Wilson, Amanda L. Tapia, Yun Li, Joseph G. Ibrahim, Jason L. Stein, and Michael I. Love. "MRLocus: Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity." PLOS Genetics 17, no. 4 (April 19, 2021): e1009455. http://dx.doi.org/10.1371/journal.pgen.1009455.
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Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.
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Thomas, Neal. "Assessing Model Sensitivity of the Imputation Methods Used in the National Assessment of Educational Progress." Journal of Educational and Behavioral Statistics 25, no. 4 (December 2000): 351–71. http://dx.doi.org/10.3102/10769986025004351.
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The National Assessment of Educational Progress (NAEP) uses latent trait item response models to summarize performance of students on assessments of educational proficiency in different subject areas such as mathematics and reading. Because of limited examination time and concerns about student motivation. NAEP employs sparse matrix sampling designs that assign a small number of examination items to each sampled student to measure broad curriculums. As a consequence, each sampled student’s latent trait is not accurately measured, and NAEP uses multiple imputation missing data statistical methods to account for the uncertainty about the latent traits. The sensitivity of these model-based estimation and reporting procedures to statistical and psychometric assumptions is assessed. Estimation of the mean of the latent trait train different subpopulations was very robust to the modeling assumptions. Many of the other currently reported summaries, however; may depend on the modeling assumptions underlying the estimation procedures; these assumptions, motivated primarily by analytic tractability, are unlikely to attain, raising concerns about current reporting practices. The results indicate that more conservative criteria should be considered when forming intervals about estimates, and when assessing significance. A possible expansion of the imputation model is suggested that may improve its performance.
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McINTYRE, LAUREN M., CYNTHIA J. COFFMAN, and R. W. DOERGE. "Detection and localization of a single binary trait locus in experimental populations." Genetical Research 78, no. 1 (August 2001): 79–92. http://dx.doi.org/10.1017/s0016672301005092.
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The advancements made in molecular technology coupled with statistical methodology have led to the successful detection and location of genomic regions (quantitative trait loci; QTL) associated with quantitative traits. Binary traits (e.g. susceptibility/resistance), while not quantitative in nature, are equally important for the purpose of detecting and locating significant associations with genomic regions. Existing interval regression methods used in binary trait analysis are adapted from quantitative trait analysis and the tests for regression coefficients are tests of effect, not detection. Additionally, estimates of recombination that fail to take into account varying penetrance perform poorly when penetrance is incomplete. In this work a complete probability model for binary trait data is developed allowing for unbiased estimation of both penetrance and recombination between a genetic marker locus and a binary trait locus for backcross and F2 experimental designs. The regression model is reparameterized allowing for tests of detection. Extensive simulations were conducted to assess the performance of estimation and testing in the proposed parameterization. The proposed parameterization was compared with interval regression via simulation. The results indicate that our parameterization shows equivalent estimation capabilities, requires less computational effort and works well with only a single marker.
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Gao, Boran, Can Yang, Jin Liu, and Xiang Zhou. "Accurate genetic and environmental covariance estimation with composite likelihood in genome-wide association studies." PLOS Genetics 17, no. 1 (January 4, 2021): e1009293. http://dx.doi.org/10.1371/journal.pgen.1009293.
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Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.
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Ronin, Yefim I., Abraham B. Korol, and Eviatar Nevo. "Single- and Multiple-Trait Mapping Analysis of Linked Quantitative Trait Loci: Some Asymptotic Analytical Approximations." Genetics 151, no. 1 (January 1, 1999): 387–96. http://dx.doi.org/10.1093/genetics/151.1.387.
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Abstract Estimating the resolution power of mapping analysis of linked quantitative trait loci (QTL) remains a difficult problem, which has been previously addressed mainly by Monte Carlo simulations. The analytical method of evaluation of the expected LOD developed in this article spreads the “deterministic sampling approach for the case of two linked QTL for single- and two-trait analysis. Several complicated questions are addressed through this evaluation: the dependence of QTL detection power on the QTL effects, residual correlation between the traits, and the effect of epistatic interaction between the QTL for one or both traits on expected LOD (ELOD), etc. Although this method gives only an asymptotic estimation of ELOD, it allows one to get an approximate assessment of a broad spectrum of mapping situations. A good correspondence was found between the ELODs predicted by the model and LOD values averaged over Monte Carlo simulations.
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Krupová, Zuzana, Emil Krupa, Ludmila Zavadilová, Eva Kašná, and Eliska Žáková. "Current challenges for trait economic values in animal breeding." Czech Journal of Animal Science 65, No. 12 (December 21, 2020): 454–62. http://dx.doi.org/10.17221/161/2020-cjas.
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Modern selection approaches are expected to bring about the cumulative and permanent improvement of animal performance and profitability of animal production. Breeding values of traits along with trait economic values (EVs) are utilised for economic selection purposes with many species all over the world. Currently, some challenges related to trait EVs in animal breeding should be considered. First, the selection response based on the higher accuracy of genomic selection may be reduced due to improper weighting of the trait breeding values of selection candidates. A comprehensive approach applied in bioeconomic models allows suitable trait EV calculations. Further challenges comprise the new breeding objectives associated with climate change, environmental mitigation and animal adaptability. The estimation of EVs for traits influencing greenhouse gas (GHG) emissions has been mostly based on including the value of CO<sub>2</sub> emission equivalent in the trait EVs, on calculating EVs for feed efficiency traits and on methane yield as a direct trait of GHG emission. Genetic improvement of production, functional, feed efficiency and methane traits through the application of multi-trait selection indices was found to be crucial for mitigation of emissions and farm profitability. Defining the non-market values of traits connected with climate protection could be a useful solution for including these traits in an economic breeding objective. While GHG emissions mostly change the costs per unit of production, animal adaptability in its complexity influences animal performance. Clear definitions of disease, fertility, mortality and other breeding objective traits allow the proper calculation of trait EVs, and an accurate estimation of trait genetic parameters could lead to sufficient economic selection response. This complex approach could be beneficial for more effective utilisation of inputs and overall economic and environmental sustainability of animal production.
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Song, Huan, Chenghui Tan, Chuanlin Zhu, Dianzhi Liu, and Wenbo Peng. "The Influence of Emotion Regulation on Estimation Strategy Execution in Individuals with Trait Anxiety." Brain Sciences 12, no. 9 (September 7, 2022): 1204. http://dx.doi.org/10.3390/brainsci12091204.
