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An increasing trend in ancestral and classical inbreeding coefficients as well as inbreeding depression for longevity were found in the German Brown population. In addition, the proportion of US Brown Swiss genes is steadily increasing in German Browns. Therefore, the aim of the present study was to analyze the presence and genomic localization of runs of homozygosity (ROH) in order to evaluate their associations with the proportion of US Brown Swiss genes and survival rates of cows to higher lactations. Genotype data were sampled in 2364 German Browns from 258 herds. The final data set included 49,693 autosomal SNPs. We identified on average 35.996 ± 7.498 ROH per individual with a mean length of 8.323 ± 1.181 Mb. The genomic inbreeding coefficient F ROH was 0.122 ± 0.032 and it decreased to 0.074, 0.031 and 0.006, when genomic homozygous segments > 8 Mb (F ROH>8 ), >16 Mb (F ROH>16 ) and >32 Mb (F ROH>32 ) were considered. New inbreeding showed the highest correlation with F ROH>32 , whereas ancestral inbreeding coefficients had the lowest correlations with F ROH>32 . The correlation between the classical inbreeding coefficient and F ROH was 0.572. We found significantly lower F ROH , F ROH>4 , F ROH>8 and F IS for US Brown Swiss proportions <60% compared to >80%. Cows surviving to the 2nd, 4th, 6th, 8th, and 10th lactation had lower genomic inbreeding for F ROH and up to F ROH>32 , which was due to a lower number of ROH and a shorter average length of ROH. The strongest ROH island and consensus ROH shared by 50% of the animals was found on BTA 6 at 85-88 Mb. The genes located in this genomic region were associated with longevity ( NPFFR2 and ADAMTS3 ), udder health and morphology ( SLC4A4 , NPFFR2 , GC and RASSF6) , milk production, milk protein percentage, coagulation properties of milk and milking speed ( CSN3 ). On BTA 2, a ROH island was detected only in animals with <60% US Brown Swiss genes. Genes within this region are predominantly important for dual-purpose cattle breeds including Original Browns. For cows reaching more than 9 lactations, an exclusive ROH island was identified on BTA 7 with genes assumed to be associated with longevity. The analysis indicated that genomic homozygous regions important for Original Browns are still present and also ROH containing genes affecting longevity may have been identified. The breeding of German Browns should prevent any further increase in genomic inbreeding and run a breeding program with balanced weights on production, robustness and longevity.

Background: Limitations of the concept of identity by descent in the presence of stratification within a breeding population may lead to an incomplete formulation of the conventional numerator relationship matrix ( A ). Combining A with the genomic relationship matrix ( G ) in a single-step approach for genetic evaluation may cause inconsistencies that can be a source of bias in the resulting predictions. The objective of this study was to identify stratification using genomic data and to transfer this information to matrix A , to improve the compatibility of A and G ., Methods: Using software to detect population stratification (ADMIXTURE), we developed an iterative approach. First, we identified 2 to 40 strata ( k ) with ADMIXTURE, which we then introduced in a stepwise manner into matrix A , to generate matrix A Γ using the metafounder methodology. Improvements in consistency between matrix G and A Γ were evaluated by regression analysis and through the comparison of the overall mean and mean diagonal values of both matrices. The approach was tested on genotype and pedigree information of European and North American Brown Swiss animals (85,249). Analyses with ADMIXTURE were initially performed on the full set of genotypes (S1). In addition, we used an alternative dataset where we avoided sampling of closely related animals (S2)., Results: Results of the regression analyses of standard A on G were - 0.489, 0.780 and 0.647 for intercept, slope and fit of the regression. When analysing S1 data results of the regression for A Γ on G corresponding values were - 0.028, 1.087 and 0.807 for k =7, while there was no clear optimum k . Analyses of S2 gave a clear optimal k =24, with - 0.020, 0.998 and 0.817 as results of the regression. For this k differences in mean and mean diagonal values between both matrices were negligible., Conclusions: The derivation of hidden stratification information based on genotyped animals and its integration into A improved compatibility of the resulting A Γ and G considerably compared to the initial situation. In dairy breeding populations with large half-sib families as sub-structures it is necessary to balance the data when applying population structure analysis to obtain meaningful results., (© 2024. The Author(s).)

