Selection system efficiencies for computer simulated progeny test field designs in loblolly pine

Summary Six simulated progeny test field designs in combination with three within-family selection systems were tested on three loblolly pine (Pinus taeda L.) progeny test sites in southeastern Oklahoma and southwestern Arkansas, to compare genetic gains for the single trait, height. Residual deviations obtained by subtraction of family and plantation mean effects for each plantation were combined with simulated genetic effects with known family variance structure. The simulated genetic populations, arranged in the following progeny test field designs — large square or almost square plots, five- and ten-tree row plots, five-tree noncontiguous plots, two tree row plots, and single-tree plots — were superimposed on the residual data for each plantation. Within-family selection methods based on deviations from block means, deviations from neighborhood means and deviations from plot means were built into the model. Realized genetic gain attained by each design — selection system combination was compared with the genetic gain theoretically possible if selection accuracy were perfect, and with expected gain estimated using the general linear model. In general, average realized genetic gain compared well with expected gain. Differences between designs with large versus small plots were generally lower than expected, although the single-tree plot design always yielded highest realized gain. Realized gain was generally higher than expected when within-family selection was based on deviations from block or neighborhood means, but equal to or lower than expected when selection was based on deviations from plot means.     

The efficiency of designs for fine-mapping of quantitative trait loci using combined linkage disequilibrium and linkage

In a simulation study, different designs were compared for efficiency of fine-mapping of QTL. The variance component method for fine-mapping of QTL was used to estimate QTL position and variance components. The design of many families with small size gave a higher mapping resolution than a design with few families of large size. However, the difference is small in half sib designs. The proportion of replicates with the QTL positioned within 3 cM of the true position is 0.71 in the best design, and 0.68 in the worst design applied to 128 animals with a phenotypic record and a QTL explaining 25% of the phenotypic variance. The design of two half sib families each of size 64 was further investigated for a hypothetical population with effective size of 1000 simulated for 6000 generations with a marker density of 0.25 cM and with marker mutation rate 4 × 10^-4 per generation. In mapping using bi-allelic markers, 42~55% of replicated simulations could position QTL within 0.75 cM of the true position whereas this was higher for multi allelic markers (48~76%). The accuracy was lowest (48%) when mutation age was 100 generations and increased to 68% and 76% for mutation ages of 200 and 500 generations, respectively, after which it was about 70% for mutation ages of 1000 generations and older. When effective size was linearly decreasing in the last 50 generations, the accuracy was decreased (56 to 70%). We show that half sib designs that have often been used for linkage mapping can have sufficient information for fine-mapping of QTL. It is suggested that the same design with the same animals for linkage mapping should be used for fine-mapping so gene mapping can be cost effective in livestock populations.     

Efficiency of check-plot designs in unreplicated field trials

Check-plot designs have a lower selection intensity than unreplicated non-check-plot designs if both the number of test lines to be selected (s) and of total plots in the trial (N) are kept constant. For a check-plot design to be more efficient, local control must effectively reduce the plot error variance and increase heritability to such a level that it compensates for the corresponding loss in selection intensity and makes the expected gain from selection at least equal to that in the non-check-plot design. To realize this goal, the required minimum reduction in plot error variance in a checkplot design (relative to that in a non-check-plot design) depends on (1) check-plot frequencyf _c , (2) fractionk = s/N, and (3) ratiow ₀= ₀ ² / _g ² of non-check-plot design plot error variance ₀ ² to genetic variance _g ² among test lines. Lowerw ₀ and higherf _c andk are found to require a relatively higher reduction in plot error variance in check-plot designs. A condition is derived to show when a check-plot design may never be more efficient.     