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Previous studies have shown that some negative emotions hinder estimation strategy execution. However, these studies rarely investigate the influence of negative emotions on the estimation strategy execution in individuals with trait anxiety. The present study examines the relationship between negative emotions and trait anxiety in individuals’ estimation strategy execution. Moreover, it looks into the influence of different emotion regulation strategies on their estimation strategy execution. In October 2010, 803 college students were evaluated using the Trait Anxiety Scale. From these participants, individuals with high and low trait anxiety were selected to complete the double-digit multiplication estimation task. The results showed that the estimation strategy’s execution speed in individuals with high trait anxiety was slower than those with low trait anxiety under negative emotions (t (113) = −2.269, p = 0.025, d = 0.427). Both expression inhibition and cognitive reappraisal could significantly improve the execution speed of the estimation strategy in low trait anxiety (p < 0.001). For individuals with high trait anxiety, cognitive reappraisal regulating negative emotions can promote the estimation strategy’s execution speed (p = 0.031). However, the use of expression inhibition has no significant effect on estimation strategy execution (p = 0.101). In summary, the present study revealed that different emotion regulation strategies moderated the arithmetic strategy execution of individuals with trait anxiety, and cognitive reappraisal had a better effect in individuals with high trait anxiety.
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Feuerstahler, Leah M. "Sources of Error in IRT Trait Estimation." Applied Psychological Measurement 42, no. 5 (October 6, 2017): 359–75. http://dx.doi.org/10.1177/0146621617733955.
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In item response theory (IRT), item response probabilities are a function of item characteristics and latent trait scores. Within an IRT framework, trait score misestimation results from (a) random error, (b) the trait score estimation method, (c) errors in item parameter estimation, and (d) model misspecification. This study investigated the relative effects of these error sources on the bias and confidence interval coverage rates for trait scores. Our results showed that overall, bias values were close to 0, and coverage rates were fairly accurate for central trait scores and trait estimation methods that did not use a strong Bayesian prior. However, certain types of model misspecifications were found to produce severely biased trait estimates with poor coverage rates, especially at extremes of the latent trait continuum. It is demonstrated that biased trait estimates result from estimated item response functions (IRFs) that exhibit systematic conditional bias, and that these conditionally biased IRFs may not be detected by model or item fit indices. One consequence of these results is that certain types of model misspecifications can lead to estimated trait scores that are nonlinearly related to the data-generating latent trait. Implications for item and trait score estimation and interpretation are discussed.
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NAGAI, J., C. Y. LIN, and A. J. McALLISTER. "SIMULTANEOUS ESTIMATION OF GENETIC PARAMETERS OF LIFETIME REPRODUCTIVE TRAITS IN MICE." Canadian Journal of Animal Science 68, no. 4 (December 1, 1988): 1291–95. http://dx.doi.org/10.4141/cjas88-145.
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Heritabilities and correlations between the length of reproductive life and number of parturitions during lifetime in mice were estimated from bivariate full-sib mixed-model analysis. Heritability estimates from sire components were low (0.01) for the two traits and those from dam components were slightly higher (0.06 and 0.05). Estimates of genetic and phenotypic correlations ranged from 0.89 to 0.99. It was concluded that the two traits are virtually the same trait biologically. Implication of these results for selection of lifetime production in mice and dairy cattle is discussed. Key words: Genetic parameters, reproductive trait, bivariate analysis
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Srivastava, Swati, Bryan Irvine Lopez, Sara de las Heras-Saldana, Jong-Eun Park, Dong-Hyun Shin, Han-Ha Chai, Woncheol Park, Seung-Hwan Lee, and Dajeong Lim. "Estimation of Genetic Parameters by Single-Trait and Multi-Trait Models for Carcass Traits in Hanwoo Cattle." Animals 9, no. 12 (December 2, 2019): 1061. http://dx.doi.org/10.3390/ani9121061.
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Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components.
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Marcondes, Rafael S. "Realistic scenarios of missing taxa in phylogenetic comparative methods and their effects on model selection and parameter estimation." PeerJ 7 (October 11, 2019): e7917. http://dx.doi.org/10.7717/peerj.7917.
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Model-based analyses of continuous trait evolution enable rich evolutionary insight. These analyses require a phylogenetic tree and a vector of trait values for the tree’s terminal taxa, but rarely do a tree and dataset include all taxa within a clade. Because the probability that a taxon is included in a dataset depends on ecological traits that have phylogenetic signal, missing taxa in real datasets should be expected to be phylogenetically clumped or correlated to the modelled trait. I examined whether those types of missing taxa represent a problem for model selection and parameter estimation. I simulated univariate traits under a suite of Brownian Motion and Ornstein-Uhlenbeck models, and assessed the performance of model selection and parameter estimation under absent, random, clumped or correlated missing taxa. I found that those analyses perform well under almost all scenarios, including situations with very sparsely sampled phylogenies. The only notable biases I detected were in parameter estimation under a very high percentage (90%) of correlated missing taxa. My results offer a degree of reassurance for studies of continuous trait evolution with missing taxa, but the problem of missing taxa in phylogenetic comparative methods still demands much further investigation. The framework I have described here might provide a starting point for future work.
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Bürkner, Paul-Christian, Niklas Schulte, and Heinz Holling. "On the Statistical and Practical Limitations of Thurstonian IRT Models." Educational and Psychological Measurement 79, no. 5 (February 22, 2019): 827–54. http://dx.doi.org/10.1177/0013164419832063.
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Forced-choice questionnaires have been proposed to avoid common response biases typically associated with rating scale questionnaires. To overcome ipsativity issues of trait scores obtained from classical scoring approaches of forced-choice items, advanced methods from item response theory (IRT) such as the Thurstonian IRT model have been proposed. For convenient model specification, we introduce the thurstonianIRT R package, which uses Mplus, lavaan, and Stan for model estimation. Based on practical considerations, we establish that items within one block need to be equally keyed to achieve similar social desirability, which is essential for creating forced-choice questionnaires that have the potential to resist faking intentions. According to extensive simulations, measuring up to five traits using blocks of only equally keyed items does not yield sufficiently accurate trait scores and inter-trait correlation estimates, neither for frequentist nor for Bayesian estimation methods. As a result, persons’ trait scores remain partially ipsative and, thus, do not allow for valid comparisons between persons. However, we demonstrate that trait scores based on only equally keyed blocks can be improved substantially by measuring a sizable number of traits. More specifically, in our simulations of 30 traits, scores based on only equally keyed blocks were non-ipsative and highly accurate. We conclude that in high-stakes situations where persons are motivated to give fake answers, Thurstonian IRT models should only be applied to tests measuring a sizable number of traits.
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Konigsberg, Lyle W., Susan R. Frankenberg, and Helen M. Liversidge. "Optimal trait scoring for age estimation." American Journal of Physical Anthropology 159, no. 4 (December 12, 2015): 557–76. http://dx.doi.org/10.1002/ajpa.22914.