Genotype x environment (GxE) interaction for production traits in Fleckvieh cattle was assessed by means of various environmental descriptors (EDs). It was also of particular interest to search for EDs useful for studying differing robustness or resilience of individuals which implies reasonable GxE interaction. The set of studied EDs included farm/herd environment (e.g. herd size, housing/feeding regimes, herd production level), geographical location (e.g. height above sea level), temperature humidity index and fat-to-protein ratio. Milk, fat and protein yield deviations for the first test day, the first 100 days and the 305-day-yield of the first lactation were used as phenotypes. Genetic correlations were estimated with bi- and multivariate sire models in case of categorical EDs and reaction norm sire models in case of continuous EDs. Further, rank correlations of bulls' estimated breeding values were calculated for different environmental levels/classes. Almost all estimated genetic correlations were significantly different from 1, but not <0.93. There were some exceptions for extremely different classes/levels of some EDs (e.g. average herd-year production, organic vs conventional farm systems, way of concentrated feed supply). Rank correlations were rarely below 0.95. In summary, no substantial GxE interactions for milk production traits were found with the studied EDs., (© 2022 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

Background: Managing beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus., Methods: We focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle., Results: Our results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator 'somatic cell score', and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only., Conclusions: Pleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits., (© 2021. The Author(s).)

Copyright of Züchtungskunde is the property of Verlag Eugen Ulmer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Test-day records for average flow rate (AFR) from the routine dairy recording from Bavarian Fleckvieh cows were analysed. Two data sets with observations on approximately 20 000 cows each were sampled from the total data set. For the estimation of variance parameters, a two-step approach was applied. In a first step multiple-trait restricted maximum likelihood (REML) analyses were carried out. For each of the first three lactations, six time periods with up to 33 days were defined. An algorithm for iterative summing of expanded part matrices was applied in order to combine the estimates. In a second step covariance functions (CF) for additive-genetic variances and non-genetic animal variances were derived using second-order Legendre polynomials plus an exponential term. Estimates of test-day heritability for AFR ranged from 0.21 to 0.40, and were largest in lactation 1. For lactations 1 and 3, heritabilities decreased considerably towards the end of lactation. Genetic correlation estimates within lactation decreased as the distance between days in milk (DIM) increased. Genetic correlations between corresponding DIM in the three lactations were generally large, ranging from 0.80 to 0.99. The largest estimates were found between DIM from lactations 2 and 3. Results from this study suggest that including AFR data from second and third lactations in genetic evaluation systems could the improve accuracy of genetic selection. [ABSTRACT FROM AUTHOR]

The breeding goal for Simmental cattle is derived for intensively managed dairy farms. Its suitability for extensive farms was addressed by analysing possible genotype by environment interaction (G × E) between the management levels for first lactation milk yield traits. A first analysis was performed with the data collected from 300 000 purebred daughters of 278 second crop bulls born in Bavaria in 1993 and 1994. The farms were classified by herd-year-effect, using the sum of fat and protein yields into two levels of management, either with 33 or 10% quantiles, corresponding to approximately 100 000 cows and 30 000 cows, respectively. The comparison was based on ‘daughter yield’ deviations (DYD). Correlations between DYD of extensive and intensive environments were 0.90, 0.91 and 0.87 for milk, fat and protein yield (kg) for 33% quantiles, respectively. Corresponding correlations for 10% quantiles were 0.85, 0.83 and 0.77. Despite high correlations, 50 out of 149 sires showed significant differences between DYD in different environments. Bulls with higher DYD for milk yield on intensive farms were superior in all environments. For the second analysis extensive and intensive farms in northern and southern Bavaria were chosen at random. Approximately 20 000 cows in each management class were used for the estimation of genetic parameters. In both regions phenotypic and additive-genetic variances were higher in the intensively managed herds. Likewise heritabilities were higher for fat and protein yield, but not for milk where higher heritabilities were observed in 33% quantiles. Genetic correlations between extensive and intensive environments were 0.97 and above (33% quantiles). Ten per cent quantiles led to lower genetic correlations (0.90–0.95). Although no serious re-ranking effects of sires were evident, the scale effect and the differences in genetic parameters should be taken into consideration in practical breeding. [ABSTRACT FROM AUTHOR]