A nested-intensity design for surveying plant diversity

Managers of natural landscapes need cost-efficient, accurate, and precise systems to inventory plant diversity. We investigated a nested-intensity sampling design to assess local and landscape-scale heterogeneity of plant species richness in aspen stands in southern Colorado, USA. The nested-intensity design used three vegetation sampling techniques: the Modified-Whittaker, a 1000-m² multiple-scale plot (n = 8); a 100-m² multiple-scale Intensive plot (n = 15); and a 100-m² single-scale Extensive plot (n = 28). The large Modified-Whittaker plot (1000 m²) recorded greater species richness per plot than the other two sampling techniques (P < 0.001), estimated cover of a greater number of species in 1-m² subplots (P < 0.018), and captured 32 species missed by the smaller, more numerous 100-m² plots of the other designs. The Intensive plots extended the environmental gradient sampled, capturing 17 species missed by the other techniques, and improved species–area calculations. The greater number of Extensive plots further expanded the gradient sampled, and captured 18 additional species. The multi-scale Modified-Whittaker and Intensive designs allowed quantification of the slopes of species–area curves in the single-scale Extensive plots. Multiple linear regressions were able to predict the slope of species–area curves (adj R ² = 0.64, P < 0.001) at each Extensive plot, allowing comparison of species richness at each sample location. Comparison of species–accumulation curves generated with each technique suggested that small, single-scale plot techniques might be very misleading because they underestimate species richness by missing locally rare species at every site. A combination of large and small multi-scale and single-scale plots greatly improves our understanding of native and exotic plant diversity patterns.     

The importance and type of non-additive genetic effects for growth in Eucalyptus globulus

The paper investigates the importance of additive and non-additive genetic variances for growth in Eucalyptus globulus (Tasmanian Blue Gum), based on a large collection of diameter growth data covering 40 sites and more than 4,200 genotypes, most of them cloned, and spanning three generations of breeding. The variance estimates were based on a model accounting for additive, full-sib family and clone within full-sib family terms. The results indicated a small amount of additive genetic variance for diameter ( [^(h)]² = 0.10 ) \left( {{{\widehat{h}}^2} = 0.10} \right) and although non-additive genetic variance was also small, it accounted for a significant proportion of the total genetic variance present, corresponding to 80% of the additive variance. The interpretation of these non-additive effects is difficult. The results suggest, however, a possible role of epistasis. The evidence for this came from a strong observed bias in additive variance when clone effects were removed from the model and a larger than expected variance due to full-sib families relative to the variance due to clones within family. The relatively large proportion of genetic variance for growth that seems to be due to non-additive genetic effects has obvious implications in the breeding and deployment options in eucalypts, and these are briefly discussed.     

Plot size for progeny selection in maize (Zea mays L.)

Summary Six progeny trials that included 147 half-sib progenies of maize (Zea mays L.) population ESALQ PB-5 were conducted for the purpose of studying plot size and its consequences in recurrent selection programs. The progenies were evaluated in three 7x7 duplicate simple lattice experiments using one-row plots of 5 m². At harvest each plot was partitioned into five sub-plots (sampling units), and data was collected from each sampling unit. At the same time and place the same progenies were evaluated in three 7x7 duplicate simple lattice experiments using 1-m² (linear row with 5 plants) plots. Data were collected for plant and ear height, ear diameter, total ear weight, and total grain yield. The data were combined by using adjacent sampling units, and the analyses were performed by considered five plot sizes in addition to those of the independent trials with 1-m² plots. The experiments with 1-m² plots were less efficient in discriminating for yield traits among progenies than those with 5-m² plots. The combination of plot size and number of progenies evaluated indicated that an optimum plot size for yield was between 3 and 4 m², or 15–20 plants per plot. With such sizes the expected gain was maximized for the four replications used in this study. If the total area covered by each progeny is constant, the maximum gain from selection, however, is attained by decreasing plot size and increasing the number of replications. The minimum size of plots is, however, limited by practical or theoretical criteria. Plot size affected the estimates of additive genetic variance, coefficient of heritability, and genetic coefficient of variation for all of the traits. No practical limitation was observed for conducting experiments with 1-m² plot.     