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Song, Jie, Yiqing Zou, Yuchang Wu, Jiacheng Miao, Ze Yu, Jason M. Fletcher, and Qiongshi Lu. "Decomposing heritability and genetic covariance by direct and indirect effect paths." PLOS Genetics 19, no. 1 (January 23, 2023): e1010620. http://dx.doi.org/10.1371/journal.pgen.1010620.
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Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.
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Brzáková, Michaela, Ludmila Zavadilová, Josef Přibyl, Petr Pešek, Eva Kašná, and Anita Kranjčevičová. "Estimation of genetic parameters for female fertility traits in the Czech Holstein population." Czech Journal of Animal Science 64, No. 5 (May 26, 2019): 199–206. http://dx.doi.org/10.17221/51/2018-cjas.
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Genetic parameters for fertility traits in Czech Holstein population were estimated. The database obtained from the Czech-Moravian Breeders Corporation with 6 414 486 insemination records between years 2005–2015 was used. Date of calving of the selected animals was taken from the database of milk records from 2005–2015. Fertility traits were age at first service (AFS), age at first calving (AFC), days open (DO), calving interval (CI) and first service to conception interval in cows (FSC-C) and heifers (FSC-H). The heritability of each trait was estimated using single-trait animal models. The model included fixed effects of herd-year-season of birth, herd-year-month of calving, lactation order, parity, last calving ease, linear and quadratic regressions on age at first insemination in heifers or on age at first calving in cows. Random effects were animal, permanent environmental effect and random residual error. After edits, the final data set included up to 599 901 observations from up to 448 037 animals dependent on traits. The range of heritability estimates was from 0.010 to 0.058. The lowest heritability was for first service to conception interval in heifers, and the highest heritability was for age at first service. Variances of random permanent effects were higher than variance of additive genetic effect in all traits manifested in mature cows. Repeatability ranged from 0.060 to 0.090. Genetic correlations between traits were estimated using a bivariate animal model. High positive genetic correlations were found between AFS–AFC, DO–CI, FSC-C–DO and FSC-C–CI. A moderate genetic correlation was found between AFS–FSC-H and between AFC. A negative correlation was found between AFS–FSC-C. Correlations between other traits were close to zero. The results suggest that the level of these reproductive traits can be improved by selection of animals with high genetic merit.
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Johnson, Ruth, Kathryn S. Burch, Kangcheng Hou, Mario Paciuc, Bogdan Pasaniuc, and Sriram Sankararaman. "Estimation of regional polygenicity from GWAS provides insights into the genetic architecture of complex traits." PLOS Computational Biology 17, no. 10 (October 21, 2021): e1009483. http://dx.doi.org/10.1371/journal.pcbi.1009483.
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The number of variants that have a non-zero effect on a trait (i.e. polygenicity) is a fundamental parameter in the study of the genetic architecture of a complex trait. Although many previous studies have investigated polygenicity at a genome-wide scale, a detailed understanding of how polygenicity varies across genomic regions is currently lacking. In this work, we propose an accurate and scalable statistical framework to estimate regional polygenicity for a complex trait. We show that our approach yields approximately unbiased estimates of regional polygenicity in simulations across a wide-range of various genetic architectures. We then partition the polygenicity of anthropometric and blood pressure traits across 6-Mb genomic regions (N = 290K, UK Biobank) and observe that all analyzed traits are highly polygenic: over one-third of regions harbor at least one causal variant for each of the traits analyzed. Additionally, we observe wide variation in regional polygenicity: on average across all traits, 48.9% of regions contain at least 5 causal SNPs, 5.44% of regions contain at least 50 causal SNPs. Finally, we find that heritability is proportional to polygenicity at the regional level, which is consistent with the hypothesis that heritability enrichments are largely driven by the variation in the number of causal SNPs.
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Deinzer, Renate, Rolf Steyer, Michael Eid, Peter Notz, Peter Schwenkmezger, Fritz Ostendorf, and Aljoscha Neubauer. "Situational effects in trait assessment: The FPI, NEOFFI, and EPI questionnaires." European Journal of Personality 9, no. 1 (March 1995): 1–23. http://dx.doi.org/10.1002/per.2410090102.
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While most researchers do agree now that situations may have an effect in the assessment of traits, the consequences have been neglected, so far: if situations affect the assessment of traits we have to take this fact into account in studies on reliability and validity of measurement instruments and their application. In the theoretical part of this article we provide a more formal exposition of this point, introducing the basic concepts of latent state–trait (LST) theory. LST theory and the associated models allow for the estimation of the situational impact on trait measures in non‐experimental, correlational studies. In the empirical part, LST theory is applied to three well known trait questionnaires: the Freiburg Personality Inventory, the NEO Five‐Factor Inventory and the Eysenck Personality Inventory. It is shown that significant proportions of the variances of the scales of these questionnaires are due to situational effects. The following consequences of this finding are discussed, (i) Instead of the reliability coefficient, the proportion of variance due to the latent trait, the consistency coefficient, should be used for the estimation of confidence intervals for trait scores, (ii) To reduce the situational effects on trait estimates it may be useful to base such an estimate on several occasions, i.e., to aggregate data across occasions. (iii) Reliability and validity studies should not only be based on a sample of persons representative of those to whom the test will be applied; they should also be conducted in situational contexts representative of the intended applications.
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Novotna, Alexandra, Alena Birovas, Hana Vostra-Vydrova, Zdenka Vesela, and Lubos Vostry. "Genetic Parameters of Performance and Conformation Traits of 3-Year-Old Warmblood Sport Horses in the Czech Republic." Animals 12, no. 21 (October 27, 2022): 2957. http://dx.doi.org/10.3390/ani12212957.
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The aim of this study was to estimate the genetic parameters of a one-day performance test together with the linear type traits of 3-year-old warmblood horses. The study of genetic parameters was based on 5958 tested horses in the period 1998–2021. A total of 22 traits of linear description, three quantitatively measured traits, and one summary mark from the performance test were tested. The model equation included the fixed effect of gender and combination effects of classifier–year of evaluation–place. A single-trait animal model was used for the estimation of heritability and genetic variance, while the two-trait animal model was applied for the estimation of variance and covariance between all traits. The heritability of the overall score of the performance test was 0.25. The range for heritability was between 0.04 and 0.33 for the linear type traits and between 0.46 and 0.57 for the quantitatively measured traits. Genetic correlations were between −0.47 and 0.92. The estimated genetic parameters suggest that the results from the performance test can be incorporated into genetic evaluation in the Czech Republic.
