Journal articles: 'Additive variance'

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Mahdavi, M., G. R. Dashab, M. Vafaye Valeh, M. Rokouei, and M. Sargolzaei. "Genomic evaluation and variance component estimation of additive and dominance effects using single nucleotide polymorphism markers in heterogeneous stock mice." Czech Journal of Animal Science 63, No. 12 (December 4, 2018): 492–506. http://dx.doi.org/10.17221/83/2017-cjas.

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Exploration of genetic variance has mostly been limited to additive effects estimated using pedigree data and non-additive effects have been ignored. This study aimed to evaluate the performance of single nucleotide polymorphisms (SNPs) marker models in the mixed and orthogonal framework including both additive and non-additive effects for estimating variances and genomic prediction in four diabetes-related traits in heterogeneous stock mice. Models have performed differently in detecting SNPs affecting traits. Dominance variances explained over 14.7 and 3.8% of genetic and phenotype variance in a Genomic prediction and variance component estimation method (GVCBLUP) framework. Reliabilities of additive Genomic best linear unbiased prediction model (GBLUP) in different traits ranged from 44.8 to 66.6%, for GVCBLUPs framework including both additive and dominance effects (MAD), and 46.1 to 69% for the model including additive effect (MA). Dominance GBLUP reliabilities ranged from 6 to 26.4% for MAD and from 22.5 to 50.5% in the model including dominance (MD). MA and MD had higher reliability for additive and dominance GBLUPs compared to MAD. Reliabilities of GBLUPs in MAD and MA for all traits were not significant except for growth slope (P < 0.01). In orthogonal framework models, epistasis variances accounted for a greater proportion (87.3, 89.1, 95.5, and 77.2%) of genetic variation for end weight, growth slope, body mass index, and body length, respectively. Heritability in a broad sense was estimated at 1.12, 1.67, 3.64, and 2.0%, in which non-additive heritability had a significant contribution. Genetic variances explained by dominance using GVCBLUPs were 16.8, 29.4, 14.6, and 14.9% for the traits. Generally, the non-additive models had a lower value of deviance information criterion (DIC) and performed better in estimating the variance component. Comparing the estimated variance by orthogonal framework models confirmed the results previously estimated by GVCBLUPs, with the difference that the estimates were shrinking. Following significant SNPs affecting diabetes-related traits by post-genome-wide studies could reveal unknown aspects and contribute to genetic control of the disease.

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Fakhfakh, Raouf. "Variance function of boolean additive convolution." Statistics & Probability Letters 163 (August 2020): 108777. http://dx.doi.org/10.1016/j.spl.2020.108777.

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Fang, Yixin, Heng Lian, Hua Liang, and David Ruppert. "Variance function additive partial linear models." Electronic Journal of Statistics 9, no. 2 (2015): 2793–827. http://dx.doi.org/10.1214/15-ejs1080.

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Taft, Heather R., and Derek A. Roff. "Do bottlenecks increase additive genetic variance?" Conservation Genetics 13, no. 2 (November 12, 2011): 333–42. http://dx.doi.org/10.1007/s10592-011-0285-y.

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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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Berguson, W. E., B. G. McMahon, and D. E. Riemenschneider. "Additive and Non-Additive Genetic Variances for Tree Growth in Several Hybrid Poplar Populations and Implications Regarding Breeding Strategy." Silvae Genetica 66, no. 1 (December 28, 2017): 33–39. http://dx.doi.org/10.1515/sg-2017-0005.

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Abstract Populus species (P. deltoides, P. maximowiczii, P. nigra) and their inter-specific hybrids were tested for growth rate over a five year period at four test locations in Minnesota, USA, to estimate genetic variance components. The breeding scheme incorporated recurrent selection of full-sib families of pure species parents, production of F1 inter-specific hybrids from selected families, and selection of clones within the F1s. Improvement of yield through time using this scheme is predicated on the assumption that additive effects comprise a significant portion of the total genetic variance. The estimates of additive and non-additive variances reported are not traditional point estimates, because a fully balanced mating design was impossible due to parental incompatibilities which result in incomplete breeding matrices. Instead, bounded estimates, not previously used in tree genetics research, are derived from linear combinations of formulae of genetic expectations observed among-family, among-clone, and environmental variances. Our results suggest that combined family and mass selection would lead to increases in growth rate of 27 % and 47 % per generation in P. deltoides and P. nigra, respectively. Broad sense-based clonal selection within the F1 could yield selection responses in excess of 90 % of the mean of such populations. Among-family variance comprised about 1/3 of total genetic variance while within-family variance was always about 2/3 of total genetic variance, regardless of pedigree. The results indicate that recurrent intraspecific selective breeding followed by interspecific hybridization and non-recurrent selection based on broad sense genetic variation would constitute an effective yield improvement strategy.

