Correlated Responses to Selection for Postweaning Gain in the Rat

Evidence for correlated responses to selection was investigated in lines of rats selected for 13 generations for high (U line) and low (D line) 3-9-week gain in comparison with random-bred control lines (R and C lines). The increase in 3-9-week gain in the U lines was shown to be due largely to an increase in 9-week weight, although 3-week weight also increased in these lines. In the D lines, where a marked decrease in 3-9-week gain was observed, this was found to be due to a large decrease in 9-week weight and no detectable change in 3-week weight. The average 2-week litter weight, a measure of the lactational performance of the dam, was significanly greater in the U lines than in the D lines. Selection for 3-9-week gain in these lines of rats led to changes of litter size at birth in the same direction as that of selection. This resulted in a significantly higher litter size in the U lines than in the D lines. The number of rats alive 2 and 9 weeks of age and the percentage of mated females pupping were similar in the U and D lines but lower in these lines than the random bred C lines, providing evidence for a reduction of "fitness" in the selected lines. Carcass composition was studied for all lines at the 11th generation of selection. Carcass composition, in terms of water, fat, ash and protein, was similar in the R and C lines. The U lines had more water and lesss fat than the R or C line. The D lines had similar carcass composition to the R and C lines. It is suggested that these selected and random-bred lines of rats are potentially useful animals to investigate further the developmental and physiological mechanisms which control growth.     

Cation regulation in the smooth muscle of frog stomach

Cation composition of frog smooth muscle cells was investigated. Fresh stomach muscle rings resembled skeletal muscle, but marked Na gain and K loss followed immersion. Mean Na (49.8–79.7 mM/kg tissue) and K (61.8–80.1 mM/kg tissue) varied between batches, but were stable for long periods in vitro. Exchange of 6–30 mM Na/kg tissue with ²²Na was extremely slow and distinct. Extracellular water was estimated from sucrose-¹⁴C uptake. Calculated exchangeable intracellular Na was 9 mM/kg cell water, and varied little. Thus steady-state transmembrane cation gradients appeared to be steep. K-free solution had only slight effects. Ouabain (10^-4 M) caused marked Na gain and reciprocal K loss; at 30°C, Na and K varied linearly with time over a wide range of contents, indicating constant net fluxes. Net fluxes decreased with temperature decrease. ²²Na exchange in ouabain-treated tissue at 20–30°C was rapid and difficult to analyze. The best minimum estimates of unidirectional Na fluxes at 30°C were 10–12 times the constant net flux; constant pump efflux may explain these findings. The rapidity of Na exchange may not reflect very high permeability, but it does require a high rate of transport work.     

Replicated selection for insulin-like growth factor-1 and body weight in mice

Summary Five generations of divergent selection for plasma concentration of insulin-like growth factor-1 (IGF-1) and for 12-week body weight were carried out in mice, including randomly selected control lines for each trait. All lines were replicated once (12 lines in total). Each replicate line consisted of eight male and eight female parents per generation. Litter size was standardized to eight pups at birth. Mass selection was applied in the selected lines and within-family random selection in the control lines. Blood was taken from the orbital sinus of individual mice at 12 weeks of age for IGF-1 assay. Realized heritabilities were 0.10±0.01 for IGF-1 and 0.41 ± 0.02 for 12-week weight. The realized genetic correlation between IGF-1 and 12-week weight was 0.58 ± 0.01, with a phenotypic correlation of 0.38. Although the genetic correlation between IGF-1 and body weight in mice is moderately positive, 12-week weight responded 3.5 times as fast to weight selection as to selection for IGF-1.     

Mixed model analysis of a selection experiment for food intake in mice

Data from 23 generations of mice selected for increased and reduced appetite were analysed by Restricted Maximum Likelihood fitting an animal model with litters as additional random effects. Traits considered were food intake between 4 and 6 weeks of age adjusted for 4-week body weight (AFI), the selection criterion, and body weight at 6 weeks (6WW). Selection was carried out within families. A high and a low selection line and a control were maintained in each of three replicates. Analyses were performed for each replicate separately taking subsets of the data spanning different numbers of generations. Overall estimates of heritabilities were 0.15 for AFI, which agreed well with realized heritability estimates, and 0.42 for 6WW. The litter variance, expressed as a proportion of the phenotypic variance, was 0.21 for both traits, yielding intraclass correlations of full-sibs of 0.29 and 0.42, respectively. Similar results were obtained for variances of each trait using univariate and multivariate analyses. From the latter, estimates of correlations between the two traits were 0.46 for additive genetic, -0.19 for litter and 0.31 for residual effects, resulting in a phenotypic correlation of 0.23. Analyses of data from generations 2-7, 8-13 and 14-23 separately showed a marked decrease in genetic variance and heritability in later generations for both traits. Heritabilities of AFI, for instance, were 0.24, 0.10 and 0.07, respectively. These changes could not be attributed to the effects of inbreeding or of selection in an infinitesimal model and suggested that some change in variance due to change in gene frequency had occurred during the course of the experiment.     

Selection for High Adult Body Weight in Drosophila Populations with Different Structures

Selection for high adult body weight in Drosophila melanogaster was practiced for 18 generations in three selection lines. These lines were genetically similar and of equal size but different in population structure. One line represented a large mass-selected, random-mating population, while the other two lines simulated large populations that had been subdivided into partial isolates or demes. Mass selection and random mating occurred within each deme. These two subdivided lines were different only in the rate of effective migration among the demes (5% and 10%). Selection intensities of approximately 20% were applied to these populations. A fourth line served as a random mating control. Heritability of adult body weight in the base population was estimated to be 0.58± 0.22. The results indicate that significantly greater responses were achieved in the subdivided lines than in the large mass-selected line, in spite of the fact that larger selection differentials were applied to the latter. No significant differences in response were observed between the two subdivided lines. Wright (1930, 1931) postulated that selection would be most efficient in subdivided populations with limited interdeme migration. The present findings appear to support this theory.     

