Genomic and phenotypic selections are pivotal in improving dairy cattle traits, including udder health and milk production, to meet the rising global demand for animal-derived food. The objective of this study was to identify single nucleotide polymorphisms (SNPs) associated with conformation traits of Polish Holstein Friesian (PHF) cows. Data from 3,205 cows were analysed, out of which 376 were genotyped using SNP microarrays. A dataset of 55,352 SNP was included in the analysis. Additionally, phenotypic data covering 56 variables related to cow conformation traits were analysed. Results revealed 24 significant (P<0.001) SNPs associated with various traits, including udder conformation, feet and legs, and body size. Among these, 4 SNPs affected Udder depth; 3 SNPs influenced Foot angle and Rear teat placement; 2 SNPs were significantly associated with Body Depth, Height at the withers, Rear Legs Rear View, Fore Udder Attachment and Udder Width. Finally, 1 SNP had a significant impact on Front teat placement, Rump Angle, and Rear Legs Set. These findings demonstrate that SNP microarray data provide a framework for identifying genetic associations with conformation traits in dairy cows. This approach holds promise, particularly when combined with performance analysis, in exploring optimal conformation traits in cows predisposed to specific milking types.

In this study, the effects of Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian alphabet methods (A, B, C and Cp) were investigated on genomic predictions and indirect estimations in the Polish Holstein Friesian (PHF) dairy cattle population. The study analysed the milk yield data (MY, kg/lactation) and 13,481 single nucleotide polymorphism (SNP) genotype records from 534 Polish Holstein Friesian (PHF) dairy cattle raised on private farms in Poland. The quality control of the genotypic data included the removal of monomorphic loci and the exclusion of samples with SNP missing rates exceeding 10%. After the quality control, 493 animals and 13,250 SNPs were retained for the genomic prediction. Marker effects and genomic breeding values (GEBVs) were calculated using the Bayesian alphabet and GBLUP. The results indicated that, for the milk yield of PHF cows, the Bayes C method outperformed other approaches. This method achieved the highest prediction accuracy among the evaluated methods. Additionally, the Bayes C method required the shortest computational time, underscoring its efficiency.

Reproductive traits in dairy cattle are crucial for herd productivity and profitability. This study investigates the influence of relatedness to high immune response (HIR) Immunity+ sires on reproductive performance indicators in Polish Holstein-Friesian cows. A total of 5094 cows were analyzed, categorized based on their relatedness to HIR Immunity+ sires, and assessed for various reproductive parameters, including age at first insemination, gestation length, days open, calving interval, and calving ease. The results showed that the level of relatedness to HIR Immunity+ sires influenced certain reproductive traits, such as service period, gestation length, and age at first and second calving. Additionally, cows related to HIR Immunity+ sires exhibited a higher frequency of twin pregnancies and more complicated births. While some benefits were observed in certain reproductive traits among cows related to HIR Immunity+ sires, such as reduced age at first insemination and shortened gestation length, the overall impact on reproductive efficiency remains inconclusive. Further studies are needed to fully elucidate the effects of using semen from HIR Immunity+ sires on reproductive performance in dairy cattle.

