Effects of data imbalance on estimation of heritability

Summary Effects of data imbalance on bias, sampling variance and mean square error of heritability estimated with variance components were examined using a random two-way nested classification. Four designs, ranging from zero imbalance (balanced data) to low, medium and high imbalance, were considered for each of four combinations of heritability (h²=0.2 and 0.4) and sample size (N=120 and 600). Observations were simulated for each design by drawing independent pseudo-random deviates from normal distributions with zero means, and variances determined by heritability. There were 100 replicates of each simulation; the same design matrix was used in all replications. Variance components were estimated by analysis of variance (Henderson's Method 1) and by maximum likelihood (ML). For the design and model used in this study, bias in heritability based on Method 1 and ML estimates of variance components was negligible. Effect of imbalance on variance of heritability was smaller for ML than for Method 1 estimation, and was smaller for heritability based on estimates of sire-plus-dam variance components than for heritability based on estimates of sire or dam variance components. Mean square error for heritability based on estimates of sire-plus-dam variance components appears to be less sensitive to data imbalance than heritability based on estimates of sire or dam variance components, especially when using Method 1 estimation. Estimation of heritability from sire-plus-dam components was insensitive to differences in data imbalance, especially for the larger sample size.Supported by grants from the Illinois Agricultural Experiment Station and the University of Illinois Research Board. Charles Smith, H. W. Norton and D. Gianola contributed valuable suggestions     

Estimation of (co)variance components,genetic parameters,and genetic trends of growth traits in community-based breeding programs of Bonga sheep

Community-based sheep breeding programs (CBBPs) have been adopted strategically to improve Bonga sheep, the most popular sheep breed in Ethiopia. The present study was undertaken to estimate genetic parameters and genetic trends for growth traits and inbreeding levels in each Bonga sheep CBBP. Data pertaining to growth traits, spanning a period of seven years (2012–2017), were collected from 14 Bonga sheep CBBPs. Data were analyzed using the General Linear Model procedure of SAS to study the performance of the breed over the years. The genetic parameters were estimated by univariate and multivariate animal model using restricted maximum likelihood method of WOMBAT software. The genetic trends were estimated by the regression of the average breeding values of the animals on the year of birth. The overall least square means ± SE of BW (kg) were 3.10 ± 0.010, 16.1 ± 0.07, 24.7 ± 0.20, 30.4 ± 0.40 and 34.0 ± 0.84 for birth weight (BWT), weaning weight (WWT), six-month weight (SMWT), nine-month weight (NMWT) and yearling weight (YWT), respectively. Direct heritability estimates from selected models were 0.56 ± 0.030, 0.36 ± 0.030, 0.22 ± 0.040, 0.17 ± 0.070 and 0.13 ± 0.150 for BWT, WWT, SMWT, NMWT and YWT, respectively. Six-month weight was the selection trait and presented positive trends for 10 CBBPs, and negative trends for four CBBPs. Moderate to high heritability estimates and positive genetic trends indicated scope for further improvement of BW. Additionally, the positive and high correlation between BW traits indicated that selection for just one trait would also improve the other traits through correlated responses.     

Application of approximate variances of variance components and their ratios in genetic tests

The application and underlying assumptions of formulae used to estimate the variance of variance components and ratios of variance components are fully described for (1) variance components estimated using Henderson's Method 3 (HM3) and Restricted Maximum Likelihood (REML) and (2) ratios of variance components commonly used in genetic tests — biased and unbiased heritabilities. A first-order Taylor series approximation is often used to estimate the variance of a ratio of two random variables (e.g., heritability), however the formula is complicated, thus making calculations prone to errors. Dickerson's approximation is considerably simpler, though relatively rarely used. In case studies using data from 148 slash pine full-sib progeny tests, Dickerson's method was found to be slightly more conservative than the Taylor series approximation when estimating the variance of heritability estimates, regardless of test size, age, or the trait (volume, which is a continuous trait, and rust resistance, which is a bernoulli trait). Both the Taylor series and Dickerson approximations compared favorably with an empirical estimate of the variance of heritability estimates, however there is some evidence of small-sample bias associated with the use of the asymptotic variance-covariances from REML variance component estimation.This is Florida Agricultural Experiment Station Journal Series No. R-03964 of the Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA     

The effects of selection for gain in mice on the direct-maternal genetic correlation

