Honeybees have attracted considerable scientific and public interest in recent years. Besides pesticides and pathogens, failure or loss of the queen have been considered the most important factors leading to colony losses worldwide. The poor quality of the queen is a factor that ranks among the top reasons for bee colony failure. There are traits that can indicate the quality of a queen bee. This study aims to investigate the diversity in reproductive and morphological traits that can be useful in selective breeding programs for improving colony performance and survivability. The studied animals came from a population bred by a professional queen breeder in Northern Italy. Heritability and genetic correlations were estimated. According to our results, some of the traits showed good variability and they could be included as breeding goals in selection programs. Improving the quality of queens could directly impact honeybee colonies’ performance and survivability. Ultimately, it represents an added value to a queen bee-breeder company.The quality of the honeybee queen has an important effect on a colony’s development, productivity, and survival. Queen failure or loss is considered a leading cause for colonies’ mortality worldwide. The queen’s quality, resulting from her genetic background, developmental conditions, mating success, and environment, can be assessed by some morphological measures. The study aims to investigate variability for traits that could assess the quality of the queen. Related animals were enrolled in this study. Variance components were estimated fitting a mixed animal model to collected data. Heritabilities of body and tagmata weights ranged from 0.46 to 0.54, whereas lower estimates were found for the tagmata width and wing length. Heritabilities estimated for the spermatheca diameter and volume, number of ovarioles, and number of sperms were 0.17, 0.88, 0.70, and 0.57, respectively. Many phenotypic correlations related to size were high and positive, while weak correlations were found between morphology and reproductive traits. Introducing a queen’s traits in a selection program could improve colonies’ survivability. Further research should focus on better defining the correlations between the individual qualities of a queen and her colony’s performance. Apis mellifera) are social insects who live in colonies characterized by a cooperative system of brood care, overlapping generations, and reproductive division of tasks [Honey bees , length of the right forewing, diameter and volume of the spermatheca, number of sperms in the spermatheca, and number of ovarioles. Ovarioles were counted instead of weighed since the ovary’s weight could be influenced by the developmental stages of the eggs they contain, as pointed out by Amiri et al. .The aim of this research was to investigate phenotypic and genetic variability of the above-mentioned traits for queen quality in a small population bred by a professional queen breeder in Northern Italy.The queens were provided by an Italian queen-breeding and beekeeping company that produces and sells about 400–600 queens per week, from spring to late summer. The rearing of the queens was characterized by a standardized production system and by traceability of both maternal and paternal lines of each queen (the pedigree). The standardized rearing system consists of using only queen-less finisher colonies. These colonies are fed and treated in a standardized way to provide uniform quality. Specifically, these colonies receive new brood from one single apiary on a regular basis. The “brood-donor” apiary is composed of genetically uniform colonies. The grafts are inserted in the finisher colonies for a week. Afterwards, the royal cells are collected and incubated at 34.5 °C for 11 days. After incubation, the cells are brought to the mating station where they are inserted in the mating nuclei. This process was carried out by the same operators following a strict timetable along the season, which minimizes any potential error variance due to management practices.n = 70) and 2018 (n = 77). The analyzed queens were bred at different times of the production season reported as the ordinal number of the week of the year in which the mated queen was harvested from the mating nucleus. All queens naturally mated in the same area within the year. The queens were bred in groups of sisters from 10 maternal lines in 2017, and 7 maternal lines in 2018. Furthermore, the maternal lines shared common ancestors. The maternal lines queens mated at an isolated mating station where each year a group of 12–15 drone-producing colonies provided drones. In this way, a pedigree could also be traced from the paternal side.This study was conducted on 147 queens bred during spring/summer seasons of 2017  . Every iIn order to assess the reproductive characteristics of the queen, the abdomen was dissected following the methods described in Porporato et al. . The abdAs soon as the abdomen was opened, the spermatheca was extracted and put on a glass slide. In 2017, the spermatheca was photographed on a slide under the microscope, and its diameter (ds) was measured with ImageJ software. In 2018, the diameters were measured by hand with a digital caliper. Since the spermatheca of a queen does not have a perfect spherical shape, the diameter was calculated as the average of three measurements. The volume of the spermatheca (vs) was calculated as the volume of a sphere.To assess the concentration of semen, the spermatheca was popped in 1 mL of physiological solution (NaCl 0.9%). The concentration of sperm (sp) was assessed using a standard haemocytometer chamber (Burker Camera) under a light microscope .Only the ovarioles of the right ovary were counted . The numFor the analysis, estimates of the genetic relationships among the studied queens are required. The methods of Brascamp and Bijma  were useith combination of the week of the year in which the queen was harvested , jth queen (j = 1147), kth observation. This model allowed us to estimate the heritability of each measured phenotype.First, a univariate mixed animal model  for eachSecondly, a bivariate approach was used for each combination of traits fitting the same model above described. The bivariate analysis allowed us to estimate both phenotypic and genetic correlations between the measured traits.Every analyzed queen was free from macroscopic external defects. However, some internal defects were observed. The most frequent defect observed in the 16% of the queens was an abnormal intestinal tract, which appeared swollen and brownish/yellowish in color; in the 9% melanosis of the ovary was observed; in the 8% of queens enteroliths were observed, which are small stones in the intestinal tract also described in Porporato et al. ; in 2.7%Mean, standard deviation (SD), and coefficient of variation (CV) of recorded traits are presented in The number of sperm and volume of spermatheca were very variable, as their CV resulted 82.8% and 41.7%, respectively. Other measures such as tagmata weights, diameter of spermatheca, and number of ovarioles were less variable and their CV ranged from 10.1% to 17.9%. Concerning body weight, our result was in good agreement with values reported by the literature ,26,46,55Genetic parameters are reported in Heritabilities ranged from 0.17 to 0.88 with rather high standard errors which might be explained by the sample size. In particular, for body weight and for individual tagmata weight, heritabilities resulted to be in a narrower range between 0.46 and 0.54. On the other hand, for tagmata width, the higher estimate was found for thorax width (0.42 ± 0.32), followed by the head width (0.26 ± 0.27) and the lowest was the estimate for the abdomen width (0.13 ± 0.26). The estimate for the length of the right forewing was 0.30 ± 0.29. Estimates for diameter and volume of the spermatheca resulted in 0.17 ± 0.34 and 0.88 ± 0.39 respectively. The increase of sv heritability compared to sd heritability could be explained by the way sv was calculated from ds. Indeed, sv was estimated from sd applying the formula of the volume of the sphere. This transformation increased the total phenotypic variance of trait sv by a coefficient (4/3 × π), without affecting the sv error variance. Therefore, the higher the genetic variance, the higher the heritability of sv trait. This result should be verified as the volume of spermatheca was approximated to that of a sphere, although this organ may show different shapes [Concerning morphological traits , phenotypic correlations ranged from 0.16 to 0.88. Highly correlated traits were body weight with abdomen weight (0.88 ± 0.03) and thorax weight (0.67 ± 0.06); moreover, a correlation of 0.80 ± 0.04 resulted between abdomen and thorax widths. Lower correlations were observed between morphological traits and the length of the right forewing. We observed very low or close to zero phenotypic correlations among reproductive traits  except for the correlation between sd and sv (0.97 ± 0.01). The latter is likely explained by the fact that sv is derived by sd using the formula to estimate the volume of a sphere. Remarkably, according to our results, reproductive traits do not seem to be associated with morphological measures. Our results are in agreement with Corbella and Gonçalves , Hatch eAs expected, our genetic correlations estimates were affected by large standard errors due to the limited number of individuals in our dataset. Therefore, estimates should be considered with caution. Among morphological traits, tagmata weights showed high genetic correlations which ranged from 0.61 to 0.99. High and positive genetic correlations were found between the weight and width of the head (0.97 ± 0.46) and thorax (0.83 ± 0.31), respectively. There was also a high genetic correlation between thorax width and abdomen width 0.74 ± 0.34). In addition, high genetic correlations were observed between wl and hw (0.92 ± 0.71), tw (0.74 ± 0.38), hwi (0.96 ± 0.48), and twi (0.98 ± 0.31). The high relationship between lw and thorax dimensions may be explained by the fact that wings grow on the thorax, the locomotive tagma which groups the legs and the wings. It could be argued that during the insect’s development they are tightly and jointly regulated. The dimension of the spermatheca positively correlated with tw (0.78 ± 0.39) and hwi (0.89 ± 0.48). As expected, the diameter and volume of the spermatheca correlation were near to one (0.99 ± 0.02). Positive genetic correlations were found between wing length and number of ovarioles (0.79 ± 0.59) and number of sperms (0.57 ± 0.52). The association between wing length and these reproductive traits may be explained by the paramount function of the wings during the mating flight, which depends on the wings’ movements. The very high negative genetic correlation between the dimension of the spermatheca and the number of sperms it contains was surprising , previous reports showed that the spermatheca is often not totally filled after mating ,34.. In addiBeekeepers have long selected queens, choosing the “best” on the basis of phenotypic desired features, mainly body size and color. The purpose set at the beginning of this study was to investigate a series of traits that could be useful for the evaluation and the selection of any qualities of a queen. Statistical analysis confirmed the existence of certain rather intuitive correlations between some morphological measures. Other results shed light on aspects that were counterintuitive in principle, i.e., the absence of phenotypic correlation between morphological measures and reproductive traits. Heritability is a parameter that generally describes how easily parents transmit to their offspring a certain phenotype and which has, on the other hand, a practical operational utility for breeders and selection programs. The heritability of body weight, spermatheca volume, number of ovarioles, and number of stored sperm look like promising breeding goals, given the observed genetic variability. It would be interesting to reproduce and extend this study to other traits, and to a larger number of individuals , to confirm or update the conclusions from this study. Moreover, it would be pivotal to extend such studies to colonies’ performances. In fact, phenotypic or genetic correlations between colony performance and queen quality, estimated with the animal model, are completely lacking in the literature.In conclusion, monitoring and introducing queen’s traits as breeding goals in a genetic improvement program represent an appealing plan of approach to the overall decline of queens reported in recent years from both the beekeepers and the scientific community. Improving the overall quality and reproductive traits of queens could directly impact colonies’ performance and survivability. Ultimately, it represents an added value to a queen bee-breeder company.