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

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

Efficiency of inventory plot patterns for the estimation of woody vegetation recruit density in a tropical dense forest in Bénin

This study assessed the effectiveness of plot patterns for estimating recruit density of woody species in the dense forest of Lama Reserve (Bénin). The experimental design consisted of thirty 0.04 ha plots randomly settled in the forest and each subdivided into four hundred 1‐m² quadrats. Within each quadrat, recruits (dbh ≤10 cm) were counted and saplings (h ≥ 2 m and 2 cm ≤ dbh < 7 cm) and young trees (h ≥ 2 m and 7 cm ≤ dbh < 10 cm) were measured in dbh. In each 0.04 ha plot, seven different plot shapes and sizes were considered by grouping adjacent 1‐m² quadrats. Relationship between mean square error of the estimation of the density of recruitments and the plot sizes was modelled using the Smith law. Results obtained showed an average value of density of recruitments of 10.7 plants/m² with Green index value of 0.01. Shape and size of plots highly influenced the estimation of the density of recruitments. Rectangular plots of length/width = 2 and size of 72 m² (12 m × 6 m) were most efficient for the estimation of the density of recruitments in tropical dense forest with standard error of 0.79 plants/m².     

A comparison of four experimental designs for the estimation of heritability

Summary The partial diallel cross, the complete diallel cross, and the designs known as North Carolina Experiments 1 and 2 are compared for their usefulness in estimating heritability. It is first shown that reliable values for the sampling mean and variance of heritability estimates are obtained from approximate expressions based on the moments of the chi-square distribution. These expressions are then applied to determine the optimum experimental designs for a range of situations.The main basis for discrimination is the amount of information per unit, defined as i = 1/(N var( ²)), where ² is the estimate of the heritability h ² and N is the number of units in the experiment, either individuals or families.The two parameters considered were the heritability of individuals and the heritability of full-sib families, and for each of these the partial diallel cross was the most preferred, followed in decreasing order of preference by design NC2, the complete diallel, and design NC1.It is first shown that there is no optimum number of parents for a partial diallel cross or male parents for designs NC1 and NC2. The number of crosses per parent for a partial diallel or dams per sire for designs NC2 and NC2 should generally be six or less. Any expansion should be in the direction of using more parents in the case of the partial diallel, or more male parents in the case of designs NC1 and NC2. For the two heritability parameters considered in this study it is inefficient to increase the number of replicates beyond two.     

THE EFFECT OF ENVIRONMENTAL VARIABILITY ON THE HERITABILITIES OF TRAITS OF A FIELD CRICKET

The presence of heritable variation in traits is a prerequisite for evolution. The great majority of heritability (h²) estimates are performed under laboratory conditions that are characterized by low levels of environmental variability. Very little is known about the effect of environmental variability on the estimation of components of quantitative variation, although theoretical extrapolations from lab studies have been attempted. Here we investigate the effects of environmental heterogeneity on variance component estimation using full-sib families of Gryllus pennsylvanicus split between a homogeneous laboratory environment and a more variable field environment. Although large standard errors prevent demonstration of statistically significant differences among h² of traits measured in the two environments for all but one trait, the values of h² are, on average, lower in the variable field environment, with a mean reduction of 19%. Developmental time is an exception, exhibiting high levels of additive variance in the field, leading to a higher value of h² in the variable environment. Underlying the lower field h² estimates are greater components of environmental variance as expected, as well as lower components of genetic variance. In this study, there is no evidence that the increase in the environmental component of variance in the field is any more important in the reduction of h² than is the decrease in the additive genetic component. The implications of the relative changes in the two components of variance are discussed.     

