Journal of Animal Breeding and Genetics最新文献

{"title":"An Increase in Male Recombination Rate With Age in Dairy Cattle Is Heritable and Polygenic.","authors":"Evelyn Jade, Mathew D Littlejohn, Katie Eketone, Richard J Spelman, Jessica Stapley, Anna W Santure","doi":"10.1111/jbg.12948","DOIUrl":"https://doi.org/10.1111/jbg.12948","url":null,"abstract":"<p><p>Meiotic recombination is an essential process for shuffling genetic diversity in sexually reproducing organisms, can vary within and between individuals in response to intrinsic and extrinsic factors, and can be heritable. Interestingly, recombination rate has been found to vary with age in some species, but to date, there have been no assessments of the heritability and genetic architecture of this age effect. Here, we leverage a large pedigree of SNP chip-genotyped Aotearoa New Zealand Holstein-Friesian and Jersey dairy cattle to test for an effect of age on male recombination rate, the heritability of recombination rate and of any such age effect on recombination, and the genetic architecture underlying these two phenotypes. We found a significant, albeit small, increase in the average number of male autosomal recombinations with age. Consistent with previous studies, we found moderate heritability (h² ≈ 0.15) of sire recombination rate and detected association with several regions on chromosome 10 encompassing genes such as REC8, REC114, RNF212B and NEK9. Further, we found novel evidence of some heritability (h² ≈ 0.05) in the rate of change in recombination with age in sires. Variation in the rate of change with age is likely also polygenic, but there is a region on chromosome 1 that is weakly associated with the rate of change. It is unclear whether the heritability of age-related recombination rate change is widespread across species, and we encourage studies in other taxa to assess its prevalence and evolutionary significance.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of Genetic Progress in the Context of Disconnection Between Two Originally Connected Populations.","authors":"M Wicki, A Legarra, J Raoul","doi":"10.1111/jbg.12946","DOIUrl":"https://doi.org/10.1111/jbg.12946","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Genetic progress of breeding programs is highly dependent on the size of reference populations and the relatedness between reference populations and selection candidates. Many reasons can lead a population to split into several subpopulations (sanitary, physiological, political reasons, etc.). More specifically, alternative (e.g., organic) farming may lead to farms breaking away from the conventional scheme to form a distinct breeding scheme, especially in organic sheep farming where the ban on hormones makes the use of artificial insemination (AI) difficult. However, these potential splits of the population into several smaller subpopulations could decrease genetic progress. The aim of our study was to investigate, using stochastic simulations, the impact of separation of the population into two subpopulations while still applying the same breeding objective and methods. We simulated a breeding program inspired by a dairy program but applicable to different species. We simulated two different initial population sizes with 5400 (10,800) females mated to 90 (180) males and a trait of heritability 0.30. This population was under selection for several discrete generations (G-9 to G-1) as a single population. Then, for the last 11 cycles of selection, the population was either maintained as a unique population (scenario \"NoSep\", which was the reference scenario) or split into two subpopulations with different ratios: 50/50, 60/40, 70/30, 80/20, and 90/10. We studied three scenarios in which the population was split: CE (separation and Common Evaluation), in which the evaluation remained common between both subpopulations; SE (separation and separate evaluation), in which the subpopulations were evaluated individually; and NoSel (Separation and No Selection), in which the breeding males were randomly selected, as opposed to the two previous scenarios in which we selected the males based on their GEBVs. We studied the evolution of differentiation of populations (F&lt;sub&gt;st&lt;/sub&gt;), accuracy of predictions, genetic progress, and rate of inbreeding over generations. We observed a faster genetic divergence in the case of an unbalanced split and separate evaluation (F&lt;sub&gt;st&lt;/sub&gt; in G11 equal to 0.134 for the ratio 90/10 scenario SE). The separate evaluation had a significant, negative effect on both the accuracy and genetic gain of the smallest population (minimal accuracy of 0.53 and maximal loss of 16.6% for ratio 90/10 with 5400 females), whereas the accuracy and genetic gain of the largest population were not