Components of genetic variation for postweaning growth traits were estimated for both control and growth stocks of mice. The effect of phenotypic selection for gain, which genetically combines selection for additive direct and maternal effects, on additive genetic variance components, heritability, and additive genetic correlationsis discussed. Quantitative genetic theory predicts that simultaneous selection for two metric traits in the same direction will cause the genetic correlation between the two traits to become more negative. The results presented in this paper conflict with this theory. The direct-maternal additive genetic correlation was more negative in the control line (with 356 mice) than in the growth-selected line (with 320 mice) for the three traits analyzed (0.310 vs 0.999 for 21-day weight, 0.316 vs 1.000 for 42-day weight, and 0.506 vs 1.000 for gain from 21–42 days). Estimates were obtained by restricted maximum likelihood (REML) computed under a derivative free algorithm (DFREML).     

Estimation of genetic parameters for semen quality traits and growth rate in a paternal rabbit line

Variance components of sperm quality traits were estimated in a paternal line of rabbits selected on the basis of daily weight gain (DG, g/day) between 28 and 63 days of age. Features of the marginal posterior distributions for the genetic variance ratios, variance due to non-additive plus environmental permanent male effects, and variance due to litter of birth effects with respect to phenotypic variance are reported. The correlation between sperm quality traits and the selection criteria were also estimated. Nine sets of two-trait analyses were performed involving 12 908 DG records, 2231 ejaculates corresponding to 412 males, and 14 700 animals in the pedigree file. Heritability values (h²) of sperm quality traits commonly evaluated in a classic spermiogram were 0.18, 0.19, and 0.12 for normal acrosome status (NAR) (%, percentage of sperm with intact acrosome), sperm abnormalities (ANR) (%, percentage of sperm abnormalities), and sperm motility (MOT) (%, percentage of total motile sperm cells), respectively. The h² of some motion computer-assisted sperm analysis (CASA) Parameters 0.09, 0.11, 0.10, 0.11, 0.11 and 0.11 for average path velocity (VAP) (μm/sec; average path velocity), straight-line velocity (VSL) (μm/sec; straight-line velocity), curvilinear velocity (VCL) (μm/sec; curvilinear velocity), linearity index (LIN) (%, linearity index), amplitude of lateral head displacement (ALH) (μm; amplitude of the lateral head displacement) and straightness (STR) (%, straightness) were also estimated. Permanent environmental effects were lower than the corresponding values of h² and varied between 0.04 and 0.14. Genetic correlations between DG and sperm traits showed a high interval of highest density of 95% (HPD)_95% (interval of highest density of 95%). However, there is some consistent evidence of the negativity of the genetic correlations of DG with NAR and MOT (−0.40 and −0.53, respectively). Permanent correlations were low, including the zero in the HPD_95%. Litter birth correlations between DG with LIN and STR showed that a favorable effect for growth could be detrimental for them (−0.47 and −0.53). Therefore, as the magnitude of the genetic correlations does not seem very high, it may be possible to define a selection index, including some sperm quality traits that allow improvement of DG without diminishing the semen quality.     

Animal Board Invited Review: Meta-analysis of genetic parameters for resilience and efficiency traits in goats and sheep