EXPRESSION OF GENETIC VARIATION IN BODY SIZE OF THE COLLARED FLYCATCHER UNDER DIFFERENT ENVIRONMENTAL CONDITIONS

Heritability of body size in two experimentally created environments, representing good and poor feeding conditions, respectively, was estimated using cross-fostered collared flycatcher Ficedula albicollis nestlings. Young raised under poor feeding conditions attained smaller body size (tarsus length) than their full-sibs raised under good feeding conditions. Parent-offspring regressions revealed lower heritability (h²) of body size under poor than under good feeding conditions. Hence, as the same set of parents were used in the estimation of h² in both environments, this suggests environment-dependent change in additive genetic component of variance (V_A), or that the genetic correlation between parental and poor offspring environment was less than that between parental and good offspring environment. However, full-sib analyses failed to find evidence for genotype-environment interactions, although the power of these tests might have been low. Full-sib heritabilities in both environments tended to be higher than estimates from parent-offspring regressions, indicating that prehatching or early posthatching common environment/maternal effects might have inflated full-sib estimates of V_A. The effect of sibling competition on estimates of V_A was probably small as the nestling size-hierarchy at day 2 posthatch was not generally correlated with size-hierarchy at fledging. Furthermore, there was no correlation between maternal body condition during the incubation and final size of offspring, indicating that direct maternal effects related to nutritional status were small. A review of earlier quantitative genetic studies of body size variation in birds revealed that in eight of nine cases, heritability of body size was lower in poor than in good environmental conditions. The main implication of this relationship will be a decreased evolutionary response to selection under poor environmental conditions. On the other hand, this will retard the loss of genetic variation by reducing the accuracy of selection and might help explain the moderate to high heritabilities of body-size traits under good environmental conditions.     

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.     

Are Plant Censuses Carried Out on Small Quadrats More Reliable than on Larger Ones?

Plant censuses are known to be significantly affected by observers’ biases. In this study, we checked whether the magnitude of observer effects (defined as the % of total variance) varied with quadrat size: we expected the census repeatability (% of the total variance that is not due to measurement errors) to be higher for small quadrats than for larger ones. Variations according to quadrat size of the repeatability of species richness, Simpson equitability and reciprocal diversity indices, Ellenberg indicator values, plant cover and plant frequency were assessed using 359 censuses of vascular plants. These were carried out independently by four professional botanists during spring 2002 on the same 18 forest plots, each comprising one 400-m² quadrat, four 4-m² and four 2-m² quadrats. Time expenditure was controlled for. General Linear Models using random effects only were applied to the ecological indices to estimate variance components and magnitude of the following effects (if possible): plot, quadrat, observer, plant species and two-way interactions. High repeatability was obtained for species richness and Ellenberg indicator values. Species richness and Ellenberg indicator values were generally more accurate but also more biased in large quadrats. Simpson reciprocal diversity and equitability indices were poorly repeatable (especially equitability) probably because plant cover estimates varied widely among observers, irrespective of quadrat size. Grouping small quadrats usually increased the repeatability of the variable considered (e.g. species richness, Simpson diversity, plant cover) but the number of plant species found on those pooled 16 m² was much lower than if large plots were sampled. We therefore recommend to use large, single quadrats for forest vegetation monitoring.     

On estimation of genetic variance within families using genome-wide identity-by-descent sharing

Background

Traditionally, heritability and other genetic parameters are estimated from between-family variation. With the advent of dense genotyping, it is now possible to compute the proportion of the genome that is shared by pairs of sibs and thus undertake the estimation within families, thereby avoiding environmental covariances of family members. Formulae for the sampling variance of estimates have been derived previously for families with two sibs, which are relevant for humans, but sampling errors are large. In livestock and plants much larger families can be obtained, and simulation has shown sampling variances are then much smaller.

Methods

Based on the assumptions that realised relationship of sibs can be obtained from genomic data and that data are analyzed by restricted maximum likelihood, formulae were derived for the sampling variance of the estimates of genetic variance for arbitrary family sizes. The analysis used statistical differentiation, assuming the variance of relationships is small.

Results

The variance of the estimate of the additive genetic variance was approximately proportional to 1/ (fn²σR2), for f families of size n and variance of relationships σR2.

Conclusions

Because the standard error of the estimate of heritability decreased in proportion to family size, the use of within-family information becomes increasingly efficient as the family size increases. There are however, limitations, such as near complete confounding of additive and dominance variances in full sib families.     