Ca 2+ -specific removal of Z lines from rabbit skeletal muscle

Removal of rabbit psoas strips immediately after death and incubation in a saline solution containing 1 mM Ca²⁺ and 5 nM Mg²⁺ for 9 hr at 37°C and pH 7.1 causes complete Z-line removal but has no ultrastructurally detectable effect on other parts of the myofibril. Z lines remain ultrastructurally intact if 1 mM 1,2-bis-(2-dicarboxymethylaminoethoxy)-ethane (EGTA) is substituted for 1 mM Ca²⁺ and the other conditions remain unchanged. Z lines are broadened and amorphous but are still present after incubation for 9 hr at 37°C if 1 mM ethylenediaminetetraacetate (EDTA) is substituted for 1 mM Ca²⁺ and 5 mM Mg²⁺ in the saline solution. A protein fraction that causes Z-line removal from myofibrils in the presence of Ca²⁺ at pH 7.0 can be isolated by extraction of ground muscle with 4 mM EDTA at pH 7.0–7.6 followed by isoelectric precipitation and fractionation between 0 and 40% ammonium sulfate saturation. Z-line removal by this protein fraction requires Ca²⁺ levels higher than 0.1 mM, but Z lines are removed without causing any other ultrastructurally detectable degradation of the myofibril. This is the first report of a protein endogenous to muscle that is able to catalyze degradation of the myofibril. The very low level of unbound Ca²⁺ in muscle cells in vivo may regulate activity of this protein fraction, or alternatively, this protein fraction may be localized in lysosomes.     

Three-way Interaction between 14-3-3 Proteins, the N-terminal Region of Tyrosine Hydroxylase, and Negatively Charged Membranes

Tyrosine hydroxylase (TH), the rate-limiting enzyme in the synthesis of catecholamines, is activated by phosphorylation-dependent binding to 14-3-3 proteins. The N-terminal domain of TH is also involved in interaction with lipid membranes. We investigated the binding of the N-terminal domain to its different partners, both in the unphosphorylated (TH-(1–43)) and Ser¹⁹-phosphorylated (THp-(1–43)) states by surface plasmon resonance. THp-(1–43) showed high affinity for 14-3-3 proteins (K_d ∼ 0.5 μm for 14-3-3γ and -ζ and 7 μm for 14-3-3η). The domains also bind to negatively charged membranes with intermediate affinity (concentration at half-maximal binding S_0.5 = 25–58 μm (TH-(1–43)) and S_0.5 = 135–475 μm (THp-(1–43)), depending on phospholipid composition) and concomitant formation of helical structure. 14-3-3γ showed a preferential binding to membranes, compared with 14-3-3ζ, both in chromaffin granules and with liposomes at neutral pH. The affinity of 14-3-3γ for negatively charged membranes (S_0.5 = 1–9 μm) is much higher than the affinity of TH for the same membranes, compatible with the formation of a ternary complex between Ser¹⁹-phosphorylated TH, 14-3-3γ, and membranes. Our results shed light on interaction mechanisms that might be relevant for the modulation of the distribution of TH in the cytoplasm and membrane fractions and regulation of l-DOPA and dopamine synthesis.     

Genomic Consequences of Background Effects on scalloped Mutant Expressivity in the Wing of Drosophila melanogaster

We have evaluated the extent to which SNPs identified by genomewide surveys as showing unusually high levels of population differentiation in humans have experienced recent positive selection, starting from a set of 32 nonsynonymous SNPs in 27 genes highlighted by the HapMap1 project. These SNPs were genotyped again in the HapMap samples and in the Human Genome Diversity Project–Centre d''Etude du Polymorphisme Humain (HGDP–CEPH) panel of 52 populations representing worldwide diversity; extended haplotype homozygosity was investigated around all of them, and full resequence data were examined for 9 genes (5 from public sources and 4 from new data sets). For 7 of the genes, genotyping errors were responsible for an artifactual signal of high population differentiation and for 2, the population differentiation did not exceed our significance threshold. For the 18 genes with confirmed high population differentiation, 3 showed evidence of positive selection as measured by unusually extended haplotypes within a population, and 7 more did in between-population analyses. The 9 genes with resequence data included 7 with high population differentiation, and 5 showed evidence of positive selection on the haplotype carrying the nonsynonymous SNP from skewed allele frequency spectra; in addition, 2 showed evidence of positive selection on unrelated haplotypes. Thus, in humans, high population differentiation is (apart from technical artifacts) an effective way of enriching for recently selected genes, but is not an infallible pointer to recent positive selection supported by other lines of evidence.IN the last 50,000–100,000 years (KY), humans have expanded from being a rare species confined to parts of Africa and the Levant to their current numbers of >6 billion with a worldwide distribution (Jobling et al. 2004). Paleontological and archaeological evidence suggests that key aspects of modern human behavior developed ∼100–50 KYA in Africa (Henshilwood et al. 2002) and behaviorally modern humans then expanded out of Africa ∼60–40 KYA (Mellars 2006). The physical and biological environments encountered outside Africa would have been very different from those inside and included climatic deterioration reaching a glacial maximum ∼20 KYA and subsequent amelioration that permitted the development of agricultural and pastoral lifestyles in multiple independent centers after ∼10 KYA. Neolithic lifestyles would have led to further changes including higher population densities, close contact with animals, and novel foods, in turn leading to new diseases (Jobling et al. 2004). It is likely that genetic adaptations accompanied many of these events.Adaptation, or positive natural selection, leaves an imprint on the pattern of genetic variation found in a population near the site of selection. This pattern can be identified by comparing the DNA variants in multiple individuals from the same and different populations and searching for signals such as unusually extended haplotypes (extended haplotype homozygosity, EHH) (Voight et al. 2006; Sabeti et al. 2007; Tang et al. 2007), high levels of population differentiation (International Hapmap Consortium 2005; Barreiro et al. 2008; Myles et al. 2008), or skewed allele frequency spectra (Carlson et al. 2005). These signals become detectable at different times after the start of selection and are all transient, being gradually eroded by both molecular processes such as mutation, recombination, or further selection and population processes such as migration or demographic fluctuations, with the survival order extended haplotypes < population differentiation < allele frequency spectra (Sabeti et al. 2006). The absolute timescales of survival are not well understood, but extended haplotype tests typically detect selection within the last 10 KY (Sabeti et al. 2006) while unusual allele frequency spectra may detect much older selection. For example, it has been suggested that the signal associated with the FOXP2 gene (Enard et al. 2002) may predate the modern human–Neanderthal split ∼300–400 KYA (Krause et al. 2007), although such an interpretation has been questioned (Coop et al. 2008). However, despite significant uncertainties and limitations, population-genetic analyses are well placed to provide insights into many of the important events within the timescale of recent human evolution.In principle, it should be possible to survey the genome for sites of selection and then interpret this catalog in the light of archaeological, climatic, and other records. Progress toward such a goal has, however, been limited: many factors can confound the detection of selection and only genotype data from previously ascertained SNPs, rather than full resequence data, have thus far been available throughout the whole genome. In practice, the strategy used has therefore been to search the genome for signals that can be detected in available genotype data, such as extended haplotypes or population differentiation, and evaluate the significance of the regions identified by comparing them with empirical distributions of the same statistic, models that incorporate information about the demography, or biological expectations (McVean and Spencer 2006). However, it remains unclear how effective this strategy is: What false positive and false negative rates are associated with its applications? Further evaluation is desirable.The International HapMap Project has carried out the highest-resolution study so far of genetic variation in a set of human populations. In an article published in 2005, genotypes of >1 million SNPs were reported from 270 individuals with ancestry from Africa (Yoruba in Ibadan, Nigeria: YRI), Europe (Utah residents with ancestry from northern and western Europe: CEU), China (Han Chinese in Beijing, China: CHB), and Japan (Japanese in Tokyo, Japan: JPT) (International HapMap Consortium 2005). This article highlighted 32 SNPs from 27 genes that showed particular evolutionary interest because of a combination of two factors: they were nonsynonymous, that is, they changed an amino acid within a protein-coding gene and thus were likely to alter biological function, and they also exhibited a high level of population differentiation equal to or exceeding that of rs2814778, a SNP that is associated with strong biological evidence for population-specific selection. This SNP underlies the FY*0 (Duffy blood group negative) phenotype; FY*0 homozygotes do not express the Duffy blood group antigen on red blood cells and are consequently highly resistant to infection by the malarial parasite, Plasmodium vivax. The *0 allele is nearly fixed in Africa and rare outside, and it is widely believed that this is due to selection for resistance to vivax malaria.However, a number of studies have emphasized that large differences in allele frequency between populations can arise without positive selection: for example, a highly differentiated SNP in the Neuregulin I gene was not accompanied by unusual patterns in adjacent SNPs (Gardner et al. 2007), and large frequency differences can be quite common in empirical data sets, particularly in comparisons between Africa or America and the rest of the world, where population bottlenecks and “allele surfing” may have occurred during the exit from and entrance to these continents, respectively (Hofer et al. 2009). We wished to measure the extent to which the high population differentiation observed at the 27 HapMap genes might have resulted from positive selection and the extent to which it reflected other origins such as demographic factors, chance, or errors. We therefore retyped the same SNPs in the HapMap samples and in a large additional set of human populations and applied alternative tests for selection, either based on long-range haplotypes or based on full resequence data. For the latter, sequence data for 5 of the genes were available from public sources, and four new data sets were generated for this project. We found that, while genotyping errors led to some artifactual high differentiation signals, population differentiation was a useful but by no means infallible guide to recent selection detected by other methods.     

