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At least 19 recordsLinked to original sources

Experimental evolution of phenotypic plasticity: how predictive are cross-environment genetic correlations?

Genetic correlations are often predictive of correlated responses of one trait to selection on another trait. There are examples, however, in which genetic correlations are not predictive of correlated responses. We examine how well a cross-environment genetic correlation predicts correlated responses to selection and the evolution of phenotypic plasticity in the seed beetle Stator limbatus. This beetle exhibits adaptive plasticity in egg size by laying large eggs on a resistant host and small eggs on a high-quality host. From a half-sib analysis, the cross-environment genetic correlation estimate was large and positive (rA=0.99). However, an artificial-selection experiment on egg size found that the realized genetic correlations were positive but asymmetrical; that is, they depended on both the host on which selection was imposed and the direction of selection. The half-sib estimate poorly predicted the evolution of egg size plasticity; plasticity evolved when selection was imposed on one host but did not evolve when selection was imposed on the other host. We use a simple two-locus additive genetic model to explore the conditions that can generate the observed realized genetic correlation and the observed pattern of plasticity evolution. Our model and experimental results indicate that the ability of genetic correlations to predict correlated responses to selection depends on the underlying genetic architecture producing the genetic correlation.

Animals↗

On the sampling variance of intraclass correlations and genetic correlations.

Widely used standard expressions for the sampling variance of intraclass correlations and genetic correlation coefficients were reviewed for small and large sample sizes. For the sampling variance of the intraclass correlation, it was shown by simulation that the commonly used expression, derived using a first-order Taylor series performs better than alternative expressions found in the literature, when the between-sire degrees of freedom were small. The expressions for the sampling variance of the genetic correlation are significantly biased for small sample sizes, in particular when the population values, or their estimates, are close to zero. It was shown, both analytically and by simulation, that this is because the estimate of the sampling variance becomes very large in these cases due to very small values of the denominator of the expressions. It was concluded, therefore, that for small samples, estimates of the heritabilities and genetic correlations should not be used in the expressions for the sampling variance of the genetic correlation. It was shown analytically that in cases where the population values of the heritabilities are known, using the estimated heritabilities rather than their true values to estimate the genetic correlation results in a lower sampling variance for the genetic correlation. Therefore, for large samples, estimates of heritabilities, and not their true values, should be used.

Analysis of Variance↗

Heritabilities, genetic correlations, and genetic change for female fertility and protein yield in Norwegian Dairy Cattle.

Data from 1,815,581 first insemination records from daughters of 2697 Norwegian Dairy Cattle (NRF) sires were analyzed. A multitrait model was used to estimate genetic parameters and genetic change for 56-d nonreturn rate in virgin heifers (NR56D0), for 56-d nonreturn rate in first lactation cows (NR56D1L), for interval from calving to first insemination (CFI1L), and for protein yield (PY(305)1L). The heritabilities for NR56D0, NR56D1L, CFI1L, and PY(305)1L were 1.08, 0.99, 3.01, and 20.80%, respectively. Genetic correlation between heifer and cow fertility was high and favorable between NR56D0 and NR56D1L (0.54) and moderate and unfavorable between NR56D0 and CFI1L (0.24). The genetic correlations between NR56D1L and CFI1L and between NR56D0 and PY(305)1L were 0.08 and 0.04, respectively. A small, unfavorable genetic correlation between NR56D1L and PY(305)1L (-0.18) was found, while the genetic correlation between PY(305)1L and CFI1L was strongly unfavorable (0.47). Since 1972, NRF sires have been selected for NR56D0 using breeding values estimated from large progeny groups and with considerable weight in the total merit index. A linear regression analysis of sire PTA on year of first insemination of daughters showed an annual genetic change of 0.14% units for NR56D0. Selection was able to stabilize the genetic change of NR56D1L (0.03%/yr) but an undesirable change for CFI1L (0.11 d/yr) was found. The change of sire PTA for PY(305)1L was 0.63 kg/yr.

Analysis of Variance↗

The ecology and genetics of fitness in Chlamydomonas. X. The relationship between genetic correlation and genetic distance.

A necessary condition for the maintenance of genetic variation in heterogeneous environments is that the relative fitnesses of a collection of genotypes vary as conditions of growth change. This can be detected by estimating the amount of gene-by-environment interaction (G x E) when a range of types are tested across a range of conditions. However it is the sign and magnitude of the genetic correlation, which is a component of G x E, that governs the ultimate fate of variation. Whether genetic variation will be preserved, then, depends on how the genetic correlation changes as a function of the ecological differences among environments and the genetic differences among genotypes. To evaluate this, we assayed the performance of 15 chlorophyte species of known genetic relation in 20 environments. We found that the quantity of G x E increased as both the environmental variance across environments and the genetic distance increased. Moreover the genetic correlation declined as the environmental variance between pairs of environments and the genetic distance between pairs of genotypes increased. These results suggest that divergent selection will be more likely to maintain genetic variation when environments are strongly contrasted and genotypes widely divergent.