A multiplicative random regression (M-RRM) test-day (TD) model was used to analyse daily milk yields from all available parities of German and Austrian Simmental dairy cattle. The method to account for heterogeneous variance (HV) was based on the multiplicative mixed model approach of Meuwissen. The variance model for the heterogeneity parameters included a fixed region × year × month × parity effect and a random herd × test-month effect with a within-herd first-order autocorrelation between test-months. Acceleration of variance model solutions after each multiplicative model cycle enabled fast convergence of adjustment factors and reduced total computing time significantly. Maximum Likelihood estimation of within-strata residual variances was enhanced by inclusion of approximated information on loss in degrees of freedom due to estimation of location parameters. This improved heterogeneity estimates for very small herds. The multiplicative model was compared with a model that assumed homogeneous variance. Re-estimated genetic variances, based on Mendelian sampling deviations, were homogeneous for the M-RRM TD model but heterogeneous for the homogeneous random regression TD model. Accounting for HV had large effect on cow ranking but moderate effect on bull ranking. [ABSTRACT FROM AUTHOR]

The long-term performance of different selection strategies was evaluated via simulation using the example of a local cattle breed, German Angler cattle. Different optimum contribution selection (OCS) approaches to maximize genetic gain were compared to a reference scenario without selection and truncation selection. The kinships and migrant contribution (MC) were estimated from genomic data. Truncation selection achieved the highest genetic gain but decreased diversity considerably at native alleles. It also caused the highest increase in MCs. Traditional OCS, which only constrains kinship, achieved almost the same genetic gain but also caused a small increase of MC and remarkably reduced the diversity of native alleles. When MC was required not to increase and the increase of kinship at native alleles was restricted, the MC levels and the diversity at native alleles were well managed, and the genetic gain was only slightly reduced. However, genetic progress was substantially lower in the scenario that aimed to recover the original genetic background. Truncation selection and traditional OCS selection both reduce the genetic originality of breeds with historical introgression. The inclusion of MC and kinship at native alleles as additional constraints in OCS showed great potential for conservation. Recovery of the original genetic background is possible but requires many generations of selection and reduces the genetic progress in performance traits. Hence, constraining MCs at their current values can be recommended to avoid further reduction of genetic originality., (Copyright © 2017 Wang et al.)

Copyright of Züchtungskunde is the property of Verlag Eugen Ulmer and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations., Results: We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation., Conclusions: Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants.