Genetic selection focused purely on production traits has proven very successful in improving the productive performance of livestock. However, heightened environmental and infectious disease challenges have raised the need to also improve the resilience of animals to such external stressors, as well as their efficiency in utilising available resources. A better understanding of the relationship between efficiency and production and health traits is needed to properly account for it in breeding programmes and to produce animals that can maintain high production performance in a range of environmental conditions with minimal environmental footprint. The aim of this study was to perform a meta-analysis of genetic parameters for production, efficiency and health traits in sheep and goats. The dataset comprised 963 estimates of heritability and 572 genetic correlations collated from 162 published studies. A threelevel meta-analysis model was fitted. Pooled heritability estimates for milk production traits ranged between 0.27 ± 0.03 and 0.48 ± 0.13 in dairy goats and between 0.21 ± 0.06 and 0.33 ± 0.07 in dairy sheep. In meat sheep, the heritability of efficiency traits ranged from 0.09 ± 0.02 (prolificacy) up to 0.32 ± 0.14 (residual feed intake). For health traits, pooled heritability was 0.07 ± 0.01 (faecal egg count) and 0.21 ± 0.01 (somatic cell score) in dairy goats and 0.14 ± 0.04 (faecal egg count) and 0.13 ± 0.02 (somatic cell score) in dairy sheep. In meat sheep, the heritability of disease resistance and survival traits ranged between 0.07 ± 0.02 (mastitis) and 0.50 ± 0.10 (breech strike). Pooled estimates of genetic correlations between resilience and efficiency traits in dairy goats were not significantly different from zero with the exception of somatic cell score and fat content (?0.19 ± 0.01). In dairy sheep, only the unfavourable genetic correlation between somatic cell score and protein content (0.12 ± 0.03) was statistically significant. In meat sheep only, the correlations between growth and faecal egg count (?0.28 ± 0.11) as well as between growth and dagginess (?0.33 ± 0.13) were statistically significant and favourable. Results of this meta-analysis provide evidence of genetic antagonism between production and health in dairy sheep and goats. This was not observed in meat sheep where most of the pooled estimates had high standard errors and were non-significant. Based on the obtained results, it seems feasible to simultaneously improve efficiency and health in addition to production by including the different types of traits in the breeding goal. However, a better understanding of potential trade-offs between these traits would be beneficial. Particularly, more studies focused on reproduction and resilience traits linked to the animal’s multi-trait response to challenges are required.     

Estimation of genetic parameters for post-weaning traits of Kermani sheep

The objective of the present study was to estimate genetic parameters for post-weaning traits in Kermani sheep. Traits were included 6-month weight (6MW), 9-month weight (9MW), yearling weight (YW), greasy fleece weight at first shearing (GFW) and greasy fleece weights at various shearings (RFW). Data and pedigree information used in this research were collected at Breeding Station of Kermani sheep during 1993–2004. Genetic parameters were estimated with single- and multi-traits analysis using restricted maximum likelihood (REML) procedures, under animal models. Log likelihood ratio test indicated the most appropriate model for 6MW and 9MW should included direct additive genetic effects as well as maternal permanent environmental effects. However the most appropriate model for YW and GFW had only the direct additive genetic effects. The effects of sex, age of dam and year of birth were significant on body weight traits (P < 0.01). GFW was influenced significantly by sex and year of birth (P < 0.01) but was not affected by age of dam (P > 0.05). Type of birth was no significant effect on studied traits (P > 0.05). Also, the age of lamb at weighing time was a significant influence on 6MW, 9MW and YW. Direct heritability estimates for 6MW, 9MW, YW and GFW were 0.32, 0.03, 0.15 and 0.15, respectively. Maternal permanent environmental estimates of 0.09 were obtained for 6MW and 9MW. Genetic correlation estimates between mentioned traits ranged from 0.51 to 0.99. Phenotypic correlations were generally lower than those of genetic correlation and varied from 0.05 to 0.79 for various traits. The environmental correlations estimates between GFW with growth traits were low, but between other traits were positive and high, ranged from 0.54 to 0.72. The value of repeatability estimated for greasy fleece weight was 0.22.     

Phenotypic and genetic variability of estimated growth curve parameters in mice

Summary Data from 1,919 outbred ICR mice were used to examine the potential usefulness of growth curve parameters as selection criteria for altering the relationship between body weight and age. A logistic growth function was used to model growth through 12 weeks of age. Estimates of asymptotic weight (A), maximum growth rate (r) and age at point of inflection (t^*) were obtained by nonlinear least-squares. A log transformation was also used to stabilize residual variance. Phenotypic and genetic parameters were estimated for the estimated growth curve parameters and for body weights at 2, 3, 4.5, 6, 8 and 12 weeks of age. Heritabilities of estimated growth curve parameters (obtained with and without a log transformation, respectively) were: A (0.28±0.07, 0.28±0.07), r (0.35±0.07, 0.53±0.09) and t^* (0.41±0.08, 0.44±0.08). Estimated genetic correlations suggest that t^* may be useful in selecting for rapid early growth without increasing mature weight.     

