Attention module incorporated transfer learning empowered deep learning-based models for classification of phenotypically similar tropical cattle breeds (Bos indicus).

Accurate breed identification in dairy cattle is essential for optimizing herd management and improving genetic standards. A smart method for correctly identifying phenotypically similar breeds can empower farmers to enhance herd productivity. A convolutional neural network (CNN) based model was developed for the identification of Sahiwal and Red Sindhi cows. To increase the classification accuracy, first, cows's pixels were segmented from the background using CNN model. Using this segmented image, a masked image was produced by retaining cows' pixels from the original image while eliminating the background. To improve the classification accuracy, models were trained on four different images of each cow: front view, side view, grayscale front view, and grayscale side view. The masked images of these views were fed to the multi-input CNN model which predicts the class of input images. The segmentation model achieved intersection-over-union (IoU) and F1-score values of 81.75% and 85.26%, respectively with an inference time of 296 ms. For the classification task, multiple variants of MobileNet and EfficientNet models were used as the backbone along with pre-trained weights. The MobileNet model achieved 80.0% accuracy for both breeds, while MobileNetV2 and MobileNetV3 reached 82.0% accuracy. CNN models with EfficientNet as backbones outperformed MobileNet models, with accuracy ranging from 84.0% to 86.0%. The F1-scores for these models were found to be above 83.0%, indicating effective breed classification with fewer false positives and negatives. Thus, the present study demonstrates that deep learning models can be used effectively to identify phenotypically similar-looking cattle breeds. To accurately identify zebu breeds, this study will reduce the dependence of farmers on experts.

Breeding practice of indigenous village chickens, and traits and breed preferences of smallholder farmers.

BACKGROUND: Indigenous village chickens, or (IVCs), significantly contribute to rural livelihoods. Although natural selection has a disproportionate impact on the genetic structure of IVCs, farmers have developed locally tailored breeding practices to get the most out of their flocks. OBJECTIVES: Small-scale farmers' insights on trait breed preferences and family flock breeding practices are presented in this cross-sectional study. METHODS: A cross-sectional study was conducted in two agroecological zones using face-to-face individual interviews with 119 general informants. RESULTS: Farmers prefer IVCs because they are multipurpose birds. Unlike policymakers, who usually underestimate the importance of IVCs, small-scale farmers acknowledge the coexistence of local and commercial chickens. Only 15.7% of farmers recruited homegrown cocks, whereas 47.9% outsourced breeding cocks from local markets and 36.4% from neighbours (χ-squared = 15.976, df = 2, p = 0.0003395). About 49.2% of small-scale farmers believed that consanguineous mating-induced inbreeding has only trivial effects. High flock turnover significantly reduces inbreeding. Regardless of the low production capacity, small-scale farmers prefer local (rank = 1.47) chickens to commercial (rank = 1.61). For cocks, fertility and growth traits were highly sought after, whereas for hens, maternal instincts and laying performance were prioritized. Compared to the highlands, the lowlands had a longer egg storage period (t = 2.677, df = 117, p = 0.009, 95% CI: -3.7607, -0.5622). CONCLUSIONS: This study documented the wisdom of small-scale farmers and encouraged the incorporation of their insights into a sustainable genetic improvement program.

Helminths of the black-headed gull (Chroicocephalus ridibundus) from breeding colonies in north-central Poland.

Among parasites of the digestive tract of the black-headed gull (Chroicocephalus ridibundus) in Poland, the best known are species of digenetic trematodes and cestodes. Nematodes of this bird species are not well known. Black-headed gulls, due to their varied diet, migration, life in a flock, and changes of habitat, can become infected with various species of helminths, and like synanthropic birds, they can spread the dispersal stages of parasites across urban and recreational areas. In the present study, an attempt was made to identify the helminth fauna of C. ridibundus from breeding colonies in north-central Poland. The aim of the study was to describe the taxonomic structure of parasites of the digestive tract of the black-headed gull and determine the quantitative parameters of their occurrence. A total of 43 black-headed gulls were examined post-mortem for gastrointestinal helminths, resulting in the identification of four cestodes (Paricterotaenia porosa, Lateriporus clerci, Anomotaenia micracantha, and Wardium fusum), three trematodes (Diplostomum pseudospathaceum, Plagiorchis laricola, and Apophallus muehlingi), and three nematodes (Eucoleus contortus, Cosmocephalus obvelatus, and Porrocaecum ensicaudatum). Lateriporus clerci (in adult form), C. obvelatus and P. ensicaudatum (in larval form) were recorded for the first time in the black-headed gull in Poland.

