Fuzzy classification of phantom parent groups in an animal model

<p>Abstract</p> <p>Background</p> <p>Genetic evaluation models often include genetic groups to account for unequal genetic level of animals with unknown parentage. The definition of phantom parent groups usually includes a time component (e.g. years). Combining several time periods to ensure sufficiently large groups may create problems since all phantom parents in a group are considered contemporaries.</p> <p>Methods</p> <p>To avoid the downside of such distinct classification, a fuzzy logic approach is suggested. A phantom parent can be assigned to several genetic groups, with proportions between zero and one that sum to one. Rules were presented for assigning coefficients to the inverse of the relationship matrix for fuzzy-classified genetic groups. This approach was illustrated with simulated data from ten generations of mass selection. Observations and pedigree records were randomly deleted. Phantom parent groups were defined on the basis of gender and generation number. In one scenario, uncertainty about generation of birth was simulated for some animals with unknown parents. In the distinct classification, one of the two possible generations of birth was randomly chosen to assign phantom parents to genetic groups for animals with simulated uncertainty, whereas the phantom parents were assigned to both possible genetic groups in the fuzzy classification.</p> <p>Results</p> <p>The empirical prediction error variance (PEV) was somewhat lower for fuzzy-classified genetic groups. The ranking of animals with unknown parents was more correct and less variable across replicates in comparison with distinct genetic groups. In another scenario, each phantom parent was assigned to three groups, one pertaining to its gender, and two pertaining to the first and last generation, with proportion depending on the (true) generation of birth. Due to the lower number of groups, the empirical PEV of breeding values was smaller when genetic groups were fuzzy-classified.</p> <p>Conclusion</p> <p>Fuzzy-classification provides the potential to describe the genetic level of unknown parents in a more parsimonious and structured manner, and thereby increases the precision of predicted breeding values.</p

Multibreed genomic prediction using summary statistics and a breed-origin-of-alleles approach

Because of an increasing interest in crossbreeding between dairy breeds in dairy cattle herds, farmers are requesting breeding values for crossbred animals. However, genomically enhanced breeding values are difficult to predict in crossbred populations because the genetic make-up of crossbred individuals is unlikely to follow the same pattern as for purebreds. Furthermore, sharing genotype and phenotype information between breed populations are not always possible, which means that genetic merit (GM) for crossbred animals may be predicted without the information needed from some pure breeds, resulting in low prediction accuracy. This simulation study investigated the consequences of using summary statistics from single-breed genomic predictions for some or all pure breeds in two- and three-breed rotational crosses, rather than their raw data. A genomic prediction model taking into account the breed-origin of alleles (BOA) was considered. Because of a high genomic correlation between the breeds simulated (0.62-0.87), the prediction accuracies using the BOA approach were similar to a joint model, assuming homogeneous SNP effects for these breeds. Having a reference population with summary statistics available from all pure breeds and full phenotype and genotype information from crossbreds yielded almost as high prediction accuracies (0.720-0.768) as having a reference population with full information from all pure breeds and crossbreds (0.753-0.789). Lacking information from the pure breeds yielded much lower prediction accuracies (0.590-0.676). Furthermore, including crossbred animals in a combined reference population also benefitted prediction accuracies in the purebred animals, especially for the smallest breed population

Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses

Assessment protocols to describe the various aspects of conformation, gait and jumping traits on a linear scale were introduced at young horse tests for Swedish Warmblood horses in 2013. The traits scored on a linear scale are assumed to be less subjective and more easily compared across populations than the traditional evaluated traits that are scored relative to the breeding goal. However, the resulting number of traits is considerable, and several of the traits are correlated. The aim of this study was to investigate the interrelationship between the different evaluated and linearly scored traits in Swedish Warmbloods using factor analysis. In total, 20,935 horses born 1996-2017 had information on evaluated traits, and 5450 of these also had linearly scored trait records assessed since 2014 when the protocol was updated. A factor analysis with varimax rotation was performed separately for evaluated and linearly scored traits using the Psych package in R. Height at withers was included in both analyses. A total of four factors for evaluated traits and 14 factors for linearly scored traits were kept for further analysis. Missing values for individual traits in horses with linearly scored trait records were imputed based on correlated traits before factor scores were calculated using factor loadings. Genetic parameters for, and correlations between, the resulting underlying factors were estimated using multiple-trait animal models in the BLUPF90 package. Heritability estimates were on a similar level as for the traits currently used in the genetic evaluation, ranging from 0.05 for the factor for linearly scored traits named L.behaviour (dominated by traits related to behaviour) to 0.59 for the factor for evaluated traits named E.size (dominated by height at withers and conformation). For both types of traits, separate factors were formed for jumping and gait traits, as well as for body size. High genetic correlations were estimated between such corresponding factors for evaluated traits and factors for linearly scored traits. In conclusion, factor analysis could be used to reduce the number of traits to be included in multiple-trait genetic evaluation or in genomic analysis for warmblood horses. It can also contribute to a better understanding of the interrelationships among the assessed traits and be useful to decide on subgroups of traits to be used in several multiple-trait evaluations on groups of original traits

Dairy cattle farmers' preferences for different breeding tools

Breeding technologies play a significant role in improving dairy cattle production. Scientifically proven tools for improved management and genetic gain in dairy herds, such as sexed semen, beef semen, genomic testing, dairy crossbreeding, and multiple ovulation embryo transfer (MOET), are readily available to dairy farmers. However, despite good accessibility, decreasing costs, and continuous development of these tools, their use in Sweden is limited. This study investigated Swedish dairy farmers' preferences for breeding tools through a survey including a discrete choice experiment. The survey was distributed online to 1 521 Swedish farmers and by an open link published through a farming magazine. In total, the study included 204 completed responses. The discrete choice experiment consisted of 10 questions with two alternative combinations, which gave 48 combinations in total. Utility values and part-worth values were computed using a conditional logit model based on the responses in the discrete choice experiment for nine groups of respondents: one group with all respondents, two groups based on respondents using dairy crossbreeding or not within the past 12 months, two based on herd size, two based on respondent age, and two based on whether respondents had used breeding advisory services or not. The strongest preferences in all groups were for using sexed semen and beef semen. Genomic testing was also significantly preferred by all groups of respondents. Except in large herds, MOET on own animals was significantly and relatively strongly disfavoured by all groups. Buying embryos had no significant utility value to any group. Dairy crossbreeding had low and insignificant utility values in the group of all respondents, but it was strongly favoured by the group that had used dairy crossbreeding within the past 12 months, and it was disfavoured by the group that had not. Part-worth values of combined breeding tools showed that combinations of sexed and beef semen, alone or with genomic testing without dairy crossbreeding, were the most preferred tools. Compared with the most common combinations of breeding tools used in the past 12 months, the part-worth values indicated that Swedish dairy farmers may prefer to use breeding tools more than they do today. Statements on the different breeding tools indicated that the respondents agreed with the benefits attributed to the breeding tools, but these benefits may not be worth the cost of genomic testing and the time consumption of MOET. These valuable insights can be used for further development of breeding tools

Conservation of a native dairy cattle breed through terminal crossbreeding with commercial dairy breeds

Farmers play a key role in conserving native livestock breeds, but without economic support, farms with native breeds may not be viable. We hypothesized that terminal crossbreeding can improve herd economy and decrease the economic support needed from society. Three scenarios were simulated using SimHerd Crossbred: a herd of purebred Swedish Polled Cattle, a herd of purebred Swedish Red, and a herd of 75% Swedish Polled Cattle and 25% F1 crossbreds. The results showed annual contribution margin per cow in the herd can be increased by euro181 by crossbreeding compared with pure-breeding with the native breed, giving a 13.6% growth in contribution margin. However, the needed cost in subsidies paid by the government will remain unchanged if the population size of the native breed is to be maintained. Combining a crossbreeding strategy with the marketing of niche products may facilitate the conservation of native cattle