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Luo, Z. W., and Chung-I. Wu. "Modeling Linkage Disequilibrium Between a Polymorphic Marker Locus and a Locus Affecting Complex Dichotomous Traits in Natural Populations." Genetics 158, no. 4 (August 1, 2001): 1785–800. http://dx.doi.org/10.1093/genetics/158.4.1785.
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AbstractLinkage disequilibrium is an important topic in evolutionary and population genetics. An issue yet to be settled is the theory required to extend the linkage disequilibrium analysis to complex traits. In this study, we present theoretical analysis and methods for detecting or estimating linkage disequilibrium (LD) between a polymorphic marker locus and any one of the loci affecting a complex dichotomous trait on the basis of samples randomly or selectively collected from natural populations. Statistical properties of these methods were investigated and their powers were compared analytically or by use of Monte Carlo simulations. The results show that the disequilibrium may be detected with a power of 80% by using phenotypic records and marker genotype when both the trait and marker variants are common (30%) and the LD is relatively high (40–100% of the theoretical maximum). The maximum-likelihood approach provides accurate estimates of the model parameters as well as detection of linkage disequilibrium. The likelihood method is preferred for its higher power and reliability in parameter estimation. The approaches developed in this article are also compared to those for analyzing a continuously distributed quantitative trait. It is shown that a larger sample size is required for the dichotomous trait model to obtain the same level of power in detecting linkage disequilibrium as the continuous trait analysis. Potential use of these estimates in mapping the trait locus is also discussed.
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Greveniotis, Vasileios, Evangelia Sioki, and Constantinos G. Ipsilandis. "Estimations of fibre trait stability and type of inheritance in cotton." Czech Journal of Genetics and Plant Breeding 54, No. 4 (November 7, 2018): 190–92. http://dx.doi.org/10.17221/12/2017-cjgpb.
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Traits affecting fibre quality were evaluated in a multi-location environmental experiment. Four main cotton regions in Greece were selected as different environments. Five commercial cotton cultivars were used for evaluation of 10 fibre quality traits. Each cultivar was sown in 10 different fields in each region. Environmental fluctuations within regions affected each quality trait differently showing a different degree of inheritance. Four traits showed the lowest stability index values indicating quantitative inheritance, further four traits with intermediate values indicated determination by a few genes, while the more stable and thus with qualitative inheritance traits were considered to indicate fibre maturity and uniformity. The mean estimation of stability in multi-location experiments was found the same as in multi-genotype evaluation. Two cultivars (Elsa and Celia) were found to be more stable across the Greek environments and two regions favoured stability for almost all traits. Correlations between regions were high and the same was found between genotypes.
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Leite, Mauro Sergio de Oliveira, Luiz Alexandre Peternelli, Márcio Henrique Pereira Barbosa, Paulo Roberto Cecon, and Cosme Damião Cruz. "Sample size for full-sib family evaluation in sugarcane." Pesquisa Agropecuária Brasileira 44, no. 12 (December 2009): 1562–74. http://dx.doi.org/10.1590/s0100-204x2009001200002.
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The objective of this study was to determine the minimum number of plants per plot that must be sampled in experiments with sugarcane (Saccharum officinarum) full-sib families in order to provide an effective estimation of genetic and phenotypic parameters of yield-related traits. The data were collected in a randomized complete block design with 18 sugarcane full-sib families and 6 replicates, with 20 plants per plot. The sample size was determined using resampling techniques with replacement, followed by an estimation of genetic and phenotypic parameters. Sample-size estimates varied according to the evaluated parameter and trait. The resampling method permits an efficient comparison of the sample-size effects on the estimation of genetic and phenotypic parameters. A sample of 16 plants per plot, or 96 individuals per family, was sufficient to obtain good estimates for all traits considered of all the characters evaluated. However, for Brix, if sample separation by trait were possible, ten plants per plot would give an efficient estimate for most of the characters evaluated.
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Sisson, S. A., and M. A. Hurn. "Bayesian Point Estimation of Quantitative Trait Loci." Biometrics 60, no. 1 (March 2004): 60–68. http://dx.doi.org/10.1111/j.0006-341x.2004.00167.x.
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Meijer, Rob R., and Michael L. Nering. "Trait Level Estimation for Nonfitting Response Vectors." Applied Psychological Measurement 21, no. 4 (December 1997): 321–36. http://dx.doi.org/10.1177/01466216970214003.
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Glas, C. A. W. "The Rasch Model and Multistage Testing." Journal of Educational Statistics 13, no. 1 (March 1988): 45–52. http://dx.doi.org/10.3102/10769986013001045.
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This paper concerns the problem of estimating the item parameters of latent trait models in a multistage testing design. It is shown that using the Rasch model and conditional maximum likelihood estimates does not lead to solvable estimation equations. It is also shown that marginal maximum likelihood estimation, which assumes a sample of subjects from a population with a specified distribution of ability, will lead to solvable estimation equations, both in the Rasch model and in the Birnbaum model.
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OLLIVIER, L., L. A. MESSER, M. F. ROTHSCHILD, and C. LEGAULT. "The use of selection experiments for detecting quantitative trait loci." Genetical Research 69, no. 3 (June 1997): 227–32. http://dx.doi.org/10.1017/s0016672397002802.
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Gene frequency changes following selection may reveal the existence of gene effects on the trait selected. Loci for the selected quantitative trait (SQTL) may thus be detected. Additionally, one can estimate the average effect (α) of a marker allele associated with an SQTL from the allele frequency change (Δq) due to selection of given intensity (i). In a sample of unrelated individuals, it is optimal to select the upper and lower 27% for generating Δq in order to estimate α. For a given number of individuals genotyped, this estimator is 0·25i2 times more efficient than the classical estimator of α, based on the regression of the trait on the genotype at the marker locus. The method is extended to selection criteria using information from relatives, showing that combined selection considerably increases the efficiency of estimation for traits of low heritability. The method has been applied to the detection of SQTL in a selection experiment in which the trait selected was pig litter size averaged over the first four parities, with i=3. Results for four genes are provided, one of which yielded a highly significant effect. The conditions required for valid application of the method are discussed, including selection experiments over several generations. Additional advantages of the method can be anticipated from determining gene frequencies on pooled samples of blood or DNA.
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Jiang, C., and Z. B. Zeng. "Multiple trait analysis of genetic mapping for quantitative trait loci." Genetics 140, no. 3 (July 1, 1995): 1111–27. http://dx.doi.org/10.1093/genetics/140.3.1111.