Stem and canopy architecture traits are superior predictors of floral biomass yield and offer a good indication of hybrid uniformity in field plantings of genetically diverse cannabinoid hemp populations. Cannabis sativa L.) is not well described, especially the segregation and inheritance of traits that are important for yield. A total of 23 families were produced from genetically diverse parents to investigate the inheritance of morphological traits and their association with biomass accumulation and cannabinoid yield. In addition, a novel classification method for canopy architecture was developed. The strong linear relationship between wet and dry biomass provided an accurate estimate of final dry stripped floral biomass. Of all field and aerial measurements, basal stem diameter was determined to be the single best selection criterion for final dry stripped floral biomass yield. Along with stem diameter, canopy architecture and stem growth predictors described the majority of the explainable variation of biomass yield. Within-family variance for morphological and cannabinoid measurements reflected the heterozygosity of the parents. While selfed populations suffered from inbreeding depression, hybrid development in hemp will require at least one inbred parent to achieve uniform growth and biomass yield. Nevertheless, floral phenology remains a confounding factor in selection because of its underlying influence on biomass production, highlighting the need to understand the genetic basis for flowering time in the breeding of uniform cultivars.The breeding of hybrid cultivars of hemp ( Cannabis sativa L.) is a multipurpose crop with incipient potential in diverse markets. Hemp is a dioecious annual (2n=20)   that, alears ago . The curears ago ; howeverC. sativa is not well understood. While monoecious cultivars are favored in grain production, all-female populations are preferred by growers of cannabinoid hemp because pollination can dramatically reduce cannabinoid yield  . Presentid yield . Feminizid yield . RepeateFloral phenology is an important component in hemp breeding programs because substantial variation can affect the uniformity of cultivar populations  varieties of At present, there is no ideotype for cannabinoid hemp because optimized agronomic management practices and efficient harvesting equipment have yet to be established. Hemp cultivation in the USA mainly uses transplants of greenhouse-grown seedlings or rooted cuttings frequently grown in plasticulture with wide row (2–3 m) and in-row (0.8–1.2 m) spacing. As the market evolves, it is likely that cannabinoid cultivation will move towards direct-seeded, high-density plantings. However, improvements in the efficiency of feminized seed production would be needed to achieve this. Nevertheless, emerging markets will continue to support small farmers and niche products, such as growing cultivars with diverse cannabinoid and terpene profiles  assess the genetic diversity of hemp; (ii) evaluate common parent families segregating for economically important traits in the field; (iii) develop novel selection criteria for plant architecture; (iv) determine important predictors of final dry floral biomass yield; and (v) establish an ideotype for field-grown cannabinoid hemp.® , and genomic DNA was extracted using a DNeasy® Plant Mini kit , following the manufacturers’ protocol. DNA quality was checked by agarose gel electrophoresis and quantified with a Qubit® fluorometer . Library construction and sequencing were based on the 96-plex genotyping-by-sequencing (GBS) protocol  platform at the University of Wisconsin Biotechnology Center DNA Sequencing Core Facility .Leaf tissue from 190 hemp genotypes representing grain, fiber, and cannabinoid cultivars, as well as US feral accessions, were obtained from Cornell Hemp seed and clonal inventories or from breeders or growers . Young lmem (Variant discovery was performed using the Tassel GBS Discovery Pipeline version 2 (mem . The meaST) and allelic richness  were estimated for each cluster using genet.dist (method=‘WC84’) and allelic.richness, respectively, in hierfstat , to generate 17 common families, and another six families were produced using two inbred SJ’s CBD’ . To ensuJ’s CBD’ . Three w® Rooting Gel . At the time of planting (16 June 2020), 15 progeny individuals were randomly selected from each family, and planted together in single plots at 1.2 m spacing within a row and 1.8 m spacing between rows  were randomly installed across the field to aid in timing of irrigation. The field was fertigated twice through a Dosatron  4 and 6 weeks after planting, using Jack’s 12-4-16 Hydro FeED RO .For each family, seeds were sown into deep 50-cell Sureroots trays  with potting mix  in the greenhouse with supplemental lighting with a 16:8 h light:dark regimen, 3 weeks before planting in the field at Cornell AgriTech . The common parent, ‘TJ’s CBD’, was planted from cuttings, but grown in the same greenhouse conditions as the seedlings. Cuttings were rooted using Clonexeen rows . GranulaFlowering date was recorded when pre-terminal  and terminal pistils (clusters of flowers at shoot termini) were observed. Weekly observations were recorded until all plants were terminally flowering, and expressed as days to flower after planting in the field.Plant height, measured as the length of the primary stem, was assessed weekly for 10 weeks, beginning the week after planting in the field. Growth rates were calculated using height measurements over time. Plant form was derived from plant height, maximum canopy diameter, and height at that diameter at 63 DAP . From thR2=0.9993) by comparing manual measurements from ImageJ with automated measurements using run.ij  in LeafArea  severity was visually rated for each plant on a continuous scale of 0–100% canopy leaf area diseased, measured at 71, 86, and 97 DAP.Chlorophyll concentration was measured using an Apogee MC-100 meter  at 50 DAP, as an average of three unique measurements on fully expanded leaves in the middle canopy, not less than 15 cm from the shoot apex. Leaves at the same position were sampled from the outer canopy at 86 DAP to measure leaflet number, leaf area, leaf length, leaf perimeter, petiole area, petiole length, and petiole perimeter. Intact leaves with petioles were measured in the Fiji distribution of ImageJ (LeafArea . Each leLeafArea  (SupplemLeafArea . Hemp por=0.98) and dry stripped biomass (r=0.96). The strong linear relationship of wet to dry biomass made accurate prediction of dry stripped biomass achievable. Utilizing the wet (WBM), dry (DBM), and dry stripped biomass (DSBM) of the sampled individuals, predictions were obtained with the following simple linear models: DBM= −0.13322+0.31174×WBM+ε, where ε~Ɲ; DSBM=0.113884+0.156749×WBM+ε, where ε~Ɲ. Dry floral biomass per unit area (kg m−2) was calculated by dividing dry floral biomass by the square of the maximum canopy diameter. Morphological data can be found in Individuals were harvested when the inflorescence was fully mature, typically 5 weeks after initiation of terminal flowering, as described in –1 and 1.39 cm px–1, respectively. A data processing pipeline was developed to analyze collected aerial images for the extraction of morphological and vegetation index traits (A Matrice 100 series drone equipped with a Zenmuse 3 RGB camera (4K 4096×2160 px)  and a MicaSense RedEdge five-band multispectral sensor (1280×960 px)  was flown 10 times during the growing season using the DroneDeploy App version 2.90.0 . Flights were completed at 10 d intervals from 15 DAP to 93 DAP, with an altitude of 20 m and 80% front and side overlap. Ground sampling distances for the Zenmuse 3 and RedEdge were 0.86 cm pxx traits . CollectPlant geo-locations were calculated using color orthoimages. A color orthoimage was converted to an excessive green index . ConnectIn the colorized point clouds, the point cloud of each plant was cropped using the calculated bounding boxes. Random sample consensus  was usedThe primary terminal inflorescence (10 cm) was sampled from each individual in the week preceding harvest for cannabinoid analysis and dried in a climate-controlled room with a maximum temperature of 30 °C and average relative humidity of 35%. Once dried, samples were milled to a fine powder for cannabinoid analysis via HPLC, further described in P<0.001) were conducted using Pearson’s correlation coefficient (r) with cor.test. Model II linear regression was conducted using the ranged major axis (RMA) method in lmodel2  in archetypes (2A=4σ 2F) and phenotypic variance as the sum of family and residual variance. Variance components were estimated using lmer in lme4 . Following ANOVA, mean separation was conducted using Tukey’s HSD in agricolae . Wilcoxo lmodel2 . Maximum lmodel2 . Absolutchetypes , using trelaimpo , using 1The relationships between architecture, foliar, physiological, and pathology traits, and high-throughput phenotyping (HTP) measurements were assessed to provide a clearer picture of hemp growth and development, and to understand their contribution to the traits of primary economic importance: floral biomass and cannabinoid yield. These were assessed in families with a common parent to reveal the degree of segregation based on parent heterozygosity and allelic diversity.ST ranging from 0.08  to 0.30  (Parent selection for this study was predicated on the genetic relatedness of a broad sample of available hemp germplasm using high-density single nucleotide polymorphism (SNP) markers , of whicd T1/R4) , reflectd T1/R4) , and allHybrid development in hemp will depend on genome-wide markers to infer kinship and heterotic group membership. Prediction of combining ability is the hallmark of hybrid breeding, however; a deficiency of published studies on hemp hybrids and the lack of a unified genotyping platform has delayed its progress in the research community. Nevertheless, the clusters identified here serve as a good starting point for hemp breeders to select representative inbred parent lines for early analysis of combining ability and stable hybrid deployment.C. sativa is quantitative , with five terminally flowering at ~35 DAP and 10 at ~85 DAP.There was substantial variation in flowering time both within and among families . Seven f1 progeny would segregate 3 late:1 early, and depending on the status of the S1 parent, S2 lines would be either all-early, all-late, or segregating. However, 3:1 ratios were not clearly observed, but were either ~1:1, ~2:1, all-early, or all-late. If 1:1 ratios could be explained as a testcross, then homozygous recessive seed parents would express the early flowering trait, yet only ‘Candida’ and ‘TJ’s CBD S1 #5’ flowered early. It is probable that more than one gene is controlling the early flowering trait observed in this population and may be evidence of epistasis. Since the seed parents were genetically diverse, there are likely to be multiple segregation models for this trait. Although some ratios could be explained by a single factor and others by two or more, larger populations would undoubtedly be required to test these hypotheses. Inbred lines can be developed that will be homozygous for those major gene alleles affecting flowering time, but it will be critical to understand how different alleles interact in the heterozygous state in F1 hybrid cultivars to determine latitudinal adaptation.For those families not clearly