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WHITLOCK, MICHAEL C. "Neutral additive genetic variance in a metapopulation." Genetical Research 74, no. 3 (December 1999): 215–21. http://dx.doi.org/10.1017/s0016672399004127.

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For neutral, additive quantitative characters, the amount of additive genetic variance within and among populations is predictable from Wright's FST, the effective population size and the mutational variance. The structure of quantitative genetic variance in a subdivided metapopulation can be predicted from results from coalescent theory, thereby allowing single-locus results to predict quantitative genetic processes. The expected total amount of additive genetic variance in a metapopulation of diploid individual is given by 2Neσ2m (1 + FST), where FST is Wright's among-population fixation index, Ne is the eigenvalue effective size of the metapopulation, and σ2m is the mutational variance. The expected additive genetic variance within populations is given by 2Neσ2e(1 − FST), and the variance among demes is given by 4FSTNeσ2m. These results are general with respect to the types of population structure involved. Furthermore, the dimensionless measure of the quantitative genetic variance among populations, QST, is shown to be generally equal to FST for the neutral additive model. Thus, for all population structures, a value of QST greater than FST for neutral loci is evidence for spatially divergent evolution by natural selection.

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LIN, C. Y., and A. J. LEE. "ESTIMATION OF ADDITIVE AND NONADDITIVE GENETIC VARIANCES IN NONINBRED POPULATIONS UNDER SIRE OR FULLSIB MODEL." Canadian Journal of Animal Science 69, no. 1 (March 1, 1989): 61–68. http://dx.doi.org/10.4141/cjas89-009.

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The separation of additive and nonadditive genetic variances has been a problem for animal breeding researchers because conventional methods of statistical analyses (least squares or ANOVA type) were not able to accomplish this task. Henderson presented computing algorithms for restricted maximum likelihood (REML) estimation of additive and nonadditive genetic variances from an animal model for noninbred populations. Unfortunately, application of this algorithm in practice requires extensive computing. This study extends Henderson's methodology to estimate additive genetic variance independently of nonadditive genetic variances under halfsib (sire), fullsib nested and fullsib cross-classified models. A numerical example illustrates the REML estimation of additive [Formula: see text] and additive by additive [Formula: see text] genetic variances using a sire model. Key words: Genetic variance, additive, nonadditive, dairy

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Liu, Li, Yayu Wang, Di Zhang, Zhuoxin Chen, Xiaoshu Chen, Zhijian Su, and Xionglei He. "The Origin of Additive Genetic Variance Driven by Positive Selection." Molecular Biology and Evolution 37, no. 8 (April 3, 2020): 2300–2308. http://dx.doi.org/10.1093/molbev/msaa085.

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Abstract Fisher’s fundamental theorem of natural selection predicts no additive variance of fitness in a natural population. Consistently, studies in a variety of wild populations show virtually no narrow-sense heritability (h2) for traits important to fitness. However, counterexamples are occasionally reported, calling for a deeper understanding on the evolution of additive variance. In this study, we propose adaptive divergence followed by population admixture as a source of the additive genetic variance of evolutionarily important traits. We experimentally tested the hypothesis by examining a panel of ∼1,000 yeast segregants produced by a hybrid of two yeast strains that experienced adaptive divergence. We measured >400 yeast cell morphological traits and found a strong positive correlation between h2 and evolutionary importance. Because adaptive divergence followed by population admixture could happen constantly, particularly in species with wide geographic distribution and strong migratory capacity (e.g., humans), the finding reconciles the observation of abundant additive variances in evolutionarily important traits with Fisher’s fundamental theorem of natural selection. Importantly, the revealed role of positive selection in promoting rather than depleting additive variance suggests a simple explanation for why additive genetic variance can be dominant in a population despite the ubiquitous between-gene epistasis observed in functional assays.

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Wu, Jixiang, Johnie N. Jenkins, Jack C. McCarty, and Dongfeng Wu. "Variance Component Estimation Using the Additive, Dominance, and Additive × Additive Model When Genotypes Vary across Environments." Crop Science 46, no. 1 (January 2006): 174–79. http://dx.doi.org/10.2135/cropsci2005.04-0025.

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11

DU, F. X., I. HOESCHELE, and K. M. GAGE-LAHTI. "Estimation of additive and dominance variance components in finite polygenic models and complex pedigrees." Genetical Research 74, no. 2 (October 1999): 179–87. http://dx.doi.org/10.1017/s0016672399003948.

