Comparing hybrid maize to open-pollinated varieties, the former is widely recognized for its higher producing capacity. However, the production potential of hybrids varies depending on the region and the season. In order to find the high yielding stable genotypes throughout both settings, this experiment was carried out in the summer of 2021 and the winter of 2021–2022, using a Randomized Complete Block Design with 11 genotypes and 3 replications. The tallest genotypes among those evaluated were CP 808 and G-25 in the winter and summer, respectively. Both P3553 in the winter and CP 808 in the summer reported earlier days to anthesis. Grain yield showed a substantial and positive links with cob characteristics and a negative correlation with reproductive traits, according to correlation analysis. When compared to the yield of both seasons, P3355 and Bisco gold 941 showed highest yield in both environments. Thus, genotypes P3355 and Bisco gold 941 are recommended for cultivation in study area over other available hybrids in the market. Genotype x environment interaction; AMMI analysis; Corn breeding; Heritability; Winter; Summer. Its culThe use of Open Pollinated variety (OPV) is high in mid-hills but in Terai region, hybrid maize tends to be dominating with increase in yield per hectare. There are several maize varieties available in Nepal's seed market from different international corporations, some of which are registered and some of which are not. This leads to a variety of issues in Nepal, from barren cob to a high disease incidence and low plant stand . Hybrid The study of genotypes in different seasons is desired as a genotype suitable for summer cultivation may not be suitable for winter and vice versa and some can perform optimally with stable production in both seasons , 4, 6. TThough there are 45 registered commercial maize hybrids in Nepal, the hybrids that performs better with stable grain yield in individual and across the season is not studied. Kandel and Shrestha  reportedDue to fragmentation, the majority of farmers in developing nations have extremely limited access to land, and the hybrid maize seed that is available is rarely used entirely in a single cropping season. Therefore, small landholder farmers may find it advantageous to adopt high yielding maize cultivars in both seasons, improving the productivity of the crop, if we were able to identify high yielding and stable varieties/hybrids for both the summer and winter seasons. The demand of hybrid seeds of maize is almost dependent on India with very few national hybrids , Chitwan) under cultivation, among which Rampur hybrid-10 is one of the most promising heat resilient maize hybrid , 5. Due 22.1The experiment was conducted in the farmers field of Itahari sub-metropolitan municipality (26°39′47″N 87°16′28″E). The study area lies in the terai region of Nepal located at 110 masl that has humid weather with cold winters and very hot summers. The climatic details of the experimental site are presented in 2.2The experiment was conducted in Completely Randomized Bock design (RCBD) with 11 genotypes and 3 replications in both the seasons (Summer 2021 and Winter 2021/2022). The genotypes used in the study were collected from local agrovets of Itahari and Biratnagar in their original sachet of 2kg quantity and all of them were commercial maize hybrids developed by various multinational companies. A total of nine multinational company hybrids, RH-10  and Arun 2 (open pollinated variety) as local check was used in the experiment. The details of the genotypes used has been presented in th of April and harvested from 21st of August to 23rd of August. In winter, sowing was done on 15th of November and was harvested from April 18-April 25 when physiological maturity has been attained.The experimental field was laid out plot size of 4 m × 3 m with row to row and plant to plant spacing of 0.75m and 0.2m, respectively. Every genotype was sown in four consecutive rows. The plot to plot distance was 0.75m and block to block distance was 1m. In summer, sowing was done on 252.3−1 of N:P2O5:K2O in the form of Urea, Diammonium Phosphate and Muriate of potash respectively [Field preparation was done by ploughing two times followed by leveling and ensuring optimal moisture for germination. Farm yard manure was applied at the rate of 15 t/ha. Chemical fertilizers were applied at the rate of 180:60:40 kg haectively . Plantin2.4Data collection was done on five randomly selected plants from all plot in both the seasons. Except for reproductive traits, all the traits were recorded after the crop has attained physiological maturity and harvesting. Reproductive traits include day to 50% tasseling (anthesis days), days to 50% silking (silking days) and Anthesis silking interval (ASI). Plant height and ear height was measured at physiological maturity from five randomly selected plants as suggested by Badu-Apraku et al. . After h2.5Data was entered in MS-Excel 2016 and was analyzed using R-studio 4.0.1 and SPSS v.20. Estimation of genetic parameters was done by using R-studio v.4.0.1. Correlation coefficient was estimated using SPSS v.20.Correlation coefficient was computed by the formula rp (xy) is phenotypic correlation, cov p (xy) is covariance due to phenotype of xy, and As recommended by Singh and Chaudary , the phe2.6Data were subjected to analysis of variance across the environments (season) using ADEL-R version 2.0. Additive Main Effect and Multiplicative Interaction (AMMI) model was used for the mean of the yield of the 11 genotype of maize from both the environments using GEA-R software. The AMMI model equation is:33.13.1.1Analysis of variance revealed that the plant height differed significantly in both the seasons and ear height differed significantly in winter season and was not significantly different in summer season . Plants CP 808 (255.3cm) was the tallest genotype in winter season followed by Bisco gold 941 236.6cm), G-25 236.67cm), and P3355 (235.3cm) . In summcm, and P.6cm, G-23.1.2Analysis of variance revealed that the reproductive traits anthesis days, silking days and anthesis silking interval showed highly significant differences (P ≤ 0.0001) in both the seasons, between the genotypes . The ant3.1.3Genotype × Environment interaction was non-significant. This might be due to the reason that the trait might be under the genetic control and has low influence on variation in environmental condition [Number of rows per cob (NORPC), Number of grains per row (NOGPR) and Cob length (CL) are significantly different in both the seasons. Cob diameter and number of ears per hectare (NOEPH) showed significantly different performance in summer and nonsignificant performance in winter season. The details of the cob characteristics have been presented in ondition  and simiondition  in threeondition . The menondition , 8.Table3.1.4Analysis of variance revealed that the grain yield differed significantly in both the seasons, However the average grain yield was higher in the summer season. Similar finding was also reported by Dhakal et al.  where th3.23.2.1Genetic parameters differed significantly in both the summer and winter seasons. PCV was larger than GCV in both the seasons in terms of all genetic parameters, i. e environment influenced in expression of traits. Similar finding was also reported by several researchers , 44, 45.Overall highest GCV was observed in ASI during winter season and summer season among all the genetic parameters. Though, GCV indicates a high level of genetic diversity, but heritability estimates and genetic gain are used to determine the amount of heritable variation in traits , 45.3.2.2In winter season, the highest heritability was reported in anthesis days 0.9407) followed by silking days (0.9131), number of grains per row (0.9004), thousand kernel weight (0.8621), cob length , anthesis silking interval (0.7402), plant height (0.7230) and grain yield (0.7205). The least heritable trait was cob diameter (0.0959). The highest heritability during summer season was reported in anthesis silking interval (0.8832) followed by number of grains per row (0.8677), cob diameter (0.7369), anthesis days 0.7148), plant height (0.6827), grain yield (0.6679), number of rows per cob (0.6621), cob length (0.6377), and silking days (0.6339). Thousand kernel weight and ear height were low heritable traits and number of ears per hectare was moderately heritable. This study reported that, in both the seasons, heritability of trait was low (<30%), moderate (30–60%) and highly heritable (>60%)  as propo followed, plant hGenetic advance as a percentage of mean ranged from 1.7% in cob diameter to 65.74% in anthesis silking interval in winter season and 6.21% in ear height to 81.68% in anthesis silking interval in summer season. Greater estimates of GAM were reported in anthesis silking interval (65.74) followed by grain yield (40.36), number of grains per row (34.82), cob length (32.57), 1000-kernel weight (22.31), and ear height (22.21) in winter season and in summer season higher estimate of GAM was reported in anthesis silking interval (81.68) followed by number of grains per row (36.9) and grain yield (31.66). Based on heritability and genetic advance of different traits in both season anthesis silking interval, number of grains per row and grain yield are under genetic control governed by additive genes. Mohana Krishna et al.  reported3.3Correlation analysis showed that grain yield is positively and significantly correlated with thousand kernel weight (TKW) r = 0.597), anthesis silking interval r = 0.446), plant height (r = 0.381), ear height (r = 0.384), number of grains per row (r = 0.446), number of rows per cob (r = 0.375), cob diameter (r = 0.431), cob length (r = 0.461) and number of ears per hectare (r = 0.499). Reproductive trait like anthesis days (r = -0.487) and silking days (r = -0.466) are negatively and significantly correlated with grain yield i.e. earlier tasseling and silking days are related with high grain yield in maize . Similar, anthesi6, plant 3.4AMMI analysis was done for all the traits under study and it is observed that the trait viz. anthesis silking interval, anthesis days, silking days, plant height, number of ear per hectare, cob diameter, number of rows per cob showed significant variations (p < 0.05) for both the main (genotypes and environment) and interaction effects revealing the presence of considerable variability among the studied genotypes, environment, and their interaction. Genotype was not significantly different for Cob length, number of ears per hectare, ear height, grain yield, number of grains per row and for environment showed a significant variation. Thousand kernel weight showed significant variation between genotypes whereas environment and interaction effects were not significantly different.In our study, AMMI analysis revealed that maximum part of total variance was attributed to environment followed by genotype and interaction for the traits anthesis days, silking days, anthesis silking interval, ear height, and plant height. Similar finding was reported by Zaid et al. . Maximum4Hybrid maize is well known for its remarkable contribution to food security as