Pi (measure of stability). Sixteen associations with GY were “environment-specific” with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For Pi, 8 associations were from chromosomes 4B and 7B, indicating ‘hot spot’ regions for stability. Epistatic interactions contributed to an additional 5–9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9–10% increase in prediction accuracy, whereas for Pi this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth investigation on the above using a large set of advanced bread wheat lines , which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index  Triticum aestivum L.) is one of the most important cereal crops for global food security (Bread wheat (security . With thsecurity . Improvesecurity . In addi® genotyping array). Due to the transformed genetic toolkit available in wheat, untangling the genetic architecture of traits by genome-wide association study (GWAS) and predicting performance by genomic selection (GS) have become feasible. Several GWAS analyses have been performed in wheat for plethora of traits including yield and yield components  markers at a low cost, have revolutionized the field of plant genomics. Hence, a paradigm shift from marker-based to sequencing-based genotyping of breeders’ germplasm panels has been observed in the post-genome sequencing era. Wheat has particularly benefited from these technological advancements; dense sets of SNPs are now available from different marker platforms , DArTseq, high-density Affymetrix Axiommponents . HoweverGenomic selection is another widely used genomics-assisted approach in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. It offers the potential to accelerate genetic gain by increasing selection accuracy and intensity and shortening the lengths of breeding cycles. Though GS is a relatively new technology for wheat breeding, significant success has been achieved in testing and validating various models for GY and other traits using markers and pedigrees , b.In animal breeding, evidences have accumulated to realize that integration of prior information of quantitative trait loci (QTL) in GS models can result in increased prediction accuracies for traits with complex genetic architecture . A comprIn this study, we used a large set of spring bread wheat lines  from the CIMMYT Global Wheat Program , genotypPlant materials consisted of 4,302 spring bread wheat lines, which formed the entries of five Elite Yield Trials (EYT) during five consecutive years  i.e., EYmax > 32°C). All trials were sown in mid-November except for HS, which was sown in the end of February. The plot size in bed planting was 2.8 m × 1.6 m (two beds of 0.8 m with three rows each) and in flat planting was 4 m × 1.6m (six rows). Trials were phenotyped for days to heading (DH), plant height (PH), and GY in each year. DH was recorded as the number of days from planting until 50% of the spikes in each plot had completely emerged above the flag leaves. PH was recorded as the average of three values for each plot measured in centimeter from the soil surface to the tip of the spike excluding awns. At maturity whole plots were harvested to estimate GY per plot. The details of the phenotyping are also described in The three stress environments included (i) mild drought stress; sown in bed and with only two irrigations (B-2IR), (ii) severe drought stress; sown in flat and with drip irrigation (SD) and (iii) heat stress; bed sowing with five irrigations  for each trait. The data were adjusted for block effects within each replication per trial using values from the two common check varieties in SAS 9.4 using the PROC GLM function and adjusted entry mean of the genotypes were calculated for GY.ij-Yi) + Yall trials, where Yij is value of the entry for a trial, Yi is mean of checks of that trial and Yall trials is the mean of checks of all trials.For DH and PH, the adjusted means were calculated by the formula Y =  ≥ 0.15 was used for constructing haplotype blocks. No further imputations were done with the filtered set of 8,443 SNPs. Since the haplotype blocks were created on all lines from five EYTs together, a high threshold for MAF i.e., MAF ≥ 0.15 was applied so that in each EYT a MAF ≥ 0.05 could be achieved. Haplotypes were generated based on the linkage disequilibrium (LD) parameter D’ using the modified R script from Ppd-D1a and Vrn-B1a were also used as covariables to avoid any further confounding effects of flowering genes.Haplotype-based GWAS was conducted in each individual EYT using Plink version 1.07 , while sAll genomic predictions were based on the G-BLUP model, using the following formula:y is a vector of phenotypes consisting of the adjusted means, β is a vector of fixed effects (depending upon the model see the below), u is a vector of random genetic values, e is the vector of residuals. X and Z are design matrices. The u was assumed to follow a Gaussian distribution G was the genomic relationship matrix and e was assumed to follow a Gaussian normal distribution where In order to include the GWAS results in genomic prediction, four types of relationship matrices were calculated and used as part of the G-BLUP models in BGLR package:(a)MG) was calculated using single markers and following the formula:The additive relationship matrix HG) was calculated using the following formula:The haplotype based relationship matrix MGG) – Marker based Gaussian Kernel wasThe single marker-based Gaussian Kernel HGG)The haplotype-based Gaussian Kernel  and absence (0) for particular haplotype allele. Epistatis was captured in the models using Reproducing Kernel Hilbert Space (RKHS) regression equation based on Gaussian kernel  were identified in only one EYT, belonging to the previously defined group 1. Of the remaining associations, 19, 8, and 7 were assigned to groups 2, 3, and 4, respectively . Twenty-Pi and ER) for GY were calculated. Of the two, Lin and Binns’s Pi is a compound index that quantifies both performance (G) and G × E interaction and thus identifies good performing and stable lines with a single parameter  explained by 58 haplotypes associated with GY ranged from 0.7 to 14.0% in different environments across EYTs, while for the associated 125 SNPs it ranged from 0.02 to 4.5%. Similarly, for Pi, R2 ranged from 4.7 to 11.0% and 2.5 to 5.2% for haplotypes and single SNPs, respectively. The average R2 explained was 6.1–9.9% higher with the haplotype-based GWAS as compared to the single marker-based GWAS for GY and Pi across all EYTs  contributing to additional 9% variation (Pi (data not shown). Pi, respectively. Epistatic interactions were also significant among significantly associated SNPs and genome-wide distributed SNPs for GY and Pi (data not shown).For GY, epistatic loci interacting with other main effect loci were identified across EYTs for each of the environments . For exaariation . Higher We compared the predictive ability of the four genomic prediction models by incorporating different relationship matrices: Model (1) Genome-wide distributed SNPs (SM model/base model), Model (2) Genome-wide distributed haplotype blocks and epistasis among haplotype blocks (H + E model), Model (3) Genome-wide distributed SNPs, epistasis among SNPs, and single SNPs as fixed effects identified from GWAS and epistasis analyses (SM + E + fixed effects model), Model (4) Genome-wide haplotype blocks, epistasis among haplotypes blocks and haplotypes as fixed effects identified from GWAS and epistasis analyses (H + E + fixed effects model).Pi. For GY under