云南香型软米水稻资源农艺性状遗传效应研究

选取5份云南地方香型软米水稻种质资源和6份自育香型软米保持系按5×6不完全双列杂交设计(NCⅡ)配制成30个组合,采用加性-显性-上位性遗传模型,分析云南香型软米11个农艺性状的遗传效应。结果表明,云南香型软米多数农艺性状的遗传主要受加×加上位性效应、加性×环境效应、显性×环境效应的影响,还存在不同程度的加性效应和显性效应,单株产量受基因加性效应、显性效应、加×加上位性效应、加性×环境效应、显性×环境效应的影响;株高、有效穗的遗传率以普通狭义遗传率为主,其他性状的普通狭义遗传率和互作狭义遗传率均达极显著水平;产量构成性状之间存在不同类型和不同程度的遗传相关,多数性状之间以加×加上位性、加性×环境和显性×环境互作效应显著。     

Utilization of pedigree information to estimate genetic parameters from large unbalanced data sets in apple

 Genetic parameters (narrow-sense heritabilities and genetic correlations) were estimated for major agronomical traits in apple from large unbalanced data sets, with the help of wide-pedigree information. The software REML VCE (Groeneveld 1996) took into account the complex pedigree of the French apple-breeding population, thanks to the restricted maximum likelihood procedure combined with the construction of the entire relationship matrix between hybrids planted in the field and their ancestors. Narrow-sense heritability estimates ranged from 0.34 to 0.68 for traits exhibiting a normal distribution. Heritability values around 0.35–0.40 were obtained for fruit characteristics (size, texture, flavour, juice content, attractiveness, russeting). Higher values of heritability were obtained for vigour, assessed by the circumference of the trunk (0.51), and powdery mildew resistance (0.68). Additive genetic correlations between traits were also estimated, showing a very high relationship between fruit-quality traits. Vigour and powdery mildew resistance were slightly correlated with the other traits. Utilization of the ‘individual genetic model’ for the estimation of genetic parameters and breeding values in apple is discussed. Received: 14 June 1997 / Accepted: 4 December 1997     

The effect of simultaneous selection on the genetic correlation

The theoretical effect of simultaneous selection on the genetic correlations between two traits over 20 generations was examined using simulation. For each generation, a population of 50 male and 50 female diploid gen otypes with 15 loci, each with two alleles, was synthesized. None of the loci exhibited dominance. Five loci affected only trait 1, 5 loci only trait 2 and 5 were pleiotropic (affected both traits). Initial allelic frequencies were equal at each locus. Phenotypes were created by adding a random normal deviation for each trait to the genotype. The size of this deviation for each trait determined its heritability (h²). Index selection with h² combinations of (0.15, 0.15), (0.15,0.45) and (0.45,0.45) and relative economic weights of (1, 1) and (1, 3) for each h² combination was employed. In each generation, the highest ranking 25 genotypes of each sex were used to generate the next generation with single-pair matings, each producing two male and two female offspring. One hundred replicates were run for both negative and positive correlations. With a positive initial value, the genetic correlation tended to decline (toward zero). The rates of change were moderately affected by index weights and h². With a negative initial value, the genetic correlation tended to decrease (towards -1). However, unequal heritabilities and unequal relative economic weights slowed the rate of change with the greatest imbalances tending to hold the correlation constant or move it toward zero. These simulations illustrate that changes in parameters over time can affect the selection practiced. Under some of the conditions simulated, the use of initial genetic parameter values without change could have potentially negative effects on overall genetic gain.     

Patterns of quantitative genetic variation in multiple dimensions

A fundamental question for both evolutionary biologists and breeders is the extent to which genetic correlations limit the ability of populations to respond to selection. Here I view this topic from three perspectives. First, I propose several nondimensional statistics to quantify the genetic variation present in a suite of traits and to describe the extent to which correlations limit their selection response. A review of five data sets suggests that the total variation differs substantially between populations. In all cases analyzed, however, the “effective number of dimensions” is less than two: more than half of the total genetic variation is explained by a single combination of traits. Second, I consider how patterns of variation affect the average evolutionary response to selection in a random direction. When genetic variation lies in a small number of dimensions but there are a large number of traits under selection, then the average selection response will be reduced substantially from its potential maximum. Third, I discuss how a low genetic correlation between male fitness and female fitness limits the ability of populations to adapt. Data from two recent studies of natural populations suggest this correlation can diminish or even erase any genetic benefit to mate choice. Together these results suggest that adaptation (in natural populations) and genetic improvement (in domesticated populations) may often be as much constrained by patterns of genetic correlation as by the overall amount of genetic variation.     