Knowledge of the values of genetic parameters is a prerequisite for conducting a breeding program. This is especially important for rumination, which is considered an indicator of cow's health. Exploring the genetic relations between rumination time, milk yield, and milking traits could make it a valuable tool in dairy cattle breeding strategies. The objective of the research was to estimate heritability, repeatability, and genetic and phenotypic correlations of rumination time (RT), as well as traits associated with milk yield and milking of dairy cows of the Polish Holstein-Friesian breed kept in herds equipped with an automatic milking system. The research takes into consideration daily results for milking in the first lactation and second lactation, from 1 486 cows of the breed milked between 2013 and 2015 year. Cows were housed in 24 free-stall barns and fed a Partial Mixed Ration feed. The barns had an automated milking system (Astronaut A4 - Lely Industry). The cows received a varied dose of the concentrate, either in the milking robot or the feeding station, depending on the level of their milk yield. Our research has shown that RT was a low heritable trait (0.140 +/- 0.039) and had a medium repeatability (0.572 +/- 0.00 7). We detected a positive genetic correlation between RT and milk yield (0.341); however, a statistically significant negative relationship was identified between RT and urea content (-0.418) in milk. Estimations of genetic correlations suggest that selecting for higher RT may correspond to reduced urea content in milk. Investigating the genetics aspect of RT and the relationship with milk yield and milking traits may turn this into one of the useful criterion selections for dairy cattle breeding strategies, but should be used carefully. Further analyses on larger data sets and different populations are necessary. (c) 2024 The Author(s). Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The dairy cattle industry has witnessed significant advancements in production traits, including milk yield, reproduction, and health, through decades of breeding efforts. Despite these improvements, cow health and milk hygiene remain essential. High Immune Response (HIR) technology identifies cattle with optimized immune responses, enabling the selection of bulls with high immune responses for breeding. Studies indicate that HIR-based breeding improves animal health without compromising production traits. This study aimed to assess the effectiveness of bulls with a high immune response (HIR) in improving milk yield, milk contents, and somatic cell count in the milk of Polish Holstein-Friesian cows. A total of 11,509 cows of the Polish Holstein-Friesian breed were used. Among those, 1,773 animals were the offspring of HIR bulls, including 926 first-generation offspring (called I50 group) and 847 second-generation offspring (I25 group). There were 9,736 animals unrelated to HIR bulls (I0 group). Selected production and functional traits were analyzed. The results show that cows with higher kinship to HIR bulls showed increased milk yield. However, their milk exhibited lower fat, protein, and casein content. A favorable impact of HIR bulls was demonstrated by the decrease in somatic cell count in their offspring's milk compared to the milk of cows from the I0 group. This research contributes valuable insights into the potential benefits of using HIR bulls in dairy cattle breeding programs, emphasizing the importance of balancing production traits with health considerations.

The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

Automatic milking systems (AMSs) give cows relative freedom to choose the time and frequency of milking throughout the day. Feeding stations also may improve the management of farms. Combining milking robots and feeding stations (FS) may improve milking efficiency and milk yield. Therefore, combining AMS and FS may be beneficial for farmers. The objective of the research was to compare selected automatic milking parameters (daily indices per cow) registered by an AMS in relation to selected features including the presence of concentrate feeding stations. We analysed 931 cows born in 2013-14, in lactations 1-8. In total, we collected data from 357 318 milking days. The following parameters were examined: milking frequency (n/24 h), number of rejected milking (n/24 h), the average number of nipple attempts (n/milking), milking speed (kg/min), time spent in the milking box (s/24 h), milk yield (kg/24 h), milking efficiency (kg/min), rumination time (min/24 h), and concentrate intake (kg) per 100 kg of milk produced. The statistical analysis was conducted using a multi-factor analysis of variance. The analysis confirmed a statistical effect of the concentrate feeding system on most of the investigated traits, except for nipple attempts, box time and rumination time. In cows in barns with an FS, the following parameters were statistically higher compared to cows in non-FS barns: milking frequency (3.04 vs 2.73n/24 h), number of rejected milking (2.24 vs 1.51n/24 h), milking speed (2.98 vs 2.64 kg/min), milk yield (33.48 vs 30.14 kg/24 h), milking efficiency (1.80 vs 1.67 kg/min), and concentrate intake per 100 kg of milk produced (14.67 vs 12.67 kg). The study results indicate that using feeding stations in combination with an AMS can increase milking efficiency, hence the milk output from a milking robot. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Mastitis is the single most expensive disease among cattle in the dairy industry and environmental pathogens such as Staphylococcus aureus and Staphylococcus warneri are among the most common culprits. Recent studies had shed the light on how such pathogens utilize lipolysis mechanisms to evade their host's immune response. In this study, computational drug discovery approaches were deployed to investigate human FDA-approved drugs that hold the potential of serving as inhibitors of lipase activity in the former 2 pathogens. Comprehensive computational analysis involving molecular docking, nanoscale molecular dynamics, and in silico binding free energy estimation has shown that Drospirenone, a unique progestogen with anti-mineralocorticoid properties commonly used in human birth control pills holds potential inhibitory activity against the lipase of Staphylococcus warneri as it had shown to form several stable hydrophobic interactions and hydrogen bonds with the formers lipWY lipase enzyme.

