Estimates Of Genetic Covariance Research Articles

Genotypic covariance between the performance of a resident species and community assembly in the field

Abstract Genetic variation in resident species can influence the assembly and dynamics of communities, but the potential for these genetic effects to persist across generations is largely unresolved. In principle, persistent, directional changes in communities are only predicted when community properties covary genetically with the fitness of resident species. Estimates of genetic covariance between the fitness of a resident species and its community are therefore necessary to “close the eco‐evolutionary loop” in studies of community genetics, but such estimates are rare. Emulating community genetics experiments in plants, we used clonal replicates of 21 genotypes of a resident species (the encrusting bryozoan, Hippopodina) to investigate the magnitude of genotypic variance contributing to assembly of a marine benthic community. Genotypes explained up to 35% of variation in community assembly. Critically, the performance of Hippopodina genotypes covaried both with the evenness of communities and with the abundances of some individual species, representing an indirect genetic effect that creates the potential for multigenerational interactions between Hippopodina and co‐existing species. Our results suggest that different genotypes will associate with different community members consistently across generations, and such non‐random associations can give rise to specialization. Further interactions between species other than Hippopodina itself may also be altered by effects of genetic variation in the focal species. Furthermore, species in the community other than Hippopodina itself will interact more commonly in the presence of some genotypes over others. Our results support the potential for genetic variation in one species to have deterministic effects on the dynamics of ecological communities. A plain language summary is available for this article.

Assessing the degree of stratification between closely related Holstein-Friesian populations

Genomic information is an important part of the routine evaluation of dairy cattle and provides the wide availability of animals genotyped using single nucleotide polymorphism (SNP) microarrays. We analyzed 2243 Polish and 2294 German Holstein-Friesian bulls genotyped using the Illumina BovineSNP50 BeadChip. For each bull, estimated breeding values (EBVs) calculated from national routine genetic evaluation were available for production traits and for somatic cell score (SCS). Separately for each population, we estimated SNP haplotypes, pairwise linkage disequilibrium (LD), and SNP effects. The SNP genetic covariance between both populations was estimated using a bivariate mixed model. The average LD was lower in the Polish than in the German population and, with increasing genomic distance, LD decays 1.7 times more rapidly in German than in Polish cattle. The comparison of SNP allele frequencies for base populations estimated separately using Polish and German data revealed a very good agreement. The comparison of genetic effects corresponding to various window lengths defined in bp emerged a systematic pattern: regardless of the length of the compared region, few significant differences were found for production traits, while many were observed for SCS. For each trait, the German population had much higher SNP variances than the Polish population and the genetic covariance estimates were all positive. Depending on traits’ inheritance mode, the additive genetic variation can be stored in many genes following the infinitesimal model (like for SCS) or distributed between genes with high effects and the polygenic “background” (like for production traits). Accounting for those differences has implications on the prospective international genomic evaluation.

Longitudinal genetic analyses of fillet traits in Nile tilapia Oreochromis niloticus

There is little published information on genetic covariance estimates among meat yield traits in Nile tilapia. Longitudinal genetic analyses to evaluate patterns of environmental and genetic covariances among such traits during growth would help to establish selection criteria and predict direct and correlated response to selection. Accordingly, longitudinal genetic studies from 106 to 245days of age were performed by random regression models for carcass and fillet weights and yields in Nile tilapia. For each analysis, the statistical model included random family common environmental and genetic effects and assuming heterogeneity of residual variances. For carcass and fillet yield, genetic correlations between closely paired ages (from 140 to 200days) were higher (>0.80) than those separated by moderate (>0.40) or longer (approaching zero) intervals. For carcass and fillet weight, genetic correlations when both ages were greater than 125days post-hatching were larger than 0.60, and those when both ages were greater than 149days post-hatching were larger than 0.80. Heritability estimates for fillet and carcass yields ranged from moderate (0.12) to high (0.52), suggesting the likelihood that selection programs could improve these traits. The manner in which fillets were removed seems to influence the results. Heritability estimates for carcass and fillet weight ranged from high (0.52) to low (0.01). The family common environmental effect was important for reducing the heritability estimates of these two traits, probably due to high correlation of each trait to body weight at slaughter, which is substantially affected by the common family environmental effect. Accordingly, to increase the accuracy of predicted breeding values for these traits, it is important to reduce family effects by minimizing the duration during which families are reared separately, then tagging individual fish and initiating communal stocking at as early a life stage as possible. Based upon estimated genetic parameters among the traits, direct selection for body weight, carcass weight and fillet weight is not recommended if the breeding goal is to increase fillet yield as a correlated response. Contrarily, the high genetic correlations between body weight and fillet and carcass weights (>0.98) suggest that body weight could be used as a selection criterion to avoid slaughtering potential breeders to acquire data and to achieve higher selection intensity.

Genetic Evaluation and Selection Response of Birth Weight and Weaning Weight in Indigenous Sabi Sheep

Genetic parameters were estimated for birth weight and weaning weight from three year (1991-1993) data totalling 1100 records of 25 rams to 205 ewes of Indigenous Sabi flock maintained at Grasslands Research Station in Zimbabwe. AIREML procedures were used fitting an Animal Model. The statistical model included the fixed effects of year of lambing, sex of lamb, birth type and the random effect of ewe. Weight of ewe when first joined with ram was included as a covariate. Direct heritability estimates of 0.27 and 0.38, and maternal heritability estimates of 0.24 and 0.09, were obtained for birth weight and weaning weight, respectively. The total heritability estimates were 0.69 and 0.77 for birth weight and weaning weight, respectively. Direct-maternal genetic correlations were high and positive. The corresponding genetic covariance estimates between direct and maternal effects were positive and low, 0.25 and 0.18 for birth weight and weaning weight, respectively. Responses to selection were 0.8 kg and 0.14 kg for birth weight and weaning weight, respectively. The estimated expected correlated response to selection for birth weight by directly selecting for weaning weight was 0.26. Direct heritabilities were moderate; as a result selection for any of these traits should be successful. Maternal heritabilities were low for weaning weight and should have less effect on selection response. Indirect selection can give lower response than direct selection. (Asian-Aust. J. Anim. Sci. 2002. Vol 15, No. 12 : 1690-1694)

Estimation of genetic covariances with method R.

Method R is a simple and computationally inexpensive method for estimating (co)variances. The objective of the study was to investigate properties of Method R for estimation of (co)variance components with emphasis on covariance estimation. Theoretical Method R formulas were developed for simplified single-variate and bivariate models. In single-trait models, the curve of the regression of Method R was continuous and monotonic and its slope depended on the amount of information on each animal and on the variance ratio. The curve became steeper as the number of records per animal decreased. For covariance, the curve of the regression was monotonic but not continuous. However, a regression coefficient of 1 still corresponded to the correct covariance. Similar curves were observed in analyses of simulated data sets. Because of the observed discontinuity, algorithms implementing Method R that require a continuous regression curve would not work in models with covariances. An alternative algorithm was based on a transformation matrix obtained by multiplying a matrix of numerators with the inverse of a matrix of denominators of the regression factors. Such an algorithm converged reliably for all models tested. Method R can be modified to estimate covariances in models too large for other methods.

Estimation of Genetic Covariances from Sire Progeny Test Results

A method of estimating genetic covariance between pairs of traits from sire progeny tests is described. The basis is a maximum likelihood procedure for multiple traits. An advantage of this method over correlations between sire proofs is that the number of progeny in each proof is taken into account. The assumptions and restrictions of the approximation method are discussed, and the calculations are illustrated by example.