When managing the rate of genomic inbreeding using genomic relationship matrices, allele frequencies tend to be moved toward the frequencies used for centering genotypes in those matrices. Here, we explore if this behavior could be exploited to purposefully change the frequency of alleles. We test the method in a simulated toy example and in the Friesian horse population to purge deleterious alleles as a case study. We show that the average entry of the numerator matrix from VanRaden's method 1 (ZZ'¯) equals 4 times the average squared difference between the populations' allele frequency (AF) and the used centering allele frequency (CAF) (ZZ'¯=4∑k=1nsnp(AFk-CAFk)2). By setting the centering frequency to the desired AF and subsequently optimizing contributions to minimize the average matrix entry, the population's AF is shifted to the desired frequency. We call this novel selection method using a matrix: "TAFT" (targeted allele frequency tuning), and use it with optimum contribution selection (OCS) in this study (TAFT-OCS). TAFT outperformed index methods for target frequencies between 0% and 100%. The index methods showed different behavior compared to TAFT by exerting different selection pressure on loci depending on the difference to the target frequency. Frequencies of all 12 deleterious alleles could be reduced within ∼6 generations in the Friesian horse population so that no affected individuals remained. At the same time, genetic gain of 3.7 cm withers height (1.11 gSD) per generation was made and the realized pedigree inbreeding rate kept below 0.5%. We propose TAFT-OCS as a novel method to directly change allele frequencies to any desired value which is particularly beneficial if intermediate allele frequencies are desired or genetic diversity should be maintained. TAFT shifts the focus of selection from individuals to alleles. It only requires to set a desired target frequency and a minimum and maximum allowed frequency if needed.

Abstract Background Recessive lethal alleles causing pre- or postnatal death in homozygous mutant animals, could lead to reduced fertility success. The Friesian horse breed has signs of reduced fertility and has faced high inbreeding rates in the past (∆F > 1%). Consequently, by genetic drift lethal alleles may have reached moderate to high frequencies in the population. Our aim was to identify lethal recessive alleles that — when homozygous — may cause pre- or postnatal death in the Friesian horse. Results We analyzed genotypes (70 K SNP) of over 8,000 Friesian horses, looking for haplotypes with a homozygous deficiency, and used available sequence data of 50 Friesian sires to pinpoint the likely causal variant. A deficit in homozygous animals suggests a lethal allele, because individuals inheriting two copies of such an allele likely die before birth or die before being genotyped, creating a detectable imbalance in genotype frequencies. We found ten candidate haplotypes in the Friesian horse with carrier frequencies ranging from 8.0 to 22.1%. We identified candidate causal variants of six haplotypes, of which two were associated with the already known genetic disorders dwarfism and hydrocephalus. The other candidate variants were a 261-kilobase-pair deletion affecting several non-coding RNA’s, and a 14-base-pair frameshift deletion in the MET gene. Three haplotypes in LD comprised the deletion in MET and were associated with a 25% reduction (P < 0.001) in insemination success in risk matings, likely caused by early embryonic lethality. Conclusions In general, considering the population characteristics of domestic horse breeds, we strongly recommend performing such analyses in other horse breeds using the increasingly available genotype data. Such analyses could provide important contribution to the improvement of fertility rates in horse populations.

In horses, genetic diversity is predominantly observed between breeds, with little variation within breeds. The studbooks of the two largest horse populations in the Netherlands, the Dutch Warmblood horse and Friesian horse population, have ongoing conservation projects including collecting large-scale genotype and sequence data. The current reference genome, derived from a Thoroughbred horse can lead to bias in genetic analyses of other horse breeds. Therefore, the aim of this study was to create high-quality breed-specific reference genomes of Dutch Warmblood and Friesian horses. We performed nanopore long-read sequencing (R10.4, Q20+) of an F1 cross between a Dutch Warmblood horse and a Friesian horse to create two breed-specific reference genomes by trio binning. This resulted in high-quality, haplotype-resolved reference genomes with contig N50 of 37 and 35 Mb and single copy gene completeness of 99.2 and 99.3