An arctic breeding songbird overheats during intense activity even at low air temperatures.

Birds maintain some of the highest body temperatures among endothermic animals. Often deemed a selective advantage for heat tolerance, high body temperatures also limits birds' thermal safety margin before reaching lethal levels. Recent modelling suggests that sustained effort in Arctic birds might be restricted at mild air temperatures, which may require reductions in activity to avoid overheating, with expected negative impacts on reproductive performance. We measured within-individual changes in body temperature in calm birds and then in response to an experimental increase in activity in an outdoor captive population of Arctic, cold-specialised snow buntings (Plectrophenax nivalis), exposed to naturally varying air temperatures (- 15 to 36 °C). Calm buntings exhibited a modal body temperature range from 39.9 to 42.6 °C. However, we detected a significant increase in body temperature within minutes of shifting calm birds to active flight, with strong evidence for a positive effect of air temperature on body temperature (slope = 0.04 °C/ °C). Importantly, by an ambient temperature of 9 °C, flying buntings were already generating body temperatures ≥ 45 °C, approaching the upper thermal limits of organismal performance (45-47 °C). With known limited evaporative heat dissipation capacities in these birds, our results support the recent prediction that free-living buntings operating at maximal sustainable rates will increasingly need to rely on behavioural thermoregulatory strategies to regulate body temperature, to the detriment of nestling growth and survival.

Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population.

BACKGROUND: Carcass weight (HCW) and marbling (MARB) are critical for meat quality and market value in beef cattle. In composite breeds like Brangus, which meld the genetics of Angus and Brahman, SNP-based analyses have illuminated some genetic influences on these traits, but they fall short in fully capturing the nuanced effects of breed of origin alleles (BOA) on these traits. Focus on the impacts of BOA on phenotypic features within Brangus populations can result in a more profound understanding of the specific influences of Angus and Brahman genetics. Moreover, the consideration of BOA becomes particularly significant when evaluating dominance effects contributing to heterosis in crossbred populations. BOA provides a more comprehensive measure of heterosis due to its ability to differentiate the distinct genetic contributions originating from each parent breed. This detailed understanding of genetic effects is essential for making informed breeding decisions to optimize the benefits of heterosis in composite breeds like Brangus. OBJECTIVE: This study aims to identify quantitative trait loci (QTL) influencing HCW and MARB by utilizing SNP and BOA information, incorporating additive, dominance, and overdominance effects within a multi-generational Brangus commercial herd. METHODS: We analyzed phenotypic data from 1,066 genotyped Brangus steers. BOA inference was performed using LAMP-LD software using Angus and Brahman reference sets. SNP-based and BOA-based GWAS were then conducted considering additive, dominance, and overdominance models. RESULTS: The study identified numerous QTLs for HCW and MARB. A notable QTL for HCW was associated to the SGCB gene, pivotal for muscle growth, and was identified solely in the BOA GWAS. Several BOA GWAS QTLs exhibited a dominance effect underscoring their importance in estimating heterosis. CONCLUSIONS: Our findings demonstrate that SNP-based methods may not detect all genetic variation affecting economically important traits in composite breeds. BOA inclusion in genomic evaluations is crucial for identifying genetic regions contributing to trait variation and for understanding the dominance value underpinning heterosis. By considering BOA, we gain a deeper understanding of genetic interactions and heterosis, which is integral to advancing breeding programs. The incorporation of BOA is recommended for comprehensive genomic evaluations to optimize trait improvements in crossbred cattle populations.

Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites.

BACKGROUND: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Design and test of novel scent enrichments to enhance breeding of zoo-housed lemurs.

Background: Zoos use environmental enrichments, including scents, which may have applications to improve breeding success for taxa, such as lemurs, which rely heavily on olfactory communication. We aimed to develop novel, biologically-relevant scent enrichments to trigger mating behaviours of zoo-housed lemur species, which are critically endangered in the wild and show a low success rate in captive breeding programmes. Methods: We examined anogenital odour secretions, released by female gentle ( Hapalemur alaotrensis) and ruffed ( Varecia variegata) lemurs, using solid-phase microextraction and gas chromatography-mass spectrometry techniques. We identified the key compounds distinguishing the volatile chemical profile of female lemurs during the breeding season and used them to develop species-specific scent enrichments. We then tested the scent enrichments, made up of synthesized mixtures conveying information about female lemur fertility, on unsuccessful breeding pairs of lemurs hosted in European zoos. We evaluated the effects of the newly designed scent enrichments on their target species by combining behavioural observations with faecal endocrinology. Results: We identified and reproduced fertility-specific signals associated with female scents. These scent mixtures triggered male sexual behaviours, including mating, during and after the enrichment condition. We also found effects on faecal testosterone levels, with increased levels after the enrichment condition albeit not statistically significant. Conclusions: Our findings suggest that biologically-relevant scent enrichments may trigger natural species-specific behaviours, with potential implications for conservation breeding of zoo-based endangered lemur species, and highlight that combining more assessment methods may assist with evaluating the impact of environmental enrichments.