impacted. Combining the evaluations led to smaller but still significant deterioration of the genetic gain of the smallest population when the ratio was very unbalanced (loss of genetic gain of 14.3% for a ratio of 90/10 with 5400 females). In conclusion, population separation has a negative impact on genetic gain, particularly for small populations. Although it does help in alleviating divergence and loss of genetic gain, joint evaluation ca","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic Evaluation in Nellore Cattle for Reproductive Traits: Multiple Ways to Account for Missing Pedigrees.","authors":"Larissa Temp, Gabriel Gubiani, Ludmilla Brunes, Claudio Magnabosco, Fernando Bussiman, Jorge Hidalgo, Daniela Lourenco, Fernando Baldi","doi":"10.1111/jbg.12947","DOIUrl":"https://doi.org/10.1111/jbg.12947","url":null,"abstract":"<p><p>Missing pedigrees are a common problem in most populations. Animals with unknown ancestors are usually treated as founders; however, this can underestimate inbreeding, not properly account for different base populations, and bias breeding values. We aimed to assess the use of unknown parent groups (UPG) or metafounders (MF) to model missing pedigrees in a beef cattle population. Phenotypic and genotypic data from the Nellore improvement programme of the Brazilian Breeders and Researchers Association were used. The pedigree contained 3.8 M animals born between 1970 and 2022, of which 51,752 were genotyped. Records for scrotal circumference at 365 days old (SC365, N = 239,806), age at first calving (AFC, N = 560,785) and accumulated cow productivity (ACP, N = 269,330) were used. Four models were implemented: single-step GBLUP without explicitly dealing with missing pedigree (G0), with UPG (G1), with MF (G2) and with <math> <semantics><mrow><mi>G</mi></mrow> <annotation>$$ mathbf{G} $$</annotation></semantics> </math> accounting for group-specific allele frequencies (G3). UPG and MF were assigned based on commercial and registered herds (S1), uncertain paternity (S2) and patriarchs (S3). The accuracy and bias of predictions were assessed using the linear regression (LR) method. Linear, single-trait animal models were used for SC365 and AFC, and multi-trait for ACP. Heritability estimates ranged from 0.07 to 0.40. Compared to G0, accuracy was slightly higher in G2_S2 and G2_S3 (0.70 vs. 0.71) for SC365, G2_S3 (0.49 vs. 0.51) for AFC, G1_S2 for ACP (0.67 vs. 0.71). Bias was small in all the scenarios (≤ 0.06 SD), except of ACP that presented a great bias, including MF. Overall, G1 and G2 had similar accuracy, possibly because of the limited number of genotyped animals linked to MF. Centring the genomic relationship matrix by patriarchs' allelic frequencies resulted in similar accuracy and bias to the MF models. Replicating the study with a larger database containing more genotyped animals connected to MF could help improve the MF estimates, and thus, prediction accuracy and bias.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Parameters for Direct and Maternal Effects on Pre-Weaning Growth and Efficiency-Related Traits in Inter-Cross Lamb.","authors":"Abdul Rahim, Rajni Chaudhary, R Pourouchottamane, Pallavi Chauhan, K S Rajaravindra, G R Gowane, Arun Kumar","doi":"10.1111/jbg.12945","DOIUrl":"https://doi.org/10.1111/jbg.12945","url":null,"abstract":"<p><p>The objective of this investigation was to estimate the (co)variance components and genetic parameters for pre-weaning growth traits in inter-cross sheep maintained at NTRS, ICAR-CSWRI, Garsa, India. Data records of 1891 lambs for birth weight (BW) and 1763 lambs for weaning weight (WW) descending from 183 rams and 667 dams, born between 2012 and 2023, were utilised in the study. The live weight was used for the calculation of pre-weaning average daily weight gain (PDWG), pre-weaning Kleiber ratio (PKR), pre-weaning growth efficiency (PGE) and pre-weaning relative growth rate (PGR). The fixed effects used in the model were birth year, genetic group, sex, type of birth, and parity of dam. Estimation of covariance components was carried out by fitting a series of animal models using the average information restricted maximum likelihood (AIREML) algorithm. The best-fitting model was determined after testing for improvement in log-likelihood values. The overall LSM ± SE for BW, WW, PADG, PKR, PGE, and PGR were 3.29 ± 0.04 kg, 14.83 ± 0.22 kg, 127.87 ± 2.46 g, 16.78 ± 0.13, 357.53 ± 7.13, and 1.66 ± 0.02, respectively. Analysis revealed that pre-weaning traits were significantly (p < 0.05) affected by year of birth, parity of dam, type of birth and sex of the lamb. Direct heritability estimates were 0.11 ± 0.04, 0.16 ± 0.04, 0.16 ± 0.04, 0.15 ± 0.06, 0.18 ± 0.06 and 0.17 ± 0.06 for BW, WW, PADG, PKR, PGE, and PGR, respectively. The corresponding