it out yielded OPV's by more than double their production. However, the hybrids that performed superior in one environment do not perform similarly in other locations and environments. In this study, locally available for sale commercial hybrids were used for evaluation and they reported a significant variation in both the seasons for yield and yield attributing traits. Correlation coefficient revealed that cob characteristics have a significant and positive association with grain yield. The field experiment over two seasons revealed that genotype P3355 and Bisco gold 941 showed promising yield when cultivated in winter and summer seasons under optimum conditions. Farmers direct evaluation for grain yield and yield attributing traits of two recommended hybrids (P3355 and Bisco Gold 941) under various planting geometry and nutrient content is suggested for optimal production and increased profitability.Not applicable.Not applicable.Sandesh Thapa; Sandhya Adhikari: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Sara Rawal: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Adzuki beans are an important food legume crop in East Asia. A large number of adzuki bean accessions are maintained in the Chinese national seed genebank. A collection of 59 elite cultivars, 389 landraces, and 27 wild adzuki beans were selected and phenotyped extensively for yield and yield-related traits at two different locations . Ten agronomic and yield-related traits were scored, and the data were subjected to analysis of variance (ANOVA), principal component analysis (PCA), correlation, and cluster analysis. Significant variation was observed for genotypes, locations, and genotype x environment interaction for most traits. Also, there were significant differences in the phenotypes among accessions of different germplasm types. The broad-sense heritability of traits studied ranged from 4.4% to 77.8%. The number of seeds per pod (77.8%), 100-seed weight (68.0%), and number of plant branches (63.9%) had a high heritability. A total of 10 traits were transformed into 3 comprehensive factors by principal component analysis, and the first three principal component factors contributed 72.31% of the total variability. Cluster analysis categorized the 475 adzuki bean accessions into five distinct groups. The results described in this study will be useful for adzuki bean breeders for the development of varieties with high end-use quality. Vigna angularis L.) is a diploid (2n = 22), self-pollinating, and annual legume which belongs to the Vigna of Leguminosae [The adzuki bean  differs from the wild adzuki bean (V. angularis var. nipponensis) in many traits, e.g., the cultivated adzuki bean forms flowers earlier and has larger seeds than the wild adzuki bean, the seed size has markedly increased, the growth habit has changed from twining to erect stems, the plant height decreased, and the maturation period has shortened significantly. These changes contributed to an increase in yield over the adzuki bean landrace and cultivated varieties. Landraces represent an intermediate stage of domestication between the wild ancestor and modern cultivated varieties, and they are a valuable source of genetic diversity. Hence, understanding and utilizing the genetic variation in adzuki bean accessions is essential for improving the crop [Domestication has triggered a wide range of morphological and physiological traits that distinguish domesticated crops from their wild ancestors. The domestication of cultivated adzuki beans from their wild ancestors represents one of the most important events of human history . The culthe crop . The yiethe crop ,14,15. Gthe crop , water cthe crop , and phothe crop ,19. EnviThe identification and evaluation of agronomic traits of germplasm resources is the basis of crop breeding. The methods used commonly include principal component analysis ,21,22, cp < 0.05). Number of seeds per pod (NSP) and 100-seed weight (100-SW) had no significant difference between the two locations. NPB, PY, NPP, and SWP had a wide variation in both two locations (CV > 50%), while DM, DF, and NNS possessed a narrow variation in two locations (CV < 20%). NSP, PY, and NNS had the highest Shannon’s diversity index (SHDI) in Nanning, while the DF and NSP had the highest SHDI in Nanyang. A high level of variability and diversity exists among genotypes and locations in response to the traits scored, indicating that traits studied were influenced by genotype x environment interactions. In this study, NSP and 100-SW were less affected by the environment (since these two traits were had no significant differences in two locations).A large number of variations were observed in the 10 traits among the accessions evaluated in two locations . The plap < 0.001) studied except NSP. The genotypes (G) varied significantly in PH (p < 0.001), NNS (p < 0.001), NPB (p < 0.001), NSP (p < 0.001), 100-SW (p < 0.001), PY (p < 0.01), and DF (p < 0.05), but had no significant difference in DM, NPP, and SWP. Significant differences (p < 0.001) were recorded among the environments (E) for all the traits measured except NSP and 100-SW. There was also a high significant effect of genotype × environment (G × E) interaction on all the traits (p < 0.001).Analysis of variance (ANOVA)  showed tFor a better understanding of how the latitude of the collection site is related to agronomic and yield traits, scatter plot and linear regression analyses were conducted . PH, NNSLinear regression analyses between DF and latitude of the collection site suggested that flowering date was delayed with increasing latitude, resulting in reproductive growth delayed and yield characters decreased. These findings indicated that accessions collected at lower latitudes have greater potential in traits related to yield, although variation is wide among accessions collected in the same latitude. The significant relationship between traits and latitude of the collection site suggests that accessions collected from different geographical latitudes are phenotypically diverse, which can be used for breeding new adzuki bean varieties adapting to various latitudinal locations.p < 0.05).A total of 475 adzuki bean accessions we selected, including 389 landraces, 59 cultivated varieties, and 27 wild accessions, and differences in phenotype were compared according to germplasm types . A very In this study, traits related to plant growth architecture such as PH, NNS, NPB, DF, and DM were higher in wild accessions and lower in cultivated varieties. However, traits related to yield had the opposite trend except NSP. NPP, SWP, 100-SW, and PY were lower in wild accessions. 100-SW was higher in cultivated varieties, and SWP was higher in landraces, while NPP and PY had no significant differences between landraces and cultivated varieties. These findings indicated that wild accessions were more taller plants, more plant branches, longer days to flowering and maturity, while cultivated varieties were earlier maturity, larger seed size, and higher yield. This probably is the result of human selection during domestication. Furthermore, according to different breeding goals, accessions of different germplasm types can be selected for adzuki bean breeding.The GCV of traits studied ranged from 2.3% to 58.7% . The higHeritability is the ratio of genetic variance to the total variance (phenotypic variance). In this study, the broad-sense heritability of traits studied ranged from 4.4% to 88.3%. The NSP, 100-SW, and NPP had a high heritability (≥60%), indicating that these traits are less influenced by the environment. However, DF, DM, NPP, SWP, and PY were observed a low heritability (≤30%). Further, the genetic advance (GA) calculated had shown that the NSP, 100-SW, and NPP also possessed a high GA. These showed that NPB, NSP, and 100-SW were more affected by genes than the environment, while the DF, DM, NPP, SWP, and PY were more affected by environment.p < 0.001) with SWP, PH, NNS, NPB, DF, DM, NPP, and NSP. SWP had a positive correlation (p < 0.05 or p < 0.01 or p < 0.001) with all other traits. However, 100-SW had a negative correlation with PH (p < 0.001), NNS (p < 0.01), DF (p < 0.05), NPP (p < 0.01), and NSP (p < 0.001). Further, NSP had a positive correlation (p < 0.001) with PH, NNS, NPB, DF, and DM, while had a negative correlation with NPP (p < 0.05). NPP had a positive correlation with NNS (p < 0.01) and NPB (p < 0.001), while a negative correlation with DF (p < 0.001) and DM (p < 0.001). Therefore, accessions had the taller plant height and more plant branches; they always had the higher yield, but smaller seed size. In addition, PH had a positive correlation (p < 0.001) with NNS, NPB, DF, and DM. DF had a positive correlation with DM (p < 0.001).The correlation matrix  showed tTo reduce the redundancy of the raw data, the principal component analysis was carried out according to the 10 traits of two environments. We selected the first three principal component factors according to the principle that the eigenvalue is greater than 1, and the loading matrix and contribution to total variability to each trait were calculated . The firThe first component (PC1) explained 31.26% of the variation and was mainly negatively correlated with PH (−0.47) and NPB (−0.41). While the second component (PC2) explained 24.49% of the variation and was more negatively correlated with DF (−0.44) and DM (−0.45). Further, the third component (PC3) explained 16.56% of the variation and was mainly correlated with NPP (0.63), 100-SW (−0.41), and SWP (0.43). In addition, the individual PCA plot  showed tThe 475 adzuki bean accessions were grouped into 5 groups based on the 10 traits . The cluMorphological markers can simply and intuitively display genetic polymorphisms of crop germplasm resources . IdentifNSP and 100-SW showed a small difference between their GCV and PCV, but other traits showed a large difference between them. The smaller the difference in value (RD) between GCV and PCV, the more genetic effects and the less environmental effects there are on the specific characters ,34. NSP p < 0.001) with SWP, PH, NNS, NPB, DF, DM, NPP, and NSP, but no significant correlation with 100-SW. A lack of correlation between seed size and yield was found in grass pea under drought conditions [V. angularis var. nipponensis, which is distributed in Japan, the Korean peninsula, China, Nepal, and Bhutan [Yield potential is thought to be partially determined by seed size, and numerous studies have tried to understand the relationship between seed size and yield in other pulses with contradictory results ,39,40,41nditions  which wanditions  and peasnditions ,43. Durinditions . Howeverd Bhutan . In compThere are differences in the selection of methods for identification and evaluation of agronomic traits of crop germplasm resources by different researchers such as correlation analysis ,45, prinA set of 475 adzuki bean accessions representing broad genetic diversity from National Crop Genebank, Institute of Crop Science, Chinese Academy of Agriculture Science (CAAS) were selected in this field trial, including 389 landraces, 59 cultivated varieties, and 27 wild adzuki beans. A total of 469 accessions were