How genomic selection has increased rates of genetic gain and inbreeding in the Australian national herd, genomic information nucleus, and bulls

Community-based breeding programs can realize sustainable genetic gain and economic benefits in tropical dairy cattle systems

Implementing an appropriate breeding program is crucial to control fluctuation in performance, enhance adaptation, and further improve the crossbred population of dairy cattle. Five alternative breeding programs (BPs) were modeled considering available breeding units in the study area, the existing crossbreeding practices, and the future prospects of dairy research and development in Ethiopia. The study targeted 143,576 crossbred cows of 54,822 smallholder households in the Arsi, West Shewa, and North Shewa zones of the Oromia Region, as well as the North Shewa zone of the Amhara Region. The alternative BPs include conventional on-station progeny testing (SPT), conventional on-farm progeny testing (FPT), conventional on-station and on-farm progeny testing (SFPT), genomic selection (GS), and genomic progeny testing (GPT). Input parameters for modeling the BPs were taken from the analysis of long-term data obtained from the Holetta Agricultural Research Center and a survey conducted in the study area. ZPLAN+ software was used to predict estimates of genetic gain (GG) and discounted profit for goal traits. The predicted genetic gains (GGs) for milk yield (MY) per year were 34.52 kg, 49.63 kg, 29.35 kg, 76.16 kg, and 77.51 kg for SPT, FPT, SFPT, GS, and GPT, respectively. The GGs of the other goal traits range from 0.69 to 1.19 days per year for age at first calving, from 1.20 to 2.35 days per year for calving interval, and from 0.06 to 0.12 days per year for herd life. Compared to conventional BPs, genomic systems (GPT and GS) enhanced the GG of MY by 53%-164%, reduced generation interval by up to 21%, and improved the accuracy of test bull selection from 0.33 to 0.43. The discounted profit of the BPs varied from 249.58 Ethiopian Birr (ETB, 1 USD = 39.55696 ETB) per year in SPT to 689.79 ETB per year in GS. Genomic selection outperforms SPT, SFPT, and FPT by 266, 227%, and 138% of discounted profit, respectively. Community-based crossbreeding accompanied by GS and gradual support with progeny testing (GPT) is recommended as the main way forward to attain better genetic progress in dairy farms in Ethiopia and similar scenarios in other tropical countries.

Genomic Analysis of Inbreeding and Coancestry in Nordic Jersey and Holstein Dairy Cattle Populations

In recent years, Genomic Selection (GS) has accelerated genetic gain in dairy cattle breeds worldwide. Despite the evident genetic progress, several dairy populations have also encountered challenges such as heightened inbreeding rates and reduced effective population sizes. The challenge has been to find a balance between achieving substantial genetic gain while managing genetic diversity within the population, thereby mitigating the negative effects of inbreeding depression. This study aims to elucidate the impact of GS on pedigree and genomic rates of inbreeding (ΔF) and coancestry (ΔC) in Nordic Jersey (NJ) and Holstein (NH) cattle populations. Furthermore, key genetic metrics including the generation interval (L), effective population size (Ne), and future effective population size (FNe) were assessed between 2 time periods, before and after GS, and across distinct animal cohorts in both breeds: females, bulls, and approved semen-producing bulls (AI-sires). Analysis of ΔF and ΔC revealed distinct trends across the studied periods and animal groups. Notably, there was a consistent increase in yearly ΔF for most animal groups in both breeds. An exception was observed in NH AI-sires, which demonstrated a slight decrease in yearly ΔF. Moreover, NJ displayed minimal changes in yearly ΔC between the periods, whereas NH exhibited elevated ΔC values across all animal groups. Particularly striking was the substantial increase in yearly ΔC within the NH female population, surging from 0.02% to 0.39% between the periods. Implementation of GS resulted in a reduction of the generation interval across all animal cohorts in both NJ and NH breeds. However, the extent of reduction was more pronounced in males compared with females. This reduction in generation interval influenced generational changes in ΔF and ΔC. Bulls and AI-sires of both breeds exhibited reduced generational ΔF between periods, in contrast to females that demonstrated an opposing pattern. Between the periods, NJ maintained a relatively stable Ne, 29.4 before and 30.3 after GS, while NH experienced a notable decline from 54.3 to 42.8. Female groups in both breeds displayed a negative Ne trend, while males demonstrated either neutral or positive Ne developments. Regarding FNe, NJ exhibited positive FNe development with an increase from 40.7 to 57.2. The opposite was observed in NH, where FNe decreased from 198.8 to 42.7. In summary, it was evident that the genomic methods could detect differences between the populations and changes in ΔF and ΔC more efficiently than pedigree methods. GS implementation yielded positive outcomes within the NJ population regarding the rate of coancestry but the opposite was observed with NH. Moreover, analysis of ΔC data hints at the potential to decrease future ΔF through informed mating strategies. Conversely, NH faces more pressing concerns, even though ΔF remains comparatively modest in contrast to what has been observed in other Holstein populations. These findings underscore the necessity of genomic control of inbreeding and coancestry with strategic changes in the Nordic breeding schemes for dairy to ensure long-term sustainability in the forthcoming years.

Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats

The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats

Introduction

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs.

Methods

This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs.

Results

It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available

BACKGROUND

It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models.

RESULTS

Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented.

CONCLUSIONS

When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

Impact of kinship matrices on genetic gain and inbreeding with optimum contribution selection in a genomic dairy cattle breeding program

BACKGROUND

Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.

RESULTS

We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.

CONCLUSIONS

Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.

Pre-Implantation Bovine Embryo Evaluation-From Optics to Omics and Beyond

Approximately 80% of the ~1.5 million bovine embryos transferred in 2021 were in vitro produced. However, only ~27% of the transferred IVP embryos will result in live births. The ~73% pregnancy failures are partly due to transferring poor-quality embryos, a result of erroneous stereomicroscopy-based morphological evaluation, the current method of choice for pre-transfer embryo evaluation. Numerous microscopic (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial-intelligence-based microscopy) and non-microscopic (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) methodologies have been tested to find an embryo evaluation technique that is superior to morphologic evaluation. Many of these research tools can accurately determine embryo quality/viability; however, most are invasive, expensive, laborious, technically sophisticated, and/or time-consuming, making them futile in the context of in-field embryo evaluation. However accurate they may be, using complex methods, such as RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy, at thousands of embryo production/collection facilities is impractical. Therefore, future research is warranted to innovate field-friendly, simple benchtop tests using findings already available, particularly from omics-based research methodologies. Time-lapse monitoring and artificial-intelligence-based automated image analysis also have the potential for accurate embryo evaluation; however, further research is warranted to innovate economically feasible options for in-field applications.

Single-Step Genomic Prediction of Superovulatory Response Traits in Japanese Black Donor Cows

We assessed the performance of single-step genomic prediction of breeding values for superovulatory response traits in Japanese Black donor cows. A total of 25,332 records of the total number of embryos and oocytes (TNE) and the number of good embryos (NGE) per flush for 1874 Japanese Black donor cows were collected during 2008 and 2022. Genotype information on 36,426 autosomal single-nucleotide polymorphisms (SNPs) for 575 out of the 1,874 cows was used. Breeding values were predicted exploiting a two-trait repeatability animal model. Two genetic relationship matrices were used, one based on pedigree information (A matrix) and the other considering both pedigree and SNP marker genotype information (H matrix). Estimated heritabilities of TNE and NGE were 0.18 and 0.11, respectively, when using the H matrix, which were both slightly lower than when using the A matrix (0.26 for TNE and 0.16 for NGE). Estimated genetic correlations between the traits were 0.61 and 0.66 when using H and A matrices, respectively. When the variance components were the same in breeding value prediction, the mean reliability was greater when using the H matrix than when using the A matrix. This advantage seems more prominent for cows with low reliability when using the A matrix. The results imply that introducing single-step genomic prediction could boost the rate of genetic improvement of superovulatory response traits, but efforts should be made to maintain genetic diversity when performing selection.