segregating for flowering date, the presumably dominant seed parent allele(s) masked that of the common parent in the hybrid. If this was a simple recessive trait, SC. sativa (narrow leaflets), C. indica (broad leaflets), C. ruderalis (three leaflets to a leaf), or hybrids and/or ‘percentages’ of each (r=0.67)  , of whic2 per leaflet, families 19-1162, 19-1170, and 19-1197  had the greatest mean leaflet area (>10.2 cm2), whereas families with the lowest mean leaflet area were 20-1072 and 19-1153 (<4.9 cm2), both of which shared the common seed parent ‘A2R4’ . In general, families with a fiber/feral seed parent background had smaller leaves and petioles, and those with a T1/R4 background were almost always larger.The mean leaf and petiole area for families with seed parents inferred to be in T1/R4 diversity group 1  were 37–r= −0.82), which is defined as the ratio of the number of leaves to stem volume. It is thought that smaller leaves of land plants are found on species that produce more of them, such that variation in leaf size can be predicted in terms of a leaf mass–number trade-off. R2=0.66) was −0.71 , but not specific leaf area (P=0.97) (P=0.62). Specific petiole area was inversely correlated with biomass accumulation (r= −0.51), stem volume (r= −0.49), and kite area (r= −0.48). While most leaf, stem, and physiological traits had inverse relationships to specific petiole area, petiole circularity (r=0.23), leaf circularity (r=0.57), and mid-canopy branch number (r=0.44) were positively associated with the trait. An increase in specific petiole area was associated with a decrease in leaf perimeter (r= −0.77). Further, the slope of the regression of log (specific petiole area) and log (leaf perimeter) was −1.46 (R2=0.59), which suggests an allometric relationship , such that plants with few leaflets per leaf had less mass per unit area and lower leaf perimeter. However, the slope of the regression of log (specific petiole area) and log (leaf dry weight) was −2.1 (R2=0.56), whereby a small increase in petiole mass per area accompanied a disproportionate increase in leaf dry mass  . Theorettionship . Similardry mass .r=0.45) (−1) of all other families  were also those segregating for early and later flowering dates.Within-family variation in growth rate and biomass accumulation is intrinsically linked to variation in floral phenology. Overall, day of maximum stem growth was positively correlated with days to flower (r=0.45) ; howeverfamilies . Not sig1 families in this study has a somewhat irregular, excurrent habit, which may be because it was propagated from cuttings. Of all the individuals surveyed, not one lacked opposite decussate branching leading to subopposite branching at shoot apices and lateral spirals on branches.Kite variables produced a useful model that accurately portrays the major architectural differences in hemp. In high-density plantings of fiber and grain hemp, apical dominance is markedly stronger, and leads to a far more columnar habit that maintains dormancy of axial buds, which often results in a significant reduction of branching. The wide planting density in this study was chosen to maximize light availability so that the variation in architectural traits would be more easily discernible. The common parent to the FPlant size (kite area)  and form1 mean of 0.76 [coefficient of variation (CV)=0.03] and common parent mean of 0.77. Kite circularity decreases when branch angles are either less or greater than 45°, which results in a more columnar or prostrate branching habit, respectively (P=0.4), selection of high-yielding individuals with low branch angles and low kite circularity could permit denser plantings without sacrificing axial branches to competition, and result in greater yield per hectare. For instance, if dry stripped floral biomass yield is considered per unit area (kg m−2), both circularity  and branch angle  become redeemable selection criteria.The least variable architectural trait, kite circularity, had an Fectively . While t2 kg−1) is indicative of small plants with a high dry biomass proportion, and vice versa, this measurement can generally be used to infer canopy density. Theoretically, wet leaf mass fraction divided by kite volume is a purer estimate of canopy density (kg m−3), and can be derived allometrically (r=0.70), leaf circularity (r=0.43), and specific petiole area (r=0.35), and inversely with final height (r= −0.82). The family with the greatest canopy leaf density was the inbred, early-flowering S2 family 19-1206. Notably, the largest plants were not necessarily the most productive per unit area, and dense canopies can come with significant costs, such as greater incidence of disease. Genetic selection on canopy architecture and canopy density traits described here could dramatically reduce grower inputs associated with pruning and maintenance, which has been shown to increase uniformity of the cannabinoid profile in greenhouse-grown, drug-type C. sativa . However, S2 family 19-1206 had the greatest mean branching angle (48.7°), which suggests that plant size (kite area) plays a more important role in maintaining branch and internode lengths, which scale linearly. Besides broken branches, there were only two instances of complete lodging, each derived from the seed parent ‘A2R4’, and were significantly taller (>2 m) overall.Incidents of broken branches all occurred at collars along the main stem and were caused by lateral movement from strong winds. Many broken branches continued to support foliage and inflorescences after breakage, but were less vigorous. Plants with greater kite area and longer internodes were more prone to breakage, especially if branching angles were shallow. Inbred Sr>0.65, P<0.005) with basal stem diameter , whereby incremental gains in stem diameter equate to considerable gains in biomass yield , but the slope of the regression reiterates that stem diameter alone is a superior selection criterion. It would be interesting to determine the earliest point at which stem diameter measurements are predictive of biomass yield, as such information could be used in early seedling selection of breeding populations.Of all traits measured, basal stem diameter offers the best return on investment as a selection criterion for biomass yield. Regression of log (biomass) and log (stem diameter) resulted in a slope of 1.7  and kite volume (r=0.67) for early flights. Family-level correlations were even stronger for height (r=0.95) (r=0.80) (r=0.56), yet this correlation was only marginally improved on a family mean basis  between flights flown before and after 56 DAP, with good correlations among measurements within but not among earlier and later flights. These differences were due to a strong windstorm between 50 DAP and 56 DAP that resulted in moderate lodging and stem breakage. Even though F flights . Canopy (r=0.95)  and volu(r=0.80) . Biomassan basis  and, forr= −0.35) and cannabidivarin  with MNLI, MSAVI2, and OSAVI at 93 DAP, but those with CBDV may be due to population structure, since only two families had individuals with >1% CBDV content. It may be possible to predict cannabinoid profiles and yield using multispectral or hyperspectral data, similar to what has been attempted with Fourier transform near-infrared spectroscopy  and inversely with days to flower (r= −0.54), green leaf index (r= −0.55), MNLI (r= −0.48) (r= −0.38). Based on these associations, prolonged exposure of early-flowering individuals to PM led to the loss of photosynthetic capacity and early desiccation of leaves and flowers, probably reducing biomass accumulation. Further, these results suggest that resistance to PM is quantitative and may be confounded by floral phenology. While later flowering individuals were less susceptible, the late-flowering phenotype is generally not leveraged in the northeastern USA because of seasonal constraints at harvest. In addition, inflorescences may not fully mature before seasonally cold temperatures, which invariably leads to yield loss. Therefore, durable genetic resistance to PM must first be identified and introgressed into elite germplasm to reduce grower inputs and maximize yield and quality traits. Selection for pest and disease resistance is still in its infancy but will probably be a key component in hemp breeding programs.Powdery mildew severity at 97 DAP ranged from 0 to 95%, with a mean of 37.9% (CV=0.52). Families with the greatest mean AUDPC were those crossed with an inbred selection of the common parent and/or derived from a selection of ‘Candida’, a highly susceptible cultivar. The lowest mean AUDPC were for families 19-1169 and 1901154, which were derived from a selection of ‘Otto II’, a known late-flowering cultivar. AUDPC was positively correlated with specific petiole area (= −0.48) , and bioConsidering that all individuals in this study were derived from a common parent, cannabinoid profiles were diverse and varied substantially within and among families . CannabiT alleles in the trial  in this study allowed for the accurate prediction of the former. On average, dry biomass was 30% of wet biomass, and dry stripped floral biomass was 60% of dry biomass, such that ideally, at harvest, 1 kg wet biomass should yield ~0.18 kg dry stripped floral biomass , height (r= −0.70), and growth rate (r= −0.67), and exemplifies agronomic considerations that still need to be met. Questions concerning the harvest index of hemp and its theoretical maximum are of obvious agronomic importance.Compared with large, high-yielding plants, smaller plants with a greater dry stripped floral biomass to wet biomass ratio could be planted at higher densities, making harvest and processing more practical on a larger scale. Although the S−2) does not necessarily favor small, early-flowering cultivars, even though their dry stripped to wet biomass ratio is superior to those flowering later. In fact, when dry floral biomass is considered on the basis of yield per unit area, the correlation of days to flower and yield is virtually negligible (r=0.18) (r=0.68)   compared(r=0.68) . Simply JXB online.The following supplementary data are available at Table S1. Genotyping-by-sequencing sample metadata and cluster membership probabilities.Table S2. Formulas for architectural traits.Table S3. Family means, Tukey HSD groupings, and common parent deviations for all traits.Table S4. ANOVA from stepwise selection and relative importance of predictors for final biomass yield.Fig. S1. Genetic diversity of hemp cultivars, crosses, and US feral accessions.Fig. S2. Field design.Fig. S3. Diagram of architectural traits within a 2-D kite.Fig. S4. Example of calculation of mean GLI by masking operations.Fig. S5. Data processing pipeline for the extraction of phenotypic traits from RGB and multispectral data.Fig. S6. Pairwise correlations of field-collected traits with aerial morphological indices on a plot-level and family-level basis.Fig. S7. Pairwise correlations of field-collected traits.Fig. S8. Pairwise correlations of cannabinoid profiles and aerial indices over time.Dataset S1. Distance matrix.Dataset S2. Morphological dataset.Dataset S3. UAS dataset.Dataset S4. Cannabinoid dataset.erab346_suppl_Supplementary_Figures_S1-S8Click here for additional data file.erab346_suppl_Supplementary_Tables_S1-S4Click here for additional data file.erab346_suppl_Supplementary_Datasets_S1-S4Click here for additional data file.