maternal heritability estimates for BW, PKR, PGE, and PGR traits were 0.19 ± 0.03, 0.11 ± 0.04, 0.13 ± 0.04, and 0.12 ± 0.04, respectively. For WW and ADWG, only direct effects were significant. The correlations of WW with efficiency-related traits and among the other traits were quite high and positive. Significant impact of maternal care for early growth traits in this breed indicates the importance of considering both direct and maternal effects for genetic evaluation. The moderate estimate of heritability for WW with high and positive genetic correlations with other traits, makes this trait a candidate for selection in the present flock. We therefore recommend the use of WW as a single trait selection criterion for the overall improvement of the growth traits in the sheep flock.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Insights: Balancing Milk Yield, Fat: Protein Ratio and Fertility in Primiparous Cows From Subtropical Regions.","authors":"Amauri Felipe Evangelista, Altair Antônio Valloto, Lenira El Faro, Rodrigo Junqueira Pereira, Laila Talarico Dias, Rodrigo de Almeida Teixeira","doi":"10.1111/jbg.12944","DOIUrl":"https://doi.org/10.1111/jbg.12944","url":null,"abstract":"<p><p>In this study, we aimed to evaluate the genetic association between fertility traits, milk yield and the fat: protein ratio (FPR) on the test day in primiparous Holstein cows. The analysed traits were milk yield (TDMY) and FPR assessed on the test day, as well as the following fertility traits: period from calving to first service (CFS), days open (DO) and calving interval (CI). Genetic parameters were estimated through bivariate analysis, using a random regression model (considering fourth-order Legendre polynomials) and the Bayesian method, with GIBBS2F90 software. Heritability estimates varied between 0.11 and 0.22 for TDMY, between 0.16 and 0.30 for FPR and between 0.03 and 0.05 for the fertility traits. Correlation estimates between TDMY and fertility traits tended to increase from early lactation until approximately day 100, then decreased slightly before continuing to grow until the end of lactation. Genetic correlations between TDMY and FPR were negative throughout lactation, ranging from -0.04 on day 5 to -0.37 in the final third of this period. The genetic correlations between FPR and fertility traits were positive in early lactation and negative in late lactation (except for CFS). These results indicate that TDMY and FPR are heritable and can be used as selection criteria in Holstein cows in Brazil. However, for fertility traits, genetic gains through direct selection may be slow. Additionally, a high level of milk production and FPRs in early lactation negatively impact fertility traits.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimates of Variance Components and Genetic Parameters for Weekly Weight in Juvenile Dairy Goats Using Random Regression Models.","authors":"Chrilukovian B Wasike, Ruth Wambui Waineina, Kiplangat Ngeno, Sophie A Miyumo, Christine M Kamidi, Josephine M Mwabili, Evans D Ilatsia","doi":"10.1111/jbg.12942","DOIUrl":"https://doi.org/10.1111/jbg.12942","url":null,"abstract":"<p><p>Growth performance of juvenile dairy goats influences their rate of growth and consequently (re)productive performance as mature goats. However, dairy goat improvement programmes focus on milk and fertility traits and seldom growth traits. This study aimed at estimating variance components and genetic parameters for growth in juvenile dairy goats to avail these estimates for inclusion in the national dairy goat improvement program. 4072 weekly weights records (from 1 week to 16th week of age) were collected on 453 goats born of 35 dams and 15 sires of Saanen, Toggenburg, Alpine and their crossbred genotypes. The records were subjected to univariate random regression analysis fitting direct additive, maternal genetic and permanent environmental [PE] effects as random effects to estimate variance components and genetic parameters. Phenotypic, additive genetic, maternal genetic and PE variances increased along the growth trajectory. Estimates of variance ratios ranged from 0.015 ± 0.14 to 0.469 ± 0.15, 0.097 ± 0.06 to 0.204 ± 0.08 and 0.407 ± 0.13 to 0.641 ± 0.08 for additive genetic, maternal genetic and PE effects, respectively. Correlation estimates were positive. Additive genetic correlations between the weekly weights were high and near unity. Maternal genetic correlations between the weekly weights were equally high. PE correlations were low between early weights and later weights, although correlations between later weights were high. The correlation estimates decreased as the interval between the weights increased for all the random effects. There is sufficient scope of variance in weekly weights to enable selective breeding. Selection to improve later weights could be done based on early weekly weights given the high additive genetic correlations.