Efficiency of population structures for mapping of Mendelian and imprinted quantitative trait loci in outbred pigs using variance component methods

In a simulation study different designs for a pure line pig population were compared for efficiency of mapping QTL using the variance component method. Phenotypes affected by a Mendelian QTL, a paternally expressed QTL, a maternally expressed QTL or by a QTL without an effect were simulated. In all alternative designs 960 progeny were phenotyped. Given the limited number of animals there is an optimum between the number of families and the family size. Estimation of Mendelian and parentally expressed QTL is more efficient in a design with large family sizes. Too small a number of sires should be avoided to minimize chances of sires to be non-segregating. When a large number of families is used, the number of haplotypes increases which reduces the accuracy of estimating the QTL effect and thereby reduces the power to show a significant QTL and to correctly position the QTL. Dense maps allow for smaller family size due to exploitation of LD-information. Given the different possible modes of inheritance of the QTL using 8 to16 boars, two litters per dam was optimal with respect to determining significance and correct location of the QTL for a data set consisting of 960 progeny. The variance component method combining linkage disequilibrium and linkage analysis seems to be an appropriate choice to analyze data sets which vary in marker density and which contain complex family structures.     

Larger wildflower plantings increase natural enemy density,diversity, and biological control of sentinel prey,without increasing herbivore density

1. An important means of conserving beneficial insects in resource‐limited habitats is to meet their ecological requirements, which may be achieved by providing areas containing flowering plants that bloom throughout the season, but little is known about the importance of wildflower plot size for supporting natural enemies or the biological control they provide. 2. Wildflowers were established in plots of sizes ranging from 1 to 100 m², and found that natural enemy density, group richness, and diversity of natural enemy groups increased with plot size. 3. The density of insect herbivores was lower in all flower plots than in the control samples, whereas the diversity of herbivore groups was significantly higher in flower plots. 4. Comparing population growth of sentinel soybean aphids (Aphis glycines Matsumura) among plot sizes, aphid colonies were smaller as plot size increased. 5. Providing beneficial insects with flowering resources resulted in significantly more natural enemies and greater pest control than in smaller flower plots or mown grass areas. 6. These results indicate that the density, diversity, and function of natural enemies are sensitive to the size of wildflower plantings, even at relatively small scales. Therefore, larger wildflower plots are more suitable for the conservation of beneficial insects and their provision of natural pest control.     

Hierarchical and factorial mating designs for quantitative genetic analysis in tetrasomic potato

Plant breeders need to quantify additive and non-additive components of genetic variance in order to determine appropriate selection methods to improve quantitative characteristics. Hierarchical and factorial mating designs (also known as North Carolina mating designs I and II, respectively) allow one to determine these variance components. The relative advantages of these two designs in the quantitative genetics of tuber yield in tetrasomic potato were investigated. Likewise, the number of female parents to include in design I was also investigated. Data were collected from two independent experiments at two contrasting Peruvian locations: La Molina in the dry coast and San Ramon in the humid mid-altitude. In the first experiment, although design I gave a negative digenic variance (σ² _D), this design provided almost the same estimate of narrow-sense heritability (h²) for tuber yield as that obtained in design II (0.291 and 0.260, respectively). Therefore, design I appears to be appropriate for quantitative genetics research in tetrasomic potato, a crop in which some clones are male sterile. The easy handling of crosses (distinct random females included in the crossing scheme) is another advantage of design I relative to design II. In the second experiment, 12 males were crossed with either two or four females following a design-I mating scheme. The additive genetic variance (σ² _A) was zero (or negative) when two females per male were included but was positive with four females. These results suggest that two females per male may not be enough for design I in tetrasomic potato. Four females per male are preferable to determine σ² _A in design I for this tetrasomic crop. Received: 19 March 2001 / Accepted: 3 July 2001     

Differences in heritable trait variation among populations of varying size in the perennial herb Phyteuma spicatum