Background: The availability of dense genotypes and whole-genome sequence variants from various sources offers the opportunity to compile large datasets consisting of tens of thousands of individuals with genotypes at millions of polymorphic sites that may enhance the power of genomic analyses. The imputation of missing genotypes ensures that all individuals have genotypes for a shared set of variants., Results: We evaluated the accuracy of imputation from dense genotypes to whole-genome sequence variants in 249 Fleckvieh and 450 Holstein cattle using Minimac and FImpute. The sequence variants of a subset of the animals were reduced to the variants that were included on the Illumina BovineHD genotyping array and subsequently inferred in silico using either within- or multi-breed reference populations. The accuracy of imputation varied considerably across chromosomes and dropped at regions where the bovine genome contains segmental duplications. Depending on the imputation strategy, the correlation between imputed and true genotypes ranged from 0.898 to 0.952. The accuracy of imputation was higher with Minimac than FImpute particularly for variants with a low minor allele frequency. Using a multi-breed reference population increased the accuracy of imputation, particularly when FImpute was used to infer genotypes. When the sequence variants were imputed using Minimac, the true genotypes were more correlated to predicted allele dosages than best-guess genotypes. The computing costs to impute 23,256,743 sequence variants in 6958 animals were ten-fold higher with Minimac than FImpute. Association studies with imputed sequence variants revealed seven quantitative trait loci (QTL) for milk fat percentage. Two causal mutations in the DGAT1 and GHR genes were the most significantly associated variants at two QTL on chromosomes 14 and 20 when Minimac was used to infer genotypes., Conclusions: The population-based imputation of millions of sequence variants in large cohorts is computationally feasible and provides accurate genotypes. However, the accuracy of imputation is low in regions where the genome contains large segmental duplications or the coverage with array-derived single nucleotide polymorphisms is poor. Using a reference population that includes individuals from many breeds increases the accuracy of imputation particularly at low-frequency variants. Considering allele dosages rather than best-guess genotypes as explanatory variables is advantageous to detect causal mutations in association studies with imputed sequence variants.

Background: Extending the reference set for genomic predictions in dairy cattle by adding large numbers of cows with genotypes and phenotypes has been proposed as a means to increase reliability of selection decisions for candidates., Methods: In this study, we explored the potential of increasing the reliability of breeding values of young selection candidates by genotyping a fixed number of first-crop daughters of each sire from one or two generations in a balanced and regular system of genotyping. Using stochastic simulation, we developed a basic population scenario that mimics the situation in dual-purpose Fleckvieh cattle with respect to important key parameters. Starting with a reference set consisting of only genotyped bulls, we extended this reference set by including increasing numbers of daughter genotypes and phenotypes. We studied the effects on model-derived reliabilities, validation reliabilities and unbiasedness of predicted values for selection candidates. We also illustrate and discuss the effects of a selected sample and an unbalanced sampling of daughters. Furthermore, we quantified the role of selection with respect to the influence on validation reliabilities and contrasted these to model-derived reliabilities., Results: In the most extended design, with 200 daughters per sire genotyped from two generations, single nucleotide polymorphism (SNP) effects were estimated from a reference set of 420,000 cows and 4200 bulls. For this design, the validation reliabilities for candidates reached 80 % or more, thereby exceeding the reliabilities that were achieved in traditional progeny-testing designs for a trait with moderate to high heritability. We demonstrate that even a moderate number of 25 genotyped daughters per sire will lead to considerable improvement in the reliability of predicted breeding values for selection candidates. Our results illustrate that the strategy applied to sample females for genotyping has a large impact on the benefits that can be achieved.

Background: The availability of whole-genome sequence data from key ancestors in bovine populations provides an exhaustive catalogue of polymorphic sites that segregate within and across cattle breeds. Sequence variants identified from the sequenced genome of key ancestors can be imputed into animals that have been genotyped using medium- and high-density genotyping arrays. Association analysis with imputed sequences, particularly when applied to multiple traits simultaneously, is a very powerful approach to detect candidate causal variants that underlie complex phenotypes., Results: We used whole-genome sequence data from 157 key ancestors of the German Fleckvieh cattle population to impute 20,561,798 sequence variants into 10,363 animals that had (partly imputed) genotypes based on 634,109 single nucleotide polymorphisms (SNPs). Rare variants were more frequent among the sequence-derived than the array-derived genotypes. Association studies with imputed sequence variants were performed using seven correlated udder conformation traits as response variables. The calculation of an approximate multi-trait test statistic enabled us to detect 12 quantitative trait loci (QTL) (P < 2.97 × 10(-9)) that affect different morphological features of the mammary gland. Among the tested variants, the most significant associations were found for imputed sequence variants at 11 QTL, whereas the top association signal was observed for an array-derived variant at a QTL on bovine chromosome 14. Seven QTL were associated with multiple phenotypes. Most QTL were located in non-coding regions of the genome but in close proximity of candidate genes that could be involved in mammary gland morphology (SP5, GC, NPFFR2, CRIM1, RXFP2, TBX5, RBM19 and ADAM12)., Conclusions: Using imputed sequence variants in association analyses allows the detection of QTL at maximum resolution. Multi-trait approaches can reveal QTL that are not detected in single-trait association studies. Most QTL for udder conformation traits were located in non-coding regions of the genome, which suggests that mutations in regulatory sequences are the major determinants of variation in mammary gland morphology in cattle.