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Abstract We present in this paper models and statistical methods for performing multiple trait analysis on mapping quantitative trait loci (QTL) based on the composite interval mapping method. By taking into account the correlated structure of multiple traits, this joint analysis has several advantages, compared with separate analyses, for mapping QTL, including the expected improvement on the statistical power of the test for QTL and on the precision of parameter estimation. Also this joint analysis provides formal procedures to test a number of biologically interesting hypotheses concerning the nature of genetic correlations between different traits. Among the testing procedures considered are those for joint mapping, pleiotropy, QTL by environment interaction, and pleiotropy vs. close linkage. The test of pleiotropy (one pleiotropic QTL at a genome position) vs. close linkage (multiple nearby nonpleiotropic QTL) can have important implications for our understanding of the nature of genetic correlations between different traits in certain regions of a genome and also for practical applications in animal and plant breeding because one of the major goals in breeding is to break unfavorable linkage. Results of extensive simulation studies are presented to illustrate various properties of the analyses.
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Di Croce, F. A., A. M. Saxton, N. R. Rohrbach, and F. N. Schrick. "138 GENETIC PARAMETER ESTIMATION FOR EMBRYO TRANSFER TRAITS." Reproduction, Fertility and Development 21, no. 1 (2009): 169. http://dx.doi.org/10.1071/rdv21n1ab138.
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Genetic selection has made tremendous progress on economically important traits in the beef industry. Most of the progress has been from quantitative genetics through use of expected progeny differences (EPD). These values allow prediction of differences in progeny of a sire compared to progeny of other sires. Development of EPD for male and female reproductive traits has largely been ignored because of low heritability of reproductive traits, even though reproduction plays a vital role in the economics of beef operations. Therefore, continued research in the area of genetic selection for fertility is becoming increasingly important. Critical limiting factors for animal breeding programs using MOET nucleus schemes include variability in superovulatory response of donor animals and resulting pregnancy of transferred embryos. Thus, the overall objective of this research was to develop genetic parameters associated with MOET to assist producers in identifying animals with greater genetic merit for these protocols. Records were examined from a large-scale MOET system in beef cattle that contained data only for cows in which at least one transferable embryo was obtained. Data on these animals were extracted and analyzed on 10 425 transferred embryos (2900 collections) from 611 donor animals (Angus, Brangus, and Charolais) utilizing semen from 215 bulls. Phenotypic traits examined included pregnancy status of the recipient following transfer (ET-preg; determined by rectal palpation at 60 days post-transfer and/or confirmed calving date of recipient), number of transferable embryos per collection (ET-trans), and number of unfertilized ova at collection (ET-UFO). Basic statistical analysis and pedigree/trait files were developed using procedures in SAS (SAS Institute, Cary, NC). Genetic parameters were estimated for a single-trait animal model using restricted maximum likelihood (REML) procedures in Wombat (Meyer K 2007 Zhejiang Uni. Science B 8, 815–821). Wombat also computed EPD and standard errors for each trait evaluated. The model included fixed effects of year as well as random animal and residual effects. The EPD for ET-preg ranged from –6.1 to 4.4% (SE = 2.2 to 4.2) for semen sires (sires of the transferred embryos) and –5.3 to 3.8% (SE = 3.2 to 4.2) for donor animals. Additionally, the heritability estimated for ET-preg was 0.03. Heritability estimated for ET-trans was 0.00, indicating minute genetic variation and thus, EPD were not presented. Heritability estimated for ET-UFO was 0.05 with EPD values (deviation of the number of UFO from the mean) ranging from –0.6 to 0.8 (SE = 0.3 to 0.6) for semen sires and –0.4 to 1.1 (SE = 0.5 to 0.6) for donor cows. As previously shown for reproductive traits, heritability of ET-preg, ET-trans, and ET-UFO was low. Genetic improvement in fertility by selection on embryo transfer traits is possible, but progress would be slow. Further studies are underway on a larger dataset to refine these estimates and to examine repeatability.
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Lamour, Julien, Kenneth J. Davidson, Kim S. Ely, Jeremiah A. Anderson, Alistair Rogers, Jin Wu, and Shawn P. Serbin. "Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy." PLOS ONE 16, no. 10 (October 19, 2021): e0258791. http://dx.doi.org/10.1371/journal.pone.0258791.
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Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (Rdark25), the maximum rate of carboxylation by the enzyme Rubisco (Vcmax25), the maximum rate of electron transport (Jmax25), and the triose phosphate utilization rate (Tp25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for Vcmax25, Jmax25, Tp25 and Rdark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m-2 s-1, and R2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.
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Ohlert, Timothy, Kaitlin Kimmel, Meghan Avolio, Cynthia Chang, Elisabeth Forrestel, Benjamin Gerstner, Sarah E. Hobbie, Kimberly Komastu, Peter Reich, and Kenneth Whitney. "Exploring the impact of trait number and type on functional diversity metrics in real-world ecosystems." PLOS ONE 17, no. 8 (August 25, 2022): e0272791. http://dx.doi.org/10.1371/journal.pone.0272791.
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The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work in studies that simulate communities and trait distributions show consistent sensitivity of functional richness and evenness measures to the number of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. Therefore, we propose to test how the number of traits used and the correlation between traits used in the calculation of functional diversity indices impacts the magnitude of eight functional diversity metrics in real plant communities. We will use trait data from three grassland plant communities in the US to assess the generality of our findings across ecosystems and experiments. We will determine how eight functional diversity metrics (functional richness, functional evenness, functional divergence, functional dispersion, kernel density estimation (KDE) richness, KDE evenness, KDE dispersion, Rao’s Q) differ based on the number of traits used in the metric calculation and on the correlation of traits when holding the number of traits constant. Without a firm understanding of how a scientist’s choices impact these metric, it will be difficult to compare results among studies with different metric parametrization and thus, limit robust conclusions about functional composition of communities across systems.
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Steinfeld, Jan, and Alexander Robitzsch. "Item Parameter Estimation in Multistage Designs: A Comparison of Different Estimation Approaches for the Rasch Model." Psych 3, no. 3 (July 8, 2021): 279–307. http://dx.doi.org/10.3390/psych3030022.
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There is some debate in the psychometric literature about item parameter estimation in multistage designs. It is occasionally argued that the conditional maximum likelihood (CML) method is superior to the marginal maximum likelihood method (MML) because no assumptions have to be made about the trait distribution. However, CML estimation in its original formulation leads to biased item parameter estimates. Zwitser and Maris (2015, Psychometrika) proposed a modified conditional maximum likelihood estimation method for multistage designs that provides practically unbiased item parameter estimates. In this article, the differences between different estimation approaches for multistage designs were investigated in a simulation study. Four different estimation conditions (CML, CML estimation with the consideration of the respective MST design, MML with the assumption of a normal distribution, and MML with log-linear smoothing) were examined using a simulation study, considering different multistage designs, number of items, sample size, and trait distributions. The results showed that in the case of the substantial violation of the normal distribution, the CML method seemed to be preferable to MML estimation employing a misspecified normal trait distribution, especially if the number of items and sample size increased. However, MML estimation using log-linear smoothing lea to results that were very similar to the CML method with the consideration of the respective MST design.