Animals↗

Data subsetting strategies for estimation of across-country genetic correlations.

International genetic evaluation of dairy cattle requires estimation of genetic correlations among populations to account for genotype-environment interaction. Simultaneous estimation of across-country genetic correlations among all populations of a widespread breed, such as the Holstein breed is, however, hampered by connectedness problems and computational challenges. The purpose of this study was to examine the effects of using bulls with across-country, balanced distribution of daughters on estimates of genetic correlations. For this purpose, dairy cattle populations undergoing selection in 6 countries were simulated. Two population-size settings were used. In the small population-size setting (S-populations), the 6 simulated countries had 2000 cows and 20 young progeny testing bulls per generation. In the larger population-size setting (L-populations), the 6 simulated countries had between 2000 and 64,000 cows and 20 to 640 young progeny testing bulls per generation. The simulated (true) across-country genetic correlations, depending on the country combination, varied between 0.5 and 0.9. Simulations comprised a base population and 10 generations and were replicated 16 times. Results for the S-populations were not conclusive. For the L-populations, results indicated that by use of data from a relatively small subset of bulls with distribution of daughters balanced across countries, genetic correlations could be estimated with very small bias (overall average of absolute value of bias across replicates was 0.03 for the L-populations). The suggested bull subsetting strategy would allow simultaneous estimation of across-country genetic correlations to be computed for a larger number of countries and in a shorter window of time than was possible previously.

Animals↗

Genetic correlations between the maternal genetic effect on chick weight and the direct genetic effects on egg composition traits in a White Leghorn line.

Selection can be a useful way to alter yolk proportion and thereby egg dry matter which, owing to its economic importance, is a trait of substantial importance for the egg-processing industry. However, the egg is primarily the chamber of embryonic development. The main purpose of this study was to estimate genetic correlations between the maternal effect on chick weight at hatching and the direct effect on different egg composition traits, in particular, yolk proportion. Additionally, genetic parameters were estimated for egg composition traits. To create a data set suitable for estimation of genetic parameters, a three-round selection experiment was set up. Birds were selected based on their predicted breeding values for the genetic maternal effect on chick weight and the direct genetic effect on yolk proportion according to the theory of elliptical selection. Genetic parameters were estimated using a multiple trait animal model and restricted maximum likelihood. The maternal heritability for chick weight was 0.5, whereas the direct heritability was dose to 0. The genetic correlations between the maternal effect on chick weight and the direct effect on yolk proportion, yolk weight, albumen weight, albumen dry matter concentration, and egg weight were 0.14, 0.76, 0.93, 0.14, and 0.99, respectively. The heritabilities for yolk proportion, yolk weight, albumen weight, albumen dry matter concentration, and egg weight were 0.33, 0.43, 0.57, 0.38, and 0.60, respectively. We conclude that breeding ought to be a useful way to increase egg dry matter with no expected unfavorable correlated effects on chick weight.

Analysis of Variance↗

An experimental evaluation of genetic correlation.

Heritability and genetic correlations realized from both single-trait and antagonistic index selection were compared with paternal half-sib estimates. Primary attention was focused on the genetic correlation between six-week body weight and six-week tail length. Parameters realized from single-trait selection were in excellent agreement with paternal half-sib estimates. However, the realized genetic correlation between six-week body weight and six-week tail length obtained from index selection was significantly greater than the other estimates. Differential inbreeding levels and realized selection intensities were considered and rejected as being causative factors for these results. Linkage disequilibrium probably was not a factor either, as the base population had been randomly mated and randomly selected with a large effective population size for many generations. It was concluded that with antagonistic index selection, the pleiotropic effects of genes may be more powerful in retarding response in aggregate genotype than current theory would suggest. Replication of all selected and control lines allowed the use of between-line estimators of sampling variances of realized genetic parameters in the above comparisons. Generally, standard errors of realized genetic parameters were much smaller than corresponding paternal half-sib standard errors. Thus, selection was an efficient method of estimation.

Animals↗

Assessment of Genetic Correlations Between Tobacco or Alcohol Use and Neurodegenerative Diseases Using East Asian Genetic Ancestry Genome-Wide Association Study Results.