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Estimation of variance components with the finite polygenic model (FPM) was evaluated. Phenotypic data for a 6300-pedigree simulated under a wide range of additive genetic models were analysed with constant homozygote difference across loci using deterministic Maximum Likelihood (DML) and a Bayesian method implemented via Gibbs sampling (BGS). Results indicate that under no selection, both DML and BGS accurately estimated the variance components, with a FPM of 5 loci or more. When both analysis methods were applied to equivalent data sets on populations that had undergone selection, the DML method produced upward biased estimates of additive genetic variation and heritability due to its use of pedigree loop cutting, while BGS provided more accurate estimation. BGS was extended to non-additive FPMs with variable homozygote differences and dominance effect across loci. This method was used to analyse data simulated under two genetic models with positive, completely dominant gene action at all loci. Results indicate that the estimates of additive and dominance variances slowly increase as the number of loci in the FPM for analysis increases, while accuracy of predicting individual breeding values and dominance deviations remains unaffected. For the simulated pedigree structure, a FPM with 10 loci or slightly fewer appears to be appropriate for variance component estimation in the presence of dominance.

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12

Park, Y. S., and D. P. Fowler. "Genetic variances among clonally propagated populations of tamarack and the implications for clonal forestry." Canadian Journal of Forest Research 17, no. 10 (October 1, 1987): 1175–80. http://dx.doi.org/10.1139/x87-181.

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Genetic variances of three tamarack (Larixlaricina (Du Roi) K. Koch.) populations in central New Brunswick were examined using vegetatively propagated materials. The component of variance due to clones within families was large for both 5-year height and survival and was partitioned into additive and nonadditive genetic variances. About 85% of the clonal variance for height was additive for the AFES and CANAAN populations but was only 18% for the NORTON population. For survival, the proportion of nonadditive variance was larger than additive variance for the AFES and CANAAN populations, whereas for the Norton population it was negligible. The possibility of clonal propagation and selection is explored as a tree improvement –reforestation option and a strategy is discussed.

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Cowley, David E., William R. Atchley, and J. J. Rutledge. "QUANTITATIVE GENETICS OF DROSOPHILA MELANOGASTER. I. SEXUAL DIMORPHISM IN GENETIC PARAMETERS FOR WING TRAITS." Genetics 114, no. 2 (October 1, 1986): 549–66. http://dx.doi.org/10.1093/genetics/114.2.549.

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ABSTRACT Sexual dimorphism in genetic parameters is examined for wing dimensions of Drosophila melanogaster. Data are fit to a quantitative genetic model where phenotypic variance is a linear function of additive genetic autosomal variance (common to both sexes), additive genetic X-linked variances distinct for each sex, variance due to common rearing environment of families, residual environmental variance, random error variance due to replication, and variance due to measurement error and developmental asymmetry (left vs. right sides). Polygenic dosage compensation and its effect on genetic variances and covariances between sexes is discussed. Variance estimates for wing length and other wing dimensions highly correlated with length support the hypothesis that the Drosophila system of dosage compensation will cause male X-linked genetic variance to be substantially larger than female X-linked variance. Results for various wing dimensions differ, suggesting that the level of dosage compensation may differ for different traits. Genetic correlations between sexes for the same trait are presented. Total additive genetic correlations are near unity for most wing traits; this indicates that selection in the same direction in both sexes would have a minor effect on changing the magnitude of difference between sexes. Additive X-linked correlations suggest some genotype × sex interactions for X-linked effects.

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Wright, A. J. "Additive variance and average effect with partial selfing." Genetical Research 50, no. 1 (August 1987): 63–68. http://dx.doi.org/10.1017/s001667230002334x.

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SummaryThe covariance of an ancestor and the average of its descendants in generation t(CPt) is formulated for a breeding system which is a mixture of selfing and outcrossing. This covariance is partitioned into least squares additive (σA. At) and non-additive (σD. Dt) components summed over individual loci, and the combined inbreeding effects of all loci (H′), such that CPt = σA. At + (s/2)t[σD. Do + H′]. For the mth locus the covariance mσA. At = 2(1 + F)Σipiα(0)iα(t)i, in which pi is the frequency of the ith allele whose additive effect (α(t)) depends on the generation for which it is defined. For distant descendants α(∞) is equal to half of the derivative of the population mean with respect to the frequency of the allele. The covariance CP∞ = σA. A∞ thus relates directly to permanent selection response measured in the equilibrium population, any additional responses observed in earlier generations being due to temporary disturbances in population genotypic structure. It is only for these distant descendants that the least squares additive component has any direct interpretation in terms of selection response. The definitions of α(0) and α(∞), lead to two distinct definitions of the average effect of an allele substitution for a model with two alleles (Fisher, 1941), and to a clarification of their significance for this breeding system.

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Chen, Zhao, Jianqing Fan, and Runze Li. "Error Variance Estimation in Ultrahigh-Dimensional Additive Models." Journal of the American Statistical Association 113, no. 521 (September 26, 2017): 315–27. http://dx.doi.org/10.1080/01621459.2016.1251440.

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Yu, Kyusang. "Conditional variance estimation via nonparametric generalized additive models." Journal of the Korean Statistical Society 48, no. 2 (June 2019): 287–96. http://dx.doi.org/10.1016/j.jkss.2018.11.007.