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Misztal, Ignacy. "295 Parameter estimation under genomic selection." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 31–32. http://dx.doi.org/10.1093/jas/skaa278.056.
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Abstract Genetic parameters are important in animal breeding for many tasks, including as input to a model for genetic evaluation, to estimate genetic gain due to selection, and to estimate correlated response due to selection on major traits. Before the genomic era, parameter estimation was facilitated by sparse structure of mixed model equations. Methods such as AI REML with sparse matrix inversion or MCMC via Gibbs sampling could estimate parameters for populations exceeding 1 million animals. With genomic selection (GS) and single-step GBLUP, the genomic matrices are mostly dense, and costs of parameter estimation increased dramatically. The estimation with 20K genotyped animals can take many days. Details in matching pedigree and genomic information influence estimated parameters. Estimation without the genomic information when GS is practiced leads to biases due to genomic-preselection. Truncating data to too few generations or to only genotyped animals leads to additional biases by excluding data on which the selection was practiced. Current studies indicate strong declines in heritability due to GS. Regular models for parameter estimation compute parameters only for the base population. Models that trace changes of parameters over time, such as random regression model on year of birth or a multiple trait model treating times slices as separate traits, are very expensive. A good compromise in parameter estimation under GS is to use slices of only 2–3 generations, with genotypes of young animals removed. When complete populations are genotyped, estimations with large number of genotyped animals are possible either with a SNP model or with GBLUP (inversion of genomic relationship matrix by APY algorithm). For simple models, Method R can provide estimates for any data size. An indirect indication of changing parameters over time is reduced predictivity or lower genetic trend despite increased data. Parameter estimation in GS would benefit from new, efficient tools.
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Krapinec, Krešimir, Vlado Jumić, Matija Balekić, Nikola Lolić, Radomir Putnik, Tihomir Florijančić, Siniša Ozimec, and Ivica Bošković. "The Reliability of Fluctuating Asymmetry in Population Estimation: The Case of Feedlot Red Deer." Symmetry 14, no. 10 (October 8, 2022): 2092. http://dx.doi.org/10.3390/sym14102092.
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Three FA indices showed correlations with age and magnitude of traits, but not in general. Significant correlations between magnitude of traits and their FA were more pronounced in AFA (12 traits) than in RFA (10 traits) in all age classes except yearlings. For the tray tine form (curvature), FA significantly correlated with its magnitude in young, middle-aged and ripe stags, which indicates that the trait is a reliable indicator of asymmetry. Significant differences in AFA among age classes were found in four traits (weight of dry antlers, volume of antlers, distal circumference of beams and total length of crown tines). By RFA, a significant difference among age classes was only found for the distal circumference of beams. Thus, AFA is a more vulnerable condition index. Contrary to other research findings, developmental instability was more pronounced in older age classes. In yearlings, no significant FA dependence on the trait of antler size was detected, but in certain traits, an asymmetry detected at an early age remains visible later as well, although in stags grown under relatively optimal (especially trophic) environment conditions, developmental instability was present anyway. This proposes two hypotheses for further research: Competition may be manifested even under controlled conditions, which might jeopardize the developmental stability of certain individuals, or some traits will show developmental instability regardless of relatively good environmental conditions.
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Azevedo Junior, Jairo, Juliana Petrini, Gerson Barreto Mourão, and José Bento Sterman Ferraz. "Categorical Visual Score Traits of a Nellore Beef Cattle Population." Journal of Agricultural Science 9, no. 8 (July 18, 2017): 63. http://dx.doi.org/10.5539/jas.v9n8p63.
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Variance components and genetic parameters of economically relevant traits in livestock, whether continuous or categorical, can be estimated by methods computationally available providing support for the selection and mating of animals in breeding programs. The objectives of this paper were to obtain and compare the variance components estimates for visual traits under continuous or categorical distribution in single-trait analysis and their correlations with continuous productive traits in two-trait analysis. Data of conformation (CONF), precocity of fat deposition (PREC) and muscling (MUSC) visual scores evaluated at 18 months of age as well as the weight at 18 months of age (YW) were collected from animals born from 2000 to 2012, in Nellore cattle herds raised in Southeastern and Central Western tropical regions of Brazil. Methods III of Henderson, Restricted Maximum Likelihood (REML), Bayesian Inference and generalized linear mixed model (GLMM) were tested. Variance components obtained from single-trait analysis were similar to those obtained from two-trait analysis. The estimates of heritability (h2) for the visual scores ranged from 0.1081 to 0.2190. Heritability estimates for traits evaluated by visual scores have moderate to high magnitude justifying the inclusion of visual scores as selection criteria in animal breeding and the selection of animals with higher scores for mating. High genetic correlations between yearling weight and morphological traits were verified. For visual scores of conformation, precocity and muscling, the most suitable model based on one-trait or two-trait analyses considered an animal model, a linear distribution of the data and the estimation method of the components of (co)variance based on Bayesian methodology.
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Qadri, Qamar Raza, Qingbo Zhao, Xueshuang Lai, Zhenyang Zhang, Wei Zhao, Yuchun Pan, and Qishan Wang. "Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models." Genes 13, no. 9 (September 2, 2022): 1580. http://dx.doi.org/10.3390/genes13091580.
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Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.
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Bi, Kaiyi, Zheng Niu, Shunfu Xiao, Jie Bai, Gang Sun, Ji Wang, Zeying Han, and Shuai Gao. "Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity." Remote Sensing 13, no. 21 (October 20, 2021): 4203. http://dx.doi.org/10.3390/rs13214203.
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High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.
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Wang, Hui, Yuan-Ming Zhang, Xinmin Li, Godfred L. Masinde, Subburaman Mohan, David J. Baylink, and Shizhong Xu. "Bayesian Shrinkage Estimation of Quantitative Trait Loci Parameters." Genetics 170, no. 1 (March 21, 2005): 465–80. http://dx.doi.org/10.1534/genetics.104.039354.
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Foster, W. W., A. E. Freeman, P. J. Berger, and A. Kuck. "Linear Type Trait Analysis with Genetic Parameter Estimation." Journal of Dairy Science 71, no. 1 (January 1988): 223–31. http://dx.doi.org/10.3168/jds.s0022-0302(88)79545-4.
