Estimation of variance components for carcass traits in Japanese Black cattle using 50K SNP genotype data

Genomic selection using high‐density single nucleotide polymorphism (SNP) genotype data may accelerate genetic improvements in livestock animals. In this study, we attempted to estimate the variance components of six carcass traits in fattened Japanese Black steers using SNP genotype data. Six hundred and seventy‐three steers were genotyped using an Illumina Bovine SNP50 BeadChip and phenotyped for cold carcass weight, ribeye area, rib thickness, subcutaneous fat thickness, estimated yield percent and marbling score. Additive polygenic variance and the variance attributable to a set of SNPs that had statistically significant effects on the trait were estimated via Gibbs sampling with two models: (i) a model with the chosen SNPs and the additive polygenic effects; and (ii) a model with the polygenic effects alone. The proportion of the estimated variance attributable to the SNPs became higher as the number of SNP effects that fit increased. High correlations between breeding values estimated with the model containing the polygenic effect alone and those estimated by chosen SNPs were obtained. No fraction of the total genetic variance was explained by SNPs associated with the trait at P ≥ 0.1. Our results suggest that for the carcass traits of Japanese Black cattle, a maximum of half of the total additive genetic variance may be explained by SNPs between 100 several tens to several 100s.     

Technical note: prediction of breeding values using marker-derived relationship matrices

In livestock populations, estimation of breeding values for selection requires a matrix describing the additive relationship between individuals in the population. This matrix can be derived from pedigree information. In some livestock populations, pedigree information may be unavailable, incomplete, or in error. Here we use simulated data to demonstrate that marker-derived relationship matrices can be used to predict breeding values and estimate additive variance components, provided the markers are sufficiently dense. The approach is demonstrated for an Angus data set with 9,323 SNP markers genotyped.     

Estimation of genetic parameters and prediction of breeding values for multivariate threshold and continuous data in a simulated horse population using Gibbs sampling and residual maximum likelihood

Simulated horse data were used to compare multivariate estimation of genetic parameters and prediction of breeding values (BV) for categorical, continuous and molecular genetic data using linear animal models via residual maximum likelihood (REML) and best linear unbiased prediction (BLUP) and mixed linear-threshold animal models via Gibbs sampling (GS). Simulation included additive genetic values, residuals and fixed effects for one continuous trait, liabilities of four binary traits, and quantitative trait locus (QTL) effects and genetic markers with different recombination rates and polymorphism information content for one of the liabilities. Analysed data sets differed in the number of animals with trait records and availability of genetic marker information. Consideration of genetic marker information in the model resulted in marked overestimation of the heritability of the QTL trait. If information on 10,000 or 5,000 animals was used, bias of heritabilities and additive genetic correlations was mostly smaller, correlation between true and predicted BV was always higher and identification of genetically superior and inferior animals was - with regard to the moderately heritable traits, in many cases - more reliable with GS than with REML/BLUP. If information on only 1,000 animals was used, neither GS nor REML/BLUP produced genetic parameter estimates with relative bias 50% for all traits. Selection decisions for binary traits should rather be based on GS than on REML/BLUP breeding values.     

Estimation of (co)variance components for birth weight in Nellore cattle using alternative data editing criteria

The aim of this study was to estimate the (co)variance components and breeding values for birthweight (BW) in Nellore cattle by considering or not identical weights that exhibit a high frequency within the contemporary group (CG). A total of 175,258 BW records of Nellore cattle born between 2002 and 2018 were used. The CG was formed by farm, year of birth, sex and feeding regime at birth. CGs with more than 16% of identical BW values were eliminated, generating a data file called BWd. Another file was created without removing these animals (BWt). A mixed linear model was used for statistical analysis, which included fixed and random effects. In both data files analysed, single-trait analysis was performed by Bayesian inference. The mean direct and maternal heritability for BW and the correlation between direct and maternal effects were 0.27, 0.07 and −0.07 for BWt, respectively, and 0.30, 0.093 and −0.07 for BWd. This method should affect the estimation of genetic merits of animals for BW, providing greater safety in the choice of sires.     