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Manstavičius, Eugenijus, and Vytautas Stepas. "Variance of additive functions defined on random assemblies." Lithuanian Mathematical Journal 57, no. 2 (April 2017): 222–35. http://dx.doi.org/10.1007/s10986-017-9356-1.

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Rigby, R. A., and D. M. Stasinopoulos. "A semi-parametric additive model for variance heterogeneity." Statistics and Computing 6, no. 1 (March 1996): 57–65. http://dx.doi.org/10.1007/bf00161574.

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Maniatis, N., and G. E. Pollott. "Genotype by environment interactions in lamb weight and carcass composition traits." Animal Science 75, no. 1 (April 2002): 3–14. http://dx.doi.org/10.1017/s1357729800052772.

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AbstractThe systematic use of the same genotype in several different environments provides information that can be used to estimate genotype by environment interaction (G ✕ E) variances and parameters. Data from the UK Suffolk Sire Referencing Scheme Ltd were used to investigate a range of sire and dam by environment interactions in lamb weight (at 8 weeks and scanning) and body composition traits (muscle and fat depth). These interactions were calculated in a DFREML mixed model containing direct additive, maternal additive, maternal environmental random variance components and the covariance between direct and maternal additive effects. Sire interactions with year, flock and flock-year and dam effects within and between litters were investigated. The addition of all G ✕ E (co)variance components resulted in an improved fit of the model for all traits. Sire interactions accounted for between 2 and 3% of the phenotypic variance in all traits, usually at the expense of both additive effects. Maternal litter environmental variance components ranged from 10% (fat depth and muscle depth) to 20% (8-week weight) of phenotypic variance. Most of this variation was found in the residual component of variance when the term was omitted from the model. When fitting sire G✕ E components in a model the covariance between direct and maternal additive genetic effects, as a proportion of phenotypic variance, was reduced to a low level (from –0·36 to –0·08 for 8-week weight). Genotype by environment interactions form a significant source of variation in lamb growth and composition traits and reduce the high negative correlation between additive effects found previously in these traits.

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Walsh, Bruce. "The struggle to exploit non-additive variation." Australian Journal of Agricultural Research 56, no. 9 (2005): 873. http://dx.doi.org/10.1071/ar05152.

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Whereas animal breeders largely focus on improvement using additive genetic variance, inbreeding and asexual reproduction allow plant breeders to at least partially exploit non-additive genetic variance as well. We briefly review various approaches used by breeders to exploit dominance and epistatic variance, discuss their constraints and limitations, and examine what (if anything) can be done to improve our ability to further use often untapped genetic variation.

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DU, F. X., and I. HOESCHELE. "Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler." Genetical Research 76, no. 2 (October 2000): 187–98. http://dx.doi.org/10.1017/s0016672300004614.

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In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

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López-Fanjul, Carlos, Almudena Fernández, and Miguel A. Toro. "Epistasis and the Conversion of Non-additive to Additive Genetic Variance at Population Bottlenecks." Theoretical Population Biology 58, no. 1 (August 2000): 49–59. http://dx.doi.org/10.1006/tpbi.2000.1470.

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Wensch-Dorendorf, M., N. Mielenz, E. Groeneveld, M. Kovac, and L. Schüler. "Varianzkomponentenschätzung unter Berücksichtigung von Dominanz an simulierten Reinzuchtlinien." Archives Animal Breeding 47, no. 4 (October 10, 2004): 387–95. http://dx.doi.org/10.5194/aab-47-387-2004.

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Abstract. Title of the paper: Estimation of variance components under dominance with simulated purebred lines A stochastic simulation based on a gene model was used to investigate the estimation of variance with dominance and additive animal models. For a heritability in broad sense of 0.5 three ratios of dominance variance (5, 10 and 25%) on the phenotypic variance were investigated under different degrees of dominance. No additionally biased estimations of the variance components as consequence of different dominance degrees were found. By using the dominance model for random mating as well as for selection the differences between true parameters and estimation values were small for all dominance degrees and ratios of dominance variance. Small, but significantly, differences can be explained by the change of the allele frequencies over the generations due to the influence of selection. By using the additive animal model, that ignores the dominance relationship, for high ratios of the dominance variance (25% or greater) important biased estimations of the variances were observed. For dominance ratios of 5% no significantly overestimation of the additive variances with the reduced model were found under selection and random mating.

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Whitlock, Michael C., and Kevin Fowler. "The Changes in Genetic and Environmental Variance With Inbreeding in Drosophila melanogaster." Genetics 152, no. 1 (May 1, 1999): 345–53. http://dx.doi.org/10.1093/genetics/152.1.345.