B-5IR, the base model showed prediction accuracies from 0.35 to 0.43  for instance in EYT2011-12 and EYT2014-15  with small phenotypic effects are identified and rare variants (<1% frequencies) of large effects remain unidentified. Furthermore, despite the increasing awareness that epistasis forms the genetic basis of complex traits, the contribution of epistasis in the genetics of GY has been rarely investigated in GWAS studies . Hence, We found 58 haplotypes associated with GY of which 16 associations were ‘environment-specific’ and 26 were more ‘robust’ across environments and/or years. Many of the genomic regions identified in the two different drought stress environments  were different from each other, pointing to the additional complexity of drought stress tolerance. On chromosome 6B, a major GY QTL (HB17.1) was identified with the favorable haplotype ‘TAC’ showing an allele effect of up to +530 and +303 kg/ha under B-2IR and SD, respectively. A recent study reported two meta-QTL for adaptation to drought stress on chromosome 6B in wheat . The QTLPi in one of the EYTs studied here and assessed the effects of Pi-associated loci on GY in multiple environments (Pi and ER) were compared on such a large dataset. Most and almost all studies in crops have done such comparisons in a handful of cultivars or varieties  coincided with QTL already reported in various studies and environments  in CIMMYT wheat germplasm. The QTL on 4B and 7B provide new genomic regions for subsequent analyses of their underlying candidate genes.In a previous study, we identified genomic regions associated with yield stability using the superiority index ronments . In thisarieties . The abienotypes  re-estabronments , while Hlong arm . The hapPi when compared to single marker analysis. These results are similar to previous studies in other crops and durum wheat  and for Pi. For GY, a general trend observed was that the model accommodating haplotype-based GWAS loci and epistatic effects was superior to other models tested and resulted in up to 9 and 10% increase in prediction accuracies in B-5IR and B-2IR environments, respectively. These results indicate that if genomic regions with moderate effects but showing significant associations across years and/or environments are identified for a complex trait, using them as fixed effects can lead to better performance of GS models. Up to date, several different strategies were tested at CIMMYT to increase prediction accuracies for GY, for example, using pedigrees and markers individually and combined, G × E models and incorporating additional secondary traits  and the epistatically interacting loci were from chromosome 4B . The lack of sufficient haplotype blocks across the genome found in this study might be the reason for the observed results.Prediction accuracies for s models . The maiWe conclude that the utility of GS incorporating GWAS results is noteworthy for GY when GWAS results identify highly significant and robust genomic regions. GS predictions were even higher when haplotypes instead of SNP were used as fixed effects. With the upsurge in dense marker data sets coming from different genotyping platforms leading to more markers than observations , haplotyhttp://hdl.handle.net/11529/10548366).The datasets analyzed for this study can be found in the CIMMYT Dataverse (DS and SD conceived the manuscript. SD designed the research. DS and UR analyzed the data. DS wrote the manuscript. SM and RS generated phenotypic data. JP generated allele called GBS marker data. All authors reviewed the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This is mainly due to its improved meat quality and adaptation to tropical climate conditions compared to Zebu and Taurine cattle breeds, respectively. This study aimed to investigate the genetic architecture of ultrasound-based carcass and meat quality traits in Montana Tropical® Composite beef cattle. Therefore, we estimated variance components and genetic parameters and performed genome-wide association studies using the weighted single-step Genomic Best Linear Unbiased Prediction (GBLUP) approach. A pedigree dataset containing 28,480 animals was used, in which 1,436 were genotyped using a moderate-density Single Nucleotide Polymorphism panel . A total of 9,358, 5,768, 7,996, and 1,972 phenotypic records for the traits Longissimus muscle area (LMA), backfat thickness (BFT), rump fat thickness (RFT), and for marbling score (MARB), respectively, were used for the analyses. Moderate to high heritability estimates were obtained and ranged from 0.16 ± 0.03 (RFT) to 0.33 ± 0.05 (MARB). A high genetic correlation was observed between BFT and RFT (0.97 ± 0.02), suggesting that a similar set of genes affects both traits. The most relevant genomic regions associated with LMA, BFT, RFT, and MARB were found on BTA10 (5.4–5.8 Mb), BTA27 (25.2–25.5 Mb), BTA18 (60.6–61.0 Mb), and BTA21 (14.8–15.4 Mb). Two overlapping genomic regions were identified for RFT and MARB (BTA13:47.9–48.1 Mb) and for BFT and RFT (BTA13:61.5–62.3 Mb). Candidate genes identified in this study, including PLAG1, LYN, WWOX, and PLAGL2, were previously reported to be associated with growth, stature, skeletal muscle growth, fat thickness, and fatty acid composition. Our results indicate that ultrasound-based carcass and meat quality traits in the Montana Tropical® Composite beef cattle are heritable, and therefore, can be improved through selective breeding. In addition, various novel and already known genomic regions related to these traits were identified, which contribute to a better understanding of the underlying genetic background of LMA, BFT, RFT, and MARB in the Montana Tropical Composite population.The Montana Tropical Longissimus muscle area (LMA), backfat thickness (BFT), rump fat thickness (RFT), and marbling score (MARB)  .Bos taurus indicus; Zebu), which are well adapted to tropical conditions (Bos taurus taurus) breeds . An alternative to improve carcass and meat quality traits, while keeping the adaptation characteristics of Zebu cattle, is through the development of composite populations , 2) Adapted Taurine breeds (Bos taurus taurus), 3) British breeds (Bos taurus taurus), and 4) Continental European breeds (Bos taurus taurus).The Montana Tropicalre USDA; . This core USDA; : 1) Zebue.g. carcass and meat quality). Recent GWAS have successfully revealed significant genomic regions in beef cattle composite populations [ in Montana Tropical® Composite beef cattle and 2) identify relevant genomic regions, candidate genes, and metabolic pathways associated with these traits, using the WssGBLUP method.Over the past few years, there has been a great interest in genetically improving this composite population and better understanding its genetic background underlying phenotypic variation of economic importance to the breeders. In this context, genome-wide association studies (GWAS) can be performed to identify Quantitative Trait Loci (QTL) associated with key traits . With regard to the reproductive breeding scheme, around 60% of cows were artificially inseminated and 40% were kept in multiple-sire lots with a cows-to-bull’ ratio of 30:1 or 25:1. The majority of calves were born between September and December (Spring season in South America and the beginning of the rainy period) and weaned at 7 months of age. Weight recording was obtained at birth and weaning. Further records of yearling weight, scrotal circumference, and other productive traits were collected between 14 and 18 months. More details are