Optimum contribution selection using traditional best linear unbiased prediction and genomic breeding values in aquaculture breeding schemes

The aim of this study was to compare genetic gain for a traditional aquaculture sib breeding scheme with breeding values based on phenotypic data (TBLUP) with a breeding scheme with genome-wide (GW) breeding values. Both breeding schemes were closed nuclei with discrete generations modeled by stochastic simulation. Optimum contribution selection was applied to restrict pedigree-based inbreeding to either 0.5 or 1% per generation. There were 1,000 selection candidates and a sib test group of either 4,000 or 8,000 fish. The number of selected dams and sires to create full sib families in each generation was determined from the optimum contribution selection method. True breeding values for a trait were simulated by summing the number of each QTL allele and the true effect of each of the 1,000 simulated QTL. Breeding values in TBLUP were predicted from phenotypic and pedigree information, whereas genomic breeding values were computed from genetic markers whose effects were estimated using a genomic BLUP model. In generation 5, genetic gain was 70 and 74% greater for the GW scheme than for the TBLUP scheme for inbreeding rates of 0.5 and 1%. The reduction in genetic variance was, however, greater for the GW scheme than for the TBLUP scheme due to fixation of some QTL. As expected, accuracy of selection increased with increasing heritability (e.g., from 0.77 with a heritability of 0.2 to 0.87 with a heritability of 0.6 for GW, and from 0.53 and 0.58 for TBLUP in generation 5 with sib information only). When the trait was measured on the selection candidate compared with only on sibs and the heritability was 0.4, accuracy increased from 0.55 to 0.69 for TBLUP and from 0.83 to 0.86 for GW. The number of selected sires to get the desired rate of inbreeding was in general less in GW than in TBLUP and was 33 for GW and 83 for TBLUP (rate of inbreeding 1% and heritability 0.4). With truncation selection, genetic gain for the scheme with GW breeding values was nearly twice as large as a scheme with traditional BLUP breeding values. The results indicate that the benefits of applying GW breeding values compared with TBLUP are reduced when contributions are optimized. In conclusion, genetic gain in aquaculture breeding schemes with optimized contributions can increase by as much as 81% by applying genome-wide breeding values compared with traditional BLUP breeding values.     

Estimation of breeding values from large‐sized routine carcass data in Japanese Black cattle using Bayesian analysis

Volumes of official data sets have been increasing rapidly in the genetic evaluation using the Japanese Black routine carcass field data. Therefore, an alternative approach with smaller memory requirement to the current one using the restricted maximum likelihood (REML) and the empirical best linear unbiased prediction (EBLUP) is desired. This study applied a Bayesian analysis using Gibbs sampling (GS) to a large data set of the routine carcass field data and practically verified its validity in the estimation of breeding values. A Bayesian analysis like REML‐EBLUP was implemented, and the posterior means were calculated using every 10th sample from 90 000 of samples after 10 000 samples discarded. Moment and rank correlations between breeding values estimated by GS and REML‐EBLUP were very close to one, and the linear regression coefficients and the intercepts of the GS on the REML‐EBLUP estimates were substantially one and zero, respectively, showing a very good agreement between breeding value estimation by the current GS and the REML‐EBLUP. The current GS required only one‐sixth of the memory space with REML‐EBLUP. It is confirmed that the current GS approach with relatively small memory requirement is valid as a genetic evaluation procedure using large routine carcass data.     

Estimation of variance components with dominance and inbreeding in dairy cattle

SUMMARY: The five variance components in the genetic (co)variance among inbred relatives for a quantitative trait with additive and dominance genetic variation were estimated by equating variances among and within different types of families of inbred cows to their expectations. The data used were milk and fat yields of 85,433 U.S. Holstein cows with inbreeding coefficients of 6.25% or higher. When all five parameters were estimated, unrealistic results were obtained. If all quantitative trait loci are biallelic, genetic (co)variance depends on only four parameters. More realistic estimates were obtained under this assumption. There was a substantial negative covariance among breeding values and dominance effects under inbreeding, and the dominance variance in inbred cows was larger than the dominance variance in the noninbred base population. ZUSAMMENFASSUNG: Varianzkomponenteneinsch?tzung bei Dominanz und Inzucht von Milchvieh Die fünf Varianzkomponenten in der genetischen (Ko)Varianz zwischen ingezüchteten Verwandten in einem quantitativen Merkmal wurden gesch?tzt durch Gleichsetzen von Varianzen zwischen und innerhalb verschiedener Familien von ingezüchteter Kühen zu ihren Erwartungswerten. Das Datenmaterial bestand aus den Milch- und Fettmengen von 85,433 U.S. Holstein Kühen mit Inzuchtkoeffizienten von 6.25% oder h?her. Die gleichzeitige Sch?tzung aller fünf Parameter führte zu unrealistischen Ergebnissen. Wenn an allen Genorten des quantitativen Merkmals nur zwei Allele vorkommen, gehen nur vier Parameter in die genetische (Ko)Varianz ein. Die Sch?tzwerte, die unter dieser Annahme berechnet wurden, waren plausibler. Eine betr?chtliche negative Kovarianz zwischen den Zuchtwerten und den Dominanzwerten bei Inzucht wurde gefunden, und die Dominanzvarianz war unter Inzucht gró?er als in der Basispopulation.     