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144175877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lactation Curve Modelling and Genetic Parameters Estimation in Murciano-Granadina Goats.","authors":"Morteza Mokhtari, Zahra Roudbari, Ehsan Mohebbinejad, Ali Esmailizadeh","doi":"10.1111/jbg.12940","DOIUrl":"https://doi.org/10.1111/jbg.12940","url":null,"abstract":"<p><p>The present study aims to determine the best non-linear model for describing lactation curves and estimating genetic parameters for the lactation curve traits in the Murciano-Granadina goats in Iran. We compared five mathematical models including the Cappio-Borlino (CB), Cobby and Le Du (CD), Narushin-Takma (NT), Wilmink (WL), and Wood (WD) to characterise the lactation curve in the first and second lactations of Murciano-Granadina does. The dataset consisted of 36,958 and 23,319 milk yield test-day records from 4964 first-parity and 3335 s-parity Murciano-Granadina does, respectively. These records were collected from 2017 to 2024 in a private dairy farm, located in Ghale-Ganj city, Kerman province, southern area of Iran. In both lactation periods, the WD model showed the lowest values for root mean squares of prediction error (RMSE) and Akaike's information criterion (AIC), as well as the highest adjusted coefficient of determination ( <math> <semantics> <mrow> <msub><msup><mi>R</mi> <mn>2</mn></msup> <mi>adj</mi></msub> </mrow> <annotation>$$ {R^2}_{adj} $$</annotation></semantics> </math> ) among the evaluated models. Additionally, positive autocorrelations were observed among the residuals for all the models considered, with the lowest positive autocorrelation obtained under the WD model. Therefore, WD was identified as the best model to characterise the lactation curve of the Murciano-Granadina does in the first and second lactation periods. Consequently, we computed the individual lactation curve traits for does in the ith parity (where i = 1 for the first parity and i = 2 for the second parity), including peak time (PTi), peak milk yield (PYi), and lactation persistency (LPi), using the parameters derived from the WD model. A multivariate animal model utilising a Bayesian approach was employed to estimate the genetic parameters of the lactation curve traits. The posterior means for heritability estimates were 0.07, 0.13, 0.05, 0.05, 0.11, and 0.08 for PT1, PY1, LP1, PT2, PY2, and LP2, respectively. In the first parity, genetic correlations among the lactation curve traits were positive estimates of 0.28, 0.96, and 0.25 for PT1-PY1, PT1-LP1, and PY1-LP1, respectively. In the second parity, the corresponding genetic correlation estimates were 0.88, 0.89, and 0.59 for PT2-PY2, PT2-LP2 and PY2-LP2, respectively. It can be concluded that the low heritability estimates for the investigated lactation curve traits suggest these traits are mainly affected by non-additive genetic and environmental effects. Consequently, direct genetic selection may not effectively modify the shape of the Murciano-Granadina lactation curve. The positive genetic correlation estimates among the traits examined within each parity, as well as among the same traits across the parities, suggest that selecting one trait will also enhance the other traits.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Evaluation Weight, Carcass and Stayability in Nellore Females.","authors":"Isabella Silva de Carvalho, Sirlene Fernandes Lázaro, Eula Regina Carrara, Matheus Rodrigues de Souza, Humberto Tonhati","doi":"10.1111/jbg.12941","DOIUrl":"https://doi.org/10.1111/jbg.12941","url":null,"abstract":"<p><p>Traits related to growth, carcass quality and stayability are key components in enhancing the profitability and sustainability of Nelore cattle production systems. This study aimed to estimate heritabilities and genetic and environmental correlations for these traits using a Bayesian approach. Data from 94,703 females were analysed for weights at 210, 365 and 450 days of age (W210, W365 and W450), loin eye area (LEA), subcutaneous fat thickness in the loin (LFT) and rump (REFT) and stayability at 48, 54 and 72 months (STAY48, STAY54 and STAY72). Heritability estimates (± standard error) were 0.14 ± 0.03 for LEA, 0.20 ± 0.03 for LFT, 0.43 for REFT, 0.12 ± 0.02 for STAY54, and 0.18 ± 0.02 for STAY72. Moderate heritabilities for W210, W365, W450, LFT and REFT indicate a substantial additive genetic component, whereas lower estimates for LEA and stayability suggest a predominant influence of environmental factors. Genetic trends were generally positive but moderate: 0.14 kg/generation (W210), 1.40 kg/generation (W365), 1.77 kg/generation (W450), 0.016 cm²/generation (LEA) and 0.0081 months/generation (STAY72). In contrast, STAY48 showed a slightly negative trend (-0.0073 months/generation). Direct selection for W450 yielded a genetic gain of 9.837 kg, whereas indirect selection via correlated traits resulted in gains ranging from 0.125 to 9.272 kg. These findings highlight the relevance of environmental effects on traits with low heritability, such as LEA and stayability, and reinforce the effectiveness of selection for weight-related traits due to their moderate heritability and favourable genetic trends.