Selection for litter size in pigs. II. Efficiency of closed and open selection lines*

A selection experiment on litter size in the pig was carried on for seventeen generations in an Inra experimental herd. The founder population was made up of 10 males and 120 females from the Large White breed. Selection was first performed for ten generations in a closed line, compared to a control line derived from the same founder population. Selection was carried on within sire family on the total number of piglets born in the first two litters of the dam (TB1 + TB2). After ten generations, the selection criterion became dam TB1 only. The control line was then discontinued and a panel of frozen semen from the 11th generation boars was created for later comparisons. The selected line was opened to gilt daughters of hyperprolific boars and sows, at a rate of 1/8 per generation, and the same selection procedure was applied irrespective of the origin of the gilt. During the whole experiment, the number of ova shed (OS) and the number of live embryos (LE) at 30 days in the 3rd pregnancy were recorded. These two parts of the experiment were analysed using REML estimation of genetic parameters and a BLUP-Animal Model in order to estimate the responses to selection. Significant heritabilities for TB1, TB2, OS and LE were obtained, i.e. 0.10, 0.05, 0.43 and 0.19, respectively. Significant common environment variances and covariances were estimated for nearly all traits. Significantly positive BLUP responses per generation were observed from G0 to G17 for TB1 (+0.086), TB2 (+0.078), OS (+0.197) and LE (+0.157). However, the responses were 3- to 4-fold higher in the G12–G17 interval compared to G0–G11, and they were also in fair agreement with previous estimates based on standard least-squares procedures, using the control line and the control frozen semen panel. Since G11, the selection intensity was increased by nearly 80 p. cent compared to the previous generations, and the proportion of hyperprolific ancestry increased up to 65 p. cent in the sows of the last generation. The total genetic gain of about 1.4 piglets at birth per litter could be shared between a gain due to immigration, of about 0.8 piglets per litter, and a within-line selection gain of about 0.6 piglets. Thus by combining selection and immigration in the second part of the experiment, advantage could be taken from both the genetic superiority of the immigrants and the higher internal selection intensity made possible by immigration.     

Choline permeability in cardiac muscle cells of the cat

Permeability of the cardiac cell membrane to choline ions was estimated by measuring radioactive choline influx and efflux in cat ventricular muscle. Maximum values for choline influx in 3.5 and 137 mM choline were respectively 0.56 and 9 pmoles/cm²·sec. In 3.5 mM choline the intracellular choline concentration was raised more than five times above the extracellular concentration after 2 hr of incubation. In 137 mM choline, choline influx corresponded to the combined loss of intracellular Na and K ions. Paper chromatography of muscle extracts indicated that choline was not metabolized to any important degree. The accumulation of intracellular choline rules out the existence of an efficient active pumping mechanism. By measuring simultaneously choline and sucrose exchange, choline efflux was analyzed in an extracellular phase, followed by two intracellular phases: a rapid and a slow one. Efflux corresponding to the rapid phase was estimated at 16–45 pmoles/cm²·sec in 137 mM choline and at 1.3–3.5 pmoles/cm²·sec in 3.5 mM choline; efflux in 3.5 mM choline was proportional to the intracellular choline concentration. The absolute figures for unidirectional efflux were much larger than the net influx values. The data are compared to Na and Li exchange in heart cells. Possible mechanisms for explaining the choline behavior in heart muscle are discussed.     

Selection for weight gain in mice at two ages and under ad libitum and restricted feeding

Summary Direct and correlated responses in weight gain and body weights were assessed for nine generations of within-family selection. Four selection criteria were used: gain between 28 and 38 or 48 and 58 days of age, and under two feeding regimes, i.e. ad libitum consumption or 80% of the control line. Direct responses to selection and realized heritabilities in the ad libitum lines were greater in the first period. Weight gain under ad libitum feeding at later ages appeared to have a lower genetic variability. In the restricted lines the responses and realized heritabilities were higher in the second period. Selection under restricted feeding in both periods led to animals that had lower weight gains than the control line when compared under ad libitum feeding.     