from China, and 6 accessions were from Japan . The latThe field trial was performed at two locations: Nanning city  in Guangxi province and Nanyang city  in Henan province in 2020. The mean daily air temperature during the experiment (June to October) was 26.9 °C and 23.0 °C in Nanning and Nanyang, respectively. Randomized complete block design was used in the field trials. Field trials were performed between June and October 2020. Each field trial had a randomized complete block design with three biological replicates per accession. Each plot had 3 rows, and the length of each plot was 5 m with 0.2 m spacing between each plant, and a spacing of 0.5 m between each row. Each accession was planted in three replicates. Weeds were manually and mechanically removed when needed. A total of 10 quantitative traits were scored , includip ≤ 0.05) were conducted by SPSS 19.0. The min, max, mean, correlation coefficient, and diversity index of each trait were calculated to determine the relationship among traits studied. According to the method of Shannon-Weaver [2B) was calculated by the method of Aydin [All phenotypic traits were analyzed using Excel 2010, and ANOVA analysis and least significant differences (LSD) (n-Weaver  to calcun-Weaver . Estimatn-Weaver . Broad-sof Aydin  and the of Aydin . The herof Aydin . GA betwof Aydin . Correlaof Aydin , princomof Aydin .In the present study, a total of 475 adzuki bean accessions were evaluated based on 10 agronomic traits and yield characters in two locations, showing a large amount of variation occurred in these evaluated traits. There were significant differences between wild adzuki bean accessions and the other two types of adzuki bean accessions. The NSP, 100-SW, and NPB had a high heritability, indicating that the heritability of these traits is less influenced by the environment. However, the DF, DM, NPP, SWP, and PY were observed as having a low heritability. Some accessions with excellent characters have been screened out which can be used for adzuki bean breeding.


R2 = 0.52 compared with SmartGrain for which R2 = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2 = 0.58. Additionally, a subpart of the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.Fusarium head blight (FHB) is an economically important disease affecting wheat and thus poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease-infected grains; however, few have looked at the final application, considering the relationship between cost and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods, were compared with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of  Triticum aestivum L.), (Fusarium graminearum Schwabe [teleomorph: Gibberella zeae (Schwein) Petch], is one of the wheat diseases with a major impact on wheat grain yield and quality. FHB can dramatically reduce grain quality and yield through the formation of sterile and wizened florets. FHB-infected grains suffer from major marketing, consumption, and processing constraints, which is the buildup of mycotoxins—mainly deoxynivalenol (DON) , . While evum L.), . The decvum L.), . Fusariuol (DON) . DON inhol (DON) . FHB disol (DON) . HoweverThe resistance components for FHB, commonly known as resistance types, have been defined into type I to type V : type I Among the investigated methods, In the face of the constraints cited earlier, automated and light-weight free software for grain image analysis have been developed ; some exIn this context, this paper has three objectives; first is to investigate the applicability of low-cost digital image analysis to predict FHB infection in harvested grains through morphological traits. This will offer more insight into the traits that are correlated to the degree of FDK. The second objective is to compare the applicability of the two methods used for grain image analysis—SmartGrain, and Cgrain Value™—in terms of consistency and throughput. The third one is to illustrate the processing chain and result interpretation with a descriptive data analysis.Wheat kernel samples were collected from an experiment under accelerated indoor growth conditions  using wi−2 s−1. At the end of the vernalization period, the climatic conditions were adjusted with a gradual increase in temperature and LI for the acclimatization of the plants to the next phase of accelerated growth conditions. Once the acclimatization period was concluded, the plants were left to grow at a constant temperature of 22°C. The accelerated growth conditions were adapted by exposing the plants to a prolonged daily light duration of 22 h, with LI at 400 μmol m−2 s−1 of uniform light intensity from LED light plates. Under these accelerated growth conditions, the plants were watered daily and fertilized weekly using first a combination of a high-phosphate and high-nitrogen soluble fertilizer SW-BOUYANT 7-1-5 + Mikro + KH2PO4, then only with a high-nitrogen fertilizer, and finally with a high-potassium soluble fertilizer Yara Tera Kristalon NPK 12-5-30 with S and Mikro.Plants were grown following an augmented block design in a climate-controlled chamber. After germination, the plants were subjected to a vernalization period of 57 days at 3°C with 8 h of daily light at medium–high light intensity (LI) of 250 μmol mF. graminearum and F. culmorum, with a concentration of 5 × 105 spore/ml. Subsequently, the plants were left to incubate at 90% rh with 16/8 h dark/light cycle at a constant temperature of 24°C for 48 h before adjusting the climatic conditions back to 60% rh. The plants were eventually left to grow under the latter conditions for 24 days before harvesting the seeds. Eight isolates from F. graminearum and F. culmorum species were used in inoculating the plants provided by the Swedish agricultural cooperative Lantmännen Lantbruk. An inoculum preparation was carried out by incubating the fungal spores at 24°C for 4 days in dark conditions to allow for mycelial growth on SNA media plates. Later, the fungal plates were exposed to near ultra-violet UV radiation for 10 h to induce macroconidia formation. Afterward, the fungal plates were incubated for 4 days at 24°C in dark conditions. Finally, macroconidia spores were collected to make the inoculation suspension with the provided concentration after adding the surfactant Tween®20 0.002% (v/v) final volume of the inoculum. A more detailed protocol is described in After completing the anthesis stage, at 33 days post-acclimatization, the plants were moved to a glasshouse chamber with relative humidity (rh) of 60% and a constant temperature of 24°C for 24 h to allow their adaptation to the new growth conditions prior to inoculation. Thereafter, the winter wheat spikes were spray-inoculated with an inoculum suspension prepared from the harvested spore of In order to evaluate FHB resistance on a large number of genotypes, a modified visual scoring of the FHB disease severity method was adopted. The method took into account the incidence of all FHB symptoms across the main tiller spike of each genotype. Therefore, disease severity was assessed as the percentage score of infected spikelets relative to all spikes, regardless of symptom continuity on the same spike. FHB development was scored at 6, 8, 10, and 12-days post-inoculation (dpi) . The FHBTwo different grain phenotyping methods were employed in this study: an automated imaging instrument with software and hardware named Cgrain Value™ which is commercially available  and the For image acquisition, the seeds were captured with a low-cost image protocol acquisition from a top-view angle of 55 cm above the seeds and placed manually on a flat surface using a digital single-lens reflex camera Canon EOS 1300D , which has a resolution of 18 megapixels, mounted on a Kaiser RS-1 repro stand. The camera was tethered to the software digiCamControl  with optAS corresponds to the total number of pixels of the segmented seed, this parameter estimates the seed size. PL refers to the length measurement of the seed outline. L corresponds to the major length measurement in the axis and W to the minor length axis measurement. CS estimates how round the region of interest is (seed), and it is calculated as LWR is calculated by DS [described in detail by The image analysis was thereafter carried out using SmartGrain software following its default protocol . Brieflyetc.), the stat file provides data per individual seed of a group , and the image file corresponds to the single seed images acquired . The result file consists of the morphological characteristics for each batch of seeds , width (W), thickness (T), average width (AVG.W), volume (V), weight (WT), light, hue, and saturation. Parameters such as L, W, and T are estimated by taking the longitudinal measurement of the axis major, higher minor, and minor, respectively. In the case of AVG.W, as the seed is received as a three-dimensional image, the measurement is referring to the mean of the average curvature. V corresponds to the seed volume obtained from the 3D image. For WT, the device has an internal balance, so while acquiring the image, it also weighs the grain. Color parameters, hue, saturation, and light are also determined by the instrument; it specifies the color base of a sample, how saturated it is, and how bright it is, respectively.Statistical analyses were conducted using R . The visily corresponds to the adjusted means of the ith wheat cultivar in the lth block, u is the general mean value, ilG is the effect of the ith wheat genotype in the lth block, β1 is the lth block effect, and ϵil is the residual. Subsequently, using the adjusted means, the best linear unbiased estimates (BLUEs) was calculated using the randomized complete block design option in META-R 6.04 , pβ corresponds to the slope coefficients for each explanatory variable, and ϵ corresponds to the error of the model . Three models were created using the morphological traits provided by both methods  as independent variables and visual scorings as the dependent variable. One model combines all the traits, and two others use the traits provided by each method. To build each model, the data set was partitioned employing the function “createDataPartition” of the caret package (Where for i=n observations:  package  into 70%P< 0.001), likewise with V, CS, and saturation (P< 0.01), indicating a clear association with FHB disease severity level. Meanwhile, the parameters WT, AS, LWR, PL, and DS did not indicate any significance but still showed slight differences between infected and non-infected grains.This study examined a total of 16 morphological traits, including size, color, and shape of winter wheat grains from the genebank and breeding sets with different levels of FHB infection. Nine traits were obtained with the instrument Cgrain Value™ and seven traits with the software SmarGrain. The distribution of all the morphological traits measured by the two methods showed a Gaussian distribution Figure 2Additionally, a principal component analysis Figure 3r = 0.65, r = 0.36, and r = 0.27, respectively). Negative correlations were also found between the visual evaluations of symptoms and the other characteristics in different levels of strength of association. There was no correlation