Investigating the genetic determination of duration-of-fertility trait in breeding hens.

The duration-of-fertility (DF), which was defined as the number of days when breeding hens lay fertile eggs following copulation or artificial insemination (AI), is an important economic trait in chick production when it has strong effects on fertile egg output and production costs. Little is known about the underlying genes and molecular markers related to DF trait to date. Here, we measured the DF of 701 Chinese Jinghong hens and 408 Jingfen hens. The DF showed high individual variability and potential for genetic improvement. Then, 192 Jinghong breeding hens were provided for a genome-wide association study, 27 SNPs respectively located in three genomic linkage regions (GGA1:41Kb; GGA3:39Kb and GGA8:39Kb) were suggested to be significantly associated with DF. Particularly, 6 of these 27 SNPs were further verified to be associated with DF in the 701 Jinghong and 408 Jingfen hens using PCR-RFLP genotyping method. These 27 SNPs were also mapped to 7 genes according to their genomic position. Furtherly, 5 of these 7 genes were tested using qPCR. Results show that the CYP2D6, WBP2NL, ESR1 and TGFBR3 mRNA expression levels of hens with long DF were significantly higher than the hens with short DF (P < 0.05). Overall, findings in our research provide new insight into the genetic basis of duration-of-fertility in breeding hens while providing new clues for further functional validation on the DF-related genetic regulation mechanism and improvement of DF through chicken breeding.

Estimation of maternal and additive effect on reproduction and productive traits in Hardhenu cattle.

The study aimed to assess performance traits in Hardhenu cattle by analysing data from 445 animals born to 59 sires and 227 dams. The investigation focused on estimating (co)variance components and genetic parameters for reproduction and production traits in dairy cattle. Results from least-squares analysis indicated a significant effect (p < .01) of the period of calving (POC) on key production traits, including first lactation milk yield (FLMY), 300-day milk yield (FLMY300), first peak yield (FPY) and total lactation milk yield (TLMY) in studied population. The least squares means for these traits were reported as follows: FLMY (2665.68 ± 45.66 kg), FLMY300 (2425.52 ± 34.41 kg), FLL (312.95 ± 3.83 days), FPY (11.52 ± 0.15 kg) and TLMY (9282.44 ± 167.03 kg) in Hardhenu cattle. In the studied population, only additive genetic variability was found to be present and there was absence of any significant maternal effect with respect to targeted traits in the resource population. Direct heritability estimates (h2) for FLMY, FLMY300, FLL, FPY, TLMY and other traits ranged from 0.03 to 0.41 in Hardhenu cattle. These findings offer valuable insights into the genetic factors influencing performance traits, contributing to the enhancement of breeding and management practices in Hardhenu cattle.

Runs of homozygosity and selection signature analyses reveal putative genomic regions for artificial selection in layer breeding.

BACKGROUND: The breeding of layers emphasizes the continual selection of egg-related traits, such as egg production, egg quality and eggshell, which enhance their productivity and meet the demand of market. As the breeding process continued, the genomic homozygosity of layers gradually increased, resulting in the emergence of runs of homozygosity (ROH). Therefore, ROH analysis can be used in conjunction with other methods to detect selection signatures and identify candidate genes associated with various important traits in layer breeding. RESULTS: In this study, we generated whole-genome sequencing data from 686 hens in a Rhode Island Red population that had undergone fifteen consecutive generations of intensive artificial selection. We performed a genome-wide ROH analysis and utilized multiple methods to detect signatures of selection. A total of 141,720 ROH segments were discovered in whole population, and most of them (97.35%) were less than 3 Mb in length. Twenty-three ROH islands were identified, and they overlapped with some regions bearing selection signatures, which were detected by the De-correlated composite of multiple signals methods (DCMS). Sixty genes were discovered and functional annotation analysis revealed the possible roles of them in growth, development, immunity and signaling in layers. Additionally, two-tailed analyses including DCMS and ROH for 44 phenotypes of layers were conducted to find out the genomic differences between subgroups of top and bottom 10% phenotype of individuals. Combining the results of GWAS, we observed that regions significantly associated with traits also exhibited selection signatures between the high and low subgroups. We identified a region significantly associated with egg weight near the 25 Mb region of GGA 1, which exhibited selection signatures and has higher genomic homozygosity in the low egg weight subpopulation. This suggests that the region may be play a role in the decline in egg weight. CONCLUSIONS: In summary, through the combined analysis of ROH, selection signatures, and GWAS, we identified several genomic regions that associated with the production traits of layers, providing reference for the study of layer genome.

