Efficiency of quantitative trait loci-assisted selection in correlations between identified and residual genotypes

This study quantified the efficiency of quantitative traits loci (QTL)‐assisted selection in the presence of correlations (ρ_qr) between identified (q) and residual (r) genotypes. Two levels of heritability (h² = 0.1 or 0.3), two levels of correlation (ρ_qr = ?0.3 or 0.3) and five proportions of genetic variance explained by QTL detected (= 0.1, 0.2, 0.4, 0.6 or 0.8) were combined to give 20 scenarios in all. QTL‐assisted selection placed a larger index weight on the QTL genotype than on the phenotype in 17 of 20 scenarios, yielding a greater response in the QTL genotype than in residual genotype. Although QTL‐assisted selection was superior to phenotypic selection in all 20 scenarios, QTL‐assisted selection showed a greater advantage over phenotypic selection when ρ_qr was positive than when ρ_qr was negative. Doubling the proportion of detected QTL variance to genetic variance does not result in a twofold increase in the genetic response to QTL‐assisted selection, suggesting that economic returns diminish for each additional cost of detecting extra QTL. The correlation between q and r would make the interpretation (or prediction) of QTL effects difficult and QTL‐assisted selection strategy must consider the joint effect of q and r. When q and r are not independent, a failure to account for ρ_qr in QTL‐assisted selection would underestimate the genetic responses when ρ_qr is positive, but overestimate the genetic responses when ρ_qr is negative. Estimation bias is more serious at high heritability than at low heritability. Accounting for ρ_qr would improve the efficiency of QTL‐assisted selection and the accuracy of QTL detection. The generalized procedure developed in this study allows for quantifying the efficiency of QTL‐assisted selection and assessing estimation bias for ignoring the correlation between q and r for all possible combinations of h², ρ_qr, and .     

Marker‐assisted selection reduces expected inbreeding but can result in large effects of hitchhiking

We used computer simulations to investigate to what extent true inbreeding, i.e. identity‐by‐descent, is affected by the use of marker‐assisted selection (MAS) relative to traditional best linear unbiased predictions (BLUP) selection. The effect was studied by varying the heritability (h² = 0.04 vs. 0.25), the marker distance (MAS vs. selection on the gene, GAS), the favourable QTL allele effect (α = 0.118 vs. 0.236) and the initial frequency of the favourable QTL allele (p = 0.01 vs. 0.1) in a population resembling the breeding nucleus of a dairy cattle population. The simulated genome consisted of two chromosomes of 100 cM each in addition to a polygenic component. On chromosome 1, a biallelic QTL as well as 4 markers were simulated in linkage disequilibrium. Chromosome 2 was selectively neutral. The results showed that, while reducing pedigree estimated inbreeding, MAS and GAS did not always reduce true inbreeding at the QTL relative to BLUP. MAS and GAS differs from BLUP by increasing the weight on Mendelian sampling terms and thereby lowering inbreeding, while increasing the fixation rate of the favourable QTL allele and thereby increasing inbreeding. The total outcome in terms of inbreeding at the QTL depends on the balance between these two effects. In addition, as a result of hitchhiking, MAS results in extra inbreeding in the region surrounding QTL, which could affect the overall genomic inbreeding.     

Muscle fibre traits as additional selection criteria for muscle growth and meat quality in pigs – results of a simulated selection

To estimate the direct and correlated responses in meatiness and meat quality, simulated selection was applied using one‐trait selection and index selection including muscle fibre traits. In a total of 2024 pigs of German Landrace, Large White, Leicoma, and Schwerfurter breeds, carcass composition, meat quality, and fibre characteristics of the M. longissimus lumborum were analysed and genetic parameters were calculated by using REML variance component estimation. Coefficients of heritability ranged from low to moderate (growth traits: h² = 0.22–0.32; meat quality traits: h² = 0.17–0.27; muscle fibre traits: h² = 0.12–0.20). The total fibre number and the frequency of white fibres correlated positively with live weight (r_g = 0.44 and 0.44, respectively) and with loin muscle area (r_g = 0.38 and 0.19, respectively) while the relationships to pH value and drip loss were not as close (r_g = ?0.29–0.19). Selection indices were constructed from live weight and muscle area, from pH value and drip loss, and from muscle structure traits. As expected, simulated selection for live weight or loin muscle area lead to direct positive effects but these were associated with negative effects on meat quality traits (selection intensities: SI = 0.1; 0.5). Using selection for an index from loin area and muscle structure (loin muscle area + total fibre number – frequency of white fibres – frequency of giant fibres) the adverse effect on meat quality was minimized (responses in pH value: ?0.05; ?0.01) or was changed towards desired direction (responses in drip loss: ?0.65%; ?0.26%). The results show that fibre characteristics of pig muscle can be used as selection criteria for simultaneous improvement of carcass composition and meat quality in pigs by including indices developed from performance and fibre traits.     

Genotyping strategies of selection candidates in livestock breeding programmes

Benefits of genomic selection (GS) in livestock breeding operations are well known particularly where traits are sex‐limited, hard to measure, have a low heritability and/or measured later in life. Sheep and beef breeders have a higher cost:benefit ratio for GS compared to dairy. Therefore, strategies for genotyping selection candidates should be explored to maximize the economic benefit of GS. The aim of the paper was to investigate, via simulation, the additional genetic gain achieved by selecting proportions of male selection candidates to be genotyped via truncation selection. A two‐trait selection index was used that contained an easy and early‐in‐life measurement (such as post‐weaning weight) as well as a hard‐to‐measure trait (such as intra‐muscular fat). We also evaluated the optimal proportion of female selection candidates to be genotyped in breeding programmes using natural mating and/or artificial insemination (NatAI), multiple ovulation and embryo transfer (MOET) or juvenile in vitro fertilization and embryo transfer (JIVET). The final aim of the project was to investigate the total dollars spent to increase the genetic merit by one genetic standard deviation (SD) using GS and/or reproductive technologies. For NatAI and MOET breeding programmes, females were selected to have progeny by 2 years of age, while 1‐month‐old females were required for JIVET. Genomic testing the top 20% of male selection candidates achieved 80% of the maximum benefit from GS when selection of male candidates prior to genomic testing had an accuracy of 0.36, while 54% needed to be tested to get the same benefit when the prior selection accuracy was 0.11. To achieve 80% of the maximum benefit in female, selection required 66%, 47% and 56% of female selection candidates to be genotyped in NatAI, MOET and JIVET breeding programmes, respectively. While JIVET and MOET breeding programmes achieved the highest annual genetic gain, genotyping male selection candidates provides the most economical way to increase rates of genetic gain facilitated by genomic testing.     

The efficiency of genome-wide selection for genetic improvement of net merit

Four methods of selection for net merit comprising 2 correlated traits were compared in this study: 1) EBV-only index (I?), which consists of the EBV of both traits (i.e., traditional 2-trait BLUP selection); 2) GEBV-only index (I?), which comprises the genomic EBV (GEBV) of both traits; 3) GEBV-assisted index (I?), which combines both the EBV and the GEBV of both traits; and 4) GBV-assisted index (I?), which combines both the EBV and the true genomic breeding value (GBV) of both traits. Comparisons of these indices were based on 3 evaluation criteria [selection accuracy, genetic response (ΔH), and relative efficiency] under 64 scenarios that arise from combining 2 levels of genetic correlation (r(G)), 2 ratios of genetic variances between traits, 2 ratios of the genomic variance to total genetic variances for trait 1, 4 accuracies of EBV, and 2 proportions of r(G) explained by the GBV. Both selection accuracy and genetic responses of the indices I?, I?, and I? increased as the accuracy of EBV increased, but the efficiency of the indices I? and I? relative to I? decreased as the accuracy of EBV increased. The relative efficiency of both I? and I? was generally greater when the accuracy of EBV was 0.6 than when it was 0.9, suggesting that the genomic markers are most useful to assist selection when the accuracy of EBV is low. The GBV-assisted index I? was superior to the GEBV-assisted I? in all 64 cases examined, indicating the importance of improving the accuracy of prediction of genomic breeding values. Other parameters being identical, increasing the genetic variance of a high heritability trait would increase the genetic response of the genomic indices (I?, I?, and I?). The genetic responses to I?, I?, and I(4) was greater when the genetic correlation between traits was positive (r(G) = 0.5) than when it was negative (r(G) = -0.5). The results of this study indicate that the effectiveness of the GEBV-assisted index I? is affected by heritability of and genetic correlation between traits, the ratio of genetic variances between traits, the genomic-genetic variance ratio of each index trait, the proportion of genetic correlation accounted for by the genomic markers, and the accuracy of predictions of both EBV and GBV. However, most of these affecting factors are genetic characteristics of a population that is beyond the control of the breeders. The key factor subject to manipulation is to maximize both the proportion of the genetic variance explained by GEBV and the accuracy of both GEBV and EBV. The developed procedures provide means to investigate the efficiency of various genomic indices for any given combination of the genetic factors studied.     

Heritability estimates for racing performance in Japanese Thoroughbred racehorses using linear and non-linear model analyses

This study evaluated the differences between linear and non‐linear modelled heritability estimates of racing performance based on lifetime earnings (LE) and lifetime ranking (LR) in Japanese Thoroughbred racehorses. The heritability estimate (h² = 0.25) obtained from a non‐linear model based on formal Japan Racing Association ranking was much higher than that obtained from a linear model based on the original trait phenotype (h² = 0.11). The linear models showed slightly higher heritability estimates under the trait categorizations than under the original phenotypes, while the non‐linear categorical trait models showed much higher heritability estimates than the linear models, especially for binary trait categorizations (h² = 0.34) with non‐winning and winning horses. The binary trait categorizations were consistent with the case and control classifications in the previous genome‐wide association study (GWAS), which identified possible sequence variants on ECA18 that affect racing performance in Japanese Thoroughbred racehorses. Those findings suggested that the different heritability estimates obtained from several trait categorizations would reflect the possible presence of susceptibility gene segregations in the analyzed population, indicating that heritability estimates from non‐linear models are useful for the selection of case and control populations in GWAS.     

Interplay between heritability,genetic correlation and economic weighting in a selection index with and without genomic information

The availability of genomic information demands proper evaluation on how the kind (phenotypic versus genomic) and the amount of information influences the interplay of heritability (h²), genetic correlation () and economic weighting of traits with regard to the standard deviation of the index (σ_I). As σ_I is directly proportional to response to selection, it was the chosen parameter for comparing the indices. Three selection indices incorporating conventional and genomic information for a two trait (i and j) breeding goal were compared. Information sources were chosen corresponding to pig breeding applications. Index I incorporating an own performance in trait j served as reference scenario. In index II, additional information in both traits was contributed by a varying number of full‐sibs (2, 7, 50). In index III, the conventional own performance in trait j was combined with genomic information for both traits. The number of animals in the reference population (N_P = 1000, 5000, 10 000) and thus the accuracy of GBVs were varied. With more information included in the index, σ_I became more independent of , and relative economic weighting. This applied for index II (more full‐sibs) and for index III (more accurate GBVs). Standard deviations of index II with seven full‐sibs and index III with N_P = 1000 were similar when both traits had the same heritability. If the heritability of trait j was reduced ( = 0.1), σ_I of index III with N_P = 1000 was clearly higher than for index II with seven full‐sibs. When enhancing the relative economic weight of trait j, the decrease in σ_I of the conventional full‐sib index was much stronger than for index III. Our results imply that N_P = 1000 can be considered a minimum size for a reference population in pig breeding. These conclusions also hold for comparing the accuracies of the indices.     

Linkage disequilibrium and selection response in two-stage marker-assisted selection of dairy cattle over several generations

A stochastic simulation was carried out to investigate the advantage of marker‐assisted selection (MAS) in comparison with traditional selection over several generations. The selection goal was a sex‐limited trait or a linear combination of traits with a polygenic component, two unlinked additive QTL and a non‐genetic component. The simulated QTL were moderate or large and the allele frequencies were varied. Two stages of selection among the male offspring were carried out. In the first stage marker information was used to select among full sibs (MAS) or one full sib was chosen at random. In the second stage young bulls were selected based on a progeny test. The response in total genetic gain was faster with MAS than with traditional selection and persisted over several generations. With a QTL of moderate size and initial allele frequencies of the favourable allele of 0.05 the response with MAS was 6% higher than with traditional selection in the sires selected after progeny test. MAS in a within‐family two‐stage selection scheme improved the genetic merit of selected bulls even when linkage disequilibrium between QTL and polygenes was initially increased.     

Estimates of phenotypic and genetic parameters for birth weight of Brown Swiss calves in Turkey using an animal model

A study was conducted to assess the influence of genetic and environmental factors on Brown Swiss calf birth weight, and to estimate variance components, genetic parameters, and breeding values. Data were collected on 1,761 Brown Swiss calves born from 1990 to 2005 in the Konuklar State Farm in Turkey. Mean birth weight for all calves was 39.3 ± 0.09 kg. Least squares mean birth weights for male and female Brown Swiss calves were 40.3 ± 0.02 and 39.0 ± 0.02 kg, respectively. Variance components, genetic parameters, and breeding values for birth weight in Brown Swiss calves were estimated by restricted error maximum likelihood (REML)–best linear unbiased prediction(BLUP) procedures using an MTDFREML (multiple trait derivative free restricted maximum likelihood) program employing an animal model. Direct heritability (h _d²), maternal heritability (h _m²), total heritability (h _T²), r _am and c _am estimates were 0.12, 0.09, 0.23, −0.58, and −0.06, respectively. The estimated maternal permanent environmental variance expressed as a proportion of the phenotypic variance (c ²) was 0.05. Breeding values were estimated for the trait and used to evaluate genetic trends across the time period investigated. The genetic trend linear regression was not different from zero. No genetic trend for birth weight was expected, since there had been no direct selection pressure on the trait. Absence of a trend confirms that there was no change due to selection pressure on correlated traits. Genetic and environmental parameter estimates were similar to literature values indicating that effective selection methods used in more developed improvement programs would be effective in Turkey as well.     

Multiple trait genetic evaluation of ewe traits in Icelandic sheep

The prolificacy of the ewes was measured as the number of lambs born per ewe mated (NLB) when the ewes were 1–4 years of age. The ewe productivity related to the same age interval was measured by special ewe production indices (EPI). The genetic parameters for these traits were estimated by a series of bivariate REML analyses using animal models. The material used for the genetic analysis contained records on 193 213 ewes. The heritability estimates for NLB were h² = 0.17, 0.13, 0.11, 0.10 for the four respective age classes. Corresponding estimates for EPI were h² = 0.16, 0.17, 0.17, 0.15. The genetic correlations among NLB at different ages ranged from 0.63 to 0.98 and among EPI from 0.82 to 0.99. The genetic correlations between NLB and EPI were generally low. The material used for estimating the breeding values by the MT‐BLUP Animal Model consisted of 1.5 million individuals in the pedigree file. In total 815 782 ewes had records for the NLB and 763 491 ewes had production index (at least 1 year). The records were registered in the years 1990–2006. All possible missing patterns were present in the data. In the iteration process expected values for missing traits were generated and solutions were obtained on canonical transformed scale. The genetic evaluations were run independently for NLB and EPI for computational convenience given the correlations between these traits were negligible.     

The sensitivity of predicted financial and genetic gains in Holsteins to changes in the economic value of traits

The objective of this study was to assess the impact of using different relative economic values (REVs) in selection indices on predicted financial and trait gains from selection of sires of cows and on the choice of leading Holstein bulls available in the UK dairy industry. Breeding objective traits were milk yield, fat yield, protein yield, lifespan, mastitis, non‐return rate, calving interval and lameness. Relative importance of a trait, as estimated by a.h², was only moderately related to the rate of financial loss or total economic merit (ΔTEM) per percentage under‐ or overestimation of REV (r = 0.38 and 0.29, respectively) as a result of the variance–covariance structure of traits. The effects on TEM of under‐ or overestimating trait REVs were non‐symmetrical. TEM was most sensitive to incorrect REVs for protein, fat, milk and lifespan and least sensitive to incorrect calving interval, lameness, non‐return and mastitis REVs. A guide to deciding which dairy traits require the most rigorous analysis in the calculation of their REVs is given. Varying the REVs within a fairly wide range resulted in different bulls being selected by index and their differing predicted transmitting abilities would result in the herds moving in different directions in the long term (20 years). It is suggested that customized indices, where the farmer creates rankings of bulls tailored to their specific farm circumstances, can be worthwhile.     

Effects of a breeding scheme combined by genomic pre‐selection and progeny testing on annual genetic gain in a dairy cattle population

The effectiveness of the incorporation of genomic pre‐selection into dairy cattle progeny testing (GS‐PT) was compared with that of progeny testing (PT) where the fraction of dam to breed bull (DB) selected was 0.01. When the fraction of sires to breed bulls (SB) selected without being progeny tested to produce young bulls (YB) in the next generation was 0.2, the annual genetic gain from GS‐PT was 13% to 43% greater when h² = 0.3 and 16% to 53% greater when h² = 0.1 compared with that from PT. Given h² = 0.3, a selection accuracy of 0.8 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, GS‐PT produced 40% to 43% greater annual genetic gain than PT. Given h² = 0.1, a selection accuracy of 0.6 for both YB and DB, and selected fractions of 0.117 for YB and 0.04 for DB, annual genetic gain from GS‐PT was 48% to 53% greater than that from PT. When h² = 0.3, progeny testing capacity had little effect on annual genetic gain from GS‐PT. However, when h² = 0.1, annual genetic gain from GS‐PT increased with increasing progeny testing capacity.     

Evaluation of genetic trends and determination of the optimal number of cumulative records of parity required in reproductive traits in a Large White pig population

Genetic improvement of the reproductive performance of pigs is important for pig breeding despite their low heritabilities. The objectives of this study were to investigate the effectiveness of selection concerning reproductive traits and to determine the optimal number of parity records required for accurate estimation of breeding values (BVs) in the open population of a commercial pig breeding company. The study used records of 2220 purebred Large White pigs (9845 litters) farrowed between 1998 and 2009 in the two herds of the Pacific Ocean Breeding Co. Ltd. The traits studied included farrowing interval (FI), total number of piglets at birth (TNB), average weaning weight per litter (AWW), and raising rate (RR). A statistical model was applied to the 4‐trait repeatability animal model. The heritabilities of FI, TNB, AWW and RR were low. The genetic trends in TNB (h² = 0.09) showed approximately 1.0 increase in 6 years from 2003 to 2008. The predicted error variances indicated that up to fourth parity records are necessary for accurate genetic evaluation. The present study results indicated that even reproductive traits with low heritability can be improved.     

Multivariate genetic analysis to account for preselection and disqualified races in the genetic evaluation of racing performances in German trotters

Abstract

The genetic associations between racing performance and preselection of horses considered as the binary trait racing status (trotters without or with at least one racing performance in life were classified as 0 and 1, respectively) as well as disqualified races (disqualified and non-disqualified trotters were classified as 1 and 0, respectively) were analysed in German trotters. Variance components for racing performance traits square root of rank at finish, racing time per km, and log of earnings with racing status were estimated based on an animal model using REML. Heritabilities of racing status, racing time and rank at finish were 0.30, 0.21, and 0.06, respectively. The genetic correlations between racing status and racing time or rank at finish were ?0.74 and ?0.32, indicating that horses started at least once showed a higher genetic potential in racing time or finishing ability than never started horses. This showed the high preselection of German trotters especially based on racing time. To account for this preselection, it was recommended for additional use of racing status in the German evaluation system. Breeding values of the three racing performance traits were estimated by two distinct models, in- or excluding racing status and compared by using three criteria. Racing time per km showed the highest correlation (r=0.98) between breeding values evaluated by these two distinct models. Therefore, incorrect selection rate of horses using breeding values from the model without racing status, was lowest for racing time per km (9.7%). Selection response increased about 1% for this trait after including racing status in the model. For the estimation of rank at finish, inclusion of racing status in the multiple trait model was much more important as indicated by a low correlation between breeding values (r=0.29) and high percentage of incorrectly selected stallions (97.5%). The trait disqualified races was first analysed using an univariate threshold model. Heritability of this trait was low (h ²=0.12) and repeatability (r=0.43) showed a moderate magnitude. Using a linear multiple trait animal model, disqualified races showed a low heritability (h ²=0.05) and a moderate favourable genetic correlation (r _g=0.43) with racing time per km. Consequently, selection on racing time per km is expected to improve indirectly the reliability of racing performance. Combined selection of reduction in disqualified races and racing time may even further improve the reliability of racing trotters.     

Aggressive behaviour of sows at mixing and maternal behaviour are heritable and genetically correlated traits

This study was designed to estimate genetic variation in aggressive behaviour of sows at mixing, and in maternal ability for the same sows. The study included 835 sows observed for number of mild or severe aggressions performed (F_A1, F_A2), or received (F_R1, F_R2) during 30 min after grouping. Maternal ability was recorded as sows' response to vocalisation from their piglets when these were handled. Maternal behaviour was studied in 1076 sows as a body reaction (MBR) to their piglets being handled after farrowing. Genetic covariances were estimated using a multi-trait animal model, assuming traits to be normally distributed. The heritabilities of performed aggression traits were intermediate (h² F_A1=0.17, h² F_A2=0.24), but lower for received aggression (h² F_R1=0.06, h² F_R2=0.04), and heritability of maternal behaviour was also low (h²=0.08). Although estimates of genetic correlations had large standard errors, they indicate that less aggressive sows were stronger responding mothers (r_g=−0.3). We conclude that performed aggression in sows is a heritable trait, and selection against aggression is possible without offsetting maternal behaviour.     

Improving management for higher reproduction accelerates genetic improvement in closed herd of swine

The present study compared responses to selection at different conception rates and litter sizes at weaning in a simulated closed herd in a swine breeding program. The base population consisted of 10 males and 50 females, and 10 generations of selection was practiced by using individual phenotype or best linear unbiased prediction of breeding values for a trait with heritability (h²) of either 0.2 or 0.5. The probability of conception in a single mating was assumed to be 0.8, 0.9 or 1.0. Litter size at weaning was sampled randomly from a normal distribution with mean 8, 10 or 12 and variance 8.1225. Genetic response increased by approximately 6% for h² = 0.2 and approximately 5% for h² = 0.5 at generation 10 when conception rate was increased from 0.8 to 1.0. However, litter size at weaning did not affect response to selection. In conclusion, improving conception rate by environmental management increases genetic response indirectly in a breeding program of a closed swine herd.     

Genetic correlations between production with disease resistance and fertility in dairy cattle and consequences for total merit selection

Twelve years of data from progeny‐test results of 1486 Swedish Red and White (SRB) and 756 Swedish Black and White (SLB) AI bulls were analysed to provide estimates of genetic correlations between yield of protein and three health‐ and fertility traits. For both breeds, the correlations were unfavourable (r_G= —0.13 to —0.37). The effects of negative genetic correlations (compared to a situation with genetic correlations with zero values) on the b‐values in the total merit index of bulls were rather small but the effects on the estimated genetic gain were large. The effect of not including health and fertility traits in the index, although included in the breeding goal, resulted in a 9–10% reduced accuracy of estimated breeding values for total merit and thus a corresponding loss in total gain.     

Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Genomic selection is based on breeding values that are estimated using genome-wide dense marker maps. The objective of this paper was to investigate the effect of including or ignoring the polygenic effect on the accuracy of total genomic breeding values, when there is coverage of the genome with approximately one SNP per cM. The importance of the polygenic effect might differ for high and low heritability traits, and might depend on the design of the reference dataset. Hence, different scenarios were evaluated using stochastic simulation. Accuracies of the total breeding value of juvenile selection candidates depended on the number of animals included in the reference data. When excluding polygenic effects, those accuracies ranged from 0.38 to 0.55 and from 0.73 to 0.79 for traits with heritabilities of 10 and 50%, respectively. Accuracies were improved by including a polygenic effect in the model for the low heritability trait, when the LD-measure r2 between adjacent markers became smaller than approximately 0.10, while for the high heritability trait there was already a small improvement at r2 between adjacent markers of 0.14. In all situations, the estimated total genetic variance was underestimated, particularly when the polygenic effect was excluded from the model. The haplotype variance was less underestimated when more animals were added in the reference dataset.     

Genetic parameter estimates for growth traits in Horro sheep

Variance components and genetic parameters were estimated for growth traits: birth weight (BWT), weaning weight (WWT), 6‐month weight (6MWT) and yearling weight (YWT) in indigenous Ethiopian Horro sheep using the average information REML (AIREML). Four different models: sire model (model 1), direct animal model (model 2), direct and maternal animal model (model 3) and direct–maternal animal model including the covariance between direct and maternal effects (model 4) were used. Bivariate analysis by model 2 was also used to estimate genetic correlation between traits. Estimates of direct heritability obtained from models 1–4, respectively, were for BWT 0.25, 0.27, 0.18 and 0.32; for WWT, 0.16, 0.26, 0.1 and 0.14; for 6MWT 0.18, 0.26, 0.16 and 0.16; and for YWT 0.30, 0.28, 0.23, and 0.31. Maternal heritability estimates of 0.12 and 0.23 for BWT; 0.19 and 0.24 for WWT; 0.09 and 0.09 for 6MWT and 0.08 and 0.14 for YWT were obtained from models 3 and 4, respectively. The correlations between direct and maternal additive genetic effects for BWT, WWT, 6MWT and YWT were –0.64, –0.42, 0.002 and –0.46, respectively. On the other hand, the genetic correlations between BWT and the rest of growth traits (WWT, 6MWT and YWT, respectively) were 0.45, 0.33 and 0.31, whereas correlations between WWT and 6MWT, WWT and YWT and 6MWT and YWT were 0.98, 0.84 and 0.87, respectively. The medium to high direct and maternal heritability estimates obtained for BWT and YWT indicate that in Horro sheep faster genetic improvement through selection is possible for these traits and it should consider both (direct and maternal) h² estimates. However, since the direct‐maternal genetic covariances were found to be negative, caution should be made in making selection decisions. The high genetic correlation among early growth traits imply that genetic improvement in any one of the traits could be made through indirect selection for correlated traits.     

Influence of maternal and additive genetic effects on offspring growth traits in Beetal goat

The purpose of the present study was to obtain estimates of variance components and genetic parameters for direct and maternal effects on various growth traits in Beetal goat by fitting four animal models, attempting to separate direct genetic, maternal genetic and maternal permanent environmental effects under restricted maximum likelihood procedure. The data of 3,308 growth trait records of Beetal kids born during the period from 2004 to 2019 were used in the present study. Based on best fitted models, the direct additive h² estimates were 0.06, 0.27, 0.37, 0.17 and 0.10 for birth weight (BWT), weight at 3 (WT3), 6 (WT6), 9 (WT9) and 12 (WT12) months of age, respectively. Maternal permanent environmental effects significantly contributed for 10% and 7% of total variance for BWT and WWT, respectively, which reduced direct heritability by 40 and 10% for respective traits from the models without these effects. For average daily gain (ADG1) and Kleiber ratios (KR1) up to weaning period (3 months) traits, maternal permanent environmental effects accounted for 7% and 8% of phenotypic variance, respectively, and resulted in a reduction of 6.6% and 5.4% in direct h² of respective traits. For post-weaning traits, the maternal effects were non-significant (p > .05) which indicates diminishing influence of mothering ability for these traits. High and positive genetic correlations were obtained among WT3-WT6, WT6-WT9 and WT9-WT12 with correlations of 0.96 ± 0.25, 0.84 ± 0.23 and 0.90 ± 0.13, respectively. Thus, early selection at weaning age can be practised taking into consideration maternal variation for effective response to selection in Beetal goat.