between FHB visual scoring and DS (r = 0.01).Considering R2 = 0.58), (R2 = 0.52), (R2 = 0.30), . These morphological traits are enumerated from most significant to least significant in The multiple linear regression model developed to identify the contributions of the 16 different morphological traits provided by Cgrain Value™ and SmartGrain expressed a high moderate prediction R2 = 0.58,This study compared the potential performances of two different image-based methods to predict FHB. The results of both indicated that morphological seed traits are functional for predicting FHB among two different sets of genotypes evaluated. Furthermore, a comparison of the applicability of the two methods was properly addressed by evaluating the cost, accuracy, and time efficiency—for instance, to extract dimension, shape, and color parameters, Cgrain Value™ utilizes a unique mirror design to inspect all possible angles of individual kernels in the sample. Additionally, image capture and processing are instantaneous, thanks to the hardware and software combination. Conversely, image acquisition using the SmartGrain system was carried out over a relatively long period, yet image processing was done relatively fast. However, compared with Cgrain Value™, the earlier approach is cheaper considering the cost of the tools used in image capture, requiring a simple RGB camera, a static frame, and the free software.On the other hand, the morphological traits, based on the statistical analysis results, that showed significant correlations to the visual scores were color traits in the HSL color representation and thickness from Cgrain Value™, length and width, from both methods 6. AlthoThe evaluated visual scores of the symptoms associated with FHB—bleached, yellowish or discolored, and stunted spikes—were previously validated by the identification of several loci by genome-wide association studies (GWAS) in a previous study with the same plants and visual scorings . The proTherefore, the prediction of both methods studied here appears to be consistent for FHB with the assigned traits concerning the phenotype–genotype association. Previous investigations showed a high correlation between symptoms that are present on wheat heads and the rate of kernel damage . TherefoAn important aspect to highlight is that the percentage of disease severity can be assessed, where, in contrast to disease spread from the point of inoculation, it offers less intensive labor by spray inoculation of a larger number of wheat genotypes. Additionally, unlike point-inoculated wheat spikelets, spray-inoculated spikes allow for evaluating the degree of damage caused by the disease to all kernels of the infected spike. Within this work frame, whole spike kernels are investigated for their characteristics rather than the damage to a limited number of kernels caused by Fusarium colonization from the point of inoculation. This, in turn, is expected to shorten the period for disease resistance assessment, lower its cost, and be less labor demanding.The results indicated that the traits with a higher correlation to FHB were length, width, thickness, and especially color values in HSL color representation. Moreover, Cgrain Value™ was advantageous to SmartGrain in terms of the time required for image capture and outperformed the latter when applied to a large number of samples, yet SmartGrain processes samples fast and is cheaper in comparison to Cgrain Value™. Although the disease prediction showed a low–moderate accuracy for SmartGrain and a high–moderate accuracy for Cgrain Value™ and the results of both methods combined, this is attributed to the prediction reference, which corresponds to FHB disease severity scorings done on the spikes. However, the novelty of this study resides in the accuracy reached even with a different reference source, but which is directly related. Additionally, as the plant material genotypes and visual scores were validated by GWAS analysis, then the results presented here are phenotype–genotype-associated.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.AC conceived the study. TH developed the breeding population set. MZ provided the material and the scores of the disease severity. MA performed the image and data acquisition with SmartGrain. RD performed the image and data acquisition with Cgrain Value™. FL analyzed the data and wrote the draft. All authors contributed to the article and approved the submitted version.This study was supported by funding from the SLU Grogrund (SLU.ltv.2019.1.1.1-623), Nordic Council of Ministers (PPP #6P3), and NordForsk (#84597). Formas (#2020-01828).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is growing interest in using genetic selection to obtain more resilient farm animals . The aims of this study were to: (i) estimate the genetic parameters of resilience indicator traits based on egg production data, (ii) assess whether these traits are genetically correlated in purebreds and crossbreds, and (iii) assess the genetic correlations of these traits with egg production (EP) as total number of eggs between 25 and 83 weeks. Purebred hens  line and 34,397 from a Rhode Island (BD) line were housed in individual cages, while crossbred hens were housed in collective cages of 6 to 8 paternal half-sibs . Deviations of a hen’s weekly egg production from the average of the corresponding batch were calculated. Resilience indicator traits investigated were the natural logarithm of the variance (LNVAR), the skewness (SKEW), and the lag-one autocorrelation (AUTO-R) of these deviations.In both purebred lines, EP was estimated to be lowly heritable (WA: 0.11 and BD: 0.12). Resilience indicators were also estimated to be lowly heritable in both lines . In both crossbred groups, EP, AUTO-R, and SKEW were estimated to be less heritable than in purebreds (EP: These results show that selection for resilience based on EP data can be considered in breeding programs for layers. Genetic improvement of resilience in crossbreds can be achieved by using information on purebreds, but would be greatly enhanced by the integration of information on crossbreds in breeding programs.The online version contains supplementary material available at 10.1186/s12711-022-00716-8. Over the past 60 years, breeding programs have resulted in very productive farm animals, at the expense of functional traits related to fitness  , 2. ThesResilience is defined as “the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance” , 6. One Previous studies in laying hens have investigated resilience indicators that were derived from body weight , egg weiOur objectives were: (i) to study the genetic parameters of resilience indicator traits that are derived from longitudinal egg production data, (ii) to assess whether these traits are genetically correlated in purebreds and crossbreds (purebred–crossbred correlation), and (iii) to assess the genetic correlations of these traits with egg production. To our knowledge, this is the first time that egg production longitudinal recording is used to study resilience in laying hens. The purebred–crossbred correlation determines whether selection for improved resilience in purebreds will result in improved resilience in crossbreds. It also determines the added value of collecting crossbred information: the lower the purebred–crossbred correlation, the higher the value of crossbred information when crossbred performance is the breeding goal , 24. EstIn this study, two purebred lines from Hendrix Genetics were studied: a White Leghorn line, called WA, and a Rhode Island line, called BD. The WA purebreds were individually housed on a nucleus farm in The Netherlands  and distributed among 13 batches from 2012 to 2017. The BD purebreds were individually housed on a nucleus farm in France  and distributed among 14 batches from 2012 to 2017 . Therefore, the raw data consisted of dates and number of eggs collected. Egg production was recorded individually for purebreds and as a pooled average for a cage for crossbreds, i.e., the total number of eggs produced during a particular period was divided by the average number of hens alive in the cage during that period. Observations for individual purebred hens with two eggs per day were not discarded from the analyses, since the first egg could have been laid after collection time on the day before and the second egg could have been laid before collection time on the day of recording. Observations of three or more eggs per day were discarded from the analyses, because this is not biologically possible, even with different collection times in successive days. Daily egg production was estimated for every day between two recording dates by dividing the total number of eggs by the number of days in the period. These daily averages were summed over 7 days to obtain the weekly egg production . The trait egg production (EP) was defined as the total number of eggs laid per hen between 25 and 83 weeks of age.The longitudinal egg production data enabled the computation of the types of resilience indicators based on deviations from the average performance of a contemporary group that had been used for other species in other studies , 16, 18.Data handling and graphs were performed in base R . Resilie package .Within sire lines, variance components were estimated using bivariate analyses, considering records of the purebreds and records of the crossbreds from the same sires’ line as two different traits, to allow estimation of the purebred–crossbred correlation. The model used for purebred phenotypes was the following animal model:The model used for crossbred phenotypes was the following sire model:The animal effects for the purebred phenotypes and the sire effects for the crossbred phenotypes were assumed to be bivariate normally distributed as:Bivariate analyses were used to study the genetic relationship between traits, using the same models presented above . Within each purebred population and for each pair of traits, the genetic effects were assumed to be distributed as:Within each crossbred population and for each pair of traits, the genetic effects were assumed to be distributed as:The residual effects were assumed to be distributed as:Reml 4.2 .For purebred phenotypes, heritability estimates were calculated from the estimates of variance components obtained with the preceding models as:For crossbred phenotypes, the calculation of the heritability needs to account for (i) the fact that the phenotypes are the average per cage of the pooled performance from all the layers in the cage and (ii) for the half-sib relationships between the grouped layers. The formula to scale heritability estimated with pooled data to an individual-based heritability was established in previous studies –30, as sentclass1pt{minimaLikelihood-ratio (LR) tests were performed to test if the estimates of heritability were statistically significantly different from 0 or if the genetic correlations were different from 0 or 1. The LR was calculated as:Reml 4.2 ). Assumi2=2.706) .The two purebred lines were high-producing layers. The observed mean EP (between 25 and 83 weeks of age) was 371 and 347 eggs in the WA and BD purebreds, respectively , and moderately