Habitat fragmentation may affect trait evolution in plants through changes in the environment. Evolutionary change, however, may be limited when fragmented populations suffer from genetic or environmental deterioration. In this study, we examined the potential of plants in fragmented populations to respond to altered selective pressures by estimating the amount of heritable variation in several phenotypic traits, using Phyteuma spicatum as study species. We grew offspring of plants of ten natural populations of varying size under common environmental conditions and assessed if population trait means or heritability estimates were related to the size and abiotic environmental conditions of the populations of origin. All traits differed significantly among populations and maternal families, suggesting that genetic effects were responsible for the observed trait variation. Narrow-sense heritabilities (h ² ) ranged between 0 and 1.13, depending on trait and population of origin. Size and/or environmental conditions of the populations of origin affected means and h ² -estimates of some of the measured traits. Heritabilities for flowering duration and mean seed mass decreased with decreasing population size, suggesting that plants in small populations may have a reduced capacity to respond and adapt to changes in the environment which alter selective pressures on these traits. Still, mean h ² -estimates were in some cases low, and patterns were generally quite variable. Further studies are therefore needed to gain more conclusive insights into the adaptive potential of small plant populations. Such knowledge is important if we want to understand how habitat fragmentation and associated changes in the environment affect trait evolution.     

An approximation of the minimum-variance estimator of heritability based on variance component analysis

An approximate minimum-variance estimate of heritability (h²) is proposed, using the sire and dam components of variance from a hierarchical analysis of variance. The minimum sampling variance is derived for unbalanced data. Optimum structures for the estimation of h² are given for the balanced case. The degree to which ĥ² is more precise than the equally weighted estimate ĥ²_S+D is a function of the size and structure of the sample used. However, computer simulation reveals that ĥ² has less desirable behavior than ĥ²_S+D. An iterative procedure improved the estimation of h², especially in small populations, when those values of ĥ²_S or ĥ²_D outside the range of the parameter were constrained to zero or unity.     

CONSTANCY OF POPULATION PARAMETERS FOR LIFE-HISTORY AND FLORAL TRAITS IN RAPHANUS SATIVUS L. II. EFFECTS OF PLANTING DENSITY ON PHENOTYPE AND HERITABILITY ESTIMATES

To determine the effect of growing conditions on population parameters in wild radish, (Raphanus sativus L.: Brassicaceae), we replicated maternal and paternal half-sib families of seed across three planting densities in an experimental garden. A nested breeding design performed in the greenhouse produced 1,800 F₁ seeds sown in the garden. We recorded survivorship, measured phenotypic correlations among and estimated narrow-sense and broad-sense heritabilities (h²) of: days to germination, days to flowering, petal area, ovule number/flower, pollen production/flower, and modal pollen grain volume. Survivorship declined with increasing density, but the relative abundances of surviving families did not differ significantly among densities. Seeds in high-density plots germinated significantly faster than seeds sown in medium- or low-density plots, but they flowered significantly later. Plants in high-density plots had fewer ovules per flower than those in the other treatments. Petal area and pollen characters did not differ significantly among densities. Densities differed with respect to the number and sign of significant phenotypic correlations. Analyses of variance were conducted to detect additive genetic variance (V_a) of each trait in each density. At low density, there were significant paternal effects on flowering time and modal pollen grain volume; in medium-density plots, germination time, flowering time and ovule number exhibited significant paternal effects; in high-density plots, only pollen grain volume differed among paternal sibships. The ability to detect maternal effects on progeny phenotype also depended on density. Narrow-sense h² estimates differed markedly among density treatments for germination time, flowering time, ovule number and pollen grain volume. Maternal, paternal and error variance components were estimated for each trait and density to examine the sources of variation in narrow-sense h² across densities. Variance components did not change consistently across densities; each trait behaved differently. To provide qualitative estimates of genetic correlations between characters, correlation coefficients were estimated using paternal family means; these correlations also differed among densities. These results demonstrate that: a) planting density influences the magnitude of maternal and paternal effects on progeny phenotype, and of h² estimates, b) traits differ with respect to the density in which heritability is greatest, c) density affects the variance components that comprise heritability, but each trait behaves differently, and d) the response to selection on any target trait should result in different correlated responses of other traits, depending on density.