In vitro maturation (IVM) of mammalian oocytes, which influences subsequent in vitro development of embryos, is affected by the macromolecule content in culture media for the success of oocyte maturation competence, in which the cytoplasmic and nuclear reprogramming events occur. The insulin-like growth factor family (IGFs) promotes the maturation of bovine oocytes and the expansion of cumulus cells and also inhibits apoptosis. This study was, therefore, designed to examine the effects of macromolecules (bovine serum albumin, BSA; fetal calf serum, FCS; and polyvinyl alcohol, PVA) on in vitro nuclear maturation, total cellular protein, glutathione peroxidase (GPx) enzyme activity, and the gene expression level of IGF1, IGF2, and their receptor in bovine oocytes. Oocytes obtained from bovine ovaries were cultured in bicarbonate-buffered medium 199 supplemented with 4 mg/mL BSA, 10% FCS, 1 mg/mL PVA, and without macromolecule supplement (control) during 22 h in the air with a humidified atmosphere and 5% CO2 at 38.5 °C temperature. Supplementation of BSA and FCS increased (χ2 = 9.84; p < 0.05) the percentages of oocytes that reached metaphase II compared to the control and PVA. The amount of protein per ml of cell extracts of oocytes matured in FCS supplemented culture media was higher (p < 0.05) than the oocytes in the PVA and control. The levels of GPx enzyme activity in cell extracts isolated from oocytes in each experimental group did not change over time, but the GPx enzyme activity in oocytes matured in PVA-supplemented culture media was lower (p < 0.05) than in oocytes in the other experimental groups. Transcript for the IGF1 gene was not detected in all experimental groups, but the supplementation of BSA and FCS significantly elevated the transcript level of the IGF2 gene. In addition, the maturation of oocytes with BSA-supplemented media increased the transcript level of the IGF1R gene, whereas the transcript level of the IGF2R gene was similar among macromolecule supplementation groups. The current study concluded that BSA and FCS could improve in vitro bovine oocyte development due to supporting nuclear maturation and increasing the total cellular protein content, GPx enzyme, and transcript activity.

Optimising daily milk yield in automatic milking systems (AMS) became one of the basic issues raised by many farmers. The aim of this study was to determine the best combination of factors and their levels suggested by the decision tree for high daily milk yield per AMS in dairy cattle herds. The study involved 4854 Polish Holstein -Friesian cows milked in 20 barns with Lely AMS. Statistical analysis was performed using two methods: multi -factorial analysis of variance and classification tree technique. Milk yield, number of cows, free robot time, milking speed, and cow treatment time (i.e. cleaning of teats before and after milking) were the significant determinants of total milk yield for all cows per milking robot and were most frequently used to construct the decision tree. The tree shows that the highest total milk yield per robot was observed for the group of cows that produced more than 30 kg of milk per day, with a milking speed of over 2.40 kg/min. and during robot days in which free time did not exceed 10% of the day. In AMS dairy herds, efforts should be made to select animals that transmit high milk yield and high milking speed while shortening free robot time and increasing daily milk yield per robot. Also, attention should be given to the percentage of box time spent on preparing the cow for milking and after milking because prolongation of this variable has a negative effect on daily milk yield per AMS.