Sodium fluxes in internally dialyzed squid axons

The effects which alterations in the concentrations of internal sodium and high energy phosphate compounds had on the sodium influx and efflux of internally dialyzed squid axons were examined. Nine naturally occurring high energy phosphate compounds were ineffective in supporting significant sodium extrusion. These compounds were: AcP, PEP, G-3-P, ADP, AMP, GTP, CTP, PA, and UTP.¹ the compound d-ATP supported 25–50% of the normal sodium extrusion, while ATP supported 80–100%. The relation between internal ATP and sodium efflux was nonlinear, rising most steeply in the range 1 to 10 µM and more gradually in the range 10 to 10,000 µM. There was no evidence of saturation of efflux even at internal ATP concentrations of 10,000 µM. The relation between internal sodium and sodium efflux was linear in the range 2 to 240 mM. The presence of external strophanthidin (10 µM) changed the sodium efflux to about 8–12 pmoles/cm² sec regardless of the initial level of efflux; this changed level was not altered by subsequent dialysis with large concentrations of ATP. Sodium influx was reduced about 50 % by removal of either ATP or Na and about 70 % by removing both ATP and Na from inside the axon.     

A High Frequency of Beneficial Mutations Across Multiple Fitness Components in Saccharomyces cerevisiae

Mutation-accumulation experiments are widely used to estimate parameters of spontaneous mutations affecting fitness. In many experiments only one component of fitness is measured. In a previous study involving the diploid yeast Saccharomyces cerevisiae, we measured the growth rate of 151 mutation-accumulation lines to estimate parameters of mutation. We found that an unexpectedly high frequency of fitness-altering mutations was beneficial. Here, we build upon our previous work by examining sporulation efficiency, spore viability, and haploid growth rate and find that these components of fitness also show a high frequency of beneficial mutations. We also examine whether mutation-acycumulation (MA) lines show any evidence of pleiotropy among accumulated mutations and find that, for most, there is none. However, MA lines that have zero fitness (i.e., lethality) for any one fitness component do show evidence for pleiotropy among accumulated mutations. We also report estimates of other parameters of mutation based on each component of fitness.ADAPTATION can occur from standing genetic variation or from newly arising mutations. The relative importance of these two sources of adaptive mutations is affected by a variety of factors, including those that alter standing levels of genetic variation (see Barrett and Schluter 2008) and those that generate new mutations. Predicting how quickly a population will adapt and the type of beneficial mutations that will fuel that adaptation requires estimates of the additive genetic variance in fitness and of the beneficial mutation rate and the distribution of beneficial effects. While additive genetic variance for fitness has been estimated in a variety of organisms (Mousseau and Roff 1987), the beneficial mutation rate and the distribution of beneficial effects have only been estimated in a few studies (Shaw et al. 2002; Joseph and Hall 2004; Perfeito et al. 2007; Dickinson 2008; Hall et al. 2008). Surprisingly, these studies estimate that between 6 (Joseph and Hall 2004) and 50% (Shaw et al. 2002) of fitness-altering mutations are beneficial. In contrast, most mutation-accumulation (MA) experiments identify few, if any, beneficial mutations. Such wildly different estimates have even been generated from studies of the same species in similar environments (Zeyl and Devisser 2001; Joseph and Hall 2004; Dickinson 2008; Hall et al. 2008). If these estimates are correct, then they would suggest that the genotypes used in these experiments have vastly different evolutionary potential with respect to their capacity to exhibit rapid adaptation from new mutations.A more likely scenario is that much of the variation in estimates of the beneficial mutation rate is due to methodological differences between studies. One possibility is the fitness component being analyzed. The beneficial mutation rate may be under- or overestimated if the fitness component is under stabilizing selection or subject to antagonistic pleiotropy. Analyses of mutation-accumulation data typically assume that selection is directional. As a result, analyses of phenotypes under stabilizing selection may falsely conclude that mutations that increase a phenotype are beneficial and mutations that lower values are deleterious (see Keightley and Lynch''s 2003 criticism of Shaw et al. 2002). Alternatively, the beneficial mutation rate may be over- (or under) estimated if mutations increase fitness in regard to one component, but lower fitness in regard to lifetime fitness or another fitness component (i.e., antagonistic pleiotropy). Here, we explore these possibilities by investigating whether the high beneficial mutation rates estimated from our previous experiments are specific to the fitness component that we examined.In two previous studies we accumulated mutations in 152 yeast, MA lines and used measures of their effects on diploid growth rate to estimate parameters of beneficial and deleterious mutations. In the first study we estimated that 6% of mutations accumulated during the first 1012 generations of accumulation improved diploid growth (Joseph and Hall 2004). To determine whether this high beneficial mutation rate was due to sampling error, we passaged the lines for an additional 1050 generations and found that 13% of mutations improved diploid growth (Hall et al. 2008). Similarly, another yeast MA experiment (Dickinson 2008) estimated an uncorrected frequency of beneficial mutations of 25%, although correction for within-colony selection reduces this estimate by approximately half. Together, these studies indicate that a substantial proportion of mutations accumulated in these yeast MA lines are beneficial for a single fitness component and that this observation cannot be explained by the chance sampling of a few beneficial mutations.In this study we return to our yeast MA lines (Joseph and Hall 2004) and examine whether the high beneficial mutation rate that we estimated after 1012 generations is an artifact of the fitness component that we examined. To test this hypothesis we examined whether our MA lines carry mutations that are beneficial across multiple fitness components: diploid growth, sporulation efficiency, spore viability, and haploid growth rate. If our previous results are due to us analyzing a fitness component that is either subject to stabilizing selection or antagonistic pleiotropy, then mutations accumulated in our lines will be conditionally beneficial and analyses of additional fitness components would yield different estimates of the beneficial mutation rate. We found that three of the four fitness components yield high estimates of the beneficial mutation rate. This suggests that multiple MA lines have accumulated beneficial mutations and that the high beneficial mutation rate that we previously estimated is not an artifact of the fitness component that we examined.Measuring multiple components of fitness also allowed us to examine the pleiotropic effects of beneficial and deleterious mutations. In general, we found that mutations altering one component of fitness have little effect on other components. However, lethal mutations were typically pleiotropic.