presented in Four ultrasound-based carcass and meat quality traits , recorded on animals born between 2008 and 2016, were included in this study. Animals were raised in 18 farms located at different Brazilian states, Paraguay and Uruguay (Longissimus muscle area (LMA) was measured in cm2, between the 12th and 13th ribs. Backfat thickness (BFT) was measured in mm, at a point three-fourths of transverse orientation over the LMA (biceps femoris and gluteus medius between the ischium and ilium (www.uspremiumbeef.com/DocumentItem.aspx?ID=21). All traits were evaluated by ultrasonography using the ALOKA 500 V device, with a 3.5 MHz linear probe. The images were analyzed using the LINCE® software  age of the animals at the ultrasound measurement was 580.27 (±75.08) days.  the LMA . Rump fand ilium . Marblinsoftware . PhenotyA total of 1,436 bulls were genotyped using a moderate-density SNP panel containing 30,105 SNPs . Genotype quality control was performed using the PREGSF90 program . In geneVariance components and breeding value prediction. Single-trait linear animal models and the average-information restricted maximum likelihood (AI-REML) procedure were used to estimate heritability and variance components, using the AIREMLF90 package from the BLUPF90 family programs (A−1) is replaced by the H−1 matrix. The H−1 is defined as follow , which was calculated as  accounting for the observed allelic frequencies; and k is a scaling parameter, defined as 2 Σ p(1–p), in which p is the observed allele frequency of each marker. The weighting factor can be derived either based on SNP frequencies  heterozygosity (described below), and age of the animal at the measurement were included as fixed effects in the model. Thus, the statistical model used in this study can be described as:ijkl is the phenotypic record for each trait  recorded on the animal l, belonging to the CG i, at age j, and direct  heterozygosity (HD) k. b1 and b2 are the linear regression coefficients related to the Age and HD effects, respectively, which were considered as deviations from the mean 1 is the direct additive genetic random effect for the animal l, and ϵijkl is the residual random effect associated with the animal l, direct  heterozygosity k, age j, and CG i. Assuming a matrix notation, the previous model can be written as:where yy is the vector of phenotypic observations for each trait; β is the vector of solutions for fixed effects; α is the vector of predictions for random additive genetic animal effect; ϵ is the vector of random residual terms; X and Z are the incidence matrices of fixed and random effects, respectively. It was assumed that: α ~ Nvariance matrix (V) of the random effects can be expressed as:where, H is the relationship matrix used in the ssGBLUP method. The non-additive effects of heterozygosity were obtained by linear regression to the coefficients of direct  heterozygosity (HD), which were calculated as ; Si and Di are the fractions of the ith biological type of sire and dam, respectively. Coefficients for biological types  were equal to the proportion of each biological type in the breed composition , and it was assumed that the sum of all proportions of biological types in one animal were equal to one. To avoid multicollinearity, direct additive effects of the biological type N were excluded from the statistical models, i.e., the effects for A, B, and C were estimated as deviations of the additive effects of N  using pedigree and genomic information. Genetic and phenotypic correlations were calculated using the AIREMLF90 package from the BLUPF90 family programs (G⊗H); ϵ ~ N; where α, H, and I are the same as above; G is the additive genetic (co)variance matrix; R is the residual (co)variance matrix. In this reasoning, the (co)variance matrix for random effects was:programs . The mulGenome-wide association studies (GWAS). The GWAS for each trait was carried out using the weighted ssGBLUP method  and the ARS-UCD1.2 version of the cattle genome  < 5 based on the Benjamini-Hochberg procedure  using the Ensembl Genome Browser  estimates for LMA, BFT, RFT, and MARB are presented in The variance components and heritability  were identified for this common genomic region. A total of 12 biological processes (BP) and two pathways were significantly enriched (Two overlapping regions were identified on BTA13: 1) at 47.7–48.5 Mb for BFT and MARB, and 2) at 61.5–63.5 Mb for BFT and RFT. The highest peaks associated with LMA, BFT, RFT, and MARB were located on BTA10 , BTA27 , BTA18 , and BTA21 , respectively . 2 estimates for BFT (0.17 ± 0.06), RFT (0.27 ± 0.07), and LMA (0.32 ± 0.02) in Nellore beef cattle. 2 estimate for LMA (0.32 ± 0.08) in a composite population of 50% Red Angus, 25% Charolais, and 25% Tarentaise beef cattle. The moderate to high heritability estimates indicate that genetic progress can be achieved for these traits through selective breeding.The genetic parameters obtained for ultrasound carcass traits in the Montana Tropical Composite population . For insr = 0.97 ± 0.02), indicating that these traits are controlled by a similar set of genes. Furthermore, this high genetic correlation suggests that indirect genetic progress can be attained by including only one of these two traits in a breeding program. Positive but unfavorable genetic correlations were estimated between BFT and MARB (r = 0.66 ± 0.01), RFT and MARB (r = 0.55 ± 0.02), LMA and BFT (r = 0.53 ± 0.08), and LMA and RFT (r = 0.39 ± 0.02). This is because the industry aims to increase MARB and LMA while keeping BFT and RFT at a constant level. However, as these correlations are far from the unit, genetic progress for all the traits can be achieved by including and properly weighting them in a selection index. A favorable correlation was observed between LMA and MARB (r = 0.23 ± 0.01). A high and favorable genetic correlation was estimated between BFT and RFT  gene was identified to play a role in the expression of all the traits included in this study. PPP1R3B has been reported to be associated with meat quality traits in cattle, including pH, meat color, and shear force  gene family proteins  has also been associated with growth and carcass traits, including body weight, hip width and withers height  located on a conserved region on BTA14, previously reported as a selective sweep region in dairy and beef cattle breeds  identified for multiple carcass traits suggests that there are important pleiotropic effects regulating phenotypic expression of these traits. This is also supported by the moderate to high genetic correlation observed here and in other studies (e.g.SCAP and ENTPD3 genes) was also reported by DNMT3B (DNA cytosine-5-methyltransferase 3 beta), associated with BFT in this study, was previously associated with marbling score, subcutaneous fat, Longissimus muscle area, body weight, carcass weight, dressing percentage in offspring of Wagyu and F1 crossbred cows of Limousin with Fuzhou Yellow cattle  of interest and the density of the panel used  are two key factors that influence the identification of important genomic regions, especially those located in regions with low levels of linkage disequilibrium or small effect on the trait. These two factors might have constrained the genomic regions that were identified in this study. However, the SNP panel used in this study contains informative SNPs identified in several breeds, which were also used to develop the Montana Tropical Composite population . This might have minimized these effects. In view of the limitations