Tomato flavour is an important goal for breeders. Volatile organic compounds (VOCs) are major determinants of tomato flavour. Although most tomato varieties for fresh market are F1 hybrids, most studies on the genetic control of flavour-related traits are performed on lines. We quantified 46 VOCs in a panel of 121 small fruited lines and in a test cross panel of 165 hybrids (the previous panel plus 44 elite cherry tomato lines crossed with a common line). High and consistent heritabilities were assessed for most VOCs in the two panels, and 65% of VOC contents were strongly correlated between lines and hybrids. Additivity was observed for most VOCs. We performed genome wide association studies (GWAS) on the two panels separately, along with a third GWAS on the test cross subset carrying only F1 hybrids corresponding to the line panel. We identified 205, 183 and 138 associations, respectively. We identified numerous overlapping associations for VOCs belonging to the same metabolic pathway within each panel; we focused on seven chromosome regions with clusters of associations simultaneously involved in several key VOCs for tomato aroma. The study highlighted the benefit of testcross panels to create tasty F1 hybrid varieties. Tomato is the vegetable crop that is most consumed and produced worldwide . NeverthSolanum lycopersicum) and the wild species Solanum pennellii concomitantly enabled the cloning of major genes regulating key VOC synthesis [Solanum Pimpinellifolium or Solanum Habrochaites have also been studied through biparental segregating populations and provided new QTLs for flavour-related traits as reviewed in [Organoleptic quality-evaluated through both flavour and texture-depends on the variety choice, the growth conditions and post-harvest storage conditions . Howeverynthesis ,19. Otheiewed in . Preciseiewed in ,22,23.Although tremendous improvement has been achieved for QTL identification on homozygous plant material , breederS. pimpinellifolium (SP) homozygous lines; a second panel grown in 2019 composed of a test cross gathering the previous panel completed with 44 elite cherry tomato, all crossed by a common big fruited S. lycopersicum (SL) parent; a third panel derived from the test cross, carrying only the 121 hybrids from the core collection to compare VOC inheritance at the line and hybrid levels  and wild relative d levels . ContrarWe explored the phenotypic diversity of three related GWAS panels representative of the genetic diversity of small fruited tomatoes . The firThe CCI panel was grown from March to July 2018 under passive glasshouse irrigated conditions–in soil-on the experimental site of INRAE GAFL, Avignon, France. The TCT-which contained the subset TCI-panel was grown from April to July 2019 under soilless and passive irrigation conditions in a plastic greenhouse on the experimental site of the seed company Gautier semences, Eyragues, France. At least three plants per genotype were cultivated in both trials.Three harvests—representing three replicates—of red ripe fruits were conducted for each trial during three successive weeks, starting from the second truss for the first harvest and finishing around the fifth truss for the third harvest. At least 10 fruits were harvested from each plot and each harvest (up to 30 fruits per harvest for SP accessions). The harvested fruits were divided in two pools: the pericarp from at least 5 fruits per plot was fast frozen in liquid nitrogen and ground to powder with a cryogenic mill. The samples were then stored at −80 °C until analysis of their VOC content. The second pool of fruits was weighted to get an average fruit weight. The fruits were then crushed and the resulting juice was used for Soluble Solid Content (SSC) measurement. Phenotypic values from the CCI and TCI panels are available in 2.2H2O and 500 µL of a 100 mM EDTA-NaOH pH 7.5 solution were added, gently mixed and sonicated for 5 min. Finally, 1 mL of the resulting paste was transferred to a 10 mL screw cap vial for analysis. Extraction of VOCs was performed by means of headspace solid phase microextraction (HS-SPME) due to its high sensitivity [Profiling of volatile compounds was performed at the Instituto de Biología Molecular y Celular de Plantas, at the Universidad Politécnica de Valencia, Spain. The same protocol was used in 2018 on CCI and in 2019 on TCT. Right before analysis, 0.5 g of each sample was weighed in a 15 mL vial. The samples were then incubated at 37 °C for 10 min. Then, 1.1 g of CaClsitivity . Brieflycar package on each panel to test for genotype effect with the following model:ij is the trait value of genotype I in harvest j, μ is the intercept, gi and rj represent the fixed effects of the genotype and the harvest, respectively, and εij the residual effect. Except for three terpenoid-derived VOCs in the CCI panel and one lipid-derived VOC in the TCT panel, all VOCs, SSC and fruit weight showed significant genotype effects. Broad-sense heritability (h2) was computed for every trait with the lme4 package by using the following linear mixed model:ij is the trait value of genotype i in harvest j, μ is the intercept, gi the random effect of genotype i, rj the fixed effect of harvest j, and εij the random residual effect. Then, heritability was derived from the variance components of the model as:G2 and σe2 are the genetic and residual variance, respectively. Skewed data were then transformed using The same statistical analyses were performed on the three panels separately using the R software v.3.6.2 . Prior tcorrplot and the ‘hclust’ clustering method. Correlations between identical traits between CCI and TCI were computed on scaled datasets with Pearson’s pairwise correlations.Correlations between traits within and among panels were computed using Pearson’s pairwise correlations on scaled datasets. The graphic representation of the pairwise correlations were produced using the R packages ComplexHeatmap and dendextend on scaled datasets. Clusters for metabolites and accession profiles were produced with the R package hclust and the ‘average’ method on scaled dataset. The factoextra package evaluated the optimal number of clusters when a bend was observed in the total within sum of square as the number of clusters increased. Metabolic clusters were further validated with the pvclust R package based on ‘euclidian’ distance, ‘average method, and n = 1000 bootstrap replications. Briefly, pvclust calculated probability values for each cluster using bootstrap resampling techniques. p-values are given as ‘approximately unbiased’ (AU) or ‘bootstrap probability’ (BP), both reflecting the frequency of the cluster appearance across the multiple resampling (Suzuki and Shimodaira 2006). Metabolic clusters gathering compounds belonging to the same metabolic pathway allowed a quality control of the VOC quantification method. PLS-DAs were computed on scaled data with the R package mixOmics. To assess the statistical link between VOCs, SSC and each genetic group, we computed p-values with the ‘catdes’ function of the FactomineR package in order to support metabolic signatures identified by the PLS-DA method.Metabolomic profiles from each accession were produced with the R packages plink software [FactomineR and the function ‘emma.kinship’ (Identity By State method), respectively [−4 with up to 15 cofactors allowed. For any significant association, we first computed the marker pairwise linkage disequilibrium (‘LD’) with each neighbour marker in a 2 Mbp interval (1 Mb on each chromosome end) with the LDcorSV package [2 between pairs of markers on a given chromosome. Thus, the confidence interval (‘CI’) around each associated SNP was defined as the interval where the computed r2 was higher than 0.5 in a 2 Mbp region. The SL4.0 tomato genome version was used for the analysis along with the annotation ITAG4.0. We excluded genes that did not map on any of the 12 chromosomes, and focused on the 33,562 remaining genes. From these CIs, we extracted the list of CGs comprised in each interval. We retrieved CGs when their functional annotations matched functions involved in VOC synthesis as reported in the bibliography, irrespective of their expression in tomato fruits. Lists of CGs per metabolic pathway were thus constituted  using bwa 0.7.17 and PCR duplicates were removed with SAMtools v1.9 [gatk4 v4.1.4.1 [Snakemake v5.8.1 [Singularity v3.5.3 [For the subset of CGs we selected, we looked for the putative effect of polymorphisms that could be found when we compared sequencing data from 47 lines from the CCI panel against the reference genome Solanum lycopersicum Heinz_1706. These lines appear in bold type in  V0.11.8  and MultiQC v1.7 . Sequencp 0.20.0  with theols v1.9  sambambav4.1.4.1  and a gey v3.5.3 . We predSince the hybrids shared half of their genome with the corresponding lines, CCI and TCI structures are identical on the PCA : the corA total of 46 VOCs plus SSC and fruit weight remained once we had applied quality filters . PairwisHeatmaps were produced for each panel for the 46 VOCs and SSC. Cluster analyses were computed beforehand on metabolites based on the accession profiles. The optimal number of clusters was evaluated at nine, eight and ten for CCI, TCI and TCT, respectively. Whatever the panel considered, metabolite clusters were consistent with those identified in the TCT panel . ClusterIn order to identify characteristic features of each genetic group identified in the genetic structure analysis, we conducted a Partial Least Square–Discriminant Analysis (‘PLS-DA’) on each panel  with fur2 test on each metabolic pathway and each panel showed significant enrichment of CG presence in the CI of associations from the three panels for SSC and both benzenoid and phenylalanine-derived VOCs. We found enrichment in CGs for the branched-chain amino acid-derived (resp. carotenoid and terpenoid-derived) metabolic pathway for the TCI (resp. TCT) panel. Both CCI and TCT (resp. CCI and TCI) panels showed associations with significant enrichment of CGs from the lipid-derived (resp. sulphur-derived) metabolic pathway. Overall, the MLMM method paired with the definition of narrow CIs allowed us to accurately retrieve CGs.From the 46 VOCs, SSC and fruit weight measured across panels, 45, 44 and 41 traits displayed at least one association in the CCI, TCI and TCT panels, respectively. The MLMM step-by-step approach allowed the calculation of the percentage of variance explained (‘PVE’) for the first association of each trait. The CCI, TCI and TCT panels had 76%, 90% and 90% of their first associations with PVE > 20%, respectively. These first associations, accounting for 24% of the 526 total associations identified, carried 21% of the total number of CGs.In order to identify promising associations among those presented in smoky gene (Solyc09g089585) cloned by [ADH2 (Solyc09g025210) cloned by [Lin5 (Solyc09g010080) [Overall, we retrieved four previously cloned genes among our associations. The loned by  was idenloned by  for the loned by  involvedg010080)  involvedp-values < 10−6 for the subset of VOCs impacting tomato aroma; (2) markers in association with balanced Minor Allele Frequency (‘MAF’) (>0.2) to ensure that both allelic classes were well represented, before estimating FTMV allelic effect on the VOC content; (3) regions displaying overlaps between VOCs belonging to the same metabolic pathway, and with consistent effect of FTMV allelic class on closely related VOCs.Apart from cloned genes, two, five and 14 associations found in the CCI, TCI and TCT panel, respectively, carried 17 different CGs previously suggested in  Table S. Ten CGsAll CGs identified in the CI of our associations are presented in Many lipid-derived VOCs are supposed to impact tomato aroma as reviewed in Regulating phenylalanine and benzenoid-derived VOCs is important for flavour improvement. Although they both derive from the amino-acid phenylalanine, they confer aromas with opposite impact on consumer liking: all the phenylalanine-derived VOCs we quantified are positively correlated to consumer liking with sweet and floral aromas, while most benzenoid-derived VOCs are negatively correlated to consumer liking with ‘smoky’ and ‘medicinal’ aromas. Identifying key regulators of their synthesis is thus of utmost interest. We suggested “CG.B.Phe.3” that showed overlapping associations for EUGEN in TCI and PHEAC in TCT on chromosome 3, both carrying a glycosyltransferase (Solyc03g118120) that showed a cis-regulation pattern, which might result from at least one of the four non-synonymous mutation in its coding region ,52. “CG.Lin5 where cherry type tomato alleles increased the amount of sugar. Adjacent to Lin5, we identified associations highlighted in “CG.Phe.9” for three phenylalanine-derived VOCs contributing to consumer liking. Choosing heterozygosity over FTMV homozygosity could thus increase SSC along with key VOCs in this region. However, we found associations for fruit weight within “Breed.2” and “Breed.3”, and near “Breed.9” and “CG.B.4”. The cloned gene fw2.2 is located two genes upstream “Breed.2”. All of these fruit weight associations showed higher fruit weight value for FTMV homozygous genotype.Based on the aroma perception of VOCs, a better aroma quality should be obtained when the markers in association in regions “Breed.2”, “Breed.3”, “Breed.9” and “Breed.11”, and also those of all CG regions from Most of the efforts in tomato flavour have been directed to understand the genetic control of VOC accumulation in homozygous lines . HoweverAs previous studies highlight , the comAs stated in , flavourGiven the common regulation of volatiles belonging to the same metabolic pathway as found in the cluster analysis, an overall regulation of key metabolic pathways could be achieved by selecting overlapping associations for VOCs clustering with one another. Since we cannot estimate yet how many favourable alleles are necessary to make significant flavour differences in the breeding material, we proposed the most important breeding regions encompassing these overlaps to achieve multi-VOC regulation. We also indicated regions where promising CGs could be found based on the same criteria. The next and final