Conclusions:

We find that for three of four fitness components examined, a high frequency of spontaneous, fitness-altering mutations in diploid yeast is beneficial. Further, we do not detect pleiotropy of small-effect mutations, while lethal mutations show high levels of pleiotropy. In most cases, pleiotropy is positive. Two lines show evidence of antagonistic pleiotropy, indicating trade-offs, although heterozygote advantage cannot be ruled out.     

Increase in Quantitative Variation After Exposure to Environmental Stresses and/or Introduction of a Major Mutation: G × E Interaction and Epistasis or Canalization?

Why does phenotypic variation increase upon exposure of the population to environmental stresses or introduction of a major mutation? It has usually been interpreted as evidence of canalization (or robustness) of the wild-type genotype; but an alternative population genetic theory has been suggested by J. Hermisson and G. Wagner: “the release of hidden genetic variation is a generic property of models with epistasis or genotype–environment interaction.” In this note we expand their model to include a pleiotropic fitness effect and a direct effect on residual variance of mutant alleles. We show that both the genetic and environmental variances increase after the genetic or environmental change, but these increases could be very limited if there is strong pleiotropic selection. On the basis of more realistic selection models, our analysis lends further support to the genetic theory of Hermisson and Wagner as an interpretation of hidden variance.A common experimental observation in quantitative genetics is a higher phenotypic variance for quantitative traits in populations that carry a major mutation or are exposed to environmental stresses (e.g., heat shock) (Scharloo 1991; for a recent review see Gibson and Dworkin 2004). Part of the added variance must be genetic because the population responds to artificial selection. The lower variability of the wild type than that of the mutants has been interpreted as evidence for robustness or canalization (Waddington 1957): that is, under the new condition the magnitudes of gene effects across all trait loci increase relative to the original condition. The importance of canalization has been recognized for a long time and has been the subject of renewed interest recently (see de Visser et al. 2003 and Hansen 2006 for reviews).An alternative population genetic theory has been proposed by Hermisson and Wagner (2004), who suggest that the increase in genetic variance V_G after the change in environmental conditions or genetic background is a generic property of the population, with no need to introduce canalization (Waddington 1957). The theory appears simple. Under mutation–selection balance (MSB), the mutant alleles are at a selective disadvantage and there is a negative correlation between frequencies and effects of mutations: mutant alleles of small effects on the trait segregate at intermediate frequencies. After the change in genetic or environmental background, gene effects consequently change due to G × E interaction or epistasis, which reduces the negative correlation because genes that were previously of small effects and at intermediate frequencies may now have large effects. That is, the frequencies of alleles are determined by the previous MSB, while their new effects are at least partly determined by the new conditions. The genetic variance will therefore increase.Hermisson and Wagner (2004) found that the predicted increase in genetic variance can be substantial; however, the predicted increase is highly sensitive to the population size and can increase without bound with increasing population size (see their Figure 2 and Equation 16). Genetic variance would enlarge with the population size within a small population (Lynch and Hill 1986; Weber and Diggins 1990), but becomes insensitive to the population size within large populations (Falconer and Mackay 1996, Chap. 20). Hence the unbounded increase under the novel environmental condition appears to us as a downside of their theory, even though the predicted increase can be reduced if the changed environmental condition is not novel but there is previous adaptation to it (see their Figure 3).Open in a separate windowFigure 2.—Influence of the pleiotropic effect (s_p) on the increase of genetic variance Δ_G in units of the interaction parameter ξ for a “typical” situation with strength of stabilizing selection ω² = 0.1μ², mutation rate λ = 0.1 per haploid genome per generation, and population size N_e = 10⁶. The allelic pleiotropic effect on fitness and its variance effect on the trait independently follow gamma distributions with shape parameters β_s and β_v, respectively. The mean of a² across loci is E(v) = E(a²) = 10⁻⁴μ².Open in a separate windowOpen in a separate windowFigure 3.—Influence of shapes of distributions of mutational effects on (a) the variances at mutation–selection balance and (b) their increases after the genetic or environmental change. The squares represent the genetic variance and its increase and the triangles the environmental variance and its increase. The mutation rate is λ= 0.1 per haploid genome per generation, the population size is N_e = 10⁹, and the strength of real stabilizing selection is ω² = 0.1μ². Allelic effects on trait value (a), fitness (s), and residual variance (b) are assumed to be independently distributed such that v = a² follows a gamma () distribution with mean 10⁻⁴μ², s follows gamma (β_s) with mean s_p = 0.05, and b follows gamma (β_b) with mean 10⁻⁴μ².The basic model that Hermisson and Wagner (2004) employed is that the quantitative trait is under real stabilizing selection and mutant alleles have effects on the focal trait only by changing its so-called locus genetic variance. At the mutation–real stabilizing selection balance, some mutants can segregate at intermediate frequencies because of their small effects and therefore weak selection; and there are more such mutants the more strongly leptokurtic is the distribution of effects at individual loci. The unbounded increase of Hermisson and Wagner (2004) results from such a gene-frequency distribution; but it has been shown (see Barton and Turelli 1989; Falconer and Mackay 1996; Lynch and Walsh 1998) that solely stabilizing selection, whether modeled with a Gaussian (Kimura 1965) or a house of-cards approximation (Turelli 1984) or even the generalized form of Hermisson and Wagner (2004) (i.e., their Equation 14), cannot provide a satisfactory explanation for the high levels of genetic variance observed in natural populations under realistic values of mutation and selection parameters.A common observation is that one trait is controlled by many genes and one gene can influence many traits; i.e., pleiotropy is ubiquitous (Barton and Turelli 1989; Barton and Keightley 2002; Mackay 2004; Ostrowski et al. 2005). Recent detailed studies suggest that pleiotropy calculated as the number of phenotypic traits affected varies considerably among quantitative trait loci (QTL) (Cooper et al. 2007; Albert et al. 2008; Kenney-Hunt et al. 2008; Wagner et al. 2008). Such pleiotropic effects must influence the magnitude of the variance. Though some genes have little effect on the focal trait, they almost certainly affect other traits and therefore are not neutral. The inclusion of pleiotropic effects on fitness strengthens the overall selection on mutant alleles and, assuming such pleiotropic effects are mainly deleterious, maintains them at low frequencies. The genetic variance for a trait is therefore likely to be maintained at lower levels than that under only real stabilizing selection on the trait alone (Tanaka 1996). Although the gene-frequency distribution is much more extreme under this joint model, the relevant rate of mutation is genomewide and hence is much larger than that where mutation affects only the focal trait as is assumed in the real stabilizing selection model (Turelli 1984; Falconer and Mackay 1996). Taking into account empirical knowledge of mutation parameters, a combination of both pleiotropic and real stabilizing selection appears to be a plausible mechanism for the maintenance of quantitative genetic variance (Zhang et al. 2004). If pleiotropic selection is much stronger than real stabilizing selection, the association between frequency and effect of mutant alleles is weaker than that for a real stabilizing selection model. Further, if overall selection is stronger than recurrent mutation, the frequency distribution of mutant alleles will be extreme. Under those situations, the increase of genetic variance after the genetic or environmental change will be kept at lower levels than that of Hermisson and Wagner (2004), and hence the unbounded increase could be avoided.Further, Hermisson and Wagner (2004) assume that the environmental variance is not under genetic control (i.e., the variance of phenotypic value given genotypic value is the same for all genotypes) and therefore is not subject to change. This assumption conflicts with the increasingly accumulating empirical data that indicate otherwise (Zhang and Hill 2005; Mulder et al. 2007 for reviews). Direct experimental evidence is available that mutation can directly affect environmental variance, V_E (Whitlock and Fowler 1999; Mackay and Lyman 2005), and Baer (2008) provides what is perhaps the first clear demonstration that mutations increase environmental variances, on the basis of data for body size and productivity of Caenorhabditis elegans, and finds that the magnitudes of the increases are of the same order as those in the genetic variance.As real stabilizing selection on phenotype favors genotypes possessing low V_E (Gavrilets and Hastings 1994; Zhang and Hill 2005), a mutant that contributes little to V_E is more favored by stabilizing selection than one that contributes a lot. With all else being the same, mutants with small effect on V_E thus segregate at relatively high frequencies at MSB. That is, there is a negative correlation between the effect on V_E and the frequency of mutant genes. After the genetic or environmental change, some mutants that were previously of small effects on V_E have large effects due to G × E interaction or epistasis while their frequencies remain roughly the same as in the previous MSB. This certainly increases environmental variance.In this note, we first assume that mutant alleles can affect only the mean value of a focal quantitative trait and otherwise affect fitness through their pleiotropic effects (Zhang et al. 2004) and try to answer the following questions: How will the conclusion of Hermisson and Wagner (2004) be affected by taking into account the pleiotropic effect of mutants? Can the “unbounded increase” be avoided? We then further assume that mutant alleles can also directly affect the environmental variance of the focal trait (Zhang and Hill 2008) and investigate how both V_G and V_E change following the genetic or environmental change in the population.     