Direct estimates of mutation rates in humans have changed our understanding of evolutionary timing and de novo mutations (DNM) have been associated with several developmental disorders in humans. Livestock species, including pigs, can contribute to the study of DNM because of their ideal population structure and routine phenotype collection. In principle, there is the potential for livestock populations to quickly accumulate new genetic variants because of short generation intervals and high selection intensity. However, the impact of DNM on the fitness of individuals is not known and with current genomic selection programs they cannot contribute to estimated breeding values. The aims of our project were to detect and validate single nucleotide DNM in two commercial pig breeding lines, estimate the single nucleotide mutation rate, and characterise DNM. We sequenced (150 bp paired end reads, 30X coverage) 46 pig trios from two commercial lines. Single nucleotide DNM were detected using a trio-aware method. We defined candidate DNM as single nucleotide variants (SNVs) found in heterozygous state in trio-offspring with both trio-parents homozygous for the reference allele. In this study, we estimate a lower threshold of the DNM rate in pigs of 6.3 × 10–9 per site per gamete. Our findings are consistent with those from other mammals and those published for a small number of livestock species. Most DNM we detected were in introns (47

ABSTRACTHolstein cattle account for the largest proportion of dairy cattle in China. The current China Performance Index (CPI) consists mainly of production traits. To derive economic values (EV) of additional traits for balanced breeding programs, a bio‐economic model is necessary. Landless and intensive dairy farms are dominant in China, wherein all feed is purchased, and in‐farm technicians and veterinarians are employed. Therefore, in the present study, a tailored bio‐economic model was developed using the parameters of a typical dairy farm in North China. The typical farm had 1500 cows and 1400 youngstock, with a replacement rate of 33.5% per year and a productive life of 1090 days. The bio‐economic model was on a per cow per year basis and described the revenues and costs from different animal categories. The EVs of 17 traits, including production, calving, fertility, longevity, and health traits, were derived and used to develop a more balanced selection index. Results showed that the bio‐economic model can represent the typical dairy farm system in North China. The EVs of production traits were 2.39 Chinese Yuan (CNY), 32.85 CNY, and 89.60 CNY per kg milk yield, fat yield and protein yield, respectively. The EVs of production traits were two to three times higher than those in some European countries, due to the higher prices on milk volume and milk solids in China. The EVs of health traits ranged from −0.45 CNY to −11.95 CNY and were nearly half of those in other countries, due to the lower in‐farm veterinarian labour costs. The EVs of most other functional traits were in line with the published values of other countries with similar economic assumptions in the model. Using the calculated EVs, a more balanced selection index was derived by including functional traits. This index had higher relative weight (46.8%) on functional traits than the current CPI (12.5%). With the high milk prices, it is still most profitable to allow for a decline in functional traits, although the decline is considerably smaller with the developed balanced index than with the CPI. Collectively, the bio‐economic model and EVs provided the foundations for implementing balanced breeding programs in the Chinese Holstein population.

In Friesian horses, withers height is an important trait as a minimum has been set to be eligible to the studbook. Several loci for withers height have been identified in horses. However, withers height has not been studied in the Friesian horse. Therefore, our aim was to identify loci associated with withers height in the Friesian horse population. We performed a genome-wide association study using 70 K SNP data of 2192 Friesian horses. We found ECA1 and ECA9 to be significantly associated with withers height, explaining 19.6% and 3.5% of the phenotypic variance, respectively. In other horse breeds, the LCORL/NCPAG locus on ECA3 showed the strongest association with withers height, but here we found that the best-associated SNP for that locus is nearly fixed in Friesian horses for the allele associated with small size. Moreover, we observed a clear decline followed by a marked increase in average withers height of the Friesian horse over time, probably owing to shifts in its primary use over the course of the years. Additionally, the frequency of the best-associated SNP on ECA1 has increased over time. Together, our study showed that ECA1 and ECA9 are associated with withers height in Friesian horses. Further studies should be performed to confirm candidate causal mutations.