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.

Identifying Regions of the Genome Associated with Conception Rate to the First Service in Holstein Heifers Bred by Artificial Insemination and as Embryo Transfer Recipients.

Heifer conception rate to the first service (HCR1) is defined as the number of heifers that become pregnant to the first breeding service compared to the heifers bred. This study aimed to identify loci associated and gene sets enriched for HCR1 for heifers that were bred by artificial insemination (AI, n = 2829) or were embryo transfer (ET, n = 2086) recipients, by completing a genome-wide association analysis and gene set enrichment analysis using SNP data (GSEA-SNP). Three unique loci, containing four positional candidate genes, were associated (p < 1 × 10-5) with HCR1 for ET recipients, while the GSEA-SNP identified four gene sets (NES ≥ 3) and sixty-two leading edge genes (LEGs) enriched for HCR1. While no loci were associated with HCR1 bred by AI, one gene set and twelve LEGs were enriched (NES ≥ 3) for HCR1 with the GSEA-SNP. This included one gene (PKD2) shared between HCR1 AI and ET services. Identifying loci associated or enriched for HCR1 provides an opportunity to use them as genomic selection tools to facilitate the selection of cattle with higher reproductive efficiency, and to better understand embryonic loss.

Genomic Characterization of Quality Wool Traits in Spanish Merino Sheep.

The native Spanish Merino breed was the founder of all the other Merino and Merino-derived breeds worldwide. Despite the fact that this breed was created and improved to produce the highest quality fine wool, the global wool market crisis led to the wholescale crossing of most of the herds with breeds for meat purposes. Nevertheless, there are still some purebred animals with a high potential for producing quality wool. The objective of this study was to characterize the current wool quality of the breed and identify genes associated with these parameters. To achieve this, over 12,800 records from the most representative animals of the breed (registered in the herd book) were analyzed using the Australian OFDA 2000 system, for parameters such as fiber diameter (FD), standard deviation (SD), coefficient of variation (CV), fibers over 15 microns (>15%), staple length (SL), and comfort factor (CRV). Additionally, animals with the most extreme FD values were whole-genome sequenced using NGS. Genome-wide association studies (GWAS) determined the association of 74 variants with the different traits studied, which were located in 70 different genes. Of these genes, EDN2, COL18A1, and LRP1B, associated with fibers over 15%, and FGF12 and ADAM17, associated with SL, play a key role in hair follicle growth and development. Our study reveals the great potential for recovering this breed for fine wool production, and identifies five candidate genes whose understanding may aid in that selection process.

Analysis of ddRAD-seq data provides new insights into the genomic structure and patterns of diversity in Italian donkey populations.

With more than 150 recognized breeds, donkeys assume relevant economic importance, especially in developing countries. Even if the estimated number of heads worldwide is 53M, this species received less attention than other livestock species. Italy has traditionally been considered one of the cradles of European donkey breeding, and despite a considerable loss of biodiversity, today still counts nine autochthonous populations. A total of 220 animals belonging to nine different populations were genotyped using the double-digest restriction site associated DNA (ddRAD) sequencing to investigate the pattern of diversity using a multi-technique approach. A total of 418,602,730 reads were generated and successfully demultiplexed to obtain a medium-density SNP genotypes panel with about 27K markers. The diversity indices showed moderate levels of variability. The genetic distances and relationships, largely agree with the breeding history of the donkey populations under investigation. The results highlighted the separation of populations based on their genetic origin or geographical proximity between breeding areas, showed low to moderate levels of admixture, and indicated a clear genetic difference in some cases. For some breeds, the results also validate the success of proper management conservation plans. Identified runs of homozygosity islands, mapped within genomic regions related to immune response and local adaptation, are consistent with the characteristics of the species known for its rusticity and adaptability. This study is the first exhaustive genome-wide analysis of the diversity of Italian donkey populations. The results emphasized the high informativeness of genome-wide markers retrieved through the ddRAD approach. The findings take on great significance in designing and implementing conservation strategies. Standardized genotype arrays for donkey species would make it possible to combine worldwide datasets to provide further insights into the evolution of the genomic structure and origin of this important genetic resource.