Mating allocations in Nordic Red Dairy Cattle using genomic information

In this study, we compared mating allocations in Nordic Red Dairy Cattle using genomic information. We used linear programming to optimize different economic scores within each herd, considering genetic level, semen cost, the economic impact of recessive genetic defects, and genetic relationships. We selected 9,841 genotyped females born in Denmark, Finland, or Sweden in 2019 for mating allocations. We used 2 different pedigree relationship coefficients, the first tracing the pedigree 3 generations back from the parents of the potential mating and the second based on all available pedigree information. We used 3 different genomic relationship coefficients, 1 SNP-by-SNP genomic relationship and 2 based on shared genomic segments. We found high correlations (≥0.83) between the pedigree and genomic relationship measures. The mating results showed that it was possible to reduce the different genetic relationships between parents with minimal effect on genetic level. Including the cost of known recessive genetic defects eliminated expression of genetic defects. It was possible to reduce genomic relationships between parents with pedigree measures, but it was best done with genomic measures. Linear programming maximized the economic score for all herds studied within seconds, which means that it is suitable for implementation in mating software to be used by advisors and farmers

Korsning med mjölkkor – effekter på besättningsdynamik och ekonomiskt resultat

I denna simuleringsstudie jämförs besättningsstruktur samt produktionsresultat och ekonomiskt resultat för besättningar med renrasiga kor och besättningar med korsningskor. Vi har använt medeltal från Kokontrollen som ingångsvärden till simuleringsprogrammet SimHerd Crossbred. Holsteinkor (SH), röda kor (SRB) och korsningskor från systematisk slutkorsning (enkelkorsning, terminal cross) ingick i studien. Fyra olika typer av besättningar studerades: bara renrasiga SRBkor, en kärna av renrasiga SRB-kor och SHxSRB-korsningskor, bara renrasiga SH-kor, samt en kärna av renrasiga SH-kor och SRBxSH-korsningskor. Resultaten presenteras både för konventionell och ekologisk produktion, eftersom medeltal för kornas resultat och kostnader och intäkter skiljer sig mellan dessa produktionssystem. Simuleringen visar att det ekonomiska resultatet för mjölkproducenter förbättras när man går från en renrasig besättning till en besättning med korsning och det gäller för såväl konventionell som ekologisk produktion. För besättningar med SRB-kärna blev förbättringen av det ekonomiska resultatet vid övergång till korsning större för ekologisk produktion än för konventionell produktion. Det är kopplat till ett högre avräkningspris för mjölk i ekologisk produktion. Besättningsstrukturen i en besättning med korsning påverkas av djurens reproduktion och funktionella egenskaper. Det medför att utrymmet för korsningskor är större i besättningar med SRB-kärna än i besättningar med SHkärna

Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p

Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

The same ELA class II risk factors confer equine insect bite hypersensitivity in two distinct populations

Insect bite hypersensitivity (IBH) is a chronic allergic dermatitis common in horses. Affected horses mainly react against antigens present in the saliva from the biting midges, Culicoides ssp, and occasionally black flies, Simulium ssp. Because of this insect dependency, the disease is clearly seasonal and prevalence varies between geographical locations. For two distinct horse breeds, we genotyped four microsatellite markers positioned within the MHC class II region and sequenced the highly polymorphic exons two from DRA and DRB3, respectively. Initially, 94 IBH-affected and 93 unaffected Swedish born Icelandic horses were tested for genetic association. These horses had previously been genotyped on the Illumina Equine SNP50 BeadChip, which made it possible to ensure that our study did not suffer from the effects of stratification. The second population consisted of 106 unaffected and 80 IBH-affected Exmoor ponies. We show that variants in the MHC class II region are associated with disease susceptibility (praw = 2.34 × 10−5), with the same allele (COR112:274) associated in two separate populations. In addition, we combined microsatellite and sequencing data in order to investigate the pattern of homozygosity and show that homozygosity across the entire MHC class II region is associated with a higher risk of developing IBH (p = 0.0013). To our knowledge this is the first time in any atopic dermatitis suffering species, including man, where the same risk allele has been identified in two distinct populations