Estimation of variance components for postpartum dysgalactia syndrome in sows

The postpartum dysgalactia syndrome (PDS) represents one of the most important diseases after parturition in sows. The genetic background of the disease has been investigated some time ago and heritability estimates around 0.10 have been obtained. To compute current estimates, a dataset of 1680 sampled sows and their 2001 clinically examined litters was used for variance components estimation with a threshold liability model. Affected sows were defined through clinical examination 12–48 h after parturition. Posterior mean of additive genetic variance was 0.10 and estimated heritability for PDS averaged 0.0879 with a 95% confidence interval of 0.0876 and 0.0881. The results are in agreement with those of other studies and emphasize the importance of considering the genetic predisposition for susceptibility to PDS as well as of additional factors including hygiene and management conditions.     

Estimation of variance components including competitive effects of Large White growing gilts

Records of on-test ADG of Large White gilts were analyzed to estimate variance components of direct and associative genetic effects. Models included the effects of contemporary group (farm-barn-batch), birth litter, pen group, and direct and associative additive genetic effects. The area of each pen was 14 m2. The additive genetic variance was a function of the number of competitors in a group, the additive relationships between the animal performing the record and its pen mates, and the additive relationships between pen mates. To partially account for differences in the number of pen mates, a covariable (qi = 1, 1/n, or 1/n(1/2)) was added to the associative genetic effect. There were 4,946 records from 2,409 litters and 362 pen groups. Pen group size ranged from 12 to 16 gilts. Analyses by REML converged very slowly. A grid search showed that the likelihood function was almost flat when the additive genetic associative effect was fitted. Estimates of direct and associative heritability were 0.15 and 0.03, respectively. Within the BLUPF90 family of programs, the mixed-model equations can be set up directly. For variance component estimation, simple programs (REMLF90 and GIBBSF90) worked without modifications, but more optimized programs did not. Estimates obtained using the three values of qi were similar. With the data structure available for this study and under an environment with relative low competition among animals, accurate estimation of associative genetic effects was not possible. Estimation of competitive effects with large pen size is difficult. The magnitude of competition effects may be larger in commercial populations, where housing is denser and food is limited.     

Estimation of maternal variance components considering cow-calf contacts under extensive pastoral systems

Traditional methods of variance component estimation for traits under maternal influence consist of partitioning the variance into direct additive genetic, maternal additive genetic, permanent maternal environmental, and error variance components. This partitioning is based on the assumption that each calf is nurtured and fed exclusively by its own dam. However, under extensive pastoral systems, voluntary cross-suckling may occur and could be quantified by using contact loggers recording cow-calf affiliations. A simulation study was conducted to test several variance models for partitioning maternal variation by including information on cow-calf contacts. The results indicated that weighting maternal genetic and permanent maternal environmental effects by the relative time calves spent with particular cows, including their own mothers, is feasible and significantly increased the log-likelihood of the models. However, the interpretation of the variance components in terms of traditional direct and maternal heritability is no longer straightforward. The need for further research and implications for the industry are discussed.     

Variance components and breeding values for growth traits from different statistical models.