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Identifiability of Genetic Parameters for Growth in Mice With a Massively Deep Pedigree.","authors":"X Ding, A A Musa, N Reinsch","doi":"10.1111/jbg.12938","DOIUrl":"https://doi.org/10.1111/jbg.12938","url":null,"abstract":"<p><p>In models with direct and maternal genetic effects, structural features of the data are a potential source of bias and low accuracy of estimates for genetic covariance parameters. One of the well-known reasons for such poor practical identifiability is the lack of dams with own observations. So far, however, no attention has been paid to the impact close relationships may have. Therefore, this genetic-statistical analysis of growth traits in two unselected mouse lines includes investigations on practical identifiability of genetic (co-)variances in the light of the observed high levels of co-ancestry, resulting from massively deep pedigrees. Body weight data had been collected over 33 years (from 1978 to 2011; 145 and 118 generations per line), amounting to approximately 115,000 observations in total for body weight at three developmental stages. Additional analyses of simulated data using the original pedigree structure of one line provided insight into the bias and precision of estimates. Further, closeness to pair-wise structural non-identifiability of genetic (co-)variances was quantified. In univariate analyses, we found genetic correlations between direct and maternal effects all positive for body mass traits at different ages up to mating, except for a single small negative estimate. Overall, multivariate analyses returned somewhat stronger correlations, whereby signs remained unchanged. Simulations showed a tendency toward an upward bias of the direct-maternal genetic correlations and other parameters, especially when the true correlations were higher. For all traits indicators for structural non-identifiability were narrowly close (> 0.998) to unity, the point at which a pair of covariance components no longer can be identified. This narrowness was stronger for separate partitions of data from later generations with higher average inbreeding and within-generation co-ancestry. In conclusion, in models with direct and maternal genetic effects, strong co-ancestry between parents is another feature of the data structure that may result in bias and inflated standard errors of estimated genetic parameters.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Step Genomic BLUP With Unknown Parent Groups and Metafounders in Norwegian Red Evaluations.","authors":"Tesfaye K Belay, Arne B Gjuvsland, Janez Jenko, Leiv S Eikje, Morten Svendsen, Theo Meuwissen","doi":"10.1111/jbg.12939","DOIUrl":"https://doi.org/10.1111/jbg.12939","url":null,"abstract":"<p><p>The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and \"Q-Q⁺\" methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The \"Q-Q⁺\" approach corrects for the combined effects of UPG and \"J factor\" in non-genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (G_rtn matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G₀₅ constructed using AF = 0.5 and w = 0.0, and G_cal constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using G_rtn), four MF models (MF14 and MF52 using G_rtn or G₀₅), and three Q-Q+ models (using G_cal, G₀₅, or G_rtn). The models were evaluated through cross-validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q-Q⁺ models using the G_cal or G₀₅ matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G₀₅ were second best for level bias and performed similarly or slightly better than Q-Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with G_rtn had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G₀₅ (1.18%), but pedigree-based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G₀₅ showed the least bias (with a small standard error) and most stable predictions, while models using G_rtn introduced biases and instability. Thus, the Q-Q⁺ and MF models combined with G₀₅ and Q-Q⁺ with G_cal are recommended for their improved validation results and genetic trends.</p>","PeriodicalId":54885,"journal":{"name":"Journal of Animal Breeding and Genetics","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}