Additive Heritability Estimates Research Articles

Estimation of dominance genetic effects on pre-weaning efficiency-related traits in Baluchi sheep

Abstract This study was conducted to estimate the relative contribution of dominance genetic effects to efficiency-related traits including Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR) in Baluchi sheep. To this end, each trait was analysed with a series of 12 animal models which were identical for fixed and additive genetic effects but differed for combinations of dominance genetic and maternal effects. The Akaike's information criterion (AIC) was used to rank models. (Co)variances between traits were estimated using bivariate analyses. For all traits studied, according to AIC values, models containing the dominance genetic effects provided a better data fit than otherwise identical models. By including dominance genetic effects in the model, additive genetic variance did not change, but a significant decrease was observed in the residual variance (24, 19 and 25% for KR, EF and RGR, respectively). Estimates of dominance heritability $( {\boldsymbol h}_{\boldsymbol d}^ 2 )$ were 0.20 ± 0.05, 0.17 ± 0.05 and 0.19 ± 0.07 for KR, EF and RGR, respectively, more than corresponding estimates of additive heritability ${\bf ( }{\boldsymbol h}_{\boldsymbol a}^{\bf 2} {\bf ) }$ as 0.14 ± 0.02, 0.09 ± 0.03 and 0.13 ± 0.02, respectively. Dominance genetic correlations between traits were 0.89 ± 0.17 (KR-EF), 0.86 ± 0.20 (KR-RGR) and 0.93 ± 0.21 (EF-RGR). Additive genetic correlations between traits were 0.84 ± 0.05 (KR-EF), 0.78 ± 0.04 (KR-RGR) and 0.83 ± 0.04 (EF-RGR). The Spearman correlation between additive breeding values including and excluding dominance genetic effects were close to unity either for all animals or top ranked animals. Since presence of dominance genetic effects increased the model power to fit the data, inclusion of these effects in the genetic evaluation models for Baluchi sheep was recommended.

Estimates of Maternal Effect and (co)Variance Components for BodyWeight at Different Ages by Animal Model in Chokla Sheep

Background: For this study, information was gathered on 6785 Chokla sheep at the Central Sheep and Wool Research Institute in Bikaner, Rajasthan, India and documented between 1974 and 2020. Methods: (Co)variance components and genetic parameters of weight at birth (BW), weaning (WW), 6, 9 and 12 months of age (6W,9W and YW, respectively) of Chokla sheep, were estimated by average algorithm restricted maximum likelihood (AIREML), fitting six different animal models with various combinations of direct and maternal effects. Result: The direct heritability estimates increased from birth to twelve months of age and values for all the body weight traits except birth weight (0.170) were moderate (0.30-0.50). The maternal influence diminished as age increases and maternal genetic effect (m2) was found to be important and sizeable at weaning stage (0.181). Maternal permanent environmental variance was found to influence the early body weight traits. Negative and high estimate of covariance between direct and maternal effects, resulted in highly inflated values of additive heritability. In this condition, it is more useful to use the total heritability (h2t) for evaluation of the response for selection based on phenotypic values to prevent the use of biased estimates of additive heritability. Genetic and phenotypic correlations among body weights at different ages were positive and ranged from medium to high.

Devising a selection strategy for the Jamunapari goat for improving lactation performance.

This study aimed to evaluate the genetic potential of the Jamunapari goat and formulate a selection strategy for improving lactation traits. The data set included 4049 phenotypic records for across parity milk yield at 90days (MY90), 140days (MY140), total milk yield (TMY), and lactation length (LL) obtained from the progeny of 83 sires and 1643 dams between the period 1990 and 2019. Animal model employing average information restricted maximum likelihood (AIREML) was used to estimate genetic parameters for milk yield traits and LL. The direct additive heritability estimates for across parity lactation traits that used repeatability model were 0.10 ± 0.03, 0.08 ± 0.03, and 0.12 ± 0.02 for MY90, MY140, and TMY, respectively, while it was low for LL (0.06 ± 0.02). The repeatability estimates were moderate ranging from 0.17 to 0.22 for milk yield traits and LL, indicating persistent performance over the parities. Animal permanent environment influence (c2) was significant in milk yield attributes, whereas direct maternal genetic effects were absent. As the early selection criteria based on first parity records are essential, we analyzed the data for the first parity separately and obtained moderate h2 estimates, viz., 0.26 ± 0.05, 0.16 ± 0.06, and 0.25 ± 0.06 for MY90, MY140, and TMY, respectively. These estimates augur further scope of selection in Jamunapari goats for higher milk yield. High and positive genetic correlation of MY90 with MY140 (0.97 ± 0.01) and TMY (0.91 ± 0.05) revealed the scope of using MY90 as the selection criterion. Based on these results, we recommend use of first parity MY90 as a single trait selection criterion for genetic improvement of all lactation traits in Jamunapari goat.

Estimation of genetic parameters and genetic trends for milk yield traits in Jamunapari goats in semiarid tropics

The Jamunapari goats are reared as dairy animals in semi-arid conditions, therefore it is necessary to evaluate the genetic potential for the genetic improvement of milk production traits. The data comprised of 2217 phenotypic records for milk yield at 90days (MY90) and 140days (MY140), total milk yield (TMY) and lactation length (LL) obtained from the progeny of 173 sires and 446 dams during the period 1990–2013. The data were analysed using mixed linear models exploring random effects due to direct additive, maternal and permanent environment variance due to animal. The most appropriate genetic models for milk yield traits were those that included permanent environment effects due to the animal. The direct additive heritability estimates were 0.15±0.05, 0.26±0.07, 0.25±0.08 for MY90, MY140 and TMY, respectively. The additive heritability estimate for LL was low and non-significant at 0.02±0.03. The repeatability estimates were moderate to high ranging from 0.68 to 0.73 for milk yield traits. The repeatability for lactation length was 0.20±0.03. Maternal variances were low ranging from 0.03 for MY90 to 0.13 for TMY. There was an increase in mean milk yield of 0.25, 0.70 and 0.72kg/year respectively at 90 and 140days, and for TMY. Genetic trends and phenotypic trends for MY90, MY140 and TMY were positive and indicated significant improvement in milk traits due to selective breeding.

Estimates of genetic parameters for growth traits in Brahman cattle using random regression and multitrait models.

Random regression models (RRM) and multitrait models (MTM) were used to estimate genetic parameters for growth traits in Brazilian Brahman cattle and to compare the estimated breeding values obtained by these 2 methodologies. For RRM, 78,641 weight records taken between 60 and 550 d of age from 16,204 cattle were analyzed, and for MTM, the analysis consisted of 17,385 weight records taken at the same ages from 12,925 cattle. All models included the fixed effects of contemporary group and the additive genetic, maternal genetic, and animal permanent environmental effects and the quadratic effect of age at calving (AAC) as covariate. For RRM, the AAC was nested in the animal's age class. The best RRM considered cubic polynomials and the residual variance heterogeneity (5 levels). For MTM, the weights were adjusted for standard ages. For RRM, additive heritability estimates ranged from 0.42 to 0.75, and for MTM, the estimates ranged from 0.44 to 0.72 for both models at 60, 120, 205, 365, and 550 d of age. The maximum maternal heritability estimate (0.08) was at 140 d for RRM, but for MTM, it was highest at weaning (0.09). The magnitude of the genetic correlations was generally from moderate to high. The RRM adequately modeled changes in variance or covariance with age, and provided there was sufficient number of samples, increased accuracy in the estimation of the genetic parameters can be expected. Correlation of bull classifications were different in both methods and at all the ages evaluated, especially at high selection intensities, which could affect the response to selection.

Quantitative genetics model as the unifying model for defining genomic relationship and inbreeding coefficient.

The traditional quantitative genetics model was used as the unifying approach to derive six existing and new definitions of genomic additive and dominance relationships. The theoretical differences of these definitions were in the assumptions of equal SNP effects (equivalent to across-SNP standardization), equal SNP variances (equivalent to within-SNP standardization), and expected or sample SNP additive and dominance variances. The six definitions of genomic additive and dominance relationships on average were consistent with the pedigree relationships, but had individual genomic specificity and large variations not observed from pedigree relationships. These large variations may allow finding least related genomes even within the same family for minimizing genomic relatedness among breeding individuals. The six definitions of genomic relationships generally had similar numerical results in genomic best linear unbiased predictions of additive effects (GBLUP) and similar genomic REML (GREML) estimates of additive heritability. Predicted SNP dominance effects and GREML estimates of dominance heritability were similar within definitions assuming equal SNP effects or within definitions assuming equal SNP variance, but had differences between these two groups of definitions. We proposed a new measure of genomic inbreeding coefficient based on parental genomic co-ancestry coefficient and genomic additive correlation as a genomic approach for predicting offspring inbreeding level. This genomic inbreeding coefficient had the highest correlation with pedigree inbreeding coefficient among the four methods evaluated for calculating genomic inbreeding coefficient in a Holstein sample and a swine sample.

A genome-wide association study of behavioral disinhibition.

We report results from a genome wide association study (GWAS) of five quantitative indicators of behavioral disinhibition: nicotine, alcohol consumption, alcohol dependence, illicit drugs, and non-substance related behavioral disinhibition. The sample, consisting of 7,188 Caucasian individuals clustered in 2,300 nuclear families, was genotyped on over 520,000 SNP markers from Illumina's Human 660W-Quad Array. Analysis of individual SNP associations revealed only one marker-component phenotype association, between rs1868152 and illicit drugs, with a p value below the standard genome-wide threshold of 5 × 10(-8). Because we had analyzed five separate phenotypes, we do not consider this single association to be significant. However, we report 13 SNPs that were associated at p < 10(-5) for one phenotype and p < 10(-3) for at least two other phenotypes, which are potential candidates for future investigations of variants associated with general behavioral disinhibition. Biometric analysis of the twin and family data yielded estimates of additive heritability for the component phenotypes ranging from 49 to 70%, GCTA estimates of heritability for the same phenotypes ranged from 8 to 37%. Consequently, even though the common variants genotyped on the GWAS array appear in aggregate to account for a sizable proportion of heritable effects in multiple indicators of behavioral disinhibition, our data suggest that most of the additive heritability remains "missing".

Genetic analysis of body weight in South African Angora kids and young goats

The data used for this study consisted of 27 485 kid records, the progeny of 599 sires and 10 077 dams, and were collected on the 2000- to 2009-born kids of 11 Angora goat studs. Variance and covariance components and ratios pertaining to direct additive genetic variation, maternal additive genetic variation, maternal permanent environmental variation, and the relationship between direct and maternal effects for birth weight (BW; kg), weaning weight (WW; kg) and body weight at 8, 12 and 16 months (W8, W12 and W16; kg) were estimated with the ASReml program. Direct additive heritability estimates of 0.22, 0.20, 0.12, 0.34 and 0.58 were obtained for BW, WW, W8, W12 and W16, respectively. Maternal heritabilities were 0.10, 0.09, 0.03 and 0.06 for BW, WW, W8 and W12, respectively, while maternal environmental effects of 0.13, 0.11, 0.06 and 0.04 were estimated for the latter traits, respectively. An unfavourable correlation of &#8211;0.38 was obtained between direct and maternal genetic effects for BW. Low to medium positive direct genetic correlations were estimated between birth weight and body weights recorded at a later stage in life. High positive direct genetic correlations were estimated among WW, W8, W12 and W16. The maternal genetic correlations obtained between birth weight and the other body weights were medium to high. Phenotypic correlations among the traits ranged from low to high. Genetic trends of body weight at different ages indicate that although not many breeders use objective measurement as a selection tool, body weight increased slightly in the 11 studs over the 10-year study period. Since reproduction and body weight should be included in a selection programme for Angora goats, the relationship between the direct and maternal additive effects should be clarified. The importance of a sufficiently structured and related pedigree, especially on the part of the dams and maternal grand dams, has been highlighted in this study. As this is one of the constraints of this data set, data collection in the Angora goat industry should continue until a suitably structured data set has been built up that could be used to estimate multi-trait breeding values for the industry.Keywords: Birth weight, genetic parameters, maternal effects, weaning weight

Maternal genetic effects on body weight and breast morphological traits in duck population under selection

Abstract. Direct and maternal additive genetic and mitochondrial variances of duck meat performance traits were estimated using AI-REML algorithm. Records of 3 099 (5 010 pedigreed birds) from six consecutive generations were included. The following four traits were investigated: body weight at 3rd week, body weight at 7th week, sternum crest length at 7th week (in cm), and breast muscles thickness at 7th week (in cm). The data were analysed via three single trait linear animal models: I – additive genetic model, II – model extended to additive maternal effects; III– as model II with mitochondrial effects as random. Adequacy of the models was examined by Akaike´s information criterion (AIC). Relatively high direct additive heritability estimates were obtained for body weight at 3rd week (0.4326–0.4546) and body weight at 7th week (0.5322–0.6088) whereas lower estimates were obtained for sternum crest length (0.1756–0.2744) and breast muscles thickness (0.1369–0.2932). The maternal heritabilities were moderate and also considerably depended on the model used. For all of the studied traits a negative covariance between direct and maternal genetic effects was found. Mitochondrial heritabilities were very low. Generally, on the basis of criteria employed, the model III can be suggested for analysis of body weight whereas for other traits model II seems to be suitable.

Estimation of variance components, genetic parameters and genetic trends for litter size of swines

Records of Large White breed animals were used to estimate variance components, genetic parameters and trends for the character total number of born piglets (TNBP) as measure of litter size. For obtaining variance components and genetic parameters, it was used the Restricted Maximum Likelihood Method using MTDFREML software. Two mixed models (additive and repeatability) were evaluated. The additive model contained fixed effect of the contemporary group and the following random effects: direct additive genetic and residual effect for the first parturition. Repeatability model had the same effects of the additive model plus parturition order fixed effect and non-correlated animal permanent environment random effect for the second, third and forth parturition. Direct additive heritability estimates for TNBP were 0.15 and 0.20 for the additive and repeatability models, respectively. The estimate of the ration among variance of the non-correlated effect of animal permanent environment effect and the phenotypic variance, expressed as total variance proportion (c2) was 0.09. The estimates of yearly genetic trends obtained in the additive and repeatability models have similar behaviors (0.02 piglets/sow/year).

Heritability of Trail Making Test performance in multiplex schizophrenia families: implications for the search for an endophenotype

The impairment of the Trail Making Test (TMT) performance as a measure of executive function deficits has been found both in patients with schizophrenia and in their unaffected first-degree relatives, suggesting that it might be considered as a familial vulnerability marker, but its heritability estimates are not well known. This study investigated the genetic heritability of impairments in TMT performance using a sample of 80 schizophrenia patients, 145 unaffected first-degree relatives and 127 healthy controls from families with multiple members with schizophrenia. Consistent with previous reports in the literature, relatives performed in between healthy controls and schizophrenia patients. Based on these results, a variance component-analysis provided small, but significant additive heritability estimates for performance indices relating performance in TMT-version A to TMT-version B. These results showed that this significant but small evidence of heritability on the one hand suggests an association with genetic predisposition to schizophrenia, but that TMT performance is also associated with epigenetic or environmental factors.

Genetic and phenotypic parameter estimates for growth traits in Boer goat

An understanding of influencing factors and genetic principles affecting the growth traits is needed to implement optimal breeding and selection programs. In this study, heritabilities (direct additive and maternal) of body weights at birth (BW0), 90 days (BW90) and 300 days (BW300) of age and average daily gains from birth to 90 days (ADG0-90), birth to 300 days (ADG0-300) and 90 days to 300 days (ADG90–300) of age in Boer goats were estimated on the basis of 1520 Boer goats at Boer Goat Breeding Station in Yidu, China, during 2002–2007. The parameters were estimated using a DFREML procedure by excluding or including maternal genetic or permanent maternal environmental effects, four analysis models were fitted in order to optimize the model for each trait. Influencing factors such as parity, litter size, kidding year and season, as well as sex of kids and some significant interactions among these factors were investigated as the fixed effects for the models. The results showed that the birth year and maternal genetic effects such as parity and litter size of dam were important determinants of the genetic parameter estimates for pre-weaning growth traits, and environmental effects such as birth year, season and sex of kids had some significant effect on post-weaning growth traits. The mean values and standard errors (SE) of direct additive heritability estimates calculated with the optimum model were 0.17 ± 0.07, 0.22 ± 0.08, 0.07 ± 0.07, 0.10 ± 0.08, 0.30 ± 0.12 and 0.08 ± 0.10 for BW0, BW90, ADG0-90, BW300, ADG0-300 and ADG90–300, respectively. For pre-weaning weights, correlation estimates between direct additive and maternal genetic ( r a–m) effect were high and negative ranging from − 0.74 to − 0.86.

Genetic variability of body weight in two goose strains under long-term selection

Body weight is one of the most important traits in any genetic improvement program in geese for at least 2 reasons. First, measurements of the trait are very easy. Second, body weight is correlated with a number of other meat performance traits. However, the genetic background of body weight shows considerable complexity. Three genetic models (with direct, maternal genetic and permanent maternal environmental effects) were employed in this study. Records of 3076 individuals of maternal strain W11 and 2656 individuals of paternal strain W33 over 6 consecutive generations, kept in the pedigree farm of Kołuda Wielka, were analysed. Body weight (in kilograms) was measured in weeks 8 (BW8) and 11 (BW11). The inbreeding levels in both populations were relatively low (0.14% and 0.02% for W11 and W33, respectively), therefore these effects were not included in the linear models to estimate genetic parameters. Three fixed effects (hatch period, sex and year) were included in each linear model. Two criteria (AIC, BIC) were used to check the goodness of fit of the models. The computations were performed by WOMBAT software. In general, the genetic parameter estimates varied across the traits, models and strains studied. Direct additive heritability estimates ranged from 0.0001 (for BW11 of W33) to 0.55 (for BW11 of W33). Maternal and total heritabilities were also variable. Estimates of ratios of direct-maternal effect covariance in phenotypic variance were both positive and negative, but they were negligible, whereas ratios of the permanent environmental maternal variance to phenotypic variance were close to zero. Both of the applied criteria of model adequacy indicate that the model with maternal genetic and environmental effects should be considered as optimal. Genetic trends were close to zero. It seems that they were influenced by long-term selection. Similar tendencies have been observed for phenotypic trends, as well.

Variance components and genetic parameters for weight and size at birth in the Boer goat

Variance components, heritability (direct additive and maternal) and correlations (additive genetic, phenotypic, maternal genetic and environmental) of body weight (BW) and body size including length (BL), height (BH) and chest girth (BCG) at birth in Boer goats were estimated on the basis of 5096 records obtained from a Boer Goat Breeding Station in Yidu, China, during 2001–2005. The parameters were estimated using a DFREML procedure by excluding or including maternal genetic or permanent maternal environmental effects, four different analysis models were fitted in order to determine the optimum model for each trait. The environmental factors such as year, season, sex and litter size (LS, number of kids) were investigated as the fixed effects. The results showed that the maternal effects were important determinants of estimated the genetic parameters for birth traits. Year and season had significant effect on birth traits. Single births and male kids had the heaviest live weight and the largest body size at birth. The mean values and standard deviation (SD) of BW, BL, BH and BCG were 3.87 ± 0.85 kg, 31.67 ± 2.87 cm, 32.92 ± 2.80 cm, 33.46 ± 3.21 cm. The mean values and standard error (SE) of direct additive heritability estimates for BW, BL, BH and BCG calculated with the optimum model were 0.19 ± 0.08, 0.14 ± 0.07, 0.24 ± 0.09 and 0.25 ± 0.10, respectively. For all the birth traits, estimates of the correlations between direct additive and maternal genetic ( r a –m ) were negative. The estimates of additive genetic and phenotypic correlations among the birth traits were high and positive, and implied no genetic antagonisms among these traits analyzed. The estimates of maternal genetic correlations also were high and positive. Medium and positive environmental correlations indicated the important effects of environmental factors on early growth traits.

Age adjustment factors, heritabilities and genetic correlations for scrotal circumference and related growth traits in Hereford and Brangus bulls.

Field data records on 10,511 Hereford and 2,522 Brangus bulls between 330 and 430 d of age were analyzed to find age of calf and age of dam adjustment factors for yearling scrotal circumference. Age of calf adjustment factors were .024 cm/d for Hereford bulls and .041 cm/d for Brangus bulls. Sons of Hereford dams were adjusted to a 6- to 8-yr dam age basis by adding .7, .3, .2, .2 or .3 cm for dams 2, 3, 4, 5 or 8 or more years old, respectively. Age of dam adjustment factors for Brangus bulls were .8, .4, .3 and .2 for dams 2, 3, 4 or 8 or more years old, respectively. Variance and covariance components for yearling scrotal circumference and several growth traits were estimated within breed using multiple-trait models and pseudo expectations involving the solutions and the right-hand sides of the mixed-model equations. Additive heritability estimates for yearling scrotal circumference of .53 and .16 were found for Hereford and Brangus bulls, respectively. Maternal heritability estimates of .12 and .10 were found for Hereford and Brangus bulls, respectively. Genetic correlations between yearling scrotal circumference and other growth traits were positive for both sets of data indicating that selection for yearling scrotal circumference should not adversely affect other growth traits in either breed. Environmental correlation estimates between yearling scrotal circumference and adjusted birth weight and between yearling scrotal circumference and adjusted 205-d weight and adjusted 365-d height were positive and moderate in magnitude for both breeds.