described here, further studies using larger datasets and denser SNP panels should be performed to validate the results reported in this study.RXRA , which has been associated with weaning weight and yearling weight in Charolais and Brahman cattle  visual behavior and 2) associative learning. The associative learning is defined as the capacity of an individual learning a behavior based on the association of two or more events . In geneThe KEGG pathway PI3K-AKT is associated with stimulation of cell growth and proliferation, and simultaneously inhibits apoptosis. In this regard, PI3Ks plays a major role in insulin metabolism , which i® Composite population, but the traits studied have a polygenic nature. Some of the genes identified were previously associated with traits such as growth, carcass, body condition score, skeletal muscle growth, carcass fatness, and meat fatty acid composition. The main biological processes and pathways identified were “muscle tissue morphogenesis” and “regulation of fatty acid biosynthetic”, which biologically validate the ultrasound-based measurements. Further studies using larger datasets  and denser SNP panels (>30 K) should be performed in order to validate the results reported in this study.Our findings indicate that ultrasound-based carcass and meat quality traits are heritable and therefore can be improved through selective breeding. The high genetic correlation between BFT and RFT indicate that indirect genetic response can be obtained by selecting for only one of them. The WssGBLUP method used to perform GWAS enabled the identification of various novel or already known candidate genes associated with the carcass and meat quality traits in the Montana Tropicaljbferraz@usp.br (JF).The data supporting the results of this article are included within the article/LG, JF, FB, and LB conceived and designed the project. LG, JF, JE, FOB, BS organized sample collection and genotyping. LG performed the analyses. LG, LB, and HO wrote the manuscript. All authors reviewed and approved the final version of the manuscript.This study was financially supported by the Sao Paulo Research Foundation (Fundação de Amparo à Pesquisa do Estado de São Paulo — FAPESP) through the grants: 2014/07566-2, 2017/11919-6, and 2018/20393-0.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer AM declared a shared affiliation, with no collaboration, with one of the authors, FB, to the handling editor at time of review.


Apis mellifera carnica subspecies of the honeybee is known for its gentleness and good honey yield. In the early 20th century, systematic breeding efforts began. Breeding progress was slow before the introduction of modern techniques of genetic evaluation in the mid 1990s. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed. From about 2000 onward, breeding progress accelerated. The result is a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The success of A. m. carnica breeding shows the potential of genetic evaluation.The Apis mellifera carnica subspecies of the honeybee has long been praised for its gentleness and good honey yield before systematic breeding efforts began in the early 20th century. However, before the introduction of modern techniques of genetic evaluation  and a computerized data management in the mid 1990s, genetic progress was slow. Here, the results of the official breeding value estimation in BeeBreed.eu are analyzed to characterize breeding progress and inbreeding. From about the year 2000 onward, the genetic progression accelerated and resulted in a considerable gain in honey yield and desirable properties without increased inbreeding coefficients. The prognostic quality of breeding values is demonstrated by a retrospective analysis. The success of A. m. carnica breeding shows the potential of BLUP-based breeding values and serves as an example for a large-scale breeding program.The  Apis mellifera carnica, the Carniolan bee, originating from an area roughly bordered by the Alps in the northwest, the Carpathian Mountains in the northeast, the Mediterranean Sea in the southwest and the Albanian Alps in the south [A. m. carnica bees began in the 18th century from the Carniolan region, now Slovenia [A. m. carnica breeding in Austria, Germany and Switzerland after 1945, evolving into the international breeding program which is the topic of this paper.he south , has atthe south . In addihe south ,4,5. TheSlovenia  had the Slovenia  by SklenAs mating control is essential for sustained breeding progress , mating A. m. carnica breeding program are (i) honey yield  and many colony .Varroa destructor [Varroa resistance is recognized as a critical breeding goal [Varroa-related breeding traits [Varroa resistance [To reduce disease susceptibility is an important breeding goal, in particular for the resistance against structor  and theistructor ,19,20. Ting goal ,22, and g traits . Hygienisistance  althoughsistance .Starting from a recollection of data from the archives of several regions in West Germany (1988) and the GDR (1990), the database BeeBreed  has deveHenderson’s best linear unbiased prediction (BLUP)  providesThe general breeding value model applied for the BeeBreed-hosted breeding value estimation has been reported before ,31 and wA. m. carnica breeding population and other properties of the breeding values.Here, we report the genetic progress of the current A. m. carnica population. The (mated) queen is tested by the properties of its colony—this wording is traditional to the breeding community, and followed throughout the manuscript. To complete the pedigree, ancestry information of further queens are added as additional datasets  is assessed by mite infestation measurements in summer and the natural mite fall measured in spring. For a full account of the performance test, see [Varroa infestation is reported, it is only a single measurement without a given date. Typically, it is taken at the 27th calender week. For colonies with more than one measurement, each measurement is adjusted to the 27th calendar week and then averaged. The adjustment uses a function obtained from an exponential regression of the multiple infestation data within the breeding data.The est, see ,42,44. IM is natural mite fall in spring given in mites per day (unit removed), n is the number of infestation measurements in summer, i-th infestation measurements in mites per 10 g bee sample, and i-th infestation measurement. The unit of the trait is “mites per 10 g bee sample”, and in the case of a single infestation measurement in the middle of 27th week with no measured mite fall the trait value equals the measurement. The adjustment term for mite fall is calibrated ,for honey yield or VID, a value outside 4-times standard deviation of an apiary/season’s values is considered as an outlier and compressed towards the apiary’s average  because the values on an apiary/season are condensed or expanded to align the phenotypical variances on the respective apiaries, similarly to breeding programs of other farm animals .The data are refined with the following procedures:queen,worker community,pseudo sire  whicpotential offspring queen.Using the queen’s studbook information , a pedigPInCo  is used A colony’s trait value ic model  as:y=Xb+Varroa-related traits hygienic behavior and VID. The split of traits was dictated by the results of model validation [Varroa-related traits were only recorded in about 15% of all performance tests and 55% of the current population. The heritabilities and genetic correlations of