Background: Genomic selection and estimation of genomic breeding values (GBV) are widely used in cattle and plant breeding. Several studies have attempted to detect population subdivision by investigating the structure of the genomic relationship matrix G. However, the question of how these effects influence GBV estimation using genomic best linear unbiased prediction (GBLUP) has received little attention., Methods: We propose a simple method to decompose G into two independent covariance matrices, one describing the covariance that results from systematic differences in allele frequencies between groups at the pedigree base (G A (*) ) and the other describing genomic relationships (G S) corrected for these differences. Using this decomposition and Fst statistics, we examined whether observed genetic distances between genotyped subgroups within populations resulted from the heterogeneous genetic structure present at the base of the pedigree and/or from breed divergence. Using this decomposition, we tested three models in a forward prediction validation scenario on six traits using Brown Swiss and dual-purpose Fleckvieh cattle data. Model 0 (M0) used both components and is equivalent to the model using the standard G-matrix. Model 1 (M1) used G S only and model 2 (M2), an extension of M1, included a fixed genetic group effect. Moreover, we analyzed the matrix of contributions of each base group (Q) and estimated the effects and prediction errors of each base group using M0 and M1., Results: The proposed decomposition of G helped to examine the relative importance of the effects of base groups and segregation in a given population. We found significant differences between the effects of base groups for each breed. In forward prediction, differences between models in terms of validation reliability of estimated direct genomic values were small but predictive power was consistently lowest for M1. The relative advantage of M0 or M2 in prediction depended on breed, trait and genetic composition of the validation group. Our approach presents a general analogy with the use of genetic groups in conventional animal models and provides proof that standard GBLUP using G yields solutions equivalent to M0, where base groups are considered as correlated random effects within the additive genetic variance assigned to the genetic base.

Most association studies for pinpointing trait-associated variants are performed within breed. The availability of sequence data from key ancestors of several cattle breeds now enables immediate assessment of the frequency of trait-associated variants in populations different from the mapping population and their imputation into large validation populations. The objective of this study was to validate the effects of 4 putatively causative variants on milk production traits, male fertility, and stature in German Fleckvieh and Holstein-Friesian animals using targeted sequence imputation. We used whole-genome sequence data of 456 animals to impute 4 missense mutations in DGAT1, GHR, PRLR, and PROP1 into 10,363 Fleckvieh and 8,812 Holstein animals. The accuracy of the imputed genotypes exceeded 95% for all variants. Association testing with imputed variants revealed consistent antagonistic effects of the DGAT1 p.A232K and GHR p.F279Y variants on milk yield and protein and fat contents, respectively, in both breeds. The allele frequency of both polymorphisms has changed considerably in the past 20 yr, indicating that they were targets of recent selection for milk production traits. The PRLR p.S18N variant was associated with yield traits in Fleckvieh but not in Holstein, suggesting that it may be in linkage disequilibrium with a mutation affecting yield traits rather than being causal. The reported effects of the PROP1 p.H173R variant on milk production, male fertility, and stature could not be confirmed. Our results demonstrate that population-wide imputation of candidate causal variants from sequence data is feasible, enabling their rapid validation in large independent populations., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The development of electrically-driven low-dimensional coherent light sources via highly-polarized polariton emission behavior has been extensively researched, but suffers from limited modulation of the exciton-photon coupling strengths. Herein, an electrically-biased near-infrared exciton-polariton lightemitting diode (LED), which includes a Ga-doped ZnO microwire (ZnO:Ga MW) and p-type GaAs substrate, is demonstrated. The well-designed LED structure is conducive to producing strong coupling between excitons and cavity photons, thus yielding highly-polarized light-emissions due to the optical birefringence in the ZnO:Ga MW microcavity. In particular, when the LED device is modified using Au&Ag alloy nanorods (AuAgNRs) with desired plasmonic properties, the electroluminescence (EL) performance is significantly boosted, especially the Rabi-splitting energy, which increases from 96 to 285 meV. The current-injection exciton-polariton emission from the LED undergoing a strong coupling regime is confirmed through angle-resolved EL measurements. This study exhibits a performanceboosted near-infrared exciton-polariton LED at room temperature, which provides a new scheme toward the realization of highly energy-efficient polariton coherent light sources. Further, the significantly lower density of polariton states induced by the incorporated metal nanostructures highlights a bright future of realizing ultralow-threshold polariton lasers much more feasibly, in comparison to conventional lasers based on narrow bandgap semiconductors. [ABSTRACT FROM AUTHOR]