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Abstract We performed a large-scale experiment on the effects of inbreeding and population bottlenecks on the additive genetic and environmental variance for morphological traits in Drosophila melanogaster. Fifty-two inbred lines were created from the progeny of single pairs, and 90 parent-offspring families on average were measured in each of these lines for six wing size and shape traits, as well as 1945 families from the outbred population from which the lines were derived. The amount of additive genetic variance has been observed to increase after such population bottlenecks in other studies; in contrast here the mean change in additive genetic variance was in very good agreement with classical additive theory, decreasing proportionally to the inbreeding coefficient of the lines. The residual, probably environmental, variance increased on average after inbreeding. Both components of variance were highly variable among inbred lines, with increases and decreases recorded for both. The variance among lines in the residual variance provides some evidence for a genetic basis of developmental stability. Changes in the phenotypic variance of these traits are largely due to changes in the genetic variance.

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Kleffe, J., N. G. N. Prasad, and J. N. K. Rao. ""Optimal" Estimation of Correlated Response Variance Under Additive Models." Journal of the American Statistical Association 86, no. 413 (March 1991): 144. http://dx.doi.org/10.2307/2289723.

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Kleffe, J., N. G. N. Prasad, and J. N. K. Rao. "“Optimal” Estimation of Correlated Response Variance under Additive Models." Journal of the American Statistical Association 86, no. 413 (March 1991): 144–50. http://dx.doi.org/10.1080/01621459.1991.10475012.

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Wittenburg, D., N. Melzer, and N. Reinsch. "Genomic additive and dominance variance of milk performance traits." Journal of Animal Breeding and Genetics 132, no. 1 (June 30, 2014): 3–8. http://dx.doi.org/10.1111/jbg.12103.

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Mirkin, Boris, Phipps Arabie, and Lawrence J. Hubert. "Additive two-mode clustering: The error-variance approach revisited." Journal of Classification 12, no. 2 (September 1995): 243–63. http://dx.doi.org/10.1007/bf03040857.

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Sadeghi, Seyed Abu Taleb, Mohammad Rokouei, Mehdi Vafaye Valleh, Mokhtar Ali Abbasi, and Hadi Faraji-Arough. "Estimation of additive and non-additive genetic variance component for growth traits in Adani goats." Tropical Animal Health and Production 52, no. 2 (October 17, 2019): 733–42. http://dx.doi.org/10.1007/s11250-019-02064-0.

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Bryant, Edwin H., Steven A. McCommas, and Lisa M. Combs. "THE EFFECT OF AN EXPERIMENTAL BOTTLENECK UPON QUANTITATIVE GENETIC VARIATION IN THE HOUSEFLY." Genetics 114, no. 4 (December 1, 1986): 1191–211. http://dx.doi.org/10.1093/genetics/114.4.1191.

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ABSTRACT Effects of a population bottleneck (founder-flush cycle) upon quantitative genetic variation of morphometric traits were examined in replicated experimental lines of the housefly founded with one, four or 16 pairs of flies. Heritability and additive genetic variances for eight morphometric traits generally increased as a result of the bottleneck, but the pattern of increase among bottleneck sizes differed among traits. Principal axes of the additive genetic correlation matrix for the control line yielded two suites of traits, one associated with general body size and another set largely independent of body size. In the former set containing five of the traits, additive genetic variance was greatest in the bottleneck size of four pairs, whereas in the latter set of two traits the largest additive genetic variance occurred in the smallest bottleneck size of one pair. One trait exhibited changes in additive genetic variance intermediate between these two major responses. These results were inconsistent with models of additive effects of alleles within loci or of additive effects among loci. An observed decline in viability measures and body size in the bottleneck lines also indicated that there was nonadditivity of allelic effects for these traits. Several possible nonadditive models were explored that increased additive genetic variance as a result of a bottleneck. These included a model with complete dominance, a model with overdominance and a model incorporating multiplicative epistasis.

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Lavi, Uri, Emanuel Lahav, Chemda Degani, Shmuel Gazit, and Jossi Hillel. "Genetic Variance Components and Heritabilities of Several Avocado Traits." Journal of the American Society for Horticultural Science 118, no. 3 (May 1993): 400–404. http://dx.doi.org/10.21273/jashs.118.3.400.

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Genetic variance components for avocado (Persea americana Mill.) traits were estimated to improve avocado breeding efficiency. The additive and nonadditive genetic variance components were calculated from the variances between and within crosses. In all nine traits examined, i.e.-anise scent, fruit density, flowering intensity, fruit weight, harvest duration, inflorescence length, seed size, softening time, and tree size-a significant nonadditive genetic variance was detected. Additive genetic variance in all traits was lower and nonsignificant. The existence of major nonadditive variance was indicated also by narrow-sense and broad-sense heritability values estimated for each trait. Therefore, parental selection should not be based solely on cultivar performance. Crosses between parents of medium and perhaps even low performance should also be included in the breeding program.