the models are given in For the calculation of breeding values, two separate models were used: a 4-trait-model for the classical traits honey yield, gentleness, calmness and swarming, and a 2-trait model for the lidation  with theThe definition of heritabilities in honeybee breeding requires nuanced consideration , as the AQAW+σE2 , where tIREMLF90 . The adde colony  was calcng PInCo . The herVarroa traits, only the correlations within the four classical traits and between the two Varroa traits are given.See The genetic correlation between two traits is calculated asFrom the BLUP solution, the sum of the queen effect and the worker effect of the potential offspring queen is used as the combined genetic effect  of the colony. The unit is the same as that of the original phenotype.To support a rational selection decision, the raw breeding values are normalized with respect to the breeding population, which is the set of 36,270 tested queens of the last 5 years. The raw breeding values are linearly transformed so that the breeding population forms a distribution with average 100 and standard deviation of 10, to produce the breeding values published in BeeBreed .Varroa traits. For VID, the normalized breeding values are reversed in that low infestation represents high breeding values.The breeding values of hygienic behavior and VID are normalized with respect to those 22,210 colonies in the last 5 testing years tested for at least one of the Varroa index at the ratio of 57:43. The share of the VID has been increased from year to year as the Varroa measurements were introduced 2006 and coverage and quality of data lagged behind hygienic behavior.The breeding values for hygienic behavior and VID are combined with the Inbreeding coefficients are calculated with PInCo as diagonal entries of the relationship-inbreeding coefficients .y from 2014 to 2019.Iterate reference year y and later.Delete all performance data from test year Calculate normalized breeding values using the full pedigree.y included but not later) and add the average apiary effect.Calculate the relative performance by subtracting the apiary effect .For hygienic behavior and VID the averages are below 100 because the normalization is restricted to colonies where these traits are measured. Thus, the low increase of See There is a large positive trend of the fixed effect which can be attributed to colony management which is directed at higher per-colony yield. In the last decade this positive trend appears to have turned. The genetic effect slowly increased from 1960 to 1985 b for a tSee The correlation of the breeding values calculated before knowing the phenotype with the phenotype finally observed is 0.150. Not suprisingly, it is highly significant because it is based on 26,549 colonies. A phenotype cannot be accurately predicted from the genotype, also indicated by the heritability of 0.14, but the reliability of prediction is sufficient for effective selection.See The increase in marks has reached 0.3 when comparing 1995 with 2018 which is remarkable as the mark ranges from 1 to 4 and the colonies considered as well-manageable are usually evaluated with marks from 3 to 4.The fixed effects varied widely without any trend until 2000 and decreased constantly since. The particular wide deviations of the apiary effect of swarming reflect very different swarming conditions from year to year. The fixed effects dropped by 0.2 when comparing 2008 with 2018 which indicates that the requirements for colonies to get good marks increased. This underlines that the perception of the properties of colonies is currently changing.See The correlation of predicted phenotype to phenotype for the gentleness and and calmness is about 0.25 which is much larger than for honey yield. For swarming drive, it is 0.17 which is still larger than for honey yield which is remarkable due to the fact that in quite a few apiaries and years colonies can not be distinguished in their swarming behavior because the seasonal weather is not prone to swarming.See The apiary effect of hygienic behavior b decreasVarroa resistance.Breeding for decreased VID c resulteVarroa-related traits. Generally, there is a considerable selection advantage of the queens with high breeding values. Comparing the average of the highest quarter with the lowest quarter, the expectation is 10% higher clearance rate in the pintest, and 0.25 less mites per 10 g bee sample.See The correlation of predicted to real phenotypes is much higher for hygienic behavior  than for VID. This corresponds well with the low heritability of VID.See A. m. carnica population, which amounts to roughly 5 kg more yield per colony, about 0.3 mark points of manageability properties, 9% of clearance rate and 0.2 less mites per 10 g bee sample, has been predominantly realized after 2000, when the breeding value estimation took effect.In a retrospective analysis, the past breeding progress of the registered The yearly genetic improvement in absolute terms , obtained from a linear regression on queens from 2009–2018, is 336 g for honey yield, 0.017 mark points for gentleness and for calmness, 0.018 mark points for swarming, 0.73 percent points for hygienic behavior and a reduction of 0.018 mites per 10g bee sample for VID.A. m. carnica population has been estimated at 0.59% per year for honey yield and 0.44% per year for gentleness for the time span 1995–2006 on the normalization basis of queens of 2001–2005, as compared to the time span 1970–1989, where it is 0.11% and 0.01%, respectively [In a previous publication, the genetic improvement for the ectively . In 2016ectively . Here, wectively ,54 becauIntriguingly, until 1990 the increase of honey yield is fully covered by the apiary effect Before 1990, colonies were mainly selected by the relative performance (compared to the apiary average) which insufficiently expressed genetic value. A large disadvantage of selection by own performance is that the direct effect is overemphasized which is counterproductive when genetic correlation with the maternal effect is highly negative . With thBefore 1990, there was a large breeding population in the former GDR. After 1990, subsidies for breeding were terminated . Thus, tThe measurement of mite infestation is hampered by some technical factors which include (i) the difficulty to obtain a representative bee sample, (ii) small deviations in humidity strongly affect the efficacy to remove mites applying the powder sugar method, and (iii) a strong effect of the environment on mite population growth and brood development. Thus, the low heritability  and the While a positive effect of available breeding values on breeding progress is very likely, it is only one of the factors that have positively affected the breeding progress. The public availability of a central registry of breeders and queens in BeeBreed may have promoted the exchange of breeding material. Furthermore, the public awareness of the relevance of beekeeping has prompted national and international funding of honeybee breeding  which motivated beekeepers to start breeding leading to the increase of the breeding population. European projects such as SmartBees have contributed to educating beekeepers to become breeders. The efforts of the D.I.B. and ACA to support breeding as a cornerstone in the challenges beekeeping faces have intensified since the 2000s.A. m. carnica breeding population in Germany and Austria is represented in BeeBreed. Thus, the breeding value progression before does not have the same level