Estimates of genetic parameters resulting from various analytical models for birth weight (BWT, n = 4,155), 205-d weight (WWT, n = 3,884), and 365-d weight (YWT, n = 3,476) were compared. Data consisted of records for Line 1 Hereford cattle selected for postweaning growth from 1934 to 1989 at ARS-USDA, Miles City, MT. Twelve models were compared. Model 1 included fixed effects of year, sex, age of dam; covariates for birth day and inbreeding coefficients of animal and of dam; and random animal genetic and residual effects. Model 2 was the same as Model 1 but ignored inbreeding coefficients. Model 3 was the same as Model 1 and included random maternal genetic effects with covariance between direct and maternal genetic effects, and maternal permanent environmental effects. Model 4 was the same as Model 3 but ignored inbreeding. Model 5 was the same as Model 1 but with a random sire effect instead of animal genetic effect. Model 6 was the same as Model 5 but ignored inbreeding. Model 7 was a sire model that considered relationships among males. Model 8 was a sire model, assuming sires to be unrelated, but with dam effects as uncorrelated random effects to account for maternal effects. Model 9 was a sire and dam model but with relationships to account for direct and maternal genetic effects; dams also were included as uncorrelated random effects to account for maternal permanent environmental effects. Model 10 was a sire model with maternal grandsire and dam effects all as uncorrelated random effects. Model 11 was a sire and maternal grandsire model, with dams as uncorrelated random effects but with sires and maternal grandsires assumed to be related using male relationships. Model 12 was the same as Model 11 but with all pedigree relationships from the full animal model for sires and maternal grandsires. Rankings on predictions of breeding values were the same regardless of whether inbreeding coefficients for animal and dam were included in the models. Heritability estimates were similar regardless of whether inbreeding effects were in the model. Models 3 and 9 best fit the data for estimation of variances and covariances for direct, maternal genetic, and permanent environmental effects. Other models resulted in changes in ranking for predicted breeding values and for estimates of direct and maternal heritability. Heritability estimates of direct effects were smallest with sire and sire-maternal grandsire models.     

Effects of excluding a set of random effects on prediction error variance of breeding value

SUMMARY: The effects of excluding a set of random effects (U-effects) uncorrelated to breeding values (BV) on prediction error variance (PEV) is studied analytically. Two situations are considered for model comparison: (a) existence of a 'true' model, (b) uncertainty about which of the competing models is 'true'. Models compared are the 'long' one, which includes BV + U-effects, and the 'short' one which includes BV's as the only random systematic effect. Expressions for PEV(BV) were obtained for the long model (PEVL); the short model (PEVS); and the short model assuming the long model is the correct one (PEVSI). It is shown that in general PEVS ≤ PEVL ≤ PEVSI. Results are exemplified by means of an example including a computer simulation. RESUMEN: En este trabajo se estudia analiticamente el efecto de excluir una variable aleatoria (efecto U) no correlacionada con el valor de cría (BV), sobre la varianza del error de predicción de este último (PEV(BV)). Para ello se utilizan dos enfoques de comparación de modelos: (a) existencia de un modelo 'verdadero', (b) incertidumbre respecto de cuál de ambos modelos alternativos es el correcto. Los modelos que se comparan son: el 'largo', que incluye BV+U, y el 'corto', el cuál solo incluye BV. Se obtienen las expresiones para PEV(BV) en las siguientes situaciones: (1) en el modelo largo (PEVL), (2) en el modelo corto (PEVS), y (3) en el modelo corto pero asumiendo que el largo es el verdadero (PEVSI). Se demuestra que en general PEVS ≤ PEVL ≤ PEVSI. Los resultados obtenidos son ilustrados mediante un ejemplo que incluye una simulación estocástica. ZUSAMMENFASSUNG: Ver?nderung der Fehlervarianz der Zuchtwertvoraussage durch Vernachl?ssigung einer Gruppe zuf?liger Wirkungen. Es wird die Auswirkung der Ausschaltung einer Gruppe zuf?lliger Wirkungen (U-effects), die mit Zuchtwerten (BV) nicht korreliert sind, auf die Varianz des Voraussage-Fehlers (PEV) analytisch untersucht. Zwei Modelle werden betrachtet: (a) Existenz eines "wahren"Modells, (b) Ungwi?heit hinsichtlich des "wahren"Modells. Es werden verglichen: ein langes Modell, das BV und U-Wirkungen umfa?t und ein kurzes, das nur BV als zuf?llige systematische Wirkung aufweist. Für PEV(BV) wurden Gr??en erhalten: (1) für das lange Modell (PEVL), (2) das kurze (PEVS) und (3) für das kurze unter der Annahme, da? das lange Modell das richtige ist (PEVSI). Im allgemeinen gilt PEVS ≤ PEVL ≤ PEVSI. Ergebnisse werden anhand einer Computersimulation erl?utert.     