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Villanueva-Verduzco, Clemente, José Antonio Ayala-Esteban, Evert Villanueva-Sanchez, Jaime Sahagen-Castellanos, and Martha Blanca Guadalupe Irizar Garza. "Changes of genetic variances and heritability by effect of selection in a Mexican local variety of Squash." Journal of Applied Biotechnology & Bioengineering 7, no. 5 (October 15, 2020): 225–30. http://dx.doi.org/10.15406/jabb.2020.07.00237.

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A local variety of squash (Cucurbita pepo L.), ‘Round Zuchinni’ type from Los Reyes Acatlixhuayan State of México, México, was simultaneously evaluated and selected. The objective was to study effect of selection on genetic variance and heritability. Additive genetic variance decreased in seven of nine traits studied: fruit height (79.93 %); fruit weight (65.72 %); fruit width (60.91 %); flesh thickness (57.66 %); flesh color (43.70 %); dry weight of seed (39.54 %); flesh flavor (16.60 %); except in width and seed length traits where it increased 63.40 % and 0.81 %, respectively. Only weight of seed had dominance genetic variance. The coefficient of additive genetic variance (CVA) fluctuated from 9.4 to 61.7 % in the first cycle, and from 9.4 to 51.8 % in the second cycle of selection-evaluation among traits. Heritability diminished in seven from nine characters. In general, the estimated genetic variances (additive and dominance) and heritability decreased as a result of combined selection of falf sib families.

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Liu, Ying, Lei Xu, Zezhao Wang, Ling Xu, Yan Chen, Lupei Zhang, Lingyang Xu, et al. "Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle." Animals 9, no. 12 (December 1, 2019): 1055. http://dx.doi.org/10.3390/ani9121055.

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Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

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Levine, Michael, and Jinguang Li. "LOCAL INSTRUMENTAL VARIABLE METHOD FOR THE GENERALIZED ADDITIVE-INTERACTIVE NONLINEAR VOLATILITY MODEL ESTIMATION." Econometric Theory 28, no. 3 (January 20, 2012): 629–69. http://dx.doi.org/10.1017/s0266466611000363.

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In this article we consider a new separable nonparametric volatility model that includes second-order interaction terms in both mean and conditional variance functions. This is a very flexible nonparametric ARCH model that can potentially explain the behavior of the wide variety of financial assets. The model is estimated using the generalized version of the local instrumental variable estimation method first introduced in Kim and Linton (2004, Econometric Theory 20, 1094–1139). This method is computationally more effective than most other nonparametric estimation methods that can potentially be used to estimate components of such a model. Asymptotic behavior of the resulting estimators is investigated and their asymptotic normality is established. Explicit expressions for asymptotic means and variances of these estimators are also obtained.

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Ghiasi, H., R. Abdollahi-Arpanahi, M. Razmkabir, M. Khaldari, and R. Taherkhani. "Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows." Journal of Agricultural Science 156, no. 4 (May 2018): 565–69. http://dx.doi.org/10.1017/s0021859618000497.

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AbstractThe aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

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LÓPEZ-FANJUL, C., A. FERNÁNDEZ, and M. A. TORO. "The role of epistasis in the increase in the additive genetic variance after population bottlenecks." Genetical Research 73, no. 1 (February 1999): 45–59. http://dx.doi.org/10.1017/s0016672398003619.

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The effect of population bottlenecks on the additive variance generated by two neutral independent epistatic loci has been studied theoretically. Three kinds of epistasis were considered: (1) additive×additive, (2) multiple dominant genotype favoured, and (3) Dobzhansky–Muller type. The additive variance in an infinitely large panmictic population (ancestral variance) was compared with its expected value at equilibrium, after t consecutive bottlenecks of equal size N (derived variance). Formulae were derived in terms of allele frequencies and effects at each locus and the corresponding epistatic effects. An increase in the additive variance after bottlenecks will occur only if its ancestral value is minimal or very small. This has been detected only for: (1) intermediate ancestral allele frequencies at both loci ; (2) extreme ancestral allele frequencies at one or both loci. The magnitude of the excess was inversely related to N and t. With dominance gene action, enhanced additive variance after bottlenecks implies a rise in the genotypic frequency of homozygous deleterious recessives, resulting in inbreeding depression. Considering multiple loci, simultaneous segregation of unfavourable alleles at intermediate frequencies, or of favourable recessives at low frequencies, cannot easily be conceived of unless there is strong genotype–environment interaction. With this possible exception, it is unlikely that the rate of evolution may be accelerated after population bottlenecks, in spite of occasional increments of the derived additive variance over its ancestral value.

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Wolpert, David H. "On Bias Plus Variance." Neural Computation 9, no. 6 (August 1, 1997): 1211–43. http://dx.doi.org/10.1162/neco.1997.9.6.1211.