of certainty. Foreign testing (exchange of queens between breeders) has become more widely implemented since 2000 as a factor for reliability of breeding values. This may have led to a dynamic cycle, where transparency, visibility, selection progress and motivation of the breeders have promoted each other and had an impact towards improved honeybees.Only after 2000, the majority of the Beekeeping has become more challenging since 1990 due to diseases, environmental change, and agricultural auxiliary substances toxic to bees, not least reflected by colony losses . As can While the stagnation of honey yield phenotype may need some explanation, for the manageability traits it is straightforward. Slowly changing properties of the bees will lead to a changing evaluation yardstick. The same is true for the pintest, where the waiting times have been successively reduced from 24 h to 3 h, resulting in a changing baseline of evaluation. The baseline for mite infestation development is also constantly changing by beekeeping procedures. While mites become more and more wide-spread, the partial resistance may prompt breeders to reduce the chemical treatments which may lead to higher mite infestations etc. In conclusion, the effect of selection cannot be directly seen on the phenotype. Only the separation of the genetic effect from the apiary effect makes the changes clearly visible.Both genetic and phenotypical correlations are positive between all traits. The general agreement among most breeders to improve all traits simultaneously avoided breeding stock which developed one trait at the the expense of the other. This is reflected in the concurrent genetic progress of all traits.Varroa resistance is occasionally associated with aggressiveness [Varroa infestation.siveness  and breesiveness . HoweverBefore 1975 and in the early 1990s the recorded pedigrees were often not complete and contained gaps and errors. Thus, the inbreeding coefficients estimated from the pedigree may be lower than the real inbreeding. In the 1980s and after 1994 the pedigrees are sufficiently complete that the estimates can be considered as reliable.The high inbreeding coefficients in the 1980s can be seen as the result of the predominant line-breeding in that time, where the population was divided into subpopulations with little exchange . AdverseA. m. carnica population follows the development of a stable breeding progress with a very limited increase in inbreeding.Consistent with simulation studies , the A. A. m. carnica population is still largely unknown. In the first half of the 20th century, there was a mixture of different bee subspecies in Germany, many of them hybridized and with problematic properties. In the effort to improve the quality of honeybees since 1950, in Germany and Austria the strategy pure breeding of A. m. carnica (and A. m. mellifera) has been adopted based on (i) controlled mating at mating stations, (ii) phenotypical analysis to select for race-typical bees, and (iii) importation from the original distribution area. While the former two decrease the genetic diversity, the latter increases it. Since the total A. m. carnica population in Europe is large, its diversity can only be assessed with genomic analysis [For all traits, the results indicate accelerated breeding progress. Is there a danger of genetic depletion? The genetic potential of the analysis . HoweverA. m. carnica increases the attractiveness for beekeepers and gives rise to the risk of losing genetic diversity [A. m. carnica has been recognized as the more sustainable strategy compared to importing highly valued A. m. carnica [The breeding progress in iversity  and endaiversity . Howeveriversity ,68. The iversity , and the carnica .


We estimated the genetic covariance matrix among four inattention (INATT) and four hyperactivity (HYP) measures in the classical twin design. Data on INATT and HYP symptom counts were obtained in mono- and dizygotic twin pairs (N = 1593) with an average age of 12.2 years (sd = .51). We analyzed maternal ratings of INATT and HYP based on the Conners’ Parent Rating Scale (CPRS), the Strengths and Weaknesses of ADHD-symptoms and Normal-behavior (SWAN), and teacher ratings based on the Conners' Teacher rating scale (CTRS) and the ASEBA Teacher Rating Form (TRF). Broad-sense heritabilities, corrected for the main effects of sex and for random teacher rater effects, were large (ranging from .658 to .912). The results reveal pervasive and strong broad-sense genetic effects on INATT and HYP phenotypes with the phenotypic covariance among the phenotypes largely due to correlated genetic effects. Specifically between 79.9 and 99.9% of the phenotypic covariance among the HYP measures, and between 81.0 and 93.5% of the INATT measures are attributable to broad-sense genetic effects. Overall, the present results, pertaining to the broad-sense heritabilities and shared genetic effects, support the current genome-wide association meta-analytic approach to identifying pleiotropic genetic variants. There is little evidence for common (or shared) environmental effects, from twin or adoption studies  is a relatively common , is complicated by systematic effects due to response styles, rating biases, method effects stemming from differences in psychometric instruments, and the situation-dependency of children's behavior  at the Vrije Universiteit Amsterdam . The TRF contains 13 items in two subscales: inattention (5 items) and hyperactivity (8) . This results in a different distribution of the measures of INATT and HYP: SWAN measures tend to follow normal distribution, while those of the other instruments are left censored and positively skewed.The phenotypic scores were obtained by computing the average item score. A phenotypic score was coded as missing if 20% or more of item responses were missing in a given participant. If less than 20% of items was missing for a subscale, the missing items were imputed by substituting the participant's average item subscale score.or shared environmental variance (C), and unshared environmental variance (E) components. In practice, the phenotypic twin correlations (rMZ and rDZ) determine the choice initial choice of model. If rMZ > 2*rDZ (which is the case with the present phenotypes), one proceeds with an ADE model.Analyses were carried out in the R program version 3.4.1 (R Core Team ΣMZ and ΣDZ represent the (2*M)  ×  (2*M) phenotypic covariance matrices in the MZ and DZ samples. We carried out both univariate and multivariate analyses. As the univariate model (M = 1) is a special case of the multivariate model (M = 8), we present the latter. The phenotypic 16 × 16 covariance matrices ΣMZ and ΣDZ were decomposed as follows:We initially analyzed the eight phenotypes separately in univariate models, and then proceeded with the multivariate modeling of the eight phenotypes simultaneously. The genetic covariance structure model is as follows. Let M denote the number of phenotypes in the model, and let ΣMZ and ΣDZ have the indicated block structure, with the blocks representing covariance matrices within individual twins  and between twin-1 and twin-2 (ΣMZ21 and ΣDZ21). ΣA, ΣD, and ΣE are 8 × 8 covariance matrices, due to additive genetic, dominance, and unshared environmental effects, respectively. Below the matrices ΣA, ΣD, and ΣE are parameterized by means of the Cholesky (or triangular) decomposition, i.e., ΣA = ΔAΔAt, where the 4 × 4 matrix ΔA is lower triangular . This parameterization has the advantage of ensuring that the A, D, and E covariance matrices are positive (semi-) definite. However, the results of interest are not the matrices ΔA, ΔD, and ΔE (which we do not report), but rather the 8 × 8 matrices SA, SD, and SE, and their relative contributions to the 8 × 8 phenotype covariance of the INATT and HYP measures (ΣA + ΣD + ΣE). In the univariate analyses (M = 1), the matrices ΣMZ and ΣDZ are 2 × 2 covariance matrices, and ΣA, ΣD, and ΣE are 1 × 1 matrices, containing simply the variances due to A, D, and E . We carried out the univariate analyses to obtain initial estimates of the univariate variance components (to inform the multivariate modeling), and to test sex differences in the magnitude of the A, D, and E variance components.