Estimation of genetic parameters among breeding soundness examination components and growth traits in yearling bulls

Data on breeding soundness examinations (BSE) and performance traits were obtained on 549 yearling beef bulls at the San Juan Basin Research Center, Hesperus, Co from 1976 to 1984. Genetic parameters estimated for components of BSE included percent motility (PMOT), percent primary abnormalities (PPRIM), percent secondary abnormalities (PSEC), percent normal sperm (PNOR), scrotal circumference (SC) and BSE score (BSESC). Performance traits included birth weight, weaning weight, yearling weight and average daily gain. The least squares model included birth year, age of dam and breed as fixed effects, sire/breed as a random variable, and age and percent inbreeding as covariates. Paternal half-sib estimates of heritability were PMOT, .08 +/- .07; PPRIM, .31 +/- .09; PSEC, .02 +/- .05; PNOR, .07 +/- .06; BSESC, .10 +/- .06 and SC, .40 +/- .09. Phenotypic correlations among BSE components and growth traits were generally favorable. Genetic correlations involving percent secondary abnormalities were highly variable with large standard errors. Seminal traits improved as age increased and became poorer as inbreeding increased.     

Estimation of (co)variance components and genetic parameters of greasy fleece weights in Muzaffarnagari sheep

Variance components and genetic parameters for greasy fleece weights of Muzaffarnagari sheep maintained at the Central Institute for Research on Goats, Makhdoom, Mathura, India, over a period of 29 years (1976 to 2004) were estimated by restricted maximum likelihood (REML), fitting six animal models including various combinations of maternal effects. Data on body weights at 6 (W6) and 12 months (W12) of age were also included in the study. Records of 2807 lambs descended from 160 rams and 1202 ewes were used for the study. Direct heritability estimates for fleece weight at 6 (FW6) and 12 months of age (FW12), and total fleece weights up to 1 year of age (TFW) were 0.14, 0.16 and 0.25, respectively. Maternal genetic and permanent environmental effects did not significantly influence any of the traits under study. Genetic correlations among fleece weights and body weights were obtained from multivariate analyses. Direct genetic correlations of FW6 with W6 and W12 were relatively large, ranging from 0.61 to 0.67, but only moderate genetic correlations existed between FW12 and W6 (0.39) and between FW12 and W12 (0.49). The genetic correlation between FW6 and FW12 was very high (0.95), but the corresponding phenotypic correlation was much lower (0.28). Heritability estimates for all traits were at least 0.15, indicating that there is potential for their improvement by selection. The moderate to high positive genetic correlations between fleece weights and body weights at 6 and 12 months of age suggest that some of the genetic factors that influence animal growth also influence wool growth. Thus selection to improve the body weights or fleece weights at 6 months of age will also result in genetic improvement of fleece weights at subsequent stages of growth.     

Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle

The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP.     

Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single‐nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic‐based [genomic best linear unbiased prediction (GBLUP)‐REML and BayesC] and pedigree‐based (PBLUP‐REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP‐REML across traits, from 0 to 0.03 with GBLUP‐REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic‐based methods were small (0.01–0.05), with GBLUP‐REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP‐REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population.     

Estimation of direct and maternal (co)variance components for pre-weaning growth traits in Muzaffarnagari sheep

Genetic parameters and (co)variance components were estimated for weight at birth and at 15, 30, 45, 60 and 75 days of age for a flock of Muzaffarnagari sheep maintained at the Central Institute for Research on Goats, Makhdoom, Mathura over a period of 27 years (1976–2002). Records on 5201 lambs descended from 1568 ewes and 170 rams were included in the analysis. Analyses were carried out by REML fitting an animal model and ignoring or including maternal genetic or permanent environmental effects. Six different animal models were fitted for all traits, and the best model was chosen after testing improvements in log-likelihood values. Direct heritability estimates were inflated substantially for all traits when maternal effects were ignored. Direct heritability estimates were 0.08 ± 0.02 for birth weight and 0.02 ± 0.02, 0.02 ± 0.02, 0.27 ± 0.08, 0.09 ± 0.04, and 0.29 ± 0.08 for weights at 15, 30, 45, 60, and 75 days, respectively. Maternal genetic effects contributed only 4 to 8% of the total phenotypic variance from birth to 30 days of age, and this effect diminished further with increasing age. Maternal heritability was low for pre-weaning growth traits and should have only a small effect on selection response. Estimates of the fraction of variance due to maternal permanent environmental effects were 0.09 ± 0.02, 0.15 ± 0.04, 0.12 ± 0.03, 0.11 ± 0.04, 0.14 ± 0.02, and 0.08 ± 0.04 for body weights at birth and at 15, 30, 45, 60, and 75 days, respectively. These results indicate that selecting for improved maternal and/or direct effects in Muzaffarnagari sheep would generate only slow genetic progress in early growth traits.     