Previous study revealed that isocitrate dehydrogenase (NADP (+)) 2, mitochondrial (IDH2), lactate dehydrogenase A (LDHA), and lactate dehydrogenase B (LDHB) genes were significantly differentially expressed in liver tissues of Holstein cows among different lactation periods and associated with lipid and protein metabolism; hence, they were considered as candidates for milk production traits. Herein, the genetic effects of the three genes on milk yield, fat, and protein traits were studied by association analysis using 926 Chinese Holstein cows from 45 sire families. As a result, five single nucleotide polymorphisms (SNPs) in IDH2, one in LDHA, and three in LDHB were identified by re-sequencing, and subsequently, they were genotyped in 926 Chinese Holstein cows by genotyping by target sequencing (GBTS). With the animal model, single-locus association analysis revealed that four SNPs in IDH2 and one SNP in LDHA were significantly associated with milk, fat, and protein yields (p ≤ 0.0491), and three SNPs in LDHB were associated with milk yield, milk fat yield, and fat percentage (p ≤ 0.0285). Further, four IDH2 SNPs were found to form a haplotype block significantly associated with milk yield, fat yield, protein yield, and protein percentage (p ≤ 0.0249). In addition, functional predictions indicated that one SNP in LDHA, g.26304153G>A, may affect transcription factor binding and two SNPs, g.88544541A>G and g.88556310T>C could alter LDHB mRNA secondary structure. In summary, this study profiled the significant genetic effects of IDH2, LDHA, and LDHB on milk yield and composition traits and provided referable genetic markers for genomic selection programs in dairy cattle. [ABSTRACT FROM AUTHOR]

Body conformation traits are linked to the health, longevity, reproductivity, and production performance of cattle. These traits are also crucial for herd selection and developing new breeds. This study utilized pedigree information and phenotypic (1185 records) and genomic (The resequencing of 496 Xinjiang Brown cattle generated approximately 74.9 billion reads.) data of Xinjiang Brown cattle to estimate the genetic parameters, perform factor analysis, and conduct a genome-wide association study (GWAS) for these traits. Our results indicated that most traits exhibit moderate to high heritability. The principal factors, which explained 59.12% of the total variance, effectively represented body frame, muscularity, rump, feet and legs, and mammary system traits. Their heritability estimates range from 0.17 to 0.73, with genetic correlations ranging from −0.53 to 0.33. The GWAS identified 102 significant SNPs associated with 12 body conformation traits. A few of the SNPs were located near previously reported genes and quantitative trait loci (QTLs), while others were novel. The key candidate genes such as LCORL, NCAPG, and FAM184B were annotated within 500 Kb upstream and downstream of the significant SNPs. Therefore, factor analysis can be used to simplify multidimensional conformation traits into new variables, thus reducing the computational burden. The identified candidate genes from GWAS can be incorporated into the genomic selection of Xinjiang Brown cattle, enhancing the reliability of breeding programs. [ABSTRACT FROM AUTHOR]