ΣA, ΣD, ΣE, i.e., the independent pathway model, a.k.a., the biometric factor model (see Martin and Eaves ΣA), was modeled by means of a 2 common factors model, in which the 4 HYP phenotypes and the 4 INATT phenotypes loaded on a common additive genetic HYP and a common additive genetic INATT model, respectively. We estimated the correlation between the common factors. The same applies to the dominance and unshared environmental covariance matrices, ΣD and ΣE. The residuals of the phenotype  were allowed to correlated between the twins. These correlations were free to vary over MZ and DZ twins, to accommodate possible genetic contributions to these residual terms.In addition to the estimation of the covariance matrices, we also fitted confirmatory common factor models to the 8 × 8 covariance matrices In fitting the ADE Cholesky models, we included sex as a main effect in all models, and tested sex differences in variance components in the univariate models. In addition, we included an additional teacher rater factor (relevant to the teacher ratings using the TRF and CTRS), to accommodate the fact that twins from the same pair may be rated by the same teacher , − 3.158 (CPRS HYP), − 1.053 (CTRS INATT), − 3.579 (CTRS HYP), − 2.737 (TRF INATT), and − 3.579 (TRF HYP). Following the transformation, which was applied to all phenotypes except the SWAN INATT and SWAN HYP, the data of all 8 phenotypes were linearly transformed so that the variances were approximately equal to one. This was done in the pooled data so that differences in phenotypic variances associated with sex, zygosity, and twin were retained. Rendering the variance to be about equal merely serves to facilitate raw data ML estimation. We considered the alternative approach of transforming the data to ordinal data , and applying liability-threshold modeling  maximum likelihood (ML) estimation to Box-Cox transformed phenotypic data (except the SWAN data), and we report robust standard errors  with an average age of 12.2 (standard deviation of 0.51). The year of birth ranged from 1986 to 1994, with 85% of the twins born in 1990 or 1991. The association of the phenotypes with age was negligible: the percentage of explained variance equaled 0.3% (CPRS HYP), 0.45% (CPRS INATT), 0.0 (CTRS HYP), 1.3% (CTRS INATT), 0.25% (TRS HYP), 0.5% (TRS INATT) and 6% (SWAN HYP) and 6% (SWAN INATT). We checked the effect of age on the ADE variance components of the SWAN phenotypes, but the effect of age was negligible. We therefore discarded age in the subsequent analyses. Of the 697 twin pairs that were rated by teachers, 57.2% were rated by the same teacher. Table MZ vs. rDZ): 0.91 vs. 0.46 (SWAN), 0.78 vs. 0.32 (CPRS), 0.64 vs. 0.25 (TRF), 0.60 vs. 0.31 (CTRS). The INATT twin correlations are: 0.85 vs. 0.40 (SWAN), 0.74 vs. 0.24 (CPRS), 0.63 vs. 0.16 (TRF), 0.72 vs. 0.41 (CTRS). The within-instrument phenotypic correlations between INATT and HYP are ~ 0.83 (SWAN), ~ 0.55 (CPRS), ~ 0.62 (TRF), and ~ 0.35 (CTRS). The across-instrument INATT correlations vary from ~ 0.31 to 0.56, and the across-instrument HYP correlations vary from ~ 0.77 to 0.20. Interrater  correlations vary from ~ 0.29 to 0.54 (INATT) and ~ 0.20 to 0.38 (HYP). These values are consistent with the results of previous articles /(ΣA + ΣD + ΣE) and ΣE/(ΣA + ΣD + ΣE), respectively.Table The broad-sense genetic correlations among the HYP measures range from 0.359 to 0.862, with an average correlation of ~ 0.50. The broad-sense genetic correlations among the INATT measures range from 0.439 to 0.627, with an average correlation of ~ 0.55. The environmental correlations are consistently lower. The proportions of phenotypic covariance attributable to A + D are highly illuminating. We find that between 79.9 and 99.9% of the phenotypic covariance among the HYP measures, and between 81.0 and 93.5% of the INATT measures are attributable to A + D. Figure The broad-sense genetic correlations between INATT and HYP are 0.866 (based on the SWAN), 0.641 (CPRS), 0.757 (TRF) and 0.416 (CTRS). The corresponding unshared environmental correlations are again generally lower. The broad sense genetic factors  accounted for most of the phenotypic correlations. The proportions of the phenotypic covariance attributable to genetic factors (A + D) equal 92.2% (SWAN), 89.6% (CPRS), 85.9% (TRF), and 83% (CTRS). Based on these results  common factor model as described in the methods section. However, this model failed to converge. We attribute this to the extensive missingness  and four measures of hyperactivity (HYP) in ~ 12 year old children. The broad-sense heritabilities (A + D) of the HYP phenotypes (~ 68–91%) and INATT phenotypes are relatively large (65–84%). The broad-sense genetic correlations between INATT and HYP vary considerably: 0.86 (SWAN), 0.64 (CPRS), 0.75 (TRF) and 0.41 (CTRS). The correlation between the INATT and HYP common factors equaled 0.75 . However, the contributions of broad sense genetic factor to the phenotypic correlations are large. The phenotypic correlations are 0.82 (SWAN), 0.54 (CPRS), 0.59 (TRF), and 0.36 (CTRS). The broad sense genetic contributions to these phenotypic correlations are 92%, 89%, 85%, and 83%, respectively. Phenotypic correlations among the four HYP measures vary between 0.331 and 0.741. Broad sense genetic factors account for 68% to 99% of these phenotypic correlations. The large genetic contributions to the phenotypic correlations among the HYP and INATT measures support the view that ADHD has a strong genetic liability . The relevant correlations are given in the third panel of Table Overall, the present results, pertaining to the broad-sense heritabilities and shared genetic effects, support the current GWAMA approach to identifying pleiotropic genetic variants contributing to ADHD variance. Specifically, we found that the HYP and INATT measures show strong genetic correlations, irrespective of the instrument used to obtain these. This suggests that the genetic effects shared between INATT and HYP are likely to be captured in the GWAMA of ADHD, notwithstanding the expected  variation in the psychometric instrument and method used to measure ADHD. It is possible that the moderating effects of rater, psychometric instrument, and situational factors introduce variation in genetic effect sizes. This may ultimately call for a random effects meta-analytic approach. We note, however, that Demontis et al.  found noWe note the following limitations of the present study. We did not consider contrast effects, although these have been demonstrated (e.g., Ebejer et al. While the results are consistent with the literature, we acknowledge that estimates may be biased. Notably, the present results are based on the classical twin design, which is characterized by many strong assumptions (Eaves et al.