In the past, small population sizes and unequal ancestor contributions have resulted in high inbreeding rates (Δ F ) in the Friesian horse. Two decades ago, the studbook implemented a mating quota and started publishing individual kinships and reduced Δ F below 1% per generation. However, since then, the breeding population size has decreased and this raises the question whether current breeding strategies are sufficient to keep Δ F below desired rates. The aim of this study was to (1) reflect on past inbreeding trends and their main determinants, using pedigree analysis and (2) evaluate the effectiveness of the current and additional breeding strategies using stochastic simulations. We estimated the current Δ F (2013–2022) at 0.72% per generation. While the total contribution of the top 10 sires to the number of offspring per year has decreased from 75% in 1980 to 35% in 2022, this was mainly due to an increased number of approved studbook sires, and not due to more equalized contributions among sires. Of the simulated breeding strategies, selecting only breeding stallions with a below average mean kinship (i.e., “mean kinship selection”) was most effective to decrease Δ F (from 0.66% to 0.33%). Increasing the number of breeding sires only had an effect when also a mating quota was applied. However, its effect remained limited. For example, a ~1.5 fold increase, combined with a mating quota of 80 offspring per sire per year, reduced Δ F from 0.55% to 0.51%. When increasing the number of breeding mares, a practically unfeasible large increase was needed for a meaningful reduction in Δ F (e.g. twice as many mares were needed to reduce Δ F from 0.66% to 0.56%). Stratified mating quotas, a novel approach in which we assigned each sire a mating quota (of 60, 80, 100 or 120 offspring per year) based on its mean kinship to recently born foals, resulted in a lower Δ F (0.43%) than a general mating quota of 90 offspring per sire per year (0.55%). Overall, while the current Δ F is below 1%, we recommend to implement additional strategies to further reduce Δ F below 0.5% in the Friesian horse population. For this breed and similar populations, we recommend to focus on breeding strategies based on kinship levels to effectively reduce Δ F .

Accurate and ex-ante prediction of cows' likelihood of conception (LC) based on milk composition information could improve reproduction management on dairy farms. Milk composition is already routinely measured by mid-infrared (MIR) spectra, which are known to change with advancing stages of pregnancy. For lactating cows, MIR spectra may also be used for predicting the LC. Our objectives were to classify the LC at first insemination using milk MIR spectra data collected from calving to first insemination and to identify the spectral regions that contribute the most to the prediction of LC at first insemination. After quality control, 4,866 MIR spectra, milk production, and reproduction records from 3,451 Holstein cows were used. The classification accuracy and area under the curve (AUC) of 6 models comprising different predictors and 3 machine learning methods were estimated and compared. The results showed that partial least square discriminant analysis (PLS-DA) and random forest had higher prediction accuracies than logistic regression. The classification accuracy of good and poor LC cows and AUC in herd-by-herd validation of the best model were 76.35 ± 10.60% and 0.77 ± 0.11, respectively. All wavenumbers with values of variable importance in the projection higher than 1.00 in PLS-DA belonged to 3 spectral regions, namely from 1,003 to 1,189, 1,794 to 2,260, and 2,300 to 2,660 cm−1. In conclusion, the model can predict LC in dairy cows from a high productive TMR system before insemination with a relatively good accuracy, allowing farmers to intervene in advance or adjust the insemination schedule for cows with a poor predicted LC.

Breeding is a promising greenhouse gas (GHG) mitigation option for the dairy sector that offers potential permanent and cumulative effects. However, there is limited understanding of how genetic traits affect GHG emissions from the dairy production chain and how breeding indices could be used to find a balance between GHG emissions and farm profit. Using a typical Chinese dairy farm as a case study, we developed a novel method to address these gaps. The farm comprised of 1523 Holstein-Friesian dairy cows and 1429 young stock. The average milk yield at the farm was 11,533 kg per cow per year. Life cycle assessment was combined with an existing bio-economic model to determine the emission intensity values (IV) of six genetic traits: milk yield, protein yield, fat yield, calving interval, productive life, and incidence of clinical mastitis. The IVs and economic values of the traits were used to form different breeding indices, of which the economic and environmental consequences were assessed. Results showed that for the next generation, breeding animals with optimal indices could reduce carbon dioxide equivalents per ton of fat-and-protein-corrected milk by six to 10 kilogrammes, while increasing profitability by 822 to 1355 Chinese Yuan per cow unit. Different indices can balance farm profit and GHG emissions to different degrees. However, the indices with higher profit showed less potential in reducing GHG emissions. This study provides insights into how breeding strategies could contribute to GHG mitigation in the dairy sector.

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.

Background Nitrate leaching to groundwater and surface water and ammonia volatilization from dairy farms have negative impacts on the environment.Meanwhile,the increasing demand for dairy products will result in more pol-lution if N losses are not controlled.Therefore,a more efficient,and environmentally friendly production system is needed,in which nitrogen use efficiency(NUE)of dairy cows plays a key role.To genetically improve NUE,extensively recorded and cost-effective proxies are essential,which can be obtained by including mid-infrared(MIR)spectra of milk in prediction models for NUE.This study aimed to develop and validate the best prediction model of NUE,nitro-gen loss(NL)and dry matter intake(DMI)for individual dairy cows in China.Results A total of 86 lactating Chinese Holstein cows were used in this study.After data editing,704 records were obtained for calibration and validation.Six prediction models with three different machine learning algorithms and three kinds of pre-processed MIR spectra were developed for each trait.Results showed that the coefficient of deter-mination(R2)of the best model in within-herd validation was 0.66 for NUE,0.58 for NL and 0.63 for DMI.For external validation,reasonable prediction results were only observed for NUE,with R2 ranging from 0.58 to 0.63,while the R2 of the other two traits was below 0.50.The infrared waves from 973.54 to 988.46 cm-1 and daily milk yield were the most important variables for prediction.Conclusion The results showed that individual NUE can be predicted with a moderate accuracy in both within-herd and external validations.The model of NUE could be used for the datasets that are similar to the calibration dataset.The prediction models for NL and 3-day moving average of DMI(DMI_a)generated lower accuracies in within-herd validation.Results also indicated that information of MIR spectra variables increased the predictive ability of models.Additionally,pre-processed MIR spectra do not result in higher accuracy than original MIR spectra in the external vali-dation.These modelswill be applied to large-scale data to further investigate the genetic architecture of N efficiency and further reduce the adverse impacts on the environment after more data is collected.

Dairy farming may have negative impacts on the environment, such as ammonia emissions to air and nitrate leaching to water. By selecting more efficient cows, the amount of nitrogen excreted per cow and consequently the adverse environmental impacts will be reduced. The aim of this study was to predict nitrogen use efficiency (NUE) of individual dairy cows using mid-infrared (MIR) spectra of milk. A total of 600 feeding and MIR records of 56 Holstein cows were collected from a farm in Beijing, China. NUE was calculated as the ratio of nitrogen in milk to nitrogen intake. The coefficient of determination of the best model was 0.69, 0.62 and 0.70 for NUE, nitrogen loss and dry mater intake, respectively. The MIR wavenumbers around 981.00 and 1506.93 cm-1 and daily milk yield were the most important variables for prediction. These results show potential for large-scale genetic evaluations of nitrogen efficiency.

Osteochondrosis (OC) is an important skeletal disease causing profound welfare concerns in horses. Although numerous studies have explored the genetics underlying OC in various breeds, the Belgian Warmblood (BW) remains unstudied despite having a concerning prevalence of 32.0%. As a result, this study aimed to conduct genome-wide association (GWA) analyses to identify candidate variants associated with OC in BWs. To achieve this, blood samples and radiographs were collected from 407 Belgian Warmbloods registered to one of two BW studbooks (Belgisch Warmbloedpaard and Zangersheide), and genotyping was performed using the 670K Axiom Equine Genotyping Array. GWA analyses using a principle component approach were then performed on OC status (OCS; presence or absence of OC at any joint), hock OC status (HOC) and stifle OC status (SOC). These analyses yielded significantly associated (P < .01) SNPs on Equus caballus chromosome (ECA) 3, ECA 12, and ECA 18 for OCS; however, no single nucleotide polymorphisms (SNPs) reached significance for HOC or SOC. Subsequent analysis of candidate genes within 500 kilobases of the significant SNPs revealed functions broadly related to cell differentiation and chondrocyte development. While this study represents another step forward in uncovering variants and biological pathways associated with OC, additional studies are needed to validate the newly identified candidate SNPs for OC in BWs. Further studies of OC in BWs, as well as other breeds, are critical in our efforts to fully understand the disease's etiopathogenesis and ultimately provide breeding programs better equipped to improve horse health and well-being.