The effect of cycles of genomic selection on the wheat (T. aestivum) genome

We documented changes in the wheat genome attributed to genomic selection including loss of diversity, and changes in population structure and linkage disequilibrium patterns. We conclude that training and prediction populations need to co-evolve instead of the use of a static training population. Genomic selection (GS) is widely used in plant breeding to shorten breeding cycles. Our objective was to assess the impact of rapid cycling GS on the wheat genome. We used 3927 markers to genotype a training population (YTP) and individuals from five cycles (YC1-YC5) of GS for grain yield. We assessed changes of allele frequency, genetic distance, population structure, and linkage disequilibrium (LD). We found 27.3% of all markers had a significant allele frequency change by YC5, 18% experienced a significant change attributed to selection, and 9.3% had a significant change due to either drift or selection. A total of 725 of 3927 markers were fixed by YC5 with selection fixing 7.3% of the 725 markers. The genetic distance between cycles increased over time. The Fst value of 0.224 between YTP and YC5 indicates their relationship was low. The number LD blocks decreased over time and the correlation between LD matrices also decreased over time. Overall, we found a reduction in genetic diversity, increased genetic differentiation of cycles from the training population, and restructuring of the LD patterns over cycles. The accuracy of GS depends on the genomic similarity of the training population and the prediction populations. Our results show that the similarity can decline rapidly over cycles of GS and compromise the predictive ability of the YTP-based model. Our results support implementing a GS scheme where the training and prediction populations co-evolve instead of the use of a static training population.

Validating the female fertility estimated breeding value in Australian commercial dairy herds

We conducted a retrospective cohort study to validate the efficacy of the Australian multitrait fertility estimated breeding value (EBV). We did this by determining its associations with phenotypic measures of reproductive performance (i.e., submission rate, first service conception rate, and early calving). Our secondary aim was to report the associations between these reproductive outcomes and management and climate-related factors hypothesized to affect fertility. Our study population included 38 pasture-based dairy herds from the northern Victorian irrigation region in Australia. We collected records for 86,974 cows with 219,156 lactations and 438,578 mating events from the date on which managers started herd recording until December 2016, comprising both fertility-related data such as insemination records, calving dates, and pregnancy test results, and systems-related data such as production, herd size, and calving pattern. We also collected hourly data from 2004 to 2017 from the closest available weather station to account for climate-related factors (i.e., temperature humidity index; THI). Multilevel Cox proportional hazard models were used to analyze time-to-event outcomes (days to first service, days to cow calving following the planned herd calving start date), and multilevel logistic regression models for binomial outcomes (conception to first service) in the Holstein-Friesian and Jersey breeds. A 1-unit increase in daughter fertility EBV was associated with a 5.4 and 8.2% increase in the daily hazard of calving in the Holstein-Friesian and Jersey breeds respectively. These are relative increases (i.e., a Holstein-Friesian herd with a 60% 6-wk in-calf rate would see an improvement to 63.2% with a 1-unit increase in herd fertility EBV). Similar results were obtained for submission and conception rate. Associations between 120-d milk yield and reproductive outcome were complicated by interactions with 120-d protein percentage and calving age, depending on the breed and outcome. In general, we found that the reproductive performance of high milk-yielding animals deteriorated faster with age than low milk-yielding animals, and high protein percentage exacerbated the differences between low and high milk-yielding animals. Climate-related factors were also associated with fertility, with a 1-unit increase in maximum THI decreasing first service conception rate by 1.2% for Holstein-Friesians but having no statistically significant association in the Jersey breed. However, THI had a negative association in both breeds on the daily hazard of calving. Our study validates the efficacy of the daughter fertility EBV for improving herd reproductive performance and identifies significant associations between 120-d milk and protein yields and THI on the fertility of Australian dairy cows.

Changes in genetic trends in US dairy cattle since the implementation of genomic selection

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.

Genomic selection in beef cattle creates additional opportunities for embryo technologies to meet industry needs

The use of genotype information to improve the predictability of Expected Progeny Difference was first implemented in American Angus cattle in 2009 and has now grown to where over 50% of all registered calves are genotyped. Animals with only a genotype now have genetic prediction accuracy equivalent to eight or more progeny records across all traits. Reproductive technologies have also been widely adopted with approximately 50% of all calves born being the result of artificial insemination. Non-surgical embryo transfer started increasing in the mid 1990s with just over 10% of calves born being the result of embryo transfer since 2005. The number of embryos created with in vitro technologies has risen sharply since 2015 and now accounts for close to 30% of all ET calves. Genomics has enabled embryo technologies to be more impactful, as females can be selected with greater accuracy and sires can be used at earlier ages with moderate accuracy. Large numbers of females genotyped each year also increases the number of selection candidates, increasing the selection intensity. Genomics, combined with increased recording, also provides more information on females. This increases the spread in the estimated index values of current dams, identifying more elite dams for selection as embryo donors. The greater scope of female selection also contributes to better inbreeding management. Commercial animals genotyped could be targeted for oocyte harvesting at slaughter, creating opportunities for low cost high value beef embryos to be used in the beef on dairy segment of the industry.

Genomic characterization of autozygosity and recent inbreeding trends in all major breeds of US dairy cattle

Maintaining a genetically diverse dairy cattle population is critical to preserving adaptability to future breeding goals and avoiding declines in fitness. This study characterized the genomic landscape of autozygosity and assessed trends in genetic diversity in 5 breeds of US dairy cattle. We analyzed a sizable genomic data set containing 4,173,679 pedigreed and genotyped animals of the Ayrshire, Brown Swiss, Guernsey, Holstein, and Jersey breeds. Runs of homozygosity (ROH) of 2 Mb or longer in length were identified in each animal. The within-breed means for number and the combined length of ROH were highest in Jerseys (62.66 ± 8.29 ROH and 426.24 ± 83.40 Mb, respectively; mean ± SD) and lowest in Ayrshires (37.24 ± 8.27 ROH and 265.05 ± 85.00 Mb, respectively). Short ROH were the most abundant, but moderate to large ROH made up the largest proportion of genome autozygosity in all breeds. In addition, we identified ROH islands in each breed. This revealed selection patterns for milk production, productive life, health, and reproduction in most breeds and evidence for parallel selective pressure for loci on chromosome 6 between Ayrshire and Brown Swiss and for loci on chromosome 20 between Holstein and Jersey. We calculated inbreeding coefficients using 3 different approaches, pedigree-based (F_PED), marker-based using a genomic relationship matrix (F_GRM), and segment-based using ROH (F_ROH). The average inbreeding coefficient ranged from 0.06 in Ayrshires and Brown Swiss to 0.08 in Jerseys and Holsteins using F_PED, from 0.22 in Holsteins to 0.29 in Guernsey and Jerseys using F_GRM, and from 0.11 in Ayrshires to 0.17 in Jerseys using F_ROH. In addition, the effective population size at past generations (5-100 generations ago), the yearly rate of inbreeding, and the effective population size in 3 recent periods (2000-2009, 2010-2014, and 2015-2018) were determined in each breed to ascertain current and historical trends of genetic diversity. We found a historical trend of decreasing effective population size in the last 100 generations in all breeds and breed differences in the effect of the recent implementation of genomic selection on inbreeding accumulation.

Breed structures in Australian dairy herds

Breed structures of Australian dairy herds over time were described for a large subset of milk-recording herds. The focus for this study was to describe the use of crossbreeding by dairy farmers, specifically proportions of herds using crossbreeding, whether they were using two-breed or three-breed crossbreeding systems, and how herd-breed structures changed over time. The most common breed structure in Australian milk-recording herds between 2000 and 2013 was two-breed crossbreeding (39% of herd-years). The next most common breed structure was purebred (35%). Over the period studied, the proportion of herds that were purebred decreased, while the proportion of herds that were crossbreeding increased (particularly three-breed crossbreeding herds). Herd-breed structures and changes over time varied with region and with the herd's calving system. There were also considerable changes in breed structure within herds, including herds changing breed structure before reverting back to their original breed structure. These results indicate that breed structures in milk-recording dairy herds in Australia are dynamic, and that farmers have commonly employed crossbreeding strategies.