Correlated responses to selection for large body size in oMt1a-oGH transgenic mice: reproductive traits

Correlated responses in female reproductive performance were evaluated following short-term selection within full-sib families for increased 8-week body weight in two replicates of four lines of mice: two ovine metallothionein-ovine growth hormone (oMt1a-oGH) transgene-carrier lines, one from a high-growth background (TM) and one from a control background (TC), and two non-transgenic lines, one from each of these genetic backgrounds (NM and NC, respectively). A fifth line (CC), not containing the transgene, served as a randomly selected control. The initial frequency of the oMt1a-oGH transgene construct in the TM and TC lines was 0.5. The frequency of transgenic females sampled at generations 7 and 8 of selection was 84.0% and 6.1% in the TC and TM lines, respectively. No significant female infertility differences were detected between transgene-carrier and non-transgenic lines or between transgenic and non-transgenic mice within carrier lines, whereas high-growth background lines had a higher infertility than control background lines (P < 0.05). Correlated responses in the TC transgene-carrier line were suggestive of reduced reproductive performance as indicated by increased post-implantation mortality (P < 0.05), number of dead fetuses plus implants (P < 0.05), and loss of fetuses from day 16 to parturition (P < 0.001). For the first two traits, the negative correlated responses were accounted for by the reduced performance of transgenic compared with non-transgenic females. Embryos carrying the transgene may also have a lower viability. In contrast, the NC non-transgenic line did not exhibit reduced reproductive performance for these traits. The low frequency of the transgene in the high-growth background TM line was associated with reduced fitness and a lower additive effect for 8-week body weight compared with the control background TC line.     

Better Estimates of Genetic Covariance Matrices by “Bending” Using Penalized Maximum Likelihood

Obtaining accurate estimates of the genetic covariance matrix for multivariate data is a fundamental task in quantitative genetics and important for both evolutionary biologists and plant or animal breeders. Classical methods for estimating are well known to suffer from substantial sampling errors; importantly, its leading eigenvalues are systematically overestimated. This article proposes a framework that exploits information in the phenotypic covariance matrix in a new way to obtain more accurate estimates of . The approach focuses on the “canonical heritabilities” (the eigenvalues of ), which may be estimated with more precision than those of because is estimated more accurately. Our method uses penalized maximum likelihood and shrinkage to reduce bias in estimates of the canonical heritabilities. This in turn can be exploited to get substantial reductions in bias for estimates of the eigenvalues of and a reduction in sampling errors for estimates of . Simulations show that improvements are greatest when sample sizes are small and the canonical heritabilities are closely spaced. An application to data from beef cattle demonstrates the efficacy this approach and the effect on estimates of heritabilities and correlations. Penalized estimation is recommended for multivariate analyses involving more than a few traits or problems with limited data.QUANTITATIVE geneticists, including evolutionary biologists and plant and animal breeders, are increasingly dependent on multivariate analyses of genetic variation, for example, to understand evolutionary constraints and design efficient selection programs. New challenges arise when one moves from estimating the genetic variance of a single phenotype to the multivariate setting. An important but unresolved issue is how best to deal with sampling variation and the corresponding bias in the eigenvalues of estimates for the genetic covariance matrix, . It is well known that estimates for the largest eigenvalues of a covariance matrix are biased upward and those for the smallest eigenvalues are biased downward (Lawley 1956; Hayes and Hill 1981). For genetic problems, where we need to estimate at least two covariance matrices simultaneously, this tends to be exacerbated, especially for . In turn, this can result in invalid estimates of , i.e., estimates with negative eigenvalues, and can produce systematic errors in predictions for the response to selection.There has been longstanding interest in “regularization” of covariance matrices, in particular for cases where the ratio between the number of observations and the number of variables is small. Various studies recently employed such techniques for the analysis of high-dimensional, genomic data. In general, this involves a compromise between additional bias and reduced sampling variation of “improved” estimators that have less statistical risk than standard methods (Bickel and Li 2006). For instance, various types of shrinkage estimators of covariance matrices have been suggested that counteract bias in estimates of eigenvalues by shrinking all sample eigenvalues toward their mean. Often this is equivalent to a weighted combination of the sample covariance matrix and a target matrix, assumed to have a simple structure. A common choice for the latter is an identity matrix. This yields a ridge regression type formulation (Hoerl and Kennard 1970). Numerous simulation studies in a variety of settings are available, which demonstrate that regularization can yield closer agreement between estimated and population covariance matrices, less variable estimates of model terms, or improved performance of statistical tests.In quantitative genetic analyses, we attempt to partition observed, overall (phenotypic) covariances into their genetic and environmental components. Typically, this results in strong sampling correlations between them. Hence, while the partitioning into sources of variation and estimates of individual covariance matrices may be subject to substantial sampling variances, their sum, i.e., the phenotypic covariance matrix, can generally be estimated much more accurately. This has led to suggestions to “borrow strength” from estimates of phenotypic components to estimate the genetic covariances. In particular, Hayes and Hill (1981) proposed a method termed “bending” that involved regressing the eigenvalues of the product of the genetic and the inverse of the phenotypic covariance matrix toward their mean. One objective of this procedure was to ensure that estimates of the genetic covariance matrix from an analysis of variance were positive definite. In addition, the authors showed by simulation that shrinking eigenvalues even further than needed to make all values nonnegative could improve the achieved response to selection when using the resulting estimates to derive weights for a selection index, especially for estimation based on small samples. Subsequent work demonstrated that bending could also be advantageous in more general scenarios such as indexes that included information from relatives (Meyer and Hill 1983).Modern, mixed model (“animal model”)-based analyses to estimate genetic parameters using maximum likelihood or Bayesian methods generally constrain estimates to the parameter space, so that—at the expense of introducing some bias—estimates of covariance matrices are positive semidefinite. However, the problems arising from substantial sampling variation in multivariate analyses remain. In spite of increasing applications of such analyses in scenarios where data sets are invariably small, e.g., the analysis of data from natural populations (e.g., Kruuk et al. 2008), there has been little interest in regularization and shrinkage techniques in genetic parameter estimation, other than through the use of informative priors in a Bayesian context. Instead, suggestions for improved estimation have focused on parsimonious modeling of covariance matrices, e.g., through reduced rank estimation or by imposing a known structure, such as a factor-analytic structure (Kirkpatrick and Meyer 2004; Meyer 2009), or by fitting covariance functions for longitudinal data (Kirkpatrick et al. 1990). While such methods can be highly advantageous when the underlying assumptions are at least approximately correct, data-driven methods of regularization may be preferable in other scenarios.This article explores the scope for improved estimation of genetic covariance matrices by implementing the equivalent to bending within animal model-type analyses. We begin with a review of the underlying statistical principles (which the impatient reader might skip), examining the concept of improved estimation, its implementation via shrinkage estimators or penalized estimation, and selected applications. We then describe a penalized restricted maximum-likelihood (REML) procedure for the estimation of genetic covariance matrices that utilizes information from its phenotypic counterparts and present a simulation study demonstrating the effect of penalties on parameter estimates and their sampling properties. The article concludes with an application to a problem relevant in genetic improvement of beef cattle and a discussion.     

Growth and body composition changes in mice selected for high post-weaning weight gain on two levels of feeding

Summary Two lines of mice were selected for high post-weaning weight gain (3 to 6 weeks) adjusted for 3 week weight. One line (F) was grown on freely available food and the other (S) on a feeding scale set at the same level for all mice. Food intake of the S line averaged 80% of the F line. The realised heritabilities after 6 generations of selection were 0.38±0.06 and 0.33±0.07 for the F and S lines, respectively. In generation 7, mice from the F and S lines and from an unselected control line (C) were compared on both free and set levels of feeding from 3 weeks to 9 weeks of age. Measurements taken were growth rate, appetite, food conversion efficiency (weight gain/food intake) and body composition (fat, protein, ash, water). The F and S lines grew more rapidly and efficiently than the C line on both levels of feeding, each line performing best on the level of feeding on which it was selected. The average genetic correlation between growth rates of the same line on the two feeding levels was 0.54±0.10. The F line grew 19% faster and was 9% more efficient than the S line on free feeding but the S line grew 15% faster and was 15% more efficient than the F line on set feeding. Relative to the C line, food intake per day on free feeding was 4% higher in the F line and 6% lower in the S line. There was no difference between the lines in food intake/g body weight. The rate of deposition of all body components increased in both selection lines. In the F, S and C lines respectively, efficiencies of gains in body components (10²x gain/food) were 1.79, 1.31 and 1.06 for fat, 1.53, 1.63 and 1.22 for protein and 5.88, 6.45 and 4.98 for protein + water. Apparently energy lost as heat was reduced in both the F and S lines. The partitioning of energy retained was altered in favour of more fat in the F line and more protein in the S line.     

Identification of Selection Signatures in Cattle Breeds Selected for Dairy Production

Genetic correlations are expected to be high among functionally related traits and lower between groups of traits with distinct functions (e.g., reproductive vs. resource-acquisition traits). Here, we explore the quantitative-genetic and QTL architecture of floral organ sizes, vegetative traits, and life history in a set of Brassica rapa recombinant inbred lines within and across field and greenhouse environments. Floral organ lengths were strongly positively correlated within both environments, and analysis of standardized G-matrices indicates that the structure of genetic correlations is ∼80% conserved across environments. Consistent with these correlations, we detected a total of 19 and 21 additive-effect floral QTL in the field and the greenhouse, respectively, and individual QTL typically affected multiple organ types. Interestingly, QTL × QTL epistasis also appeared to contribute to observed genetic correlations; i.e., interactions between two QTL had similar effects on filament length and two estimates of petal size. Although floral and nonfloral traits are hypothesized to be genetically decoupled, correlations between floral organ size and both vegetative and life-history traits were highly significant in the greenhouse; G-matrices of floral and vegetative traits as well as floral and life-history traits differed across environments. Correspondingly, many QTL (45% of those mapped in the greenhouse) showed environmental interactions, including approximately even numbers of floral and nonfloral QTL. Most instances of QTL × QTL epistasis for floral traits were environment dependent.EVOLUTIONARY responses to selection are dependent on genetic architecture. The proportion of phenotypic variation with a heritable genetic basis affects the response to selection, as does the structure of genetic correlations among selected traits. For example, an evolutionary response will be constrained if selection favors an increase in the value of two traits that are negatively correlated; i.e., a negative correlation is antagonistic to the joint vector of selection. Alternatively, if the vector of selection is parallel to the genetic correlation, then trait covariation is reinforcing and the population mean may more rapidly approach favored trait values (Etterson and Shaw 2001; Merilä and Björklund 2004). One measure of genetic architecture is the G-matrix (Lynch and Walsh 1998), which is composed of genetic variances (diagonal matrix elements) and genetic covariances among traits (off-diagonal matrix elements). G-matrices have been shown to vary across environments (Donohue et al. 2000; Conner et al. 2003; Brock and Weinig 2007), indicating that the molecular-genetic underpinnings of matrix elements (e.g., identity and/or relative effect of additive and epistatic loci, degree of pleiotropy, etc.) and the traits'' evolutionary potential vary across environments. Few studies, however, have related matrix and QTL architectures; and, therefore, the molecular-genetic underpinnings of quantitative-genetic estimates remain unclear (but see Gardner and Latta 2007; Kelly 2009).In angiosperms, covariances between floral whorls (e.g., petal and stamen length) are frequently positive among functionally related traits. These positive correlations can arise from pollinator-mediated (or pollination-mediated) selection for specific allometric relationships among floral traits and ensuing linkage disequilibrium (LD) among causal loci (Berg 1959, 1960; also referred to as phenotypic integration, see Pigliucci 2003; Klingenberg 2008). For example, in outcrossing species, male fitness may be more dependent on the frequency and efficiency of pollinator visitation than female fitness (Bell 1985; but see Hodgins and Barrett 2008). Anther placement relative to the corolla opening can affect the efficiency of pollen dissemination (Conner and Via 1993; Morgan and Conner 2001); in addition, comparative work indicates that petal–stamen length correlations are stronger than stamen–pistil length correlations in outcrossers, whereas species that reproduce via autogamous selfing show the opposite pattern (Ushimaru and Nakata 2002). Alternatively, strong floral integration could be attributed to the developmental hypothesis that genetic correlations arise due to pleiotropic genes coregulating floral whorls (Herrera 2001; Herrera et al. 2002). Strong correlations resulting from linkage disequilibrium or from developmentally based pleiotropy may constrain the evolution of novel reproductive morphologies when biotic or abiotic factors (and selection) change (Cheverud 1984; Clark 1987; Smith and Rausher 2008; Agrawal and Stinchcombe 2009).Similar to genetic covariances among floral traits, covariances between floral and nonfloral traits could also alter the evolutionary response of reproductive traits. In contrast to hypotheses regarding the adaptive significance of floral-trait integration, genetic correlations between floral and nonfloral traits (e.g., vegetative or phenological traits) are hypothesized to be disadvantageous (Berg 1960). More specifically, floral allometry may be shaped by selection for reproductive success, as described above, whereas vegetative morphology is shaped primarily by selection to optimize other functions, such as light capture. If floral and nonfloral traits have a common genetic basis, then selection on phenological or morphological traits may result in maladaptive expression of floral organ size. As a result, functionally integrated floral traits are predicted to be genetically decoupled from vegetative and phenological traits (Berg 1960).QTL mapping provides a powerful tool to explore the genetic architecture of evolutionarily important traits. The QTL architecture of interspecific floral traits has been explored in diverse systems (Bradshaw et al. 1995; Fishman et al. 2002; Goodwillie et al. 2006; Bouck et al. 2007; Moyle 2007); however, insight into the molecular genetic basis of intraspecific floral variation comes almost exclusively from Arabidopsis thaliana (Juenger et al. 2000, 2005) and Mimulus guttatus (Hall et al. 2006). Floral traits in these intraspecific crosses are polygenic with a majority of detected QTL being of small to moderate effect size. Consistent with other quantitative-genetic studies (reviewed in Ashman and Majetic 2006), floral traits in A. thaliana and M. guttatus mapping populations exhibited moderate to high genetic correlations. In both systems, mapped QTL often affected multiple floral traits. In the few cases where QTL underlying intraspecific floral morphology have been evaluated, only a single growth environment was used; estimation of floral quantitative genetics across environments and subsequent comparison with the QTL architecture underlying observed across-environment patterns are lacking.Using a segregating progeny of Brassica rapa (recombinant inbred lines, RILs) and a small sample of crop and wild accessions, we examine the quantitative-genetic and QTL architecture of floral traits under field and greenhouse environments. Specifically, we address the following questions: (1) Does this RIL population express significant genetic (co)variation for floral traits when growing in the field or greenhouse? (2) Is there significant genetic variation for vegetative traits and days to flowering in field and greenhouse environments, and is there evidence for genetic correlations between floral and nonfloral traits? (3) Does the genetic architecture of floral and nonfloral traits, as measured by the G-matrix, differ across environments? (4) What is the number and effect size of additive and epistatic QTL in field and greenhouse environments? (5) What is the relationship between mapped QTL and quantitative genetic estimates of trait (co)variation within and between floral and nonfloral traits? And (6) what is the relationship between the quantitative-genetic architecture of floral traits in the RILs vs. in the accessions?     

Analysis of response to 20 generations of selection for body composition in mice: fit to infinitesimal model assumptions

Data were analysed from a divergent selection experiment for an indicator of body composition in the mouse, the ratio of gonadal fat pad to body weight (GFPR). Lines were selected for 20 generations for fat (F), lean (L) or were unselected (C), with three replicates of each. Selection was within full-sib families, 16 families per replicate for the first seven generations, eight subsequently. At generation 20, GFPR in the F lines was twice and in the L lines half that of C. A log transformation removed both asymmetry of response and heterogeneity of variance among lines, and so was used throughout. Estimates of genetic variance and heritability (approximately 50%) obtained using REML with an animal model were very similar, whether estimated from the first few generations of selection, or from all 20 generations, or from late generations having fitted pedigree. The estimates were also similar when estimated from selected or control lines. Estimates from REML also agreed with estimates of realised heritability. The results all accord with expectations under the infinitesimal model, despite the four-fold changes in mean. Relaxed selection lines, derived from generation 20, showed little regression in fatness after 40 generations without selection.