criteria was selecting among these regions those that displayed a consistent effect of FTMV homozygous genotype across VOCs belonging to the same metabolic pathway and on VOCs with similar impact on consumer liking in order to (i) control and understand the taste differences that could be accomplished and (ii) increase VOCs with positive impact on consumer liking while simultaneously decreasing those leading to off-flavours.2 test we performed on each metabolic pathway and each panel separately mostly showed significant enrichment of CG presence in the CI of our associations. This underlined that the MLMM method paired with the definition of narrow CIs allowed us to retrieve CGs accurately.For this strategy to work on F1 hybrids, we highlighted regions with F1 specific associations, or overlapping between F1 hybrid (TCI TCT) and line (CCI) panels. With only nine and five associations commonly found between CCI and TCI and CCI and TCT, respectively, and six associations shared between the three panels, we underlined the utmost importance of considering associations fit to design F1 hybrid varieties instead of considering QTLs identified only on homozygous plant material. We revealed novel regions where phenotypic means for a given volatile are more contrasted when we compare the homozygous and heterozygous locus versions versus the two homozygous genotypes at the locus. Although the environmental effect causing lack of overlaps between line and hybrid panels cannot be ruled out, the high correlations calculated between CCI and TCI are evidence that novel associations in F1 hybrids did not result solely from an environmental effect. Additionally, we defined stringent CIs around associated markers by accounting for both population structure and kinship when calculating local LD between neighbouring markers, and we called “overlap” CI boundaries separated by less than 0.1 Mbp. Those parameters led to few overlaps. With less stringent parameters, more overlaps would be detected. Apart from the four cloned genes that we found back in hybrid panel associations and 6% of hybrid specific associations for VOCs carrying CGs previously suggested in , we idenAs part of our strategy, we also gave insight into the best allelic combinations for breeding by giving the relative position of the heterozygous class compared to FTMV homozygous genotype for a given trait-marker association. For each breeding region we selected, and even more in the elite plant material, we found associated markers with balanced MAF, which highlighted that many regions in small fruited accessions showed the allelic version of the common parent FTMV entirely lacking flavour. This finding raised awareness about the improvement that could be achieved given the number of novel levers for key VOCs we herein proposed, and that remained dormant up until now.fw2.2, lc and fw11.2, respectively. Only one fruit weight association, close to Lin5, overlapped between the line and hybrid panels. We indicated regions where flavour improvement could be accomplished, though at the expense of fruit weight. Thus, “Breed.2” with the different metabolic pathways it regulated and “Breed.3”, “Breed.9” and “CG.B.4” with global regulation of the lipid, phenylalanine and benzenoid-derived VOCs, respectively, were rather aimed at cherry type tomato breeding than big fruit type. Otherwise, we showed that improved flavour could be achieved without trade-off with yield for other breeding regions. Thus, our results can be of interest to breeders seeking to create parental lines with better aroma composition potential while being less likely to have an effect on fruit weight.Despite growing interest in organoleptic quality in commercialized hybrids, maintaining yield and other agronomical traits remains an important concern. We therefore assessed fruit weight in our experimental analyses. We chose a SL common tester to: (i) increase heterozygosity occurrence; (ii) study its impact on VOC content; and (iii) address both cherry type and big fruited tomato flavour improvement in light of fruit weight associations among the other VOC quality trait associations we identified. We found 11 hybrid specific associations for fruit weight, three of them mapping within 1 Mb of the cloned genes


The reliability of Criteria for Assessing Prescription Quality in Chinese Hospitals (CAPQCH) has never been rigorously verified. This study was designed to verify the reliability of the CAPQCH among pharmacists in China.Fourteen pharmacists, 5 from hospitals and 9 from the communities were recruited. We randomly selected 200 prescriptions, and made the testing prescriptions including appropriate and inappropriate testing prescriptions. Pharmacists assessed these testing prescriptions according to criteria in CAPQCH. Three test sets  were evaluated at 6-month intervals. Before administration of Set 3, pharmacists were informed that achievement on Set 3 would be reflected in their performance appraisal. We also evaluated the performance based on prescription comments before and after combining several confusing criteria. Cohen’s Kappa statistic, Fleiss’ Kappa statistic, and accuracy were employed to evaluate reliability among pharmacists.Median values of Cohen’s Kappa were 0.61 in Set 1, 0.66 in Set 2, and 0.80 in Set 3; reliability is thus substantial. Our data indicate no significant differences between Set 1 and Set 2, whereas Set 3 indicates significantly improved performance. Moreover, combinations of confusing criteria contributed little to improvement of performance in prescription comments.Our results verified the reliability of CAPQCH application by working pharmacists. Adding performance based on prescription comments to personal appraisals was effective in improving the quality of prescription comments. These findings may be useful when future modification of the CAPQCH is considered. Moreover, this study contributes to improving the understanding of the prescription assessment situation in China.The online version contains supplementary material available at 10.1186/s12913-022-07854-1. Inappropriate prescription is common in aging populations and is a global public health concern because it leads to negative clinical outcomes, such as increasing adverse drug events, morbidity, and costs of hospitalization and care. The global population is aging, and reduction of numbers of inappropriate prescriptions to improve safe drug use is a challenge that must be faced by governments worldwide. Our earlier studies indicated that improving the spontaneous reporting system  may contReliability of prescription comments is a critical issue. The reliability of the widely used tools, such as Beer’s criteria, STOPP, START, PCNE, and MAI, has been verified. Inter-rater reliability is typically used to assess reliability of such tools. Higher inter-rater reliability indicates higher consistency among different examiners. This approach can provide compelling evidence for overall reliability of screening tools. Ryan et al. evaluated inter-rater reliability of STOPP and START with 10 pharmacists from hospitals and the communities. Finally, inter-rater reliability of STOPP and START was judged to be “good” for both pharmacists in hospitals and the communities . Stuijt The reliability of CAPQCH has never been rigorously verified. During practical application, some reports in Chinese documented that some criteria, namely “unsuitable usage and dosage,” “no or incomplete clinical diagnosis,” “improper drug combination,” and “incompatibility or bad interactions between drugs,” might be confusing , 13. We Fourteen pharmacists were recruited, 5 from the hospital sector (recorded as “HPs” in this study) and 9 from the community sector (recorded as “CPs” in this study). Subjects work in hospitals within the Jinshan district in the south of Shanghai city, China Table . The worPrescriptions for the study were first selected randomly by two experienced pharmacists from the electronic prescription system. A total of 200 prescriptions written from September to October 2019 were selected, which were then evaluated and modified to form two serials of testing prescriptions, namely “appropriate” prescriptions and “inappropriate” prescriptions using CAPQCH. All inappropriate prescriptions were modified to ensure that there is only “one mistake” in it. Mistakes were chosen to match expected professional knowledge and experience of raters. The “mistake” was defined according to CAPQCH and a previous study concerning CAPQCH  as 1) irAll recruited pharmacists participated in three tests, given in September 2019 (Set 1), March 2020 (Set 2), and September 2020 (Set 3). The pharmacists assessed test prescriptions and identified “mistakes” according to guidance of prescription comments. Answers (raters 1–14) were then reviewed by the expert group. Set 1 and Set 2 tests were free from any connection to pharmacists’ appraisal. Before Set 3 (1 month before implementation), all pharmacists were informed that their achievement with Set 3 would be recorded in the assessment of the quality of their prescription comments, which is related to their performance appraisal. A pharmacist who achieved good performance on the test would earn a “good” score on his/her appraisal. Conversely, poor performance would earn a “poor” score. The performance appraisal is regularly associated with the ranking of their department in the local public health administrative department.All the answers were entered into a database and analyzed using SPSS software . Cohen’s Kappa statistic was used to assess the consistency between raters 1–14 and rater A . The degOverall consistency within a single test was assessed using Fleiss’ Kappa statistic. Accuracy was calculated, by pharmacists, as the number of “right raters” (raters in agreement with rater A) divided by the number of ratings.According to the previous studies , 12, somp < 0.05 was considered statistically significant.Normality of data distributions was assessed using the Kolmogorov–Smirnov test. Normally distributed data with Kolmogorov–Smirnov values over 0.05 were submitted for Levene’s test to verify homogeneity of variances. Paired t-tests were then used to compare differences between raters and rater A for each pharmacist. Independent sample t-tests were used to compare the difference between HPs and CPs. Data not normally distributed were compared using the Wilcoxon test. Seventy-nine percent of raters were female, five pharmacists were from hospitals, and nine were from the community. The average age was 38.14 ± 1.94 y (first quartile–third quartile: 33.75–42.50 y). Average work experience was 16.79 ± 2.18 y (first quartile–third quartile:10.75–22.25 y) (Table p < 0.001) and Set 2 (p < 0.05). Further, the lowest values of Cohen’s Kappa statistic were 0.33  in Set 1 and 0.42 (0.25–0.58) in Set 2, indicating a “moderate” Cohen’s Kappa statistic in the first two tested sets. Accordingly, proportions of values lower than 0.6 were 50% in Set 1, 29% in Set 2, and 0% in Set 3 (Table Sp < 0.05), but was significantly higher than only Set 1 (p < 0.05) in HPs , 0.66 (0.55–0.75), and 0.80 (0.74–0.87), respectively. These values exceed 0.6, indicating good reliability for all tests. An increasing tendency in the statistic is seen from sets 1 to 3. No significant difference was found between Set 1 and Set 2. The statistic for Set 3 was significantly greater than those for Set 1   to standard answers (rater A) Tables  and S2. Evaluation of overall consistency using the Fleiss’ Kappa statistic produced analogous results Table . No signWith respects to the influence of “confusing items”. Finally, our results imply that these criteria have a little effect on performance for prescription comments. In sets 1 and 2, only TPs displayed significant changes Tables  and 5. WIn the light of the findings of the present study, we understand that CAPQCH is a reliable tool to evaluate the potentially inappropriate prescription in China. It can be widely used in both HPs and CPs. Adding quality of prescription comments to personal performance appraisals of the pharmacists might improve the performance for the prescription comments, which should be noticed by those who are in charge of the hospital management. In addition, the “confusing items” seldom affected the performance for prescription comments, which should be taken into account in the future modification of the CAPQCH.There are several limitations in this study. First, regarding the recruitment of pharmacists, although we had recruited all the pharmacists working in our hospital (five) and our community (nine), the sample size was not determined by a strict statistical method. This might lead a sampling bias. In addition, the small size of the participants might lead a biased conclusion. Second, we compared the potentially inappropriate medications between CAPQH and the other tools like NCC MERP, STOPP, and START directly using Kappa values. CAPQH is a list of items to check for formal content. Simply comparison of the Kappa values between CAPQH and the other tools might lead a biased conclusion because the purpose and contents of each tool are very different. We need to consider the results of CAPQH in light of its content. These issues will be fully considered and improved in our future investigations.This study verified the reliability of CAPQCH using a traditional inter-rater reliability test, which indicated that CAPQCH is a reliable tool to classify the inappropriate prescription in China. Our results suggest a little influence of reported confusing criteria. These items should be considered in the future modification of the CAPQCH. Instead, adding quality of prescription comments to personal performance appraisals might be more useful for improving comments. These findings contribute to our understanding of reliability of CAPQCH and may be useful for future modifications. In addition, this study contributes to improving the understanding of the prescription assessment situation in China.Additional file 1: Table S1. CAPQCH items. Table S2. The inter-rater reliability of criteria between rater A and raters 1–14 for three sets of prescriptions using Cohen’s Kappa statistics.