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This article presents several additive corrections to the conventional quadratic loss bias-plus-variance formula. One of these corrections is appropriate when both the target is not fixed (as in Bayesian analysis) and training sets are averaged over (as in the conventional bias plus variance formula). Another additive correction casts conventional fixed-trainingset Bayesian analysis directly in terms of bias plus variance. Another correction is appropriate for measuring full generalization error over a test set rather than (as with conventional bias plus variance) error at a single point. Yet another correction can help explain the recent counterintuitive bias-variance decomposition of Friedman for zero-one loss. After presenting these corrections, this article discusses some other loss function-specific aspects of supervised learning. In particular, there is a discussion of the fact that if the loss function is a metric (e.g., zero-one loss), then there is bound on the change in generalization error accompanying changing the algorithm's guess from h1 to h2, a bound that depends only on h1 and h2 and not on the target. This article ends by presenting versions of the bias-plus-variance formula appropriate for logarithmic and quadratic scoring, and then all the additive corrections appropriate to those formulas. All the correction terms presented are a covariance, between the learning algorithm and the posterior distribution over targets. Accordingly, in the (very common) contexts in which those terms apply, there is not a “bias-variance trade-off” or a “bias-variance dilemma,” as one often hears. Rather there is a bias-variance-covariance trade-off.

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van der Werf, J. H., and I. J. de Boer. "Estimation of additive genetic variance when base populations are selected." Journal of Animal Science 68, no. 10 (1990): 3124. http://dx.doi.org/10.2527/1990.68103124x.

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Turelli, Michael. "Phenotypic Evolution, Constant Covariances, and the Maintenance of Additive Variance." Evolution 42, no. 6 (November 1988): 1342. http://dx.doi.org/10.2307/2409017.

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Goutte, Stéphane, Nadia Oudjane, and Francesco Russo. "Variance optimal hedging for continuous time additive processes and applications." Stochastics 86, no. 1 (March 18, 2013): 147–85. http://dx.doi.org/10.1080/17442508.2013.774402.

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Turelli, Michael. "PHENOTYPIC EVOLUTION, CONSTANT COVARIANCES, AND THE MAINTENANCE OF ADDITIVE VARIANCE." Evolution 42, no. 6 (November 1988): 1342–47. http://dx.doi.org/10.1111/j.1558-5646.1988.tb04193.x.

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Hoeschele, Ina. "Additive and Nonadditive Genetic Variance in Female Fertility of Holsteins." Journal of Dairy Science 74, no. 5 (May 1991): 1743–52. http://dx.doi.org/10.3168/jds.s0022-0302(91)78337-9.

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Turelli, Michael, and N. H. Barton. "WILL POPULATION BOTTLENECKS AND MULTILOCUS EPISTASIS INCREASE ADDITIVE GENETIC VARIANCE?" Evolution 60, no. 9 (2006): 1763. http://dx.doi.org/10.1554/05-585.1.

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Turelli, Michael, and N. H. Barton. "WILL POPULATION BOTTLENECKS AND MULTILOCUS EPISTASIS INCREASE ADDITIVE GENETIC VARIANCE?" Evolution 60, no. 9 (September 2006): 1763–76. http://dx.doi.org/10.1111/j.0014-3820.2006.tb00521.x.

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Harding, J., H. Huang, and T. Byrne. "Maternal, paternal, additive, and dominance components of variance in Gerbera." Theoretical and Applied Genetics 82, no. 6 (October 1991): 756–60. http://dx.doi.org/10.1007/bf00227321.

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Cunha, Elizângela Emídio, Ricardo Frederico Euclydes, Paulo Sávio Lopes, Robledo de Almeida Torres, and Paulo Luiz Souza Carneiro. "Behavior of genetic (co)variance components in populations simulated from non-additive genetic models of dominance and overdominance." Revista Brasileira de Zootecnia 39, no. 9 (September 2010): 1952–60. http://dx.doi.org/10.1590/s1516-35982010000900013.

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The aim of this work was to investigate the short-term behavior of the genetic variability of quantitative traits simulated from models with additive and non-additive gene action in control and phenotypic selection populations. Both traits, one with low (h² = 0.10) and the other with high (h² = 0.60) heritability, were controlled by 600 biallelic loci. From a standard genome, it was obtained six genetic models which included the following: only the additive gene effects; complete and positive dominance for 25, 50, 75 and 100% of the loci; and positive overdominance for 50% of the loci. In the models with dominance deviation, the additive allelic effects were also included for 100% of the loci. Genetic variability was quantified from generation to generation using the genetic variance components. In the absence of selection, genotypic and additive genetic variances were higher. In the models with non-additive gene action, a small magnitude covariance component raised between the additive and dominance genetic effects whose correlation tended to be positive on the control population and negative under selection. Dominance variance increased as the number of loci with dominance deviation or the value of the deviation increased, implying on the increase in genotypic and additive genetic variances among the successive models.

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Cheverud, J. M., and E. J. Routman. "Epistasis and its contribution to genetic variance components." Genetics 139, no. 3 (March 1, 1995): 1455–61. http://dx.doi.org/10.1093/genetics/139.3.1455.