Estimation of Variances at Different Significance Levels

Variance components and their ratios broad-sense heritability and constancy were estimated for some quantitative traits in northwestern Spain populations of maize. Estimations were carried out at the significance levels of 5% and 25% and without significance limits. It can be concluded that estimation without significance limits is more efficient than the estimations at the usual levels in the method of analysis of experimental data from fields experiments.     

REML estimation of variance parameters in nonlinear mixed effects models using the SAEM algorithm

Nonlinear mixed effects models are now widely used in biometrical studies, especially in pharmacokinetic research or for the analysis of growth traits for agricultural and laboratory species. Most of these studies, however, are often based on ML estimation procedures, which are known to be biased downwards. A few REML extensions have been proposed, but only for approximated methods. The aim of this paper is to present a REML implementation for nonlinear mixed effects models within an exact estimation scheme, based on an integration of the fixed effects and a stochastic estimation procedure. This method was implemented via a stochastic EM, namely the SAEM algorithm. The simulation study showed that the proposed REML estimation procedure considerably reduced the bias observed with the ML estimation, as well as the residual mean squared error of the variance parameter estimations, especially in the unbalanced cases. ML and REML based estimators of fixed effects were also compared via simulation. Although the two kinds of estimates were very close in terms of bias and mean square error, predictions of individual profiles were clearly improved when using REML vs. ML. An application of this estimation procedure is presented for the modelling of growth in lines of chicken.     

Effect of selection on genetic parameters of correlated traits

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

Genetic analysis of ewe productivity traits in Moghani sheep

Genetic and environmental (co)variance components for productivity traits in Moghani sheep were estimated using data from 1344 ewes. The data were collected in the Jafarabad breeding station, north-east of Iran, during a 13-year period (1995-2008). The studied traits were litter size at birth (LSB), litter size at weaning (LSW), litter mean weight per lamb born (LMWLB), litter mean weight per lamb weaned (LMWLW), total litter weight at birth (TLWB) and total litter weight at weaning (TLWW). A model including direct additive genetic effects as well as permanent environmental effects related to repeated records of ewe was the most appropriate model for all the studied traits. Genetic parameters were estimated applying restricted maximum likelihood (REML) procedure. Direct heritability estimate for LSB, LSW, LMWLB, LMWLW, TLWB and TLWW were 0.11, 0.02, 0.15, 0.07, 0.07 and 0.06, respectively. Corresponding values for repeatability estimates were 0.16, 0.19, 0.18, 0.11, 0.13 and 0.09. Genetic correlations between the studied traits ranged from −0.99 for LSB-LMWLB and LSW-LMWLB to 0.99 for LSB-TLWB. Phenotypic and environmental correlation estimates were generally lower than those of genetic ones. Estimates of permanent environmental correlation among traits were positive and medium to high. Although low direct heritabilities were estimated for the reproductive traits, as these traits are of interest then they should be included in a breeding program.     

Relationships between some genetic parameters and test environments in open-pollinated families of Pinus elliottii in South Africa

Summary Growth traits, stem form and branch characteristics in three series of well-replicated trials, comprising open-pollinated families of Pinus elliottii Engelmann, were analysed to investigate whether the absolute value of the component of variance for family effect and its relative value (which is equivalent to one-quarter of heritability on individual values) were related, with trial averages of the traits studied taken as productivity indices of the sites tested. The first series, called the foreign selection series, consisted of four trials comprising open-pollinated families from single clones from a Florida seed orchard and from selected trees in Queensland, Australia. Two trials were established in the eastern Transvaal, at Tweefontein and Frankfort State Forests (SF), and two in Zululand, at Dukuduku and KwaMbonambi SF. The second series, called the Zululand selection series, consisted of open-pollinated families from single trees selected in Zululand that were compared in three trials: in the eastern Transvaal at Wilgeboom SF, in Zululand at Dukuduku SF and in the southern Cape at Lottering SF. The third series, called the South African selection series, consisted of open-pollinated families from single trees selected in the different forest areas of South Africa and compared in four trials: two trials in the eastern Transvaal at Tweefontein and Mariti SF and two in the Natal province at Dukuduku and Weza SF. Some positive and linear relationships between absolute sizes of the variance component for family and trial average for all traits studied, except stem form, were discernible for the foreign selections and for the Zululand selections. The South African selections presented a more erratic pattern of variation. However, when the origin of the genetic material was disregarded, the positive trends were undeniable. Curvilinear relationships between relative values and site averages were discernible, when the origin of the genetic material was disregarded. Further research is needed in order to confirm the suggested effect of site on genetic expressivity of open-pollinated families of slash pine grown in South Africa.     