Accurate and early identification of the likelihood of conception (LC) in cows is imperative for a profitable dairy farm. This study aims to use the milk mid-infrared (MIR) spectra in different intervals before the first insemination and partial least squares discriminant analysis (PLS-DA) to predict LC. The results show that the MIR data within 30 to 50 d after calving and close to insemination had a better prediction in LC (accuracy = 73.9 and 72.3%) than 0 to 30 d. And specificity (74.4 to 84.9%) was higher than sensitivity (67.8 to 73.1%). Once the expected date of insemination is given, the model can predict LC before the actual insemination, and intervene in advance for cows predicted poor LC such as delayed insemination and treatment can be initiated. The predicted LC also provide a novel and convenient way to accumulate extra reproductive phenotypes for genetic evaluation.

Abstract Background The effect of heat stress on livestock production is a worldwide issue. Animal performance is influenced by exposure to harsh environmental conditions potentially causing genotype-by-environment interactions (G × E), especially in highproducing animals. In this context, the main objectives of this study were to (1) detect the time periods in which heifer fertility traits are more sensitive to the exposure to high environmental temperature and/or humidity, (2) investigate G × E due to heat stress in heifer fertility traits, and, (3) identify genomic regions associated with heifer fertility and heat tolerance in Holstein cattle. Results Phenotypic records for three heifer fertility traits (i.e., age at first calving, interval from first to last service, and conception rate at the first service) were collected, from 2005 to 2018, for 56,998 Holstein heifers raised in 15 herds in the Beijing area (China). By integrating environmental data, including hourly air temperature and relative humidity, the critical periods in which the heifers are more sensitive to heat stress were located in more than 30 days before the first service for age at first calving and interval from first to last service, or 10 days before and less than 60 days after the first service for conception rate. Using reaction norm models, significant G × E was detected for all three traits regarding both environmental gradients, proportion of days exceeding heat threshold, and minimum temperature-humidity index. Through single-step genome-wide association studies, PLAG1, AMHR2, SP1, KRT8, KRT18, MLH1, and EOMES were suggested as candidate genes for heifer fertility. The genes HCRTR1, AGRP, PC, and GUCY1B1 are strong candidates for association with heat tolerance. Conclusions The critical periods in which the reproductive performance of heifers is more sensitive to heat stress are trait-dependent. Thus, detailed analysis should be conducted to determine this particular period for other fertility traits. The considerable magnitude of G × E and sire re-ranking indicates the necessity to consider G × E in dairy cattle breeding schemes. This will enable selection of more heat-tolerant animals with high reproductive efficiency under harsh climatic conditions. Lastly, the candidate genes identified to be linked with response to heat stress provide a better understanding of the underlying biological mechanisms of heat tolerance in dairy cattle.

Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.

Cow survival is a complex trait that combines traits like milk production, fertility, health and environmental factors such as farm management. This complexity makes survival difficult to predict accurately. This is probably the reason why few studies attempted to address this problem and no studies are published that use ensemble methods for this purpose. We explored if we could improve prediction of cow survival to second lactation, when predicted at five different moments in a cow’s life, by combining the predictions of multiple (weak) methods in an ensemble method. We tested four ensemble methods: majority voting rule, multiple logistic regression, random forest and naive Bayes. Precision, recall, balanced accuracy, area under the curve (AUC) and gains in proportion of surviving cows in a scenario where the best 50% were selected were used to evaluate the ensemble model performance. We also calculated correlations between the ensemble models and obtained McNemar’s test statistics. We compared the performance of the ensemble methods against those of the individual methods. We also tested if there was a difference in performance metrics when continuous (from 0 to 1) and binary (0 or 1) prediction outcomes were used. In general, using continuous prediction output resulted in higher performance metrics than binary ones. AUCs for models ranged from 0.561 to 0.731, with generally increasing performance at moments later in life. Precision, AUC and balanced accuracy values improved significantly for the naive Bayes and multiple logistic regression ensembles in at least one data set, although performance metrics did remain low overall. The multiple logistic regression ensemble method resulted in equal or better precision, AUC, balanced accuracy and proportion of animals surviving on all datasets and was significantly different from the other ensembles in three out of five moments. The random forest ensemble method resulted in the least significant improvement over the individual methods.