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Cheng, Philip E., and Michelle Liou. "Estimation of Trait Level in Computerized Adaptive Testing." Applied Psychological Measurement 24, no. 3 (September 2000): 257–65. http://dx.doi.org/10.1177/01466210022031723.
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Hagemann, Dirk, and David Meyerhoff. "A Simplified Estimation of Latent State—Trait Parameters." Structural Equation Modeling: A Multidisciplinary Journal 15, no. 4 (October 8, 2008): 627–50. http://dx.doi.org/10.1080/10705510802339049.
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Krupa, E., and J. Wolf. "Simultaneous estimation of genetic parameters for production and litter size traits in Czech Large White and Czech Landrace pigs." Czech Journal of Animal Science 58, No. 9 (August 29, 2013): 429–36. http://dx.doi.org/10.17221/6943-cjas.
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Genetic parameters for total number of piglets born per litter, number of piglets weaned per litter, lean meat content, and average daily gain from birth till the end of the field test were estimated for Czech Large White (445 589 records) and Czech Landrace (149 057 records) pigs using a four-trait animal model. The following heritabilities were estimated (first number: Large White, second number: Landrace): 0.10 ± 0.004 and 0.09 ± 0.007 for total number born; 0.09 ± 0.005 and 0.07 ± 0.008 for number weaned; 0.39 ± 0.004 and 0.36 ± 0.009 for lean meat content; 0.21 ± 0.004 and 0.18 ± 0.006 for daily gain. The highest genetic correlation (approximately 0.85 in both breeds) was estimated between both litter size traits. In Czech Landrace, all remaining genetic correlations were < 0.20 in their absolute value. Negative correlations of approximately ‑0.25 were estimated in Czech Large White between daily gain and both reproduction traits. All remaining correlations in Czech Large White were also < 0.20 in their absolute value. The estimated non-zero correlations between production and reproduction traits are, besides of other arguments, one reason to recommend a joint genetic evaluation of production and reproduction traits. If more than one litter trait is included in the genetic evaluation, repeatability models should be used instead of separate treating the first and the second and subsequent litters; this is because of the high correlations among litter size traits which are expected to cause numerical problems if multi-parity models are used.
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Southwood, O. I. "Estimation of genetic and phenotypic trends for litter size in canadian yorkshire and landrace swine." Proceedings of the British Society of Animal Production (1972) 1990 (March 1990): 12. http://dx.doi.org/10.1017/s0308229600017967.
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Litter size in swine, although lowly heritable, has been receiving increasing interest especially with the availability of computer programs to allow estimation of genetic merit for this trait using family information. Genetic changes in a trait over time can be monitored by estimating best linear unbiased prediction (BLUP) of breeding value for individuals exhibiting the trait. Data from a recording programme within a Canadian province allowed estimation of genetic and phenotypic trends for three measures of litter size.Data from purebred Yorkshire and Landrace litters were obtained from the Quebec Record of Performance Sow Productivity Program. First parity litters born between 1977 and 1987 were analysed for total numbers born, numbers born alive and numbers weaned. Data were edited to include only herds with ten or more litters in Yorkshire and 40 or more litters in Landrace. Also, in order to reduce the number of records further, due to computing limitations, a sow was only allowed one daughter per full-sib family. A total of 2024 Yorkshire gilts (from 467 sires and 1539 dams) and 1920 Landrace gilts (from 421 sires and 1436 dams) provided records.
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Robitzsch, Alexander. "About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model." Mathematics 9, no. 13 (June 22, 2021): 1465. http://dx.doi.org/10.3390/math9131465.
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This article shows that the recently proposed latent D-scoring model of Dimitrov is statistically equivalent to the two-parameter logistic item response model. An analytical derivation and a numerical illustration are employed for demonstrating this finding. Hence, estimation techniques for the two-parameter logistic model can be used for estimating the latent D-scoring model. In an empirical example using PISA data, differences of country ranks are investigated when using different metrics for the latent trait. In the example, the choice of the latent trait metric matters for the ranking of countries. Finally, it is argued that an item response model with bounded latent trait values like the latent D-scoring model might have advantages for reporting results in terms of interpretation.
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Baes, Christine F., Filippo Miglior, Flavio S. Schenkel, Ellen Goddard, Gerrit Kistemaker, Nienke van Staaveren, Ronaldo Cerri, Marc Andre A. Sirard, and Paul Stothard. "166 Livestock Resiliency: Concepts and Approaches." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 89–90. http://dx.doi.org/10.1093/jas/skab235.159.
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Abstract Genetic improvement of health, welfare, efficiency, and fertility traits is challenging due to expensive and fuzzy phenotypes, the polygenic nature of traits, antagonistic genetic correlations to production traits and low heritabilities. Nevertheless, many organizations have introduced large-scale genetic evaluations for such traits in routine selection indexes. Medium and high-density arrays can be applied in genomic selection strategies to improve breeding value accuracy, and also in genome-wide association studies (GWAS) to identify causative mutations responsible for economically important traits. Genomic information is particularly helpful when traits have low heritability. The objective here is to provide a framework for including health, welfare, efficiency, and fertility traits taken from large-scale genetic and genomic analyses and identifying areas of potential improvement in terms of trait definition and performance testing. General tendencies between trait groups confirmed that a number of moderate unfavourable correlations (+/-0.20 or higher) exist between economically important trait complexes and health, welfare, and fertility traits. A number of trait complexes were identified in which “closer-to-biology” phenotypes could provide clear improvements to routine genetic and genomic selection programs. Here we outline development of these phenotypes and describe their collection. While conventional variance component estimation methods have underpinned the genomic component of some traits of economic interest, performance testing for health, welfare, efficiency, and fertility traits remains an elusive goal for breeding programs. Although our results are encouraging, there is much to be done in terms of trait definition and obtaining better measures of physiological parameters for wide-scale application in breeding programs. Close collaboration between veterinarians, physiologists, and geneticists is necessary to attain meaningful advancement in such areas. We would like to acknowledge the support and funding from all national and international partners involved in the RDGP project through the Large Scale Applied Research Project program from Genome Canada
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TAN, QIHUA, LENE CHRISTIANSEN, KAARE CHRISTENSEN, LISE BATHUM, SHUXIA LI, JING HUA ZHAO, and TORBEN A. KRUSE. "Haplotype association analysis of human disease traits using genotype data of unrelated individuals." Genetical Research 86, no. 3 (November 25, 2005): 223–31. http://dx.doi.org/10.1017/s0016672305007792.