Leaf physiological aspects of nitrogen-use efficiency in Brassica campestris L.: quantitative genetic variation across nutrient treatments

Summary Quantitative genetic parameters for leaf physiological and whole-plant aspects of nitrogen-use efficiency in Brassica camprestris L. were estimated in three nutrient treatments in the greenhouse. Narrow-sense heritabilities and genetic correlations varied across treatments for some traits. Sire effects were significant for leaf nitrogen content in near-optimal and super-optimal, but not in suboptimal nutrient treatments. Additive genetic variation for two estimates of leaf physiological nitrogen-use efficiency (nitrogen-based photosynthetic capacity and leaf carbon: nitrogen ratio) was significant only in the suboptimal nutrient treatment. Area-based photosynthetic capacity, on the other hand, exhibited no heritable variation in any nutrient treatment. Heritability estimates of aboveground biomass and flower production were greatest in sub- and super-optimal treatments, respectively. Negative genetic correlations between leaf nitrogen content and both estimates of leaf nitrogen-use efficiency were evident in the super-optimal treatment. Aboveground biomass and leaf nitrogen-use efficiency were positively correlated in the suboptimal treatment, suggesting that growth differences were due in part to the efficiency with which nitrogen was utilized in physiological processes. Although implications for breeding may differ for different sources of germ plasm or different measures of performance or yield, selection for improved whole-plant performance through increased nitrogen-use efficiency should proceed best in suboptimal nutrient treatments.     

The consistency of quantitative genetic estimates in field and laboratory in the yellow dung fly

How consistent quantitative genetic estimates are across environments is unclear and under discussion. Heritability (h ²) estimates of hind tibia length (body size), development time and diapause induction in the yellow dung fly, Scathophaga stercoraria, generated with various methods in various environments are reported and compared. Estimates varied considerably within and among studies, but yielded good overall averages. The genetic correlations between the sexes for body size and development time were expectedly high (r(sex)=0.57–0.78) but clearly less than unity, implying independent evolution of both traits in males and females of this sexually dimorphic species. Genetic and environmental variance components increased in proportion at variable field relative to constant laboratory conditions, resulting in overall similar h ². Heritabilities for males and females were also similar, and h ² of the morphological trait hind tibia length was not necessarily greater than that of the two life history traits. Full-sib (broad-sense) estimates (h ²=0.7–1.1) were 2–3 times greater than half-sib and parent/offspring (narrow-sense) estimates (h ²=0–0.6). Common environment (i.e., among-container) variance averaged 38.3% (body size) and 16.8% (development time) of the broad-sense genetic variance in two laboratory studies. The broad-sense h ², therefore, may contain substantial amounts (12–50%) of dominance variance and/or variance due to maternal effects. A general conclusion emerging from this and similar studies appears to be that whether field and laboratory genetic estimates differ depends on the environment, trait and species under consideration.     

Genetic variation in basic density and modulus of elasticity of coastal Douglas-fir

Douglas-fir trees from 39 open-pollinated families at four test locations were assessed to estimate heritability of modulus of elasticity (MOE) and basic density. After trees were felled, sound velocity was measured on 4-m logs with the Director HM200. Disks were taken to estimate dry and green wood density; dynamic MOE was estimated as green density × (sound velocity)². Heritability estimates of MOE (across-site h ²=0.55) were larger than those for total height (0.15) and diameter at breast height (DBH; 0.29), and similar to those for density (0.59). Negative genetic correlations were found for MOE with height (r _A=−0.30) and DBH (r _A=−0.51), and were similar to those found for density with height (r _A=−0.52) and DBH (r _A=−0.57). The partial correlations of height with MOE and density, while holding DBH constant, were positive, implying that the observed negative correlations between height and the wood properties were a function of the high positive correlation between height and DBH and the strong negative correlations between DBH and the wood properties. Taper [DBH/(height−1.4)] was found to be negatively associated with MOE. Selection for MOE may produce greater gains than selection for density because MOE had a larger coefficient of additive variation (9.6%) than density (5.1%). Conversely, selection for growth may have a more negative impact on MOE than density because of the greater genetic variation associated with MOE. Family mean correlations of the wood quality traits with stem form and crown health were mostly nonsignificant.