Estimation of variance and covariance components to determine heritabilities and repeatability of weaning weight in American Simmental cattle

Components of (co)variance for weaning weight were estimated from field data provided by the American Simmental Association. These components were obtained for the observational components of variance corresponding to a sire, maternal grandsire, and dam within maternal grandsire model. From these estimates, direct additive genetic variance (Sigma2A), maternal additive genetic variance (Sigma2M), covariance between direct and maternal additive genetic effects (SigmaAM), variance of permanent environment(Sigma2pe) and temporary environment variance(Sigma2te) were determined. A procedure to approximate restricted maximum likelihood (REML) estimates of the observational components of variance based on the expectation-maximization (EM) algorithm is described. From these results, phenotypic variance ( ) of weaning weight was 667.88 kg2. Values forSigma2A, Sigma2M, Sigma2pe and Sigma2te were 79,30,58,38,49.45, and 469.97 kg2, respectively. Genetic correlation between direct and maternal additive genetic effects was .16.     

Full pedigree quantitative trait locus analysis in commercial pigs using variance components

In commercial livestock populations, QTL detection methods often use existing half-sib family structures and ignore additional relationships within and between families. We reanalyzed the data from a large QTL confirmation experiment with 10 pig lines and 10 chromosome regions using identity-by-descent (IBD) scores and variance component analyses. The IBD scores were obtained using a Monte Carlo Markov Chain method, as implemented in the LOKI software, and were used to model a putative QTL in a mixed animal model. The analyses revealed 61 QTL at a nominal 5% level (out of 650 tests). Twenty-seven QTL mapped to areas where QTL have been reported, and eight of these exceeded the threshold to claim confirmed linkage (P < 0.01). Forty-two of the putative QTL were detected previously using half-sib analyses, whereas 46 QTL previously identified by half-sib analyses could not be confirmed using the variance component approach. Some of the differences could be traced back to the underlying assumptions between the two methods. Using a deterministic approach to estimate IBD scores on a subset of the data gave very similar results to LOKI. We have demonstrated the feasibility of applying variance component QTL analysis to a large amount of data, equivalent to a genome scan. In many situations, the deterministic IBD approach offers a fast alternative to LOKI.     

Estimation of variance components due to imprinting effects with DFREML and VCE: results of a simulation study

Treating gametes as homozygous diploid individuals, TIER and SÖLKNER (Theor. Appl. Genet. 85: 868–872, 1993) proposed a method which manages the use of available computer programs with a common animal model to estimate variance components caused by imprinting effects. Despite some relevant model restrictions, this approach has already been used in some field data analyses by an adapted version of the widely used DFREML computer program, subsequently indicated by DFREML ^a. The main objective of this study was to ascertain the properties of DFREML ^a by computer simulation and to examine other alternative estimation approaches. The most important results may be summarized as follows: (1) Treating gametes as homozygous diploid individuals has the consequence that one‐half of the actually realized gametic effect is totally abstracted in variance component estimation. Thus, an additional adjustment of the phenotypic variance calculated by DFREML ^a is necessary to get correct values of estimated variance component ratios. (2) Adjusted DFREML ^a estimates yielded correct results when animals were unselected and only maternal or paternal imprinting (not both simultaneously) occurred. (3) When the model did not adequately account for the additive genetic component within a maternal lineage, significant upward biases for the cytoplasmic component were observed. (4) The use of a simple dam and sire model with appropriate relationship matrices can be recommended when only the difference of maternal and paternal imprinting effects is of primary interest and the covariance between maternal halfsibs is not substantially increased by common environmental effects. (5) An adequate estimation of variance components for all possible imprinting situations requires the use of an animal model augmented by both maternal and paternal gametic effects. Unfortunately, a computer program on the basis of such a model does not yet exist.