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Abstract We present a new parameterization of physiological epistasis that allows the measurement of epistasis separate from its effects on the interaction (epistatic) genetic variance component. Epistasis is the deviation of two-locus genotypic values from the sum of the contributing single-locus genotypic values. This parameterization leads to statistical tests for epistasis given estimates of two-locus genotypic values such as can be obtained from quantitative trait locus studies. The contributions of epistasis to the additive, dominance and interaction genetic variances are specified. Epistasis can make substantial contributions to each of these variance components. This parameterization of epistasis allows general consideration of the role of epistasis in evolution by defining its contribution to the additive genetic variance.

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Tachida, Hidenori, and C. Clark Cockerham. "Variance components of fitness under stabilizing selection." Genetical Research 51, no. 1 (February 1988): 47–53. http://dx.doi.org/10.1017/s0016672300023934.

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SummaryVariance components of fitness under the stabilizing selection scheme of Wright (1935) for metric characters are calculated, extending his original analysis to the case with any number of alleles and multiple characters assuming additivity of gene effects. They are calculated in terms of the moments of the effects of alleles at individual loci for the metric characters. From these formulas, the variance components of fitness are evaluated at the mutation–selection equilibria predicted by the ‘Gaussian’ approximation (Lande, 1976), which is applicable if the per locus mutation rate is high, and the ‘House of Cards’ approximation (Turelli, 1984), which is applicable if the per locus mutation rate is low. It is found that the additive variance of fitness is small compared to non-additive variance in the ‘Gaussian’ case, whereas the additive variance is larger than non-additive variance in the ‘House of Cards’ case if the number of loci per character and the number of characters affected by each locus are not too large. With the assumption that a significant portion of fitness is due to this type of stabilizing selection, it is suggested that the real parameters are in the range where the ‘House of Cards’ approximation is applicable, since available data on variance components of fitness components in Drosophila show that the additive variance is far larger than the non-additive variance. It is noted that the present method does not discriminate the two approximations if the average values of the metric characters deviate from the optimum values. Other limitations of the present method are also discussed.

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Gimelfarb, A. "Additive-multiplicative approximation of genotype-environment interaction." Genetics 138, no. 4 (December 1, 1994): 1339–49. http://dx.doi.org/10.1093/genetics/138.4.1339.

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Abstract A model of genotype-environment interaction in quantitative traits is considered. The model represents an expansion of the traditional additive (first degree polynomial) approximation of genotypic and environmental effects to a second degree polynomial incorporating a multiplicative term besides the additive terms. An experimental evaluation of the model is suggested and applied to a trait in Drosophila melanogaster. The environmental variance of a genotype in the model is shown to be a function of the genotypic value: it is a convex parabola. The broad sense heritability in a population depends not only on the genotypic and environmental variances, but also on the position of the genotypic mean in the population relative to the minimum of the parabola. It is demonstrated, using the model, that G x E interaction may cause a substantial non-linearity in offspring-parent regression and a reversed response to directional selection. It is also shown that directional selection may be accompanied by an increase in the heritability.

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Ruiz, A., M. Santos, A. Barbadilla, J. E. Quezada-Díaz, E. Hasson, and A. Fontdevila. "Genetic variance for body size in a natural population of Drosophila buzzatii." Genetics 128, no. 4 (August 1, 1991): 739–50. http://dx.doi.org/10.1093/genetics/128.4.739.

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Abstract Previous work has shown thorax length to be under directional selection in the Drosophila buzzatii population of Carboneras. In order to predict the genetic consequences of natural selection, genetic variation for this trait was investigated in two ways. First, narrow sense heritability was estimated in the laboratory F2 generation of a sample of wild flies by means of the offspring-parent regression. A relatively high value, 0.59, was obtained. Because the phenotypic variance of wild flies was 7-9 times that of the flies raised in the laboratory, "natural" heritability may be estimated as one-seventh to one-ninth that value. Second, the contribution of the second and fourth chromosomes, which are polymorphic for paracentric inversions, to the genetic variance of thorax length was estimated in the field and in the laboratory. This was done with the assistance of a simple genetic model which shows that the variance among chromosome arrangements and the variance among karyotypes provide minimum estimates of the chromosome's contribution to the additive and genetic variances of the trait, respectively. In males raised under optimal conditions in the laboratory, the variance among second-chromosome karyotypes accounted for 11.43% of the total phenotypic variance and most of this variance was additive; by contrast, the contribution of the fourth chromosome was nonsignificant. The variance among second-chromosome karyotypes accounted for 1.56-1.78% of the total phenotypic variance in wild males and was nonsignificant in wild females. The variance among fourth chromosome karyotypes accounted for 0.14-3.48% of the total phenotypic variance in wild flies. At both chromosomes, the proportion of additive variance was higher in mating flies than in nonmating flies.

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Journal articles on the topic 'Additive variance'

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