negative between SKEW and AUTO-R in both lines Table .Table 4EIn crossbreds, the estimate of the genetic correlation between LNVAR and SKEW was − 0.67 in cages of eight BD crossbreds and was not statistically significantly different from 0 for the other crossbreds Table . EstimatIn summary, our results indicate synergetic genetic correlations between the resilience indicator traits at least in the purebreds. This suggests that including one trait as a selection criterion will lead to genetic improvements of the other traits.In both purebred lines, the genetic correlations were highly negative between EP and LNVAR, from lowly negative to moderately positive between EP and SKEW, and moderately positive between EP and AUTO-R Table .In the crossbreds, genetic correlation estimates were highly negative between EP and LNVAR, ranged from − 0.38 to 0.40 between EP and SKEW Tables  and 6. AIn summary, estimates of genetic correlations of EP with LNVAR were in all cases negative and therefore favorable. In contrast, estimates of genetic correlations of EP with SKEW and AUTO-R were sometimes positive and sometimes negative, but never very strong.Estimates of genetic correlations for the same trait between purebreds and crossbreds . InLNVAR is expected to be an indicator of both the severity and of the duration of a perturbation that the animal is experiencing, i.e. animals with a small variance are less affected by a perturbation than those with a large variance . In our Our estimates of heritability for LNVAR of purebred EP (estimates of 0.11 in WA and 0.12 in BD) were similar to the estimate of heritability for LNVAR of body weight (repeated records) of layers related to the WA line (estimate of 0.10) . Few stuIn our study, the genetic correlation of LNVAR with EP was favorable and negative (ranging from − 0.83 to − 0.54). In other species, LNVAR has also been shown to have favorable genetic correlations with other traits. In pigs, lower day-to-day variance in feed intake (root mean square error of prediction of feed intake) was genetically correlated with lower mortality and fewer health treatments . In dairTo our knowledge, our study is the first to report genetic correlations of LNVAR with EP in layers. These correlations imply that selecting for a reduced LNVAR would increase EP, which is desirable. EP has been a selection criterion for many generations and, therefore, has probably contributed to indirect genetic improvement of LNVAR. Given the low heritability of EP, adding LNVAR in the breeding goal is an opportunity to improve not only resilience but also production, in spite of the low heritability of LNVAR. The next step towards the inclusion of LNVAR in a breeding program is to derive economic values or to set the targeted genetic gain. A simplified simulation study has already shown that selecting for resilience indicators has a beneficial economic impact, and even more if health traits are not included in the breeding program, because of reduced labor and treatments .In summary, although selecting layers on EP already improves LNVAR, including LNVAR in breeding programs may further enhance the improvement of resilience. The absence of trade-offs makes it relatively easy to increase resilience without a loss in selection response in EP. In addition, literature on other species shows that LNVAR is favorably associated with general immunity and health , 45.The resilience measure AUTO-R is expected to be informative regarding the duration of a perturbation and thus to be an indicator of an animal’s recovery capacity: close to 0 (subsequent deviations of EP are uncorrelated) for animals with high recovery rates, close to + 1 (subsequent deviations of EP are correlated) for animals with a slow recovery rate; and close to − 1 (subsequent deviations of EP are opposite) for animals showing a compensatory response to the perturbation . In our Before using AUTO-R as a selection criterion, its definition as a trait needs to be further discussed to reach a consensus, and its biological impact on production traits and its relationship with health needs to be understood. Knowing both the LNVAR and AUTO-R of an animal can lead to complicated interpretations: phenotypically, animals with a low LNVAR could display an AUTO-R close to 1 because they are hardly affected by perturbations, and their subsequent deviations from EP are more alike. In the present study, we found that LNVAR and AUTO-R were favorably genetically correlated in all populations but their genetic correlations were less than 1. However, if layers have genetically a high AUTO-R when faced with a severe perturbation, they will show a slow recovery. Recovery from a perturbation may be an interesting trait in the case of severe perturbations (such as a virus outbreak or a heatwave), thus it may be more appropriate to calculate AUTO-R during periods with large deviations instead of over the whole period.In summary, selecting purebred layers on EP is expected to increase AUTO-R, which could decrease recovery rates from perturbation. Including AUTO-R in breeding programs may further enhance the improvement of resilience because it would put more emphasis on recovery rate. The trade-off between AUTO-R and EP also highlights the need to quantify economic values when considering the inclusion of AUTO-R in breeding programs.SKEW is expected to be an indicator of both the direction (positive or negative) and severity of a perturbation that an animal is experiencing, i.e. animals with a negative SKEW experience more negative deviations than others, which is interpreted as poor resilience . HoweverIn our study, all  in pigs  and poul in pigs . Three mfinition , 21.First, crossbreds only share half of the genetic characteristics of each purebred parental line. Allele frequencies likely differ between purebreds and crossbreds and such differences may have direct effects on the trait  or indirect effects , depending on the loci involved.Second, G × E interactions can contribute to the same trait having a different genetic architecture in different environments. Purebreds and crossbreds were raised in very different environments in our study in terms of climate, management, feed, barns, disease pressure, etc. The farms where purebreds were kept have a higher level of biosecurity than regular farms because the breeding companies cannot take any risk with the population under selection. RT-farms generally belong to farmers who collaborate with breeding companies and, although their health and care practices are usually among the best ones, the layers will experience more challenges on an RT-farm than on a nucleus farm. Commercial farm environments can still be very different from RT-farm environments . This diversity of environments and their susceptibility to changes  may influence the resilience performance of hens due to differences in types, severity, and duration of the perturbations. In addition, purebreds were housed individually, while crossbreds were in group cages, in which case the presence of cage mates creates positive (e.g. grooming) as well as negative (e.g. pecking) social interactions that can affect the individuals’ wellbeing and performance. As a consequence of G × E interactions, re-ranking of sires may occur. In both lines, estimates of t itself .Third, differences in the definition or measurement of the traits may contribute to rivation ).All these elements can contribute to the value of recording the traits in crossbreds in a commercial environment. Breeding programs would benefit from considering both pure- and crossbred data to improve response to selection for crossbred performance in commercial environments , 48, 49,Our study shows that longitudinal recording offers opportunities to develop resilience indicators that are heritable. Here, resilience indicator traits are based on deviations of production performance from the undisturbed phenotype (here defined as the contemporary group average). An alternative is to estimate the undisturbed phenotype of an individual based on various modeling approaches , 16, 50.General resilience is likely a combination of resilience of various biological functions, including food intake, digestion, growth, maintenance, reproduction, thermoregulation, immunity, behavior, etc. , 6, 10. Tools are available now for phenotype recording in layers, such as electronic nests, feeders, weighing scales, cameras, microphones, RFID chips (radio-frequency identification), other embedded sensors, NIRS or MIRS (near- or mid-infrared spectroscopy), etc., which allow the concomitant high-throughput phenotyping of different traits. Based on our results and those reported in other livestock species that show the potential of resilience indicator traits for genetic improvement of resilience, especially LNVAR, further investigations in chickens are needed (i) to find the best (combination of) production traits to calculate resilience indicators, and (ii) to record available perturbation-related indicators and relate them with the resilience indicator traits.This study shows that LNVAR and AUTO-R of weekly EP are lowly heritable in purebreds as well as in crossbreds, while SKEW had very low heritability estimates. We found that these three resilience indicator traits have favorable genetic correlations with each other and with EP. Compared to purebreds, EP and the resilience indicator traits show lower heritability estimates in crossbreds, although estimates of genetic additive variance were roughly at the same level in crossbreds and purebreds. Studies on LNVAR can go forward towards its inclusion in breeding programs, i.e. by estimating economic values, and evaluating the economic and environmental impacts. AUTO-R may add complementary information, but both its definition and its joint inclusion with LNVAR in breeding programs need to be further studied. Estimates of genetic correlations between purebreds and crossbreds of production and resilience indicator traits were low in both lines, which suggests that resilience indicator traits in crossbreds are genetically different from resilience indicator traits in purebreds, which may be partly due to individual recording in the purebreds and group recording in the crossbreds. This implies that strategies that include information on crossbreds in breeding programs would be beneficial to the genetic improvement of resilience in crossbreds.Additional file 1: Table S1. Distribution of hens from the WA line according to country and farms. Table S2. Distribution of hens from the BD line according to country and farms.Additional file 2. Heritability estimated with pooled records of half-sibs, for regular and resilience indicator traits [r traits , 30, 52.Additional file 3: Table S3. Phenotypic correlations among traits in purebreds, and their standard error below in italic characters, for the WA line  and the BD line . Table S4. Phenotypic correlations among traits in the WA crossbreds, and their standard error below in italic characters, for cages of 6 hens  and cages of 7 hens . Table S5. Phenotypic correlations among traits in the BD crossbreds, and their standard error below in italic characters, for cages of 7 hens  and cages of 8 hens .