29 Background: The effect of heat stress on livestock production is a worldwide 30 issue, where animal performance is influenced by exposure to high environmental 31 temperatures, indicating the existence of possible genotype-by-environment 32 interactions (GE). The main objectives of this study were to (1) detect the time periods 33 in which heifer fertility traits are more sensitive to the exposure to high environmental 34 temperature and/or humidity, (2) investigate GE due to heat stress in heifer fertility 35 traits, and (3) identify genomic regions associated with heifer fertility and heat stress in 36 Holstein cattle. 37 Results: Phenotypic records for three heifer fertility traits (i.e., age at first 38 calving, interval from first to last service, and conception rate at the first service) were 39 collected, from 2005 to 2018, for 56,998 Holstein heifers raised in 15 herds in the 40 Beijing area (China). By integrating environmental data including hourly air 41 temperature and relative humidity, the critical periods in which the heifers are more 42 sensitive to heat stress were defined as <=30 days before the first service for age at first 43 calving and interval from first to last service, or 10 days before and <= 60 days after 44

Appropriate evaluation of BW and gain during rearing is required for optimal extended laying performance in laying hens. The objective of this study was to compare monophasic, diphasic, and triphasic Gompertz and logistic models describing BW and gain in individually fed free-run laying hens and to study the variation between individuals in shape parameters. Fifteen Lohmann Brown Lite hens were fed ad libitum from week 0 to 43 with a precision feeding system, measuring feed intake and BW individually in a group housed setting. Random variables related to mature weight and timing of maximum gain during the pubertal growth phase were introduced into the multiphasic model for BW with the best fit. For both the weight-age and gain-age functions, the diphasic and triphasic Gompertz and logistic model models fitted the data better than the monophasic models. The Gompertz model was able to identify the ages at the highest gain at similar time points for both BW and gain, whereas the logistic models failed to do so. The derivative of the multiphasic Gompertz models for the gain-age relationship identified age at the highest gain at similar ages as compared with the logistic models for gain. The mixed models predicted that the individual mature BW ranged from 1.83 kg to 2.10 kg and the variability in the timing of the highest rate of gain during the pubertal growth spurt ranged from 15.26 wk to 19.79 wk. Including random terms associated with the mature BW and the second inflection point of the diphasic Gompertz growth model allowed for identification of variability in the growth curve shape between individuals, which can be a tool to study the relationship between the individual growth curve shape and performance parameters.

Equine insect bite hypersensitivity (IBH) is a pruritic skin allergy caused primarily by biting midges, Culicoides spp. IBH susceptibility has polygenic inheritance and occurs at high frequencies in several horse breeds worldwide, causing increased costs and reduced welfare of affected horses. The aim of this study was to identify and validate single nucleotide polymorphisms (SNPs) associated with equine IBH susceptibility. After quality control, 33,523 SNPs were included in a Bayesian genome-wide association study on 177 affected and 178 unaffected Icelandic horses. We report associated regions in E. caballus (ECA) 1, 3, 15 and 18, overlapping with known IBH QTLs in horses, and novel regions containing several genes, together explaining 11.46% of the total genetic variance. For validation, three SNPs on ECA 1 and ECA X (explaining the largest percentage of genetic variance) within 1-mb genomic windows for IBH were genotyped in an independent population of 280 Exmoor ponies. The associated genomic region (152-153 mb) on ECA 1 was confirmed in Exmoor ponies and contains the AQR gene involved in splicing processes and a long non-coding RNA. This study confirms the polygenic nature of IBH susceptibility and suggests a role of transcriptional regulatory mechanisms (e.g., alternative splicing) for IBH predisposition in these horse breeds.