This study was aimed to determinate the best model to predict milk dry matter in high milk yielding dairy cattle. Level of milk dry matter (MDM) (%) is of great importance. The material of this study consisted of 2208 milking records of dairy cattle yielding more than 40 l per day from Polish Holstein Friesian population. In this study to estimate the milk dry matter, regression of daily milk yield (MY) (l), milk urea (MU), milk protein (MP) (%) and milk fat (MF) (%) as explanatory variables were used. To estimate the best fitting, curve estimation was used. Estimation of the curves showed that milk urea was cubic, milk yield, milk protein and milk fat were quadratic. To avoid multicollinearity where VIF value greater than 10, stepwise variable selection procedure was used. After variable selection the regression equation was obtained as MDM=2.879+1.290*MF+2.395*MP-0.039*MF^2–0.225*MP^2 with 0.946 coefficient of determination. Our results showed that milk fat (%) and milk protein (%) can be used to estimate the milk dry matter (%) with a great achievement in high milk yielding dairy cattle.

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein–Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61–75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151–250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

Sheep farmers' perceptions of climate change and its impacts and the adaptation strategies they consider to address these risks are of great importance in ensuring the resilience of farming practice. This study focused on sheep farmers' perception of climate change and the risks and actions taken to mitigate these impacts. A total of 68 surveys were carried out among sheep farmers (39 transhumance and 29 semi-intensive farmers) by two different representative production systems in Türkiye. Variables regarding the socio-economic profile, climate change impacts, and adaptation strategies were identified and analyzed. Principal component analysis and a Pearson Chi-square test were used to evaluate the data. Both farmers' groups accepted and perceived climate change, showing good awareness and perception. The farmers' attitudes towards adaptation to climate change were associated with production systems. Transhumance farmers had limited adaptation and coping strategies compared to semi-intensive farmers. Transhumance farmers focused mainly on selling livestock (mostly to cope with degraded natural grassland/feed deficiency) as an adaptive strategy. In contrast, semi-intensive farmers focused on modifying their farm management and feed operations, such as changing the feed ratio and supplement use, improving water and feed storage, and considering crop feed production. The knowledge obtained from this study could be helpful for farmers and policymakers who develop long-term small ruminant production strategies that consider the effects of climate change and adapt them to different farming systems in the Türkiye.

The study covers milk yield and composition data for 17,468 Polish Holstein-Friesian cows. Methane production (g/lactation per cow, MP) for dairy cow were predicted using three methane production equations (MPE) that took into account: milk yield (MPE1), energy corrected milk (MPE2) and both milk protein concentration (%), and energy-corrected milk (MPE3). The average amounts of methane produced for each cow per lactation were 31,089 g, 46,487 g, and 51,768 g for MPE1, MPE2, and MPE3, respectively. Repeatability models were used to estimate genetic parameters for MP. The estimated heritabilities for MPE1, MPE2, and MPE3 were 0.30, 0.24, and 0.24, respectively, with a standard error of 0.01. High genetic correlations (>0.76) were obtained between methane and milk yield, protein, fat, lactose and dry matter contents in milk for MPE1, MPE2 and MPE3. Still, a moderate genetic correlation (0.34) was obtained between methane and fat content (MPE1); the standard error of the estimated genetic correlation was less than 0.05. The results of the current study indicate that genetic selection aimed to reduce MP in dairy cows is possible. However, such direct genetic selection could cause a negative genetic response in milk yield and composition due to negative genetic correlations between MP and milk yield and composition.