Whorls have been used to indicate the temperaments of domestic animals; however, little is known about the biological events that drive this association. The present study is the first that aims to find the main genomic regions that influence the whorl traits in livestock, with horses as a model. Genes related to hair follicle growth were found. Interestingly, some of these genes also influence psychiatric diseases and neurological disorders, thus evidencing a consistent biological explanation for the association.The position and number of hair whorls have been associated with the behavior, temperament, and laterality of horses. The easy observation of whorls assists in the prediction of reactivity, and thus permits the development of better measures of handling, training, mounting, and riding horses. However, little is known about the genetics involved in the formation of hair whorls. Therefore, the aim of this study was to perform a genome-wide association analysis to identify chromosome regions and candidate genes associated with hair whorl traits. Data from 342 Quarter Horses genotyped for approximately 53,000 SNPs were used in an association study using a single-step procedure. The following traits were analyzed: vertical position of hair whorl on the head, number of whorls on the head, and number of whorls on the left and right sides of the neck. The traits had between one and three genomic windows associated. Each of them explained at least 4% of the additive variance. The windows accounted for 20–80% of additive variance for each trait analyzed. Many of the prospected genes are related to hair follicle growth. Some of these genes exert a pleiotropic effect on neurological and behavioral traits. This is the first indication of biological and physiological activity that might explain the association of hair whorls and temperament. Quarter Horses have great visibility and economic importance, mainly due to their versatility in different equestrian modalities. The breed is subdivided into different populations according to their competencies, resulting from different selection objectives. The racing population has animals with the ability to run short distances, while the stock population is used in functional tests, exploring skills such as agility, temperament, obedience, and cow sense .Horses are animals that are managed directly and routinely by humans. Behavioral differences are easily observed in this species . HoweverHair whorl traits in horses show high heritability ,9,10 andThis study used 340 registered Quarter Horses of the racing line, 265 females and 75 males, born between 1985 and 2012. For the evaluation of whorl position, animals with hair whorls in more than one vertical position on the head were excluded, resulting in 252 females and 73 males. The total sample was established to best represent the diversity of racing Quarter Horses in Brazil. Animals with many offspring, as well as descendants of individuals that were influential in the formation of the breed, were selected to be genotyped. Sampling of full siblings was avoided. The breeders of these animals agreed to the sampling and genotyping. The animal procedures were approved by the Ethics Committee on Animal Use of FMVZ, Unesp, Botucatu (protocol number 157/2014–CEUA).The hair whorl phenotypes were obtained from the official identification document (outline diagram) kindly provided by the Brazilian Association of Quarter Horse Breeders (ABQM), which contains graphical and discretionary representations of the location of the marks that identify the animals .The following traits were studied: the position (POS) and number (NUM) of hair whorls on the head, and the number of hair whorls on the left (NUML) and right (NUMR) side of the neck. For POS, the horses were classified into three groups according to whorl position: (1) above the upper eye line; (2) between the upper and lower eye line; and (3) below the lower eye line. For NUM, the hair whorls arranged on the animal’s head were counted, considering the head area to end at the appearance of the ears. For NUML and NUMR, the number of hair whorls located throughout the neck was counted, considering the appearance of the cranium and thorax as the upper and lower limits, respectively. https://www.r-project.org). p > 0.05).Preliminary analyses of data consistency and descriptive statistics were performed using the R package  were genotyped with the Illumina Equine SNP50 BeadChip (54K)  and the rest (n = 240) with the Illumina Equine SNP70 BeadChip (65K) . Quality control of genotyped individuals and SNPs and genotype imputation were performed as described by . AnimalsGenomic association analysis was performed using four independent phenotypes: (1) POS; (2) NUM; (3) NUML; and (4) NUMR, in a single-trait model for each phenotype evaluated.l is the liability; a is the random effect of the animal; and ije is the residual random vector. The animal model using Bayesian inference assumes that A is the numerator relationship matrix, and R is the variance matrix of the residual vector. The residual variance  scale has a normal continuous distribution as follows:Quality control of the SNPs after imputation was performed using PEGSF90 software  which coSingle-step GBLUP was used for analysis. The variance components were estimated with the THRGIBBS1F90 software , posteriThe variations explained by windows comprising 100 adjacent SNPs were used to identify genome regions with a major effect on the traits evaluated. Major-effect genes present in the windows were prospected in the NCBI database  using EquCab 3.0 as the reference genome, filtered by species, chromosome, and position of the window. The present results of GWAS of the position and number of hair whorls on the head and neck of horses are the first reported in the literature for domestic animals, using horses as a study model . For theFor POS, three windows were found that explained more than 4% of the additive variance in the trait, in decreasing order: 51.31% (ECA17), 4.56% (ECA2), and 4.41% (ECA5) . The sumThree windows that explained more than 4% of the additive genetic variance were also identified for NUM: 26.80% (ECA1), 6.46% (ECA19), and 4.25% (ECA23) . The winThe other two traits evaluated, NUML and NUMR, followed the same patterns of genetic architecture of the others: few genomic regions with a large influence on the trait. There were two windows explaining 14.06% (ECA5) and 8.48% (ECA11) of the additive genetic variance in NUML (sum of 22.54%), and only one window explaining 81.96% (ECA7) of the variance in NUMR .The genes were prospected according to their location in the genome, and arranged as shown in This is the first GWAS of hair whorl traits in domestic animals, using horses as a study model. Hair whorl traits are used as indicators of behavior in horses ,4,5,6. TKLF5 gene, which is known for its role in epidermal biology, as well as controlling repair and growth in the root of the hair follicle in humans [KLF5 was found in the second window for POS, located on ECA2. The IL2 gene is related to hair follicle growth, and has been associated with hair loss  in humans [For POS, the window that explained most of the additive variance was located on ECA17. This window harbors the n humans . This gen humans . This isn humans . In addin humans  has beenCandidate genes found in the main windows associated with hair whorl position on the head of horses exert an effect on the formation and growth of hair follicles, and might be associated with whorl formation. Interestingly, the genes identified here have a pleiotropic effect on psychiatric characteristics. This is the first indication of biological and physiological activity that provides a plausible explanation for hair whorl position as an indicator of behavior. Furthermore, it is the beginning of the observation/validation of the genetic mechanisms underlying the phenotypic observation.SIRT1 gene, which is expressed in the hair follicle [SIRT1 were found in the second window for NUM. The CD47 gene is known for its biological role in follicular development and hair follicle formation [CD200 gene is expressed in hair follicle stem cells [ALDH1A1 gene, which is involved in signaling processes and morphogenesis of the hair follicle [For NUM, the window explaining most of the additive variance, located on ECA1, contains the follicle ,20, and follicle . Other gormation , in addiormation  and reguormation . The CD2em cells , and is em cells , in addiem cells . The thifollicle . An assofollicle . Similar to the position trait, genes associated with the number of hair whorls on the head exert activity in both the hair follicle and neurodegenerative diseases, which are indicators of behavioral and neurological alterations. The number of hair whorls on the head has also been associated with behavior in horses , providiNECTIN4 [PPOX [CCDC190 [NCSTN [PBX1 [VANGL2 [KLHDC9 [NCSTN [PBX1 [VANGL2 [TP53 [AURKB [PER1 [ALOXE3 and ALOX12B [DVL2 [ALOX15B [SHBG [ALOX15 [PER1 [SHBG [DVL2 [Analysis of the main window associated with NUML, located on ECA5, revealed a series of genes expressed in the hair follicle: NECTIN4 , PPOX [3N4 [PPOX , CCDC190[CCDC190 , NCSTN [0 [NCSTN , PBX1 [3TN [PBX1 , VANGL2  [VANGL2  and KLHD [KLHDC9 . The num9 [NCSTN , PBX1 [3TN [PBX1 ,38, and  [VANGL2 . A serieL2 [TP53 , AURKB [3 [AURKB , PER1 [4KB [PER1 , ALOXE3  ALOX12B  and DVL22B [DVL2 . Some of[ALOX15B , SHBG [45B [SHBG , and ALO [ALOX15 . In addi15 [PER1 , SHBG [4R1 [SHBG ,50, and BG [DVL2 . RHOG [UCP2 [UCP3 [IL18BP [RHOG [UCP2 [UCP3 [For NUMR, some genes related to hair follicle development and growth were found in the window that explained the highest proportion of variance, located on ECA7; these are listed as candidates: RHOG , UCP2 [5OG [UCP2 , UCP3 [5P2 [UCP3  and IL18 [IL18BP . AlthougBP [RHOG , UCP2 [5OG [UCP2 , and UCPP2 [UCP3 .Unfortunately, the low SNP density leads to large genomic windows with many genes. This makes it difficult to identify reliable candidates. The use of high-density panels would improve finding them and fine-map causative mutations. Genomic regions associated with hair whorl traits in horses were identified in the present study. In these regions, many genes whose metabolic activity is related to hair follicle growth were prospected, and they were indicated as candidates that may influence these traits. Curiously, some of these genes also have known neurological and behavioral functions. The possible pleiotropic effect of these genes is the first indication of genetic–biological validation and elucidation of the association between hair whorls and temperament in animals.


Simulation can be an efficient approach to design, evaluate, and optimize breeding programs. In the era of modern agriculture, breeding programs can benefit from a simulator that integrates various sources of big data and accommodates state-of-the-art statistical models. The initial release of XSim, in which stochastic descendants can be efficiently simulated with a drop-down strategy, has mainly been used to validate genomic selection results. In this article, we present XSim Version 2 that is an open-source tool and has been extensively redesigned with additional features to meet the needs in modern breeding programs. It seamlessly incorporates multiple statistical models for genetic evaluations, such as GBLUP, Bayesian alphabets, and neural networks, and it can effortlessly simulate successive generations of descendants based on complex mating schemes by the aid of its modular design. Case studies are presented to demonstrate the flexibility of XSim Version 2 in simulating crossbreeding in animal and plant populations. Modern biotechnology, including double haploids and embryo transfer, can all be simultaneously integrated into the mating plans that drive the simulation. From a computing perspective, XSim Version 2 is implemented in Julia, which is a computer language that retains the readability of scripting languages (e.g. R and Python) without sacrificing much computational speed compared to compiled languages (e.g. C). This makes XSim Version 2 a simulation tool that is relatively easy for both champions and community members to maintain, modify, or extend in order to improve their breeding programs. Functions and operators are overloaded for a better user interface so they may concatenate, subset, summarize, and organize simulated populations at each breeding step. With the strong and foreseeable demands in the community, XSim Version 2 will serve as a modern simulator bridging the gaps between theories and experiments with its flexibility, extensibility, and friendly interface. Computer simulation is an efficient approach to design, evaluate, and optimize breeding programs. Simulation usually acts as a bridge between theories and experiments to provide development and preliminary validation of theories as well as planning guidelines on empirical experiments. Instead of risking years real world effort, new breeding strategies supported by quantitative genetics theories can mostly be evaluated in a day through simulations with proper assumptions and an appropriate tool.Integration of various sources of big data including phenotypic, pedigree, genomic, biological, and environmental information for genetic improvement in modern animal and plant agriculture requires an efficient and flexible simulator to both simulate and compare alternative breeding programs using all available information. Such a simulator needs to accommodate state-of-the-art statistical models for genetic evaluation.As an open-source tool, the initial version of XSim , GBLUP , the BayModern breeding programs usually involve complex mating designs, such as crossbreeding in cattle and the use of multiparent populations in plants. XSimV2 is able to effortlessly mimic these designs by allowing the creation of novel mating plans together with modern biotechnology such as using double haploids (DHs) in plants and embryo transfer in animals. In addition, XSimV2 can preload up-to-date information on reference genomes for multiple major breeding species  such that genomic locations and recombination hot or cold spots can be simulated. XSimV2 is implemented in the modern programming language Julia , which rhttps://github.com/reworkhow/XSim.jl, last accessed date: Feb. 22nd, 2022).In this article, we will illustrate the latest software interface for XSimV2 including the manner in which genomes and phenomes are defined, the approach for generating founders, the methods for choosing the type of mating, the integration of selection based on real-time genetic evaluations, and others. We demonstrate the extensibility with case studies from both animal and plant breeding. The documentation of XSimV2 can be found on the GitHub repository , where the lines beginning with “#” are comments.A simulation is initialized by defining the genome and phenome with the functions build_genome and build_phenome. Multiple parameters in build_genome can be used to define the genome, including numbers of chromosomes and loci, genetic position, physical position and the minor allele frequency (MAF) of each locus, mutation rates, and genotyping error rates. Phenome-related parameters can be defined in the build_phenome function including positions and effects of causal variants [quantitative traits loci (QTL)], genetic variance-covariance matrices, residual variance-covariance matrices, phenotypic variance-covariance matrices, or heritability. The genome can also be user defined by providing a map file, in which each row represents one locus. The valid header names include “chr,” “cM,” “bp,” and “maf,” which refer to chromosome identification codes, positions in centimorgans (genetic position), positions in base pairs , and MAFs, respectively:# Define a genome with a user-provided file# An example map.csv file is in the Appendix.# Header in map.csv:”chr”,”cM”,”bp”,and”maf”build_genome(”map.csv”)Note that XSimV2 uses genetic position information to simulate crossover events, and thus, this information encodes recombination hot or cold spots. If information on genetic positions is not provided by the user, XSimV2 can infer genetic positions from physical positions provided using the preloaded genetic and physical representation of reference genomes in XSimV2 through the species argument in build_genome. XSimV2 preloads genetic and physical maps reported in literature for multiple major livestock and crop species, including cattle , and maiPhenomics is another aspect of the simulation, and it defines how traits are simulated in XSimV2. The allele substitution effects of QTL can be defined by adding columns with eff_ prefixes to the map files used in build_genome. For example, when there are two traits being simulated, columns eff_1 and eff_2 are added to assign the QTL effects. A zero value should be assigned for a locus that is not QTL. QTL with pleiotropic effects can also be simulated in multitrait simulation. An example of the map file is shown in the Appendix.# Define a phenome with heritability 0.3 by a user- provided file# Header in map.csv:”eff_” prefixed for marker effectsbuild_phenomeQuick Start approaches are available in XSimV2 to allow users to quickly set up their genome and phenome in order to focus on the simulated schemes for proof of concept (more details are in documentation). One example is for a user to define the genome and phenome by providing number of chromosomes, number of loci, numbers of QTL for each trait, genetic variances, and heritability. Genetic positions of all loci will be uniformly distributed along the genome. QTL effects and environmental effects will be sampled from a standard normal distribution. These effects will be scaled and transformed to obtain the predefined genetic variance and heritability. By default, the genetic variance vg and the residual variance ve are assumed to be scalars in single-trait simulation and diagonal matrices in multitrait simulation. Correlations are introduced by assigning nonzero values to the off-diagonal elements of vg and/or ve in multitrait simulation.The example below shows an approach to simulate two correlated traits controlled by 2 and 1 QTL among 4 SNPs on 2 chromosomes. The traits are simulated to have heritability of 0.3 and 0.8, respectively, with uncorrelated residual effects.# A quick start of genome with 4 loci on 2 chromosomesbuild_genome       # Phenome with 2 correlated traits of heritability 0.3 and 0.8,controlled by 2 and 1 QTL, respectively.build_phenome         In XSimV2, a Julia object, Cohort, was designed to represent a group of individuals and to store their heterogeneous information of simulated genotypes, pedigree, and breeding values. Users can obtain the Cohort object either through the function Founders, which takes an integer as the input argument specifying the number of simulated founders, or from the results of select or mate functions, which will be described in the later sections. If a cohort is created by Founders without known haplotypes, the cohort’s haplotypes are simulated by sampling haplotypes of each locus from a Bernoulli distribution with the event probability equals to 1 - MAF. The codes below demonstrate how to initialize a cohort containing ten simulated founders. Note that, as will be shown in the Case Studies section, random mating over a number of generations may be required to generate linkage disequilibrium (LD).# Generate 10 founders and get a cohort objectcohort_A = Founders(10)Alternatively, users can specify the genomes of all the founders and directly control their relatedness. This requires users to have known haplotypes or genotypes for founders, which XSimV2 can read from a text file. The haplotypes should have individuals recorded by row with two columns per locus, recording the paternal and maternal alleles at this locus. The haplotype is coded as 0 or 1 to represent the existence of a reference allele. If genotypes are provided, haplotypes will be further inferred from genotypes randomly. In the genotype file, alleles are coded as 0, 1, and 2 to represent the allele dosage, and these values should be arranged as individuals (rows) by loci (columns). Missing haplotypes and genotypes can be denoted as −1 or 9.# Generate founders from a known haplotype file# An example haplotypes.csv file is in the Appendixcohort_B = Founders(”haplotypes.csv”)Many mating schemes conceptually consist of parents that are sampled from two cohorts (cohorts A and B). XSimV2 allows a parent from cohort A to be mated with a specified number of parents from cohort B. The function mate takes cohort A and B, which both are Cohort objects, as the first two arguments, it can adapt to different scenarios with multiple arguments: nA common parents will be randomly selected from cohort A, and each common parent will mate with nB_per_A individuals randomly sampled from cohort B. Individuals will be sampled with replacement from cohorts A or B depending on if the arguments replace_A or replace_B are set to true. From a pair of parents sampled from two cohorts, n_per_mate offspring will be reproduced with ratio ratio_malefemale of male over female.For example, in the mating scheme below, we have 5 sires from cohort A, and each sire is mated with 10 dams from cohort B, which are sampled with replacement, to generate 1 progeny with the # A mating function exampleargs = Dict     male, female = mateThe default value for ratio_malefemale is false , and only one Cohort object will be returned if ratio_malefemale is set to false or 0. This mating function is versatile to accommodate most mating schemes  by assigning different arguments in mate.A “random mating” scheme between two cohorts is presented in the example below: Random mating between cohort A and B is performed without replacement, in which 1 progeny is generated from each mating. By default, if only the first two arguments (i.e. cohort A and B) are included in the mate function, the random mating scheme described above is performed. This scheme can also be expressed by using the overloaded operator “*” as will be shown in the “overloaded operators and functions” subsection.# Random mating schemeargs = Dictoffspring = mate# Equivalent results without providing any argumentoffspring = mate# Equivalent results by specifying scheme argumentoffspring = mate# Equivalent results with overloaded operator ’*’offspring = cohort_A * cohort_BIn the “diallel cross” scheme, each individual from cohort A mates with all individuals from cohort B. In the example below, each individual from cohort A is mated to all individuals in cohort B with one offspring from each mating.# Diallel cross mating schemeargs = Dictoffspring = mate# Equivalent results by specifying scheme argumentoffspring = mateIn the “selfing” scheme, self-fertilization (selfing) is performed if the argument scheme =“selfing” in mate. For example, if one wants to produce 10 families of 50 individuals each from selfing of 10 individuals from cohort A, the mating scheme can be defined as:# Selfing mating schemeargs = Dictoffspring = mateAs an alternative to specifying the mating scheme, users can provide a pedigree that identifies the parents for each individual. This is done by providing a pedigree file in the mate function.# Pedigree mating scheme# An example pedigree.csv file is in the Appendixcohort = mate(”pedigree.csv”)This option in mate function allows XSimV2 to simulate data from actual pedigrees.XSimV2 allows a cohort of individuals to be selected based on phenotypes (i.e. mass selection) or estimated breeding values (EBV) from real-time genetic evaluations. The selection is implemented by the function select taking a Cohort object as the first argument, and the number of individuals to be selected from the input cohort as the second argument. By default, mass selection is conducted and the argument criteria = “phenotypes” is used. To estimate breeding values, a genome-enabled analysis package “JWAS” , GBLUP , BayesiaAfter EBV (or phenotypes in phenotypic selection) are obtained, a selection index can be used to combine information on multiple traits for selection. The argument weights can be assigned as weights for traits in the selection index. Note that positive or negative weights enable selection in either ascending or descending order of EBV.In the example below, two correlated traits are simulated to be controlled by 10 and 20 QTL out of 50 locus on one chromosome, respectively. Individuals are selected based on EBV. Multitrait GBLUP is used to obtain EBV for both traits by default. The selection index weights for these two traits are set to 3.0 and −2.0. If no weight are assigned, weights are assumed to be 1 for each trait.# Genetic evaluationargs = Dictoffspring = selectHere, we introduce a function breed as a wrapper function that combines the functions for mate and select. The value of n_gens defines how many generations are simulated, and n_select_A and n_select_B define how many descendants are selected to form the cohort A and cohort B in the next generation, respectively. For example, we can have 10 sires (cohort A) and mate each sire with 5 dams (cohort B) for 3 generations. And in each generation we mass select 10 male offspring as sires and all female offspring as parents for the next generation. The code for such a breeding scheme is given below:# Mating and selection cross 5 generationsargs = Dict# Breed cohorts based on the defined argumentscohort_A, cohort_B = breed# The result is equivalent to the following mate-select iterations:for _ in 1:3 males, females = mate cohort_A = select cohort_B = femalesendSeveral species-specific features are included in XSimV2. Two examples are shown in this section including DHs in plant breeding and embryo transfer in animal breeding.DHs production is an important tool in plant breeding for reducing costs and speeding up the fixation of inbred lines. To generate breeding lines with high homozygosity, plant breeders may use a number of generations of selfing, back-crossing, or advancing generations by single seed descent (SSD). Instead, the production of DH breeding lines can derive 100% homozygous individuals in just one generation. In XSimV2, users can call the function get_DH and generate DH lines as below:# The offsprings“DHs” have the same population size asthe parents“cohort”DHs = get_DH(cohort)Ovum pick-up is a technology, where a cow is super ovulated and then the unfertilized eggs are flushed out and in vitro fertilized with potentially different sires. It can be straightforward to implement these technologies in XSimV2 by defining cows (dams) as cohort A, which will be treated as a common parent. The mating scheme can be setup as:# Example to demonstrate ovum pick-up in cattle breedingargs = Dictoffspring = mateIn XSimV2, as cohorts are the basic unit in both of the functions mate and select, we made a Cohort “object” for a collection of individuals and overloaded multiple base operators and functions in XSimV2. For example, users can simply use subset or + operators to get a subset of a cohort or to concatenate multiple cohorts into one larger group. In the example below, we concatenate the first 5 individuals in cohort A, and all individuals in cohort B into one single large cohort C.# Concatenate multiple cohortscohort_C = cohort_A[1:5] + cohort_BThe base functions sort and sample are also overloaded for Cohort. Users can sample random individuals from a cohort, or sort individuals using similar syntax (more examples are available in the documentation).# Sample 5 individuals from a cohort without replacementnew_cohort = sample# Sort the cohort by true breeding values (BV).“BV” is the default value.sort_cohort = sort# Or sort the cohort based on their pedigree in an order from the oldest to the youngest. Other options include estimated breeding values (EBV) or phenotypes (e.g.y1).sort_cohort = sortGenerating Founders), or simulated based on allele frequencies, assuming linkage and Hardy–Weinberg equilibria, with random mating over a number of generations to generate LD. For example, we might start with a population of 1,500 individuals. We let them randomly mate for 1,000 discrete (nonoverlapping) generations. In order to expand the LD range, we subject the population to a bottleneck that reduces its size to 100 individuals, and then allow random matings for another 15 generations. This approach is similar to that proposed in An example of rotational cross-breeding is shown in # Build Genome and Phenomebuild_genomebuild_phenome# Initialize a population with 1,500 parents of linkage and Hardy Weinberg equilibriaparents = Founders(1500)# Let parents random mate with each otherfor 1,000 generationsfor _ in 1:1000 parents = mate(parents)end# Drop the population size to 100 individuals andcontinue the random mating for another 15 generationsfor _ in 1:15 parents = mate(parents[1:100])endsires_base = dams_base = parentsSecond, the ancestors of what will become three pure breeds  are generated in generation # Simulate three pure breedsargs_A = Dictargs_BC = Dict# Breed A, B, and Csires_A, dams_A = breedsires_B, dams_B = breedsires_C, dams_C = breedThird, we create first-cross-offspring, which contain 2,000 individuals, using all 100 sires from breed B and 1,000 dams from breed C as two mating cohorts. Each sire will mate with 10 dams and produce two offspring which leads to 1,000 male and 1,000 female offspring at generation # Rotation parametersargs_XA = Dict args_XBC = Dictargs_A[:n_gens] = 1args_BC[:n_gens] = 1# Rotation (G1)sires_A1, dams_A1  = breedsires_B1, dams_B1 = breedsires_C1, dams_C1  = breedmales_G1, females_G1 = mate# Rotation (G2)sires_A2, dams_A2 = breedsires_B2, dams_B2 = breedsires_C2, dams_C2 = breedmales_G2, females_G2 = mate# Rotation (G3)sires_A3, dams_A3 = breedsires_B3, dams_B3 = breedsires_C3, dams_C3 = breedmales_G3, females_G3 = mateAn example of nested association mapping is shown in # Load demo datadata_map = DATA(”maize_map”)data_snp = DATA(”maize_snp”)# Build genome using the real databuild_genome(data_map)# Simulate a trait controlled by 30 QTLsbuild_phenome(30)# Randomly sample 26 founders to become the base populationfounders = Cohortcommon_parents = founders[1]diverse_parents = founders[2:26]We let each founder mate with the common parent separately to generate 25 families in the generation # Create an empty cohort to be concatenated with newly generated offspringF1 = Founders# Cross each diverse parent with the common parentfor parent in diverse_parentsF1 += common_parents * parentend# Each family produce 200 RILs to derive NAM populationargs = DictNAM = Foundersfor family in F1F2 = matefor seed in F2# single seed decentNAM += breedendendAn efficient way to design, evaluate, and optimize modern breeding programs with complicated mating systems and state-of-art biotechnologies and statistical models is provided by XSimV2. Beyond the case studies presented in this article, XSimV2 can be flexibly extended to desired settings with the aid of its modular construction. Core functions of mate and select cover the two major breeding steps and can function as independent modules. It is possible to reassemble them in arbitrary combinations and achieve as many possible designs as each user desires. XSimV2 is extensible to accommodate newly released technologies and statistical models.Breeding programs of diploid species can be adequately simulated in XSimV2. When the species is polyploid, users can add an alphabet to chromosome codes  in the map file to indicate their subgenome for allopolyploid species. XSimV2 will treat chromosomes from different subgenomes as independent pairs of chromosomes. Otherwise, users can always extend it to other polyploid behaviors with extensible development in XSimV2.The initial version of XSim . However, the computing speed and memory efficiency of XSimV2 still has room for further improvement. For example, when we simulate a population, computations for each individual at the same generation are mostly independent. Additional computing resources, if applicable, can be utilized to resolve the intensive simulation with the aid of parallel computing. Large matrices, such as genotypes, can be stored in a sparse matrix coded in 8-bit integers instead of a regular dense matrix to improve memory efficiency.Several breeding program simulators have been developed, e.g. QMSim , SBVB (Phttps://github.com/reworkhow/XSim.jl).The authors state that all data necessary for confirming the conclusions presented in the article are represented fully within the article. The documentation of XSimV2 can be found on the GitHub repository (This work was partially supported by the United States Department of Agriculture, Agriculture and Food Research Initiative National Institute of Food and Agriculture Competitive Grant No. 2018-67015-27957 and No. 2021-67015-33412.None declared.