Full textAbstract:
Haplotype inference has become an important part of human genetic data analysis due to its functional and statistical advantages over the single-locus approach in linkage disequilibrium mapping. Different statistical methods have been proposed for detecting haplotype – disease associations using unphased multi-locus genotype data, ranging from the early approach by the simple gene-counting method to the recent work using the generalized linear model. However, these methods are either confined to case – control design or unable to yield unbiased point and interval estimates of haplotype effects. Based on the popular logistic regression model, we present a new approach for haplotype association analysis of human disease traits. Using haplotype-based parameterization, our model infers the effects of specific haplotypes (point estimation) and constructs confidence interval for the risks of haplotypes (interval estimation). Based on the estimated parameters, the model calculates haplotype frequency conditional on the trait value for both discrete and continuous traits. Moreover, our model provides an overall significance level for the association between the disease trait and a group or all of the haplotypes. Featured by the direct maximization in haplotype estimation, our method also facilitates a computer simulation approach for correcting the significance level of individual haplotype to adjust for multiple testing. We show, by applying the model to an empirical data set, that our method based on the well-known logistic regression model is a useful tool for haplotype association analysis of human disease traits.
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Khmelnychiy, L. M., and V. V. Vechorka. "THE LIFETIME OF COWS UKRAINIAN RED-AND-WHITE DAIRY BREED DEPENDING ON THE LINEAR TRAITS ESTIMATION." Animal Breeding and Genetics 53 (April 27, 2017): 197–208. http://dx.doi.org/10.31073/abg.53.27.
Full textAbstract:
Studies conducted in the herd for breeding Ukrainian Red-and-White Dairy breed (n=250). Evaluation of exterior-type heifers were conducted by the method of linear classification according to the latest recommendations of the ICAR at the age of 2-4 months after calving. Such descriptive traits, that characterize the body structure of cows – chest width, body depth, angularity, the fatness, the position and rump width had been studied. The results of studies showed reliable influence of the score level of exterior descriptive traits on the lifetime of cows. The degree of variability of relationship between these traits depended on the score level and specific point of the body structure. The effect of the chest width on the lifetime of cows has curvilinear variability. Longer lifespan have been identified in animals in 3-5 scores for the development of this trait and was 2452-2505 days. With the increase from the average value in 5 scores, the lifetime of cows decreased from 2351 (6 scores), to 2041 days (9 scores). Comparing group of animals valued in 5 score with groups in 6-9 scores revealed a reliable variance in favor of the former, which ranged from 184 (P<0,05), to 464 days (P<0,001). Studies of the effect of body depth on lifetime showed that the longest periods of longevity inherent in animals evaluated the development of trait in 6-9 scores, with the highest value of 2531 days estimated in 8 scores. Cows with excessive angularity and maximum lifespan (2455-2503 days) had higher scores (7-9). A significant decrease is observed when reducing the score for this trait starting from 6 scores (-193 days; P<0,001) to 1 score (-648 days; P<0,001) in comparison with the best result in 8 scores. The relationship between the assessment for condition of rump angle and lifespan of cows has a curvilinear nature. Animals with an optimal rating of the trait in 5 scores had a high lifetime in 2517 days, whereas with the increase and decrease of scores, the number of cows days of life decreased. The difference in life expectancy between cows valued at 5 scores with groups of animals assessed in 6-9 scores ranges from 12 to 284 days with a reliable variance only compared with 8 and 9 scores (P<0,05). Compared with groups of animals estimated by 1-4 scores, the variability of variance was 100-509 days with reliability compared with 1 and 2 scores (P<0,01). The lifetime of cows is in straight dependence on the score level for trait chest width. Cows with the highest estimate for the development of trait in 9 scores was used for 462 days longer compared to animals with 1 score (P<0,01). Among the evaluated population, the greatest number of cows (n=88) estimated in 7 scores, next (n=56) in 6. In general, the vast number of cows (n=189), or 75,6% are for the development of this trait above the average, i.e. is characterized by a rather wide rump. The highest average lifetime of animals in 5 scores for fatness is on average – 2523 days. A sufficient lifetime of cows valued 1 to 4 scores with variability 2276-2459 days inferior for animals in 5 scores on 64-247 days with a significant difference between the groups estimated in 1 and 3 scores (P<0,05). A significant reduction in lifetime observed in cows with body condition score in 7-9. They are significantly worse in comparison with groups of animals with 5 scores, for a high reliable difference 282-566 days (P<0,001). A significant effect on the development of linear type traits was installed on longevity of cows Ukrainian Red-and-White Dairy breed. Each of the estimated descriptive traits influence on life expectancy of cows with different variability within grading scores in accordance with desirable development. Selection of Dairy cattle for desirable development of exterior type traits by results of linear classification will enhance the duration use of cows.
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Garriga, M., C. Ovalle, S. Espinoza, G. A. Lobos, and A. del Pozo. "Use of Vis–NIR reflectance data and regression models to estimate physiological and productivity traits in lucerne (Medicago sativa)." Crop and Pasture Science 71, no. 1 (2020): 90. http://dx.doi.org/10.1071/cp19182.
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Lucerne (alfalfa, Medicago sativa L.) is grown extensively worldwide owing to its high forage biomass production and nutritional value. Although this crop is characterised as being tolerant to drought, its production and persistence are affected by water stress. Selection of genotypes of high yield potential and persistence after a long period of drought is a major objective for lucerne-breeding programmes in Mediterranean environments. This selection could be enhanced and accelerated by the use of physiological and productivity traits and their estimation through remote-sensing methods. A set of nine cultivars of lucerne from Australia and the USA were assessed in four locations in Mediterranean central-south Chile. Several physiological and productivity traits were evaluated: forage yield (FY), stomatal conductance (gs), water potential (WP), leaf area index (LAI), nitrogen (N) content, and isotope composition (δ13C and δ18O) of the dry matter. Spectral-reflectance data were used to estimate the traits through spectral-reflectance indices (SRIs) and multivariate regression methods. For the SRI-based estimations, the R2 values for each assessment were <0.65. However, traits such as LAI, WP, gs, and N content showed higher R2 values when data from the different assessments were combined. Regression-based estimation showed prediction power similar to or higher than the SRI-based approaches. The highest R2 value was for δ13C (0.78), but for most traits the combination of data from different assessments led to higher trait estimation, with respective R2 values for LAI, FY, WP and gs of 0.67, 0.71, 0.63 and 0.85. Among regression methods, the best estimation was achieved by using support vector machine regression. The use of spectral-reflectance data collected at field level and multivariate regression models has great potential to estimate physiological and productivity traits in lucerne under water deficit and could be useful in lucerne-breeding programmes.