This study was conducted to estimate effect of single nucleotide polymorphisms (SNP) on the estimated breeding value of Hungarian Grey (HG) bulls and to find markers associated with horn colour.Genotypes 136 HG animals were determined on Geneseek high-density Bovine SNP 150K BeadChip. A multi-locus mixed-model was applied for statistical analyses.10P>10) with green and white horn. These loci are located on chromosome 1, 3, 9, 18, and 25. Seven loci  showed considerable association (−log10P>10) with the estimated breeding value.Six SNPs were identified to be associated (−logAnalysis provides markers for further research of horn colour and supplies markers to achieve more effective selection work regarding estimated breeding value of HG. The phenotypic trits of Hungarian Grey (HG) cattle are characterised by strong pigmentation, long dark eyelashes and well-developed dewlap. The breed was used as a draught animal but it has been bred also for its beef quality. In the 19th century 1884), the majority of the 4.9 million cattle in Hungary were registered as HG. Later at the turn of the century (1900) half of the 6.7 million cattle still belonged to this breed  and supp, the majHorn of HGs represent a variety in form and in colour. There are white, green and cardy colour varieties where cardy is a mixture of green and white at different ratios . In the Nowadays, there is considerable interest in the application of genomic breeding value estimation to promote rapid and efficient selection in farm animals. In the last few years, advances in molecular genetics have enabled the application of DNA-chip technique to pave the way of achieving different breeding objectives. Genom-wide association study based on typing of single nucleotide polymorphisms (SNPs) by DNA chip technique is suitable to find loci associated with economically important traits in different cattle breeds , since cAt present, breeding decisions in AHGCB are based mainly on maintaining the rotation scheme of the bulls devised to keep diversity at the highest possible level . InclusiEstimated breeding values (EBV) signify level of the breeding potential of animals. It is a score of genetic merit combining the relative economic values of several traits. Genetic background of such an EBV evaluating system was investigated in several breeds –14. SuchIn this study a genome-wide association analysis has been performed regarding horn colour and EBV  of HG cattle.Samples were collected during routine practice of health investigations organised by the AHGCB previously and independently from the research presented here. Blood samples of 136 HG bulls collected from 16 farms of the country were selected to be genotyped on GeneSeek GGP Bovine 150K SNP chip. Samples have been selected by the AHGCB i) based on their high or low breeding value (ranging from 44 to 188 points), ii) and based on their horn colour.Evaluation of EBV of HG bulls at 100 to 300 days of age was based on birth weight, daily weight gain, 205 day weight and on maternal data. EBV calculation  was basewhere y is the EBV, X is the matrix of fixed effects , Z is the matrix of effects of the individual, W is the matrix of maternal genetic effect, S is the maternal permanent environment effect, e is the residual error, b, u, m and pe are the vectors of the corresponding matrices.Series of quality control (QC) procedures were conducted on the raw data  using SNP) asFor identification of loci associated with horn colour and EBV, multilocus mixed-models were used . In greeMEAN is the mean of estimated breeding value, σEBV is the deviation of EBV.where EBVFor the correction of population structure, genomic kinship matrix was used in a multi-locus mixed model . The usewhere y is the phenotypic value, X is the matrix of fixed effects composed of SNPs and covariates (year of birth and farm), Z is the matrix of random animal effects, e means the residual effects, and β and u are vectors representing coefficients of fixed and random effects, respectively.10P>10) on chromosome 1, 3, 9, 18, and 25 to be associated with horn colour (10P>18) are as follows.We have found six loci  and impairs extracellular matrix homeostasis [Cromosome 9: Primates and cow EPH receptor A7 were identified as evolutionarily conserved targets of the WNT/beta-catenin signaling pathway  and in eeostasis .On chromosome 18 close to rs110433116 apolipoprotein E has many functions listed in gene ontology (GO) database, including locomotory exploration behavior (GO:0035641), involved in immune processes , metal chelating activity (GO:0046911) and also in heparan sulfate proteoglycan binding (GO:0043395).FSCN1), actin beta, F-box and leucine rich repeat protein 18 are listed in immune related processes in Panther database. FSCN1 also plays role in microspike (GO:0030035), microvillus (GO:0032534), and positive regulation of lamellipodium (GO:0010592) assembly.On cromosome 25, around rs108961742 there are densely packed genes. Three of them, fascin actin-bundling protein 1 (10P>10) associated to EBV on chromosome 1, 3, 6, 9, 10, and 28  is similar to immunoglobulin superfamily 4 within their Ig-like C2-type domains, which might play role in tumor supression [pression  in humanpression . Chondropression  and its pression . CXADR Ipression .Chromosome 6: the actin regulating gene WD repeat domain 1 is downregulated in lactating mammary gland of cattle [NKX3-2) gene is strongly influencing the fate of cells, e.g. myogenic program of chick muscle satellite cells can be suppressed in vitro by chondrogenic factor like NKX3-2 [f cattle  and its f cattle . Heparanf cattle . NK3 home NKX3-2 . It alsoe NKX3-2 .Chromosome 9: Serine incorporator 1 is overexpressed in medium follicles of bovine [f bovine . Heat shf bovine . Gap junf bovine . Accordif bovine . Minichrf bovine . Anti-sif bovine . Solute f bovine .Chromosome 10: VPS33B interacting protein, apical-basolateral polarity regulator, Spe-39 homolog is associated with infectious hoof lesions in Holstein cattle [Escherichia coli and Streptococcus uberis [Cooperia oncophora [n cattle . It is as uberis . Iodothys uberis . DIO2 wancophora . Thyroidncophora . They alncophora .Chromosome 28: Expression levels of DPY30 domain containing 1 and 2  and tetraspanin 14 are lower in spermatozoa of high fertility dairy sires [ry sires . Growth ry sires . Coiled-ry sires .As for our horn-colour association study, previously Radácsi et al  already The presented candidate genes around the SNPs associated with EBV are influencing fertility of both sexes, behaviour, body and muscle composition and take part in immune processes.For the breeders, desiring a shift in green-white colour ratio or wanting to exploit molecular approach in breeding value estimation, the presented markers could be useful in achieving their breeding plans.