The aim of the present study was to evaluate the genotype and allele frequencies of 24 polymorphisms in casein alpha S1 (CSN1S1), casein alpha S2 (CSN1S2), beta-casein (CSN2), kappa-casein (CSN3), and progestagen-associated endometrial protein (PAEP) genes. The study included 1900 Polish Black and White Holstein-Friesian dairy cows that were subjected to genotyping via microarrays. A total of 24 SNPs (Single Nucleotide Polymorphisms) within tested genes were investigated. Two CSN1S1 SNPs were monomorphic, while allele CSN1S1_3*G in CSN1S1_3 SNP dominated with a frequency of 99.39%. Out of seven CSN2 SNPs, four were polymorphic; however, only for CSN2_3 all three genotypes were detected. Only three out of nine SNPs within CSN3 were monomorphic. Three PAEP SNPs were also found to be polymorphic with heterozygotes being most frequent. Hardy–Weinberg equilibrium (HWE) was observed for eight variants. It was shown that only CSN3_6 was not in HWE. The fact that many of investigated SNPs were monomorphic may suggest that in the past the reproduction program favored one of these genotypes. SNPs that are included in commercially available microarrays should be monitored in relation to changes in their frequencies. If a SNP has turned monomorphic, maybe it should be considered for removal from the microarray.

BLAD (bovine leukocyte adhesion deficiency) and DUMPS (deficiency of uridine monophosphate synthase) are hereditary lethal autosomal recessive disorders that may affect Holstein cattle. The aim of the present study was to evaluate the frequency of BLAD and DUMPS genotypes in the Polish population of dairy cows. In the study 5 732 Polish Holstein-Friesian females were genetically tested for BLAD and DUMPS. Results of the present study indicate that the Polish population of dairy cattle is free from DUMPS as there were not carriers nor sick animals. For BLAD the study confirmed the present of carriers in the population to be at a low value (0.21%). Both BLAD and DUMPS may strongly affect the profitability of a dairy farm, therefore, in order to avoid the economic losses caused by the presence of these genetic disorders in the herd it is important to screen the population and detect carriers as soon as possible.

Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.

Abstract The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

The aim of this study was to compare selected parameters of automatic milking in various European Union countries and United States recorded between 2018 and 2020. Statistical analysis showed highly significant effect of country on all tested milking parameters. It was noted that i.e. the average number of cows per one robot, depending on country, ranged between 51.49 (the Netherlands) and 60.03 (Germany). Cows were milked on the average 2.50 (France) – 2.83 (Latvia) per day, with milking speed ranging from 2.30 kg/min (Lithuania) to 2.99 kg/min (US). Daily milk yield obtained from one cow ranged from 25.12 kg (Lithuania) to 34.11 kg (US), while milk efficiency from 1.44 (Lithuania) to 1.77 kg/min (US). Results also showed that the daily milk yield from one robot ranged from 1 351 kg (Latvia) to 1 930 kg (US). The statistical differences between the milking parameters in the compared countries may be the result of the diversified genetic potential of milked cows and the diversity of the feed base.

OBJECTIVE:The progestogen-associated endometrial protein (PAEP) gene encodes the main whey protein in milk, β-lactoglobulin. The aim of the study was to investigate polymorphism in the PAEP gene and its association with milk yield, composition, and quality.METHODS:Test-day records for 782 dairy cows were analysed. A total of 10 single nucleotide polymorphisms (SNP) within the PAEP gene were investigated. The following parameters were recorded: milk yield (MY, kg/d), percent milk fat (%), protein (PP, %), dry matter (DMP, %) and lactose (LP, %), urea content (UC, mg/L) as well as natural logarithm for somatic cell count (LnSCC, ln). Effect on genomic estimated breeding values accuracy was evaluated with pedigree and single step model.RESULTS:Results show that only three SNPs were polymorphic, creating 5 composite genotypes: P1 to P5. Differences in MY between composite genotypes were noted in the two tested herds. Cows with P5 composite genotypes were characterised by the highest PP and LnSCC and the lowest LP and UC (p<0.05). P4 was linked to an increased DMP and UC, while P3 to an increase in LP and decrease in PP and LnSCC. Both factors are important markers in herd management and have high influences on the herds economics. For 5 out of 7 traits the accuracy of prediction was improved by including the haplotype as a fixed effect.CONCLUSION:Presented results may suggest a new way to optimise breeding programmes and demonstrate the impact of using genomic data during that process.