Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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    Kringlooplandbouw - Robuuste Teelt veehouderij
    Veerkamp, R.F. - \ 2020
    Wageningen University & Research
    circular agriculture - livestock farming - cows - breeds - Groningen White Headed - rare breeds
    In deze video is hoogleraar Fokkerijsystemen Roel Veerkamp in gesprek met Maurits Tepper, die Groningse Blaarkoppen houdt, over robuuste koeienrassen. Welke koe robuust is, hangt volgens hen af van het hele bedrijfssysteem, niet alleen van de eigenschappen van de koe.
    Robuuste teeltsystemen - kringlooplandbouw
    Apeldoorn, Dirk van; Mommer, Liesje ; Veerkamp, Roel ; Philipsen, Bert ; Wolf, Pieter de - \ 2020
    circular agriculture - biodiversity - agri-environment schemes - technical progress - innovations - cropping systems - soil-landscape relationships - crop mixtures - crop management - perennial cropping - mixed cropping - alley cropping - arable farming - livestock farming - animal health - feeds
    Snelle genomics vragen aandacht inteelt
    Veerkamp, Roel - \ 2020
    Locomotion Data Breed4Food: Educational Files
    Schokker, Dirkjan ; Athanasiadis, Ioannis ; Visser, Bram ; