Category Archives: Journal of Animal Breeding and Genetics

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B‐spline functions

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Abstract

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

Alternative SNP weighting for multi‐step and single‐step genomic BLUP in the presence of causative variants

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The accuracy of genetic selection in dairy can be increased by the adoption of new technologies, such as the inclusion of sequence data. In simulation studies, assigning different weights to causative single-nucleotide polymorphism (SNP) markers led to better predictions depending on the genomic prediction method used. However, it is still not clear how the weights should be calculated. Our objective was to evaluate the accuracy of a multi-step method (GBLUP) and single-step GBLUP with simulated data using regular SNP, causatives variants (QTN) and the combination of both. Additionally, we compared the accuracies of all previous scenarios using alternatives for SNP weighting. The data were simulated assuming a single trait with a heritability of 0.3. The effective population size (Ne) was approximately 200. The pedigree contained 440,000 animals, and approximately 16,800 individuals were genotyped. A total of 49,974 SNP markers were evenly placed throughout the genome, and 100, 1000 and 2000 causative QTN were simulated. Both GBLUP and ssGBLUP were used in this study. We evaluated quadratic and nonlinear SNP weights in addition to the unweighted G. The inclusion of QTN to panels led to significant accuracy gains. Nonlinear A was demonstrated to be superior to quadratic weighting and unweighted approaches; however, results from Nonlinear A were dependent on the equation parameters. The unweighted approach was more suitable for less polygenic scenarios. Finally, SNP weighting might help elucidate trait architecture features based on changes in the accuracy of genomic prediction.

Population genomic structures and signatures of selection define the genetic uniqueness of several fancy and meat rabbit breeds

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Following the recent domestication process of the European rabbit (Oryctolagus cuniculus), many different breeds and lines, distinguished primarily by exterior traits such as coat colour, fur structure and body size and shape, have been constituted. In this study, we genotyped, with a high-density single-nucleotide polymorphism panel, a total of 645 rabbits from 10 fancy breeds (Belgian Hare, Champagne d'Argent, Checkered Giant, Coloured Dwarf, Dwarf Lop, Ermine, Giant Grey, Giant White, Rex and Rhinelander) and three meat breeds (Italian White, Italian Spotted and Italian Silver). ADMIXTURE analysis indicated that breeds with similar phenotypic traits (e.g. coat colour and body size) shared common ancestries. Signatures of selection using two haplotype-based approaches (iHS and XP-EHH), combined with the results obtained with other methods previously reported that we applied to the same breeds, we identified a total of 5079 independent genomic regions with some signatures of selection, covering about 1777 Mb of the rabbit genome. These regions consistently encompassed many genes involved in pigmentation processes (ASIP, EDNRA, EDNRB, KIT, KITLG, MITF, OCA2, TYR and TYRP1), coat structure (LIPH) and body size, including two major genes (LCORL and HMGA2) among many others. This study revealed novel genomic regions under signatures of selection and further demonstrated that population structures and signatures of selection, left into the genome of these rabbit breeds, may contribute to understanding the genetic events that led to their constitution and the complex genetic mechanisms determining the broad phenotypic variability present in these untapped rabbit genetic resources.

Genetic parameter estimation for pork production and litter performance traits of Landrace, Large White, and Duroc pigs in Japan

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We estimated genetic parameters for two pork production and six litter performance traits of Landrace, Large White, and Duroc pigs reared in Japan. Pork production traits were average daily gain from birth to end of performance testing and backfat thickness at end of testing (46,042 records for Landrace, 40,467 records for Large White, and 42,920 records for Duroc). Litter performance traits were number born alive, litter size at weaning (LSW), number of piglets dead during suckling (ND), survival rate of piglets during suckling (SV), total piglet weight at weaning (TWW), and average piglet weight at weaning (AWW) (27,410, 26,716, and 12,430 records for Landrace, Large White, and Duroc, respectively). ND was calculated as the difference between LSW and litter size at start of suckling (LSS). SV was calculated as LSW/LSS. AWW was calculated as TWW/LSW. Pedigree data for Landrace, Large White, and Duroc breeds contained 50,193, 44,077, and 45,336 pigs, respectively. Trait heritability was estimated via single-trait analysis and genetic correlation between two traits was estimated via two-trait analysis. When considering the linear covariate of LSS in the statistical model for LSW and TWW, for all breeds, the heritability was estimated to be 0.4–0.5 for pork production traits and below 0.2 for litter performance traits. Estimated genetic correlation between average daily gain and backfat thickness was small, ranging from 0.057 to 0.112, and those between pork production traits and litter performance traits were negligible to moderate, ranging from −0.493 to 0.487. A wide range of genetic correlation values among the litter performance traits was estimated, while that between LSW and ND could not be obtained. The results of genetic parameter estimation were affected by whether the linear covariate of LSS was included in the statistical model for LSW and TWW or not. This finding implies the necessity of carefully interpreting the results according to the choice of statistical model. Our results could give fundamental information on simultaneously improving productivity and female reproductivity for pigs.

Impact of multi‐output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms

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Feeding represents the largest economic cost in meat production; therefore, selection to improve traits related to feed efficiency is a goal in most livestock breeding programs. Residual feed intake (RFI), that is, the difference between the actual and the expected feed intake based on animal's requirements, has been used as the selection criteria to improve feed efficiency since it was proposed by Kotch in 1963. In growing pigs, it is computed as the residual of the multiple regression model of daily feed intake (DFI), on average daily gain (ADG), backfat thickness (BFT), and metabolic body weight (MW). Recently, prediction using single-output machine learning algorithms and information from SNPs as predictor variables have been proposed for genomic selection in growing pigs, but like in other species, the prediction quality achieved for RFI has been generally poor. However, it has been suggested that it could be improved through multi-output or stacking methods. For this purpose, four strategies were implemented to predict RFI. Two of them correspond to the computation of RFI in an indirect way using the predicted values of its components obtained from (i) individual (multiple single-output strategy) or (ii) simultaneous predictions (multi-output strategy). The other two correspond to the direct prediction of RFI using (iii) the individual predictions of its components as predictor variables jointly with the genotype (stacking strategy), or (iv) using only the genotypes as predictors of RFI (single-output strategy). The single-output strategy was considered the benchmark. This research aimed to test the former three hypotheses using data recorded from 5828 growing pigs and 45,610 SNPs. For all the strategies two different learning methods were fitted: random forest (RF) and support vector regression (SVR). A nested cross-validation (CV) with an outer 10-folds CV and an inner threefold CV for hyperparameter tuning was implemented to test all strategies. This scheme was repeated using as predictor variables different subsets with an increasing number (from 200 to 3000) of the most informative SNPs identified with RF. Results showed that the highest prediction performance was achieved with 1000 SNPs, although the stability of feature selection was poor (0.13 points out of 1). For all SNP subsets, the benchmark showed the best prediction performance. Using the RF as a learner and the 1000 most informative SNPs as predictors, the mean (SD) of the 10 values obtained in the test sets were: 0.23 (0.04) for the Spearman correlation, 0.83 (0.04) for the zero–one loss, and 0.33 (0.03) for the rank distance loss. We conclude that the information on predicted components of RFI (DFI, ADG, MW, and BFT) does not contribute to improve the quality of the prediction of this trait in relation to the one obtained with the single-output strategy.

Genome‐wide association studies for epistatic genetic effects on fertility and reproduction traits in Holstein cattle

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Non-additive genetic effects are well known to play an important role in the phenotypic expression of complex traits, such as fertility and reproduction. In this study, a genome scan was performed using 41,640 single nucleotide polymorphism (SNP) markers to identify genomic regions associated with epistatic (additive-by-additive) effects in fertility and reproduction traits in Holstein cattle. Nine fertility and reproduction traits were analysed on 5825 and 6090 Holstein heifers and cows with phenotypes and genotypes, respectively. The Marginal Epistasis Test (MAPIT) was used to identify SNPs with significant marginal epistatic effects at a chromosome-wise 5% and 10% false discovery rate (FDR) level. The −log10(p) values were adjusted by the genomic inflation factor (λ) to correct for the potential bias on the p-values and minimize the possible effects of population stratification. After adjustments, MAPIT enabled the identification of genomic regions with significant marginal epistatic effects for heifers on BTA5 for age at first insemination, BTA3 and BTA24 for non-return rate (NRR); BTA16 and BTA28 for gestation length (GL); BTA1, BTA4 and BTA17 for stillbirth (SB). For the cow traits, MAPIT enabled the identification of regions on BTA11 for GL, BTA11 and BTA16 for SB and BTA19 for calf size (CZ). An additional approach for mapping epistasis in a genome-wide association study was also proposed, in which the genome scan was performed using estimates of epistatic values as the input pseudo-phenotypes, computed using single-trait animal models. Significant SNPs were identified at the chromosome-wise 5% and 10% FDR levels for all traits. For the heifer traits, significant regions were found on BTA7 for AFS; BTA12 for NRR; BTA14 and BTA19 for GL; BTA19 for calving ease (CE); BTA5, BTA24, BTA25 and in the X chromosome for SB; BTA23 and in the X chromosome for CZ and in the X chromosome for the number of services (NS). For the cow traits, significant regions were found on BTA29 and in the X chromosome for NRR, BTA11, BTA16 and in the X chromosome for SB, BTA2 for GL, BTA28 for CZ, BTA19 for calving to first insemination, and in the X chromosome for NS and first insemination to conception. The results suggest that the epistatic genetic effects are likely due to many loci with a small effect rather than few loci with a large effect and/or a single SNP marker alone do not capture the epistatic effects well. The genomic architecture of fertility and reproduction traits is complex, and these results should be validated in independent dairy cattle populations and using alternative statistical models.

Genomic inbreeding estimation, runs of homozygosity, and heterozygosity‐enriched regions uncover signals of selection in the Quarter Horse racing line

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With the advent of genomics, significant progress has been made in the genetic improvement of livestock species, particularly through increased accuracy in predicting breeding values for selecting superior animals and the possibility of performing a high-resolution genetic scan throughout the genome of an individual. The main objectives of this study were to estimate the individual genomic inbreeding coefficient based on runs of homozygosity (F ROH), to identify and characterize runs of homozygosity and heterozygosity (ROH and ROHet, respectively; length and distribution) throughout the genome, and to map selection signatures in relevant chromosomal regions in the Quarter Horse racing line. A total of 336 animals registered with the Brazilian Association of Quarter Horse Breeders (ABQM) were genotyped. One hundred and twelve animals were genotyped using the Equine SNP50 BeadChip (Illumina, USA), with 54,602 single nucleotide polymorphisms (SNPs; 54K). The remaining 224 samples were genotyped using the Equine SNP70 BeadChip (Illumina, USA) with 65,157 SNPs (65K). To ensure data quality, we excluded animals with a call rate below 0.9. We also excluded SNPs located on non-autosomal chromosomes, as well as those with a call rate below 0.9 or a p-value below 1 × 10−5 for Hardy–Weinberg equilibrium. The results indicate moderate to high genomic inbreeding, with 46,594 ROH and 16,101 ROHet detected. In total, 30 and 14 candidate genes overlap with ROH and ROHet regions, respectively. The ROH islands showed genes linked to crucial biological processes, such as cell differentiation (CTBP1, WNT5B, and TMEM120B), regulation of glucose metabolic process (MAEA and NKX1-1), heme transport (PGRMC2), and negative regulation of calcium ion import (VDAC1). In ROHet, the islands showed genes related to respiratory capacity (OR7D19, OR7D4G, OR7D4E, and OR7D4J) and muscle repair (EGFR and BCL9). These findings could aid in selecting animals with greater regenerative capacity and developing treatments for muscle disorders in the QH breed. This study serves as a foundation for future research on equine breeds. It can contribute to developing reproductive strategies in animal breeding programs to improve and preserve the Quarter Horse breed.

Inbreeding depression and its effect on sperm quality traits in Pietrain pigs

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In most cases, inbreeding is expected to have unfavourable effects on traits in livestock. The consequences of inbreeding depression could be substantial, primarily in reproductive and sperm quality traits, and thus lead to decreased fertility. Therefore, the objectives of this study were (i) to compute inbreeding coefficients using pedigree (F PED) and genomic data based on runs of homozygosity (ROH) in the genome (F ROH) of Austrian Pietrain pigs, and (ii) to assess inbreeding depression on four sperm quality traits. In total, 74,734 ejaculate records from 1034 Pietrain boars were used for inbreeding depression analyses. Traits were regressed on inbreeding coefficients using repeatability animal models. Pedigree-based inbreeding coefficients were lower than ROH-based inbreeding values. The correlations between pedigree and ROH-based inbreeding coefficients ranged from 0.186 to 0.357. Pedigree-based inbreeding affected only sperm motility while ROH-based inbreeding affected semen volume, number of spermatozoa, and motility. For example, a 1% increase in pedigree inbreeding considering 10 ancestor generations (F PED10) was significantly (p < 0.05) associated with a 0.231% decrease in sperm motility. Almost all estimated effects of inbreeding on the traits studied were unfavourable. It is advisable to properly manage the level of inbreeding to avoid high inbreeding depression in the future. Further, analysis of effects of inbreeding depression for other traits, including growth and litter size for the Austrian Pietrain population is strongly advised.

Quantitative analysis of parent‐of‐origin effect in reproductive and morphological selection criteria in the Pura Raza Española horse

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It is generally assumed that parents make a genetically equal contribution to their offspring, but this assumption might not always hold. This is because the expression of a gene can be blocked by methylation during gametogenesis, and the degree of methylation can depend on the origin of the parental gene (imprinting) or by preferential management associated with genetic merit. The first consequences of this for quantitative genetics is that the mean phenotypes of reciprocal heterozygotes need no longer be the same, as would be expected according to Mendelian heritage. We analysed three mare reproductive traits (reproductive efficiency, age at first foaling and foaling number) and three morphological traits (height at withers, thoracic circumference, and scapula-ischial length) in the Pura Raza Española (PRE) horse population, which possesses a deep and reliable pedigree, making it a perfect breed for analysing the quantitative effect of parent-of-origin. The number of animals analysed ranged from 44,038 to 144,191, all of them with both parents known. The model comparison between a model without parent-of-origin effects and three different models with parent-of-origin effects revealed that both maternal and paternal gametic effects influence all the analysed traits. The maternal gametic effect had a higher influence on most traits, accounting for between 3% and 11% of the total phenotypic variance, while the paternal gametic effect accounted for a higher proportion of variance in one trait, age at first foaling (4%). As expected, the Pearson's correlations between additive breeding values of models that consider parent-of-origin and that do not consider parent-of-origin were very high; however, the percentage of coincident animals slightly decreases when comparing animals with the highest estimated breeding values. Ultimately, this work demonstrates that parent-of-origin effects exist in horse gene transmission from a quantitative point of view. Additionally, including an estimate of the parent-of-origin effect within the PRE horse breeding program could be a great tool for a better parent's selection and that could be of interest for breeders, as this value will determine whether the animals acquire genetic categories and are much more highly valued.