Moniezia spp., Eimeria spp.) resistance in Akkaraman sheep by performing genomic heritability estimations and conducting GWAS to uncover responsible genomic regions. This is one of the first studies to examine the genetic resistance of Akkaraman sheep to the tapeworm parasite. The samples from 475 animals were genotyped using the Axiom 50K Ovine Genotyping Array. Genomic heritability estimates ranged from 0.00 to 0.34 for parasite resistance traits. This indicates that measured phenotypes have low to moderate heritability estimates. A total of two genome-wide significant SNP associated with TNEM3 and ATRNL1 genes and 10 chromosome-wide significant SNPs related with 10 genes namely NELL1, ST6GALNAC3, HIPK1, SYT1, ALK, ZNF596, TMCO5A, PTH2R, LARGE1, and SCG2 were suggested as candidates for parasite resistance traits. The majority of these candidate genes were involved in several basic biological processes that are essential and important for immune system functions and cellular growth; specifically, inflammatory responses, cellular transport, cell apoptosis, cell differentiation, histone de-acetylation, and endocytosis. These results have implications for animal breeding program studies due to the effect that the genetic background has on parasite resistance, which underlies many productive, health, and wellness-related traits. Genome-wide association studies (GWAS) have been used as an effective tool to understand the genetics of complex traits such as gastrointestinal parasite (GIP) resistance. The aim of this study was to understand the genetics of gastrointestinal parasite (nematodes,  Moniezia spp.), coccidian (Eimeria spp.), and nematode  parasites are the most common parasites detected in sheep in Turkey, similar to many other countries. GIP infections adversely affect productivity and quality of welfare in animals through clinical signs like weight loss, anemia, and lethargy. Animals at an early growth stage cause considerable damage to both the farm economy and the national economy when carrying GIP, due to effects such as growth retardation.Gastrointestinal parasite (GIP) infections are among one the most important health problems in pasture-based livestock production systems . TapeworThe economic damage caused by parasitic infection is twofold: directly, is the loss of animal productivity, such as a diminished growth phase, weight loss, and deaths in juvenile lambs; indirectly, is the cost of intervention strategies which consist of chemical drug and veterinary service costs. It has been reported that the annual economic loss due to parasitic diseases in Australia, where sheep and goat farming is intensive, is around $436 million . MoreoveStrongyles spp., Nematodirus spp. eggs and Coccidian (Eimeria spp.) oocysts were found to be 0.17, 0.09, and 0.09, respectively [Moniezia expansa) egg and coccidian (Eimeria spp.) oocyst numbers varied between genetically different animals [It is imperative to understand the genetic parameters  of measured traits in order to achieve rapid and effective genetic progress for population production  and wellness  traits. Moreover, understanding these genetic parameters will allow for more accurate estimate breeding values (EBV) to be calculated and, accordingly, will generate more accurate selection decisions for a population ,8,9. Stuectively . AdditioTLR4 gene were found associated with coccidian oocyst count [Moniezia spp.) carriage to sheep genomic loci did not return any considerable results. Therefore, this is one of the first studies to examine the genetic resistance of domestic sheep to the tapeworm parasite. An association analysis employing an SNP panel that represents the entire sheep genome will begin to elucidate the genetic background of GIP resistance. Therefore, the aim of the present study was to investigate the genetic basis of GIP resistance in Akkaraman sheep. For this purpose, genetic parameter estimates and genome-wide association analyses were performed using phenotypes obtained from fecal egg counts and genotypes characterized by a 50K ovine SNP panel. Recent technological developments have enabled the effective use of molecular genetics in animal husbandry ,21,22,23st count . Search The study was conducted in Ankara, Turkey . The place is known for its continental climate with severe, snowy winters, and dry summers. The average annual precipitation is 389 mm, and the average annual temperature is 11.7 °C. The average altitude of the region is 938 m with most of the pastureland being of low quality. A total of 475 Akkaraman lambs  were used for the study. The animals were randomly selected from three herds that are members of the National Community-based Small Ruminant Breeding Program. Mating strategies are applied within these herds based on pre-weaning growth traits. Lambs were born in January and February 2021 and weaned at approximately three months of age. After weaning, 370 lambs were kept on pasture during the summer without supplemental feeding, while 105 lambs received 750–1000 g concentrate feed per day during the 90-day finishing period. Environmental factors  were recorded for animals recruited into the study. Fecal samples were collected from 129 animals at three months of age (May 2021) and 478 animals at six months of age (August 2021), including the earlier 129 subsets. A total of 20–30 grams of feces were collected directly from the rectum of the animals to minimize contamination and it was ensured that at least 60 days had passed since the last anti-parasite treatment of animals. Samples were transported to the laboratory in a cold chain and stored at 4 °C until the feces were examined. During the August sampling, approximately 6 ml of blood (in vacuum EDTA tubes) was collected from the jugular vein of the animals.NemFEC3 and NemFEC6, respectively. The phenotypes for Moniezia spp. (tapeworm) at three months and six months of age were MonFEC3 and MonFEC6, respectively. Finally, the phenotypes for the number of fecal oocysts of coccidian parasites were CocFOC3 at three months of age and CocFOC6 at six months of age. The number of eggs of nematodes, tapeworms, and oocysts of coccidian parasites was counted using the McMaster technique [GIP resistance phenotypes in sheep were based on fecal egg counts (FEC) of three different parasite species. The phenotypes for nematode egg counts at three months and six months of age were echnique . NematodThe blood samples were transferred to International Center for Livestock Research and Training (ICLRT), Genetic Laboratory, for DNA extraction. To avoid contamination risk, DNA extraction was implemented with the Qiacube HT automated device using a commercial kit . Quality control of the extracted DNA was performed before further analysis, where samples meeting the quality criteria  were kept at −20 °C until genotyping. Genotyping was performed using the Axiom 50K Ovine Genotyping Array  with the GeneTitane MultiChanel instrument at the ICLRT Genetics Laboratory. The 96-array format of the Axiom 2.0 Assay was followed as per manual workflow protocols. p < 0.05. Furthermore, samples that had a lower call rate than 90% and identity by state (IBS) higher than 95% were removed from the data set. Genotyping quality control (QC) began with the Axiom Analysis Suite program. In addition to the Axiom Analysis Suit program, one more quality control step was applied to the data by using the “check.marker” function of the GenABEL package within R ,36. The Imputation was applied to the quality-controlled genomic data before proceeding to genomic heritability estimation and genome-wide association analyses. SNPs that did not meet QC following genotyping were imputed according to the current status of this SNP in the population. The expected maximization (EM) algorithm was used in this study and the process was carried out by R GenABEL packages .µ is the population mean regarding the trait of interest, β is the vector of fixed environmental effects such as sex (2 levels), birth type (2 levels), birth month (3 levels), u ~ 0, e ~ 0, X and Z are the design matrices mapping fixed effects and polygenic background effects to each observation, respectively. Furthermore, G represents the genomic relationship matrix derived by VanRaden (2008) [The Animal Mixed Model and ‘Restricted Maximum Likelihood’ (REML) approach were used to estimate genomic heritability by using the ‘sommer’ package of R . Measuren (2008) . The forAfter estimating the variance component of each trait, the narrow sense heritability estimates of each trait were obtained by using the model given below;p-values showing the significance of the SNP effects. The p-values of the SNP effects were calculated based on the association analyses by ordering the position information of the SNPs on each chromosome on a −log10 scale. A statistical significance threshold value was used to avoid Type 1 Error (probability of rejection if the null hypothesis is true) accumulation in multiple SNP tests. The approach of Bonferroni correction for multiple tests was used to determine genome- and chromosome-wide significance thresholds for the traits.The same general model was used to estimate the SNP effect in genome-wide association studies. In the model, the additive effect estimated with the genomic relationship matrix (SNP-based identity-by-state) was included to account for the fixed genotypic effect of each SNP. A widely used graphical method, the Manhattan plot, was used to visualize the Using the Oar_v4.0 genome assembly, we managed to get positional information for associated SNPs and candidate genes from the NCBI Genome Data Viewer . SNPs thFecal egg counts for nematodes and tapeworms and fecal oocyst counts for coccidian parasites were quantified per gram of feces for 129 animals at three months of age and 475 animals at six months of age. Descriptive statistics and prevalence are shown in The number of samples for each tested trait is reported in the descriptive statistics table see . The rawHeritability estimates for parasite resistance phenotypes ranged from low to moderate in Akkaraman sheep. As can be seen in Genome-wide association analysis was performed for six different traits  belonging to three different parasite groups  associated with GIP carriage. A Linear Mixed Model (LMM) was used to identify important SNPs associated with phenotypes. The greatest advantage of LMMs in GWAS analysis is that false positive results can be eliminated by employing population stratification and animal relationships. The genotypic effect of SNPs and the random genetic effect (additive effect) of the animal were employed to calculate the SNP-based genomic relationship matrix (G). The dependent variable in the LMM equation was the measured phenotypes. SNP effects and environmental factors were included in the model as fixed effects, whereas the individual effects of animals and the effect arising from the genetic background of the animal were included in the model as random effects.p-values as defined by Devlin and Roeder, 1999. Manhattan plots were used to visualize the −log10  of all SNPs respective to their position on the relevant chromosome  and the chromosome-wide significant threshold was 3.21 × 10−5 .The observed test statistics for each SNP were compared to the test statistics expected under the null hypothesis using quantile-quantile (Q-Q) plots . A Q-Q promosome . Genome-The age at which the lambs were sampled was determined to have a significant impact on all traits associated with parasite resistance. The one exception to this was for the MonFEC6 trait. The herd effect was found to be significant for the MonFEC3 and NemFEC3 traits, while the sex of the animal was found to only affect the MonFEC3 trait. Notably, no environmental factor was found to affect the MonFEC6 trait. Finally, for the CocFOC3 trait, there was an interaction effect between the feed type and sex, while the NemFEC6 trait had a three-way interaction between the herd, sex, and feed type.A total of 12 SNPs were found to be statistically significant as a result of the GWAS studying traits associated with GIP resistance see . Two of NELL1), Teneurin Transmembrane Protein 3 (TENM3), ST6 N-Acetylgalactosaminide Alpha-2,6-Sialyltransferase 3 (ST6GALNAC3), Attractin Like 1 (ATRNL1), Homeodomain Interacting Protein Kinase 1 (HIPK1), Synaptotagmin 1 (SYT1) and ALK Receptor Tyrosine Kinase (ALK) in the genome. The other five of these SNPs are located in regions nearby specific genes namely Zinc Finger Protein 596 (ZNF596), Transmembrane and Coiled-Coil Domains 5A (TMCO5A), Parathyroid Hormone 2 Receptor (PTH2R), LARGE Xylosyl- and Glucuronyltransferase 1 (LARGE1), and Secretogranin II (SCG2) on the genome.Using the Oar_v4.0 genome assembly, we obtained positional information for associated SNPs and candidate genes from the NCBI Genome Data Viewer . CandidaPasture-based production systems which raise small ruminants have long incurred economic losses due to parasitic diseases. Global warming and associated climate change have increased the frequency of parasite infections and the diversity of pathogens in a region. On the other hand, the resistance of parasites to anti-parasitic drugs is increasing day by day. Considering all these factors together, it will be more difficult to reduce parasite infections in farm animals in the future, and alternative strategies for control will be needed. While there are present studies focused on the production and reproductive traits of the native Akkaraman sheep breed and its crossbreeds, there are not enough studies on the genetics of health traits such as parasite resistance ,49,50,51In our study, we estimated the heritability of egg/oocyst counts of nematodes, tapeworms, and coccidian parasites in Akkaraman lambs at three and six months of age. Heritability estimates for the traits ranged from 0 (MonFEC3) to 0.34 (NemFEC6). Overall, it was found that the error margin of the heritability estimates for traits measured at three months of age was higher than that of the traits at six months of age. One possible reason for this could be that the animals were not sufficiently exposed to parasites during this period. In relation to this, it is possible that animals that are three months old may not have had enough time to acquire a sufficient or remarkable egg/oocyst load, where it is known that the immune system is not fully developed at this time. Thus, it can be assumed that animals of this age do not adequately reflect the effects of genetic potential on their immune system. Lastly, it is feasible that the small sample size affected the statistical power of the analysis.Haemonchus contortus FEC in Santa Ines sheep, raised in a tropical climate, provided a heritability estimate of approximately 0.10 [Teladorsagia circumcincta ranged from 0.09 to 0.12 [Studies estimating genetic parameters of indicator phenotypes for resistance to gastrointestinal parasites have mostly focused on the nematode species because of their high economic damage. Prior studies conducted on varying nematode species in alternate sheep breeds have calculated heritability estimates ranging from low to moderate when assessing resistance to gastrointestinal nematodes ,13,15,52ely 0.10 . In addiely 0.10 . FurtherMoniezia expansa) eggs and coccidian (Eimeria spp.) oocysts [Strongyles spp. eggs, Nematodirus spp. eggs and Coccidian (Eimeria spp.) oocysts were found to be 0.17, 0.09, and 0.09, respectively [Studies on coccidian and tapeworm infections are rather sparse compared to nematode studies. In a study conducted in Texel, Ile France, Morada Nova sheep, and their crossbreds were reported to carry alternate numbers of tapeworm  was found on OAR26 for the NemFEC6 trait with a p-value of 1.08 × 10−7. The SNP (rs401554073) with the second lowest p-value (6.92 × 10−7) was associated with the MonFEC6 trait and was found on OAR22. Considering that the phenotypes belong to three different parasite groups acquired at three and six months of age, no SNPs with pleiotropic effects associated with these traits were found. This situation may be a result of the immune system acquiring alternate defense mechanisms against alternate parasite species. Significant SNPs were annotated according to their positions on the reference genome (OAR_v4.0) using the National Center for Biotechnology Information (NCBI) Genome Data Viewer. While seven of the SNPs were located within a particulate gene defined within the genome, five of them were found nearby at least one particular gene. Accordingly, the suggested candidate genes for parasite resistance were NELL1, TENM3, ST6GALNAC3, ATRNL1, HIPK1, SYT1, ALK, ZNF596, TMCO5A, PTH2R, LARGE1, SCG2.As a result of the GWAS, 12 distinct SNPs were found to be statistically significant for measured traits associated with parasite resistance. While two of these SNPs were significant at the genome-wide threshold, 10 SNPs were found to be significant at the chromosome-wide level. The most significant SNP  as a result of GWAS. The resultant protein from this gene is a teneurin protein, which is employed in multiple cell components, for example, the cell membrane (GO:0016020), and takes part in many biological functions, such as cell-cell adhesion (GO:0098609), cell adhesion (GO:0007155) and signal transduction (GO:0007165). Studies show that the Wnt signaling system, which moderates the innate immune response and adaptive immune response with the production of inflammatory cytokines, upregulates the Teneurin-3 protein [TENM3 has been identified in viral control . Further, TENM3 has been associated with hip dysplasia, suggesting a role in development . ST6GALNAC3 was another associated gene for the NemFEC6 trait located on OAR1. This gene is a member of sialyltransferase family, which transfers sialic acids from CMP-sialic acid to terminal carbohydrate groups on glycoproteins, supporting the active part ST6GALNAC3 plays in the glycoprotein metabolic process (GO:0009100) [TMCO5A (Transmembrane and Coiled-Coil Domains 5A). This gene encodes a protein particularly integral to membrane components (GO:0016021) and plays a role in protein binding (GO:0005515). For the NemFEC6 trait,  protein . This fa0009100) . Glycopr GO:00071 and signPTH2R and ATRNL1 genes were found to be associated with the fecal egg count phenotypes of Moniezia spp. (MonFEC3 and MonFEC6) on OAR2 and 22, respectively. The rs401554073 SNP was associated with the ATRNL1 gene at a genome-wide significance level with a p-value of 6.92 × 10−7. While only one of the present genes was significant at a genome-wide level, it is interesting to note that both of these genes are directly related to G-Protein-Coupled Receptors (GPCRs). While PTHR2G gene is a member of the G-protein coupled receptor 2 family, the ATRNL1 gene plays an active role upstream of or within G protein-coupled receptor signaling pathways. More specifically, the protein encoded by PTR2G is located within the cell membrane  and actively involved in cell surface receptor signaling (GO:0007166), G protein-coupled receptor signaling (GO:0007186) and adenylate cyclase-modulating G protein-coupled receptor signaling (GO:0007188). Similar to the PTHR2G gene, ATRNL1 codes for a cell membrane protein involved in G protein-coupled receptor signaling pathways (GO:0007186). By acting as gate-keepers, these GPCRs play an important role in the induction of cytokines and other signals responsible for the stimulation and manipulation of the adaptive immune system [ATRNL1 has been identified in viral responses  with a potential impact on immune responses . GPCRs play an important role in recognizing pathogens and antigens by macrophages, which are critical to innate immune system function. Therefore, it can be thought that the recognition of tapeworm infections and the resultant immune response can be related to the density of these receptors. GPCRs, and therefore the PTH2R and ATRNL1 genes, were suggested as candidate genes for resistance to Moniezia spp. infection in sheep.After the GWAS analysis, e system . They spe system ,58. ImpoHIPK1, SYT1, SCG2, and ALK were discussed as partaking in sheep resistance to coccidian parasite infection. ALK is a protein-coding gene responsible for the regulation of the dopamine receptor signaling pathway (GO:0060159). Dopamine is a neurotransmitter that actively plays a role in the regulation of the immune system [Eimeria spp. [HIPK1, SCG2, and ALK genes identified during GWAS analysis for Coccidian parasite resistance traits (CocFOC3 and CocFOC6), characterized these genes as partaking in the biological process of cell apoptosis. In particular, the HIPK1 gene plays a role in the endothelial cell apoptotic process (GO:0072577) and extrinsic apoptotic signaling pathway (GO:0097191), where the SCG2 gene directly downregulates these same two processes (GO:2000352 and GO:2001237). During coccidiosis, sporozoites are ingested and penetrate the epithelial cells to settle into their replicative niche. These sporozoites divide asexually and form merozoites as a result of this division. After a number of generations, the parasite has increased enough in number to rupture the host cell, resulting in cell death [Eimeria spp. resistance due to the effect of merozoites in the cell on apoptotic (intrinsic and extrinsic) factors in the cell. Alternatively, ALK and SCG2 genes were also identified as being associated with the mitogen-activated protein kinase (MAPK) signaling pathway, where both genes are involved in the positive regulation of said cascade . It has been reported that MAPK signaling is suppressed in host intestinal epithelial cells in an infection caused by Cryptosporidium, which is a protozoan parasite that infects intestinal epithelial cells similar to the Eimeria species [SCG2 has been determined to play a very active role in the formation of innate immune and adaptive immune responses in the body. In particular, SCG2 is involved in biological processes such as inflammatory responses (GO:0006954), eosinophil chemotaxis (GO:0048245), positive chemotaxis (GO:0050918), induction of positive chemotaxis (GO:0050930) and endothelial cell migration (GO:0043542). Within human studies, the HIPK1 gene is associated with chronic inflammatory diseases such as Crohn’s disease, primary sclerosing cholangitis, and ulcerative colitis, all of which localize to the gastrointestinal tract [As a result of GWAS for CocFOC3 and CocFOC6, four important genes namely  GO:00601. Dopaminll death . In a stll death . It is f species . Along wal tract . PTH2R and ATRNL1 genes found to be related to tapeworm numbers were involved in the synthesis of G-Protein-Coupled Receptors (GPCRs). Furthermore, HIPK1, SCG2, and ALK genes were all associated with cellular apoptosis, which may occur as a result of coccidian infection of the intestinal epithelial cell. Alternatively, SCG2 and ALK genes associated with coccidian parasite resistance are also actively involved in the MAPK signaling pathway, which has a significant role in immune responses against parasite infection.In general, many genes identified within the present study are heavily intercalated within basic biological functions that are essential to inflammation. These include processes such as eosinophil chemotaxis, inflammatory response, cell apoptosis, immune-related protein synthesis, and intercellular signaling. It was also noted that candidate genes responsible for similar host cell processes were grouped within parasite species. For instance, the In conclusion, genomic heritability for parasite resistance traits was estimated and found to be low to moderate. Furthermore, GWAS returned two genome-wide and 10 chromosome-wide significant SNPs for measured parasite resistance traits. In this study, we provided possible explanations and meaningful results about the genetics of parasite resistance. Considering that parasite resistance is directly related to the health and survivability of sheep, the resultant data may promote the development of breeding approaches that may produce more resistant and sustainable animals against disease, leading to remarkable economic impact. Similar studies with larger animal numbers and encompassing different sheep populations are required in order to transfer the present knowledge to employed applications against present parasites. The next step would be a sequencing of candidate genes in order to identify the SNPs responsible for this resistance in order to use them more easily in a marker-assisted selection approach.


Crossbreeding schemes have been widely used in global beef and dairy cattle . This brLi et al. evaluated the predictive performance of carcass traits in Chinese Simmental cattle using haplotypes in comparison to the traditional genomic prediction approaches. In comparison to the individual SNP-based genomic prediction methods, the authors reported that using haplotypes resulted in more accurate genomic breeding values for carcass traits in Chinese Simmental cattle. Furthermore, the authors indicated that a haplotype strategy considering biological information can also be used to identify variants affecting complex traits of economic importance.This special Research Topic includes six papers presenting novel and exciting breeding strategies and methods such as genomic prediction of breeding values, genome-wide association studies, beef-cross-dairy performance, and reproductive technologies. Fitting haplotypes instead of single nucleotide polymorphism (SNP) markers in genomic prediction models was investigated as an approach to improve the accuracy of genomic breeding values . In thisMartín et al. evaluated beef-x-dairy offspring performance based on their sire predicted breeding values and concluded that estimated breeding values derived from beef-breed data can be useful, albeit the response to selection in beef-on-dairy progeny was half that in beef-on-beef progeny. The study concluded that New Zealand farmers should consider the use of sire estimated breeding values for improved offspring performance in terms of beef production since the performance of beef-cross-dairy offspring was a function of their sire’s EBV. In addition to sire selection for beef-on-dairy matings, Berry and Ring advised how understanding dairy farmer attitude to sire selection depending on the market outlet for the resulting surplus calves can provide valuable information for the animal breeding chain. Using a large dataset from Irish commercial dairy herds, Berry and Ring concluded that dairy producers who rear their surplus progeny post weaning, on average, chose dairy and beef sires with superior expected genetic merit for carcass traits.Verification that genetic evaluations materialize as phenotypic differences is fundamental to stakeholder acceptance and uptake. Nengovhela et al. evaluated the impact of using reproductive technologies to improve productivity in low-income beef farms. Their study revealed that many factors may affect the adoption of reproductive technologies by the low-income beef sector but its implementation is still recommended because of the high success rate and better reproductive performance observed where this technology was implemented.According to Khan et al. conducted an interesting review on the association of the DGAT1 gene with milk and meat production traits in cattle, buffalo, goats, and sheep. These authors demonstrated that DGTA1 has a positive influence on meat and carcass fatness in beef cattle along with meat production in sheep and goats; DGTA1 also regulates milk production in sheep, goats, and buffaloes. On the other hand, Cai et al. studied the miRNA profiles of mammary glands from twelve Chinese Holstein cows and provided new insights into the mechanism of milk protein synthesis.Milk fatty acids and milk protein are key traits in the dairy industry. in vitro production (IVP) in beef-on-dairy systems, environmental credentials of dairy-beef vs. beef systems, development of new genomic evaluation systems for crossbred performance, genotype-by-environment (GxE) interactions in different production systems, the impact of non-additive genetic effects on the phenotypic variability of relevant traits in crossbred populations, and breeding strategies considering the combining ability of beef and dairy animals with particular reference to 1) exploiting location-specific heterosis across the genome through mating advice algorithms, and 2) achieving a compromise in traits such as calving difficulty from beef-on-dairy matings. We hope that this special research topic will motivate future studies aiming to maximize the success of beef-on-dairy production system.Although the six articles published in this section encompasses broad subjects, gaps in knowledge still exist. For instance, knowledge gaps exist in the use of sexed semen and