Donkeys assume relevant economic importance in several countries worldwide. However, the genetic structure of these populations is less investigated compared to other species. The aim of this study was to investigate the genetic background of nine different Italian donkey populations. A total of 220 animals were genotyped with about 27K markers extracted by the double-digest restriction site associated DNA sequencing. The consistency of the results across different approaches agreed with the demographic history, the origin, and previous results on the nine donkey populations, suggesting that our conclusions are robust. Moreover, the results of the present study highlighted low to moderate levels of admixture and, for some breeds, confirmed the success of proper management conservation plans.

Mitochondrial DNA and Y chromosome reveal the genetic structure of the native Polish Konik horse population.

Polish Konik remains one of the most important horse breeds in Poland. The primitive, native horses with a stocky body and mouse-like coat color are protected by a conservation program, while their Polish population consists of about 3,480 individuals, representing 16 dam and six sire lines. To define the population's genetic structure, mitochondrial DNA and Y chromosome sequence variables were identified. The mtDNA whole hypervariable region analysis was carried out using the Sanger sequencing method on 233 Polish Koniks belonging to all dam lines, while the Y chromosome analysis was performed with the competitive allele-specific PCR genotyping method on 36 horses belonging to all sire lines. The analysis of the mtDNA hypervariable region detected 47 SNPs, which assigned all tested horses to 43 haplotypes. Most dam lines presented more than one haplotype; however, five dam lines were represented by only one haplotype. The haplotypes were classified into six (A, B, E, J, G, R) recognized mtDNA haplogroups, with most horses belonging to haplogroup A, common among Asian horse populations. Y chromosome analysis allocated Polish Koniks in the Crown group, condensing all modern horse breeds, and divided them into three haplotypes clustering with coldblood breeds (28 horses), warmblood breeds (two horses), and Duelmener Pony (six horses). The clustering of all Wicek sire line stallions with Duelmener horses may suggest a historical relationship between the breeds. Additionally, both mtDNA and Y chromosome sequence variability results indicate crossbreeding before the studbooks closure or irregularities in the pedigrees occurred before the DNA testing introduction.

Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle.

Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

afila, the origin and nature of a major innovation in the history of pea breeding.

The afila (af) mutation causes the replacement of leaflets by a branched mass of tendrils in the compound leaves of pea - Pisum sativum L. This mutation was first described in 1953, and several reports of spontaneous af mutations and induced mutants with a similar phenotype exist. Despite widespread introgression into breeding material, the nature of af and the origin of the alleles used remain unknown. Here, we combine comparative genomics with reverse genetic approaches to elucidate the genetic determinants of af. We also investigate haplotype diversity using a set of AfAf and afaf cultivars and breeding lines and molecular markers linked to seven consecutive genes. Our results show that deletion of two tandemly arranged genes encoding Q-type Cys(2)His(2) zinc finger transcription factors, PsPALM1a and PsPALM1b, is responsible for the af phenotype in pea. Eight haplotypes were identified in the af-harbouring genomic region on chromosome 2. These haplotypes differ in the size of the deletion, covering more or less genes. Diversity at the af locus is valuable for crop improvement and sheds light on the history of pea breeding for improved standing ability. The results will be used to understand the function of PsPALM1a/b and to transfer the knowledge for innovation in related crops.

Variability and Number of Circulating Complementary Sex Determiner (Csd) Alleles in a Breeding Population of Italian Honeybees under Controlled Mating.

In Apis mellifera, csd is the primary gene involved in sex determination: haploid hemizygous eggs develop as drones, while females develop from eggs heterozygous for the csd gene. If diploid eggs are homozygous for the csd gene, diploid drones will develop, but will be eaten by worker bees before they are born. Therefore, high csd allelic diversity is a priority for colony survival and breeding. This study aims to investigate the variability of the hypervariable region (HVR) of the csd gene in bees sampled in an apiary under a selection scheme. To this end, an existing dataset of 100 whole-genome sequences was analyzed with a validated pipeline based on de novo assembly of sequences within the HVR region. In total, 102 allelic sequences were reconstructed and translated into amino acid sequences. Among these, 47 different alleles were identified, 44 of which had previously been observed, while 3 are novel alleles. The results show a high variability in the csd region in this breeding population of honeybees.