Alzheimer's disease (AD) and Parkinson's disease (PD) are the most prevalent late-onset neurodegenerative diseases worldwide. Both are influenced in part by genetic factors and are currently incurable. Tobacco and alcohol, the two most common substances used among the general adult population, are potential AD/PD risk factors and are also heritable. Although important progress has been made, most existing research on the genetics of AD and PD has been carried out in individuals of European genetic ancestry. Investigations in a broad range of groups are crucial to understand disease mechanisms. Given the current availability of ancestry-specific tobacco and alcohol use as well as AD and PD genome-wide association study summary statistics, we performed global and local genetic correlation analyses using East Asian datasets. Genes within the correlated genetic regions were subsequently used to identify potentially enriched biological pathways between substance use and neurodegenerative diseases. We identified a global genetic correlation between smoking cessation and PD, which we confirmed in complementary European genetic ancestry data. Gene set enrichment analyses highlighted potentially shared genetic mechanisms between breast cancer and AD, which warrants further exploration. This work aims to promote further analyses across genetic ancestry groups.

Female↗

Estimating genetic correlations from inbred strains.

Genetic correlations measure the extent of pleiotropic effects of polygenes on pairs of characters or the closeness of linkage between sets of loci influencing the traits and held in allelic (gametic) disequilibrium. Their importance for research lies primarily in predicting correlated responses of one trait to selection based on values for another, and secondarily in analyzing the complex organization of biological systems. Genetic correlations appear to limit the rate and set the direction of multivariate evolution. In view of this, efficient methods for estimating genetic correlations may be essential for understanding the role of behavior in adaptation and for predicting behavioral change in evolution. In this paper we present methods for the estimation of genetic correlations from inbred strain comparisons. Estimates from inbred strains are relatively easy to obtain and appear to be valid when compared to those derived from more demanding parent-offspring comparisons and to correlated responses to selection.

Alleles↗

Back to the future: genetic correlations, adaptation and speciation.

Genetic correlations can affect the course of phenotypic evolution. Although genetic correlations among traits are a common feature of quantitative genetic analyses, they have played a very minor role in recent linkage-map based analyses of the genetic architecture of quantitative traits. Here, we use our work on host-associated races in pea aphids to illustrate how quantitative trait locus (QTL) mapping can be used to test specific hypotheses about how genetic correlations may facilitate ecological specialization and speciation.

Adaptation, Biological↗

Negative genetic correlation between traits of the Drosophila head, and interspecific divergence in head shape.

For morphological traits that are negatively correlated, the genetic correlation (rg) between them might strongly influence patterns of morphological divergence and shape. Here, the pattern of divergence between two sibling species of cactophilic Drosophila, D. buzzatii and D. koepferae, is examined for two traits that are known to be negatively correlated in other Drosophila species: face width (FW) and width of both eyes (EW). Head width (HW, the sum of FW and EW, i. e. the total width of the head capsule) was also examined. Genetic and phenotypic correlations were estimated in the laboratory G2 generation of a sample of wild D. buzzatii derived from a population where D. koepferae is not present. Phenotypic correlations were also estimated in D. buzzatii and D. koepferae from another, very different, population where the species are sympatric. Consistent with studies in other Drosophila species, rg was negative and significant for the correlation between FW and EW, and positive (but nonsignificant at a matrix-wide P-value of 0.05) for the correlations of HW with both FW and EW. This well-defined correlation pattern was also consistent with the phenotypic correlations in both D. buzzatii and D. koepferae. No significant difference in these traits was detected between D. buzzatii populations, but head shape has diverged between D. buzzatii and D. koepferae. Specifically, the two negatively correlated traits, FW and EW, have evolved in opposite directions in these two species, with HW showing no significant interspecific difference. The overall picture of this divergence pattern shows a striking concordance with the present evidence of negative correlations between FW and EW, and is consistent with the notion of rg-related constraints on the pattern of interspecific differentiation.

Animals↗

Short communication: Difficulties in estimating across-country genetic correlations for weakly linked bull populations.

Genetic correlations across 9 weakly linked bull populations were estimated with various models, methods, and data sets. Estimates of the same genetic correlation differed noticeably depending on estimation strategy (up to 1.03 units) for country-pairs with no or few direct genetic links. Differences in estimated genetic correlations decreased with an increasing number of genetic ties between countries, and, essentially, no differences were found between estimated genetic correlations based on 61 common bulls, regardless of the estimation strategy used.

Analysis of Variance↗

Estimation of genetic correlations among countries in international dairy sire evaluations with structural models.

The increase in the number of participating countries and the lack of genetic ties between some countries has lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield and other traits. Structural models have been proposed to reduce the number of parameters to estimate by exploiting patterns in the genetic correlation matrix. Genetic correlations between countries are described as a simple function of unspecified country characteristics that can be mapped in a space of limited dimensions. Two link functions equal to the exponential of minus the Euclidian distance between the coordinates of two countries and the exponential of minus the square of this Euclidian distance were used for the study on international simulated and field data. On simulated data, it was shown that structural models might allow an easier estimation of genetic correlations close to the border of the parameter space. This is not always possible with an unstructured model. On milk yield data, genetic correlations obtained from 22 countries for structural models based on 2 and 7 dimensions, respectively, were analyzed. Only a structural model with a large number of axes gave reasonable estimates of genetic correlations compared with correlations obtained for an unstructured model: 76.7% of correlations deviated by less than 0.030. Such a model reduces the number of parameters from 231 genetic correlations to 126 coordinates. On foot angle data, large deviations were observed between genetic correlations estimated with an unstructured model and correlations estimated with a structural model, regardless of the number of axes taken into account.

Algorithms↗

Genetic correlations between reproduction and production traits in swine.

Genetic correlations between reproduction and production traits were estimated in swine. Reproduction traits investigated were age at first service (AFS), number of live-born piglets in the first litter (NBA1), interval from weaning to first service after first litter (WTS1), number of live-born piglets in the second litter (NBA2), and interval from weaning to first service after the second litter (WTS2). Females generating the data were Norwegian Landrace born in nucleus herds between 1990 and 2000, and the number of records ranged from 13,792 to 56,932. Genetic correlations were estimated among the main production traits in the breeding goal: adjusted age at 100 kg live weight (A100), percentage of lean meat content (LMC), individual feed consumption from 25 to 100 kg (FC), and bacon side quality (BSQ). Average adjusted backfat thickness (BF) was included as a production trait. The A100 and BF traits were recorded on gilts on-farm with 190,454 records, whereas LMC, BSQ, and FC were recorded on-station with the number of records ranging from 12,487 to 12,992. Analyses were carried out with a multivariate animal model using average information restricted maximum likelihood procedures by first running each reproduction trait with A100 and BF, followed by each reproduction trait with LMC, BSQ, and FC. Average heritabilities for reproduction traits were as follows: AFS (0.38), NBA1 (0.11), WTS1 (0.06), NBA2 (0.12), and WTS2 (0.03); and for production traits: A100 (0.30), BF (0.44), FC (0.22), LMC (0.58), and BSQ (0.23). The highest genetic correlation was estimated between A100 and AFS (r(g)= 0.68), also resulting in a positive genetic correlation between FC and AFS. Growth (A100) was negatively (i.e., unfavorably) genetically correlated to NBA1 and NBA2 (r(g) = 0.60 and rg = 0.42 respectively), and so the genetic correlation to FC also became unfavorable (r(g)= 0.23 and r(g) = 0.20). Single-trait selection for enhanced LMC would also affect NBA1 and NBA2 unfavorably (r(g)= -0.12 and r(g)= -0.24). Correlations between BF at 100 kg live weight and reproduction traits were close to zero; however, a low genetic correlation between BF and WTS1 was obtained (r(g)= -0.12), indicating that selection toward reduced BF at 100 kg live weight may have an unfavorable impact on WTS1.

Animals↗

Genetic correlations, tradeoffs and environmental variation.

Negative genetic correlations among traits are often used as evidence for tradeoffs that can influence evolutionary trajectories in populations. While there may be evidence for negative correlations within a particular environment, genetic correlations can shift when populations encounter different environmental conditions. Here we review the evidence for these shifts by focusing on experiments that have examined genetic correlations in more than one environment. In many studies, there are significant changes in correlations and these can even switch sign across environments. This raises questions about the validity of deducing genetic constraints from studies in one environment and suggests that the interaction between environmental conditions and the expression of genetic covariation is an important avenue for future work.

Animals↗

Influence of postweaning gain interval on estimates of heritability and genetic correlations.

Heritability and phenotypic environmental and genetic correlations of postweaning gains for intervals of 28 to 224 d were calculated from data on 2,410 crossbred steers and 3,088 Hereford bulls. Gains were essentially linear for the 224-d interval. As the length of the performance interval increased from 28 to 224 d, heritability of gain increased from .12 to .55 among the crossbred steers and from .09 to .24 among the Hereford bulls. There was no tendency for heritability to be larger or smaller for intervals of like duration initiated early or late in the 224-d interval. Heritability of individual animal regressions of weight on days was evaluated as an alternative measure of gain/d. Heritabilities were not different between daily gain and regressions when measured over the same intervals. Genetic correlations were generally positive and much larger than the environmental or phenotypic correlations, indicating that genetic influences tended to persist from one interval to another. Genetic correlations between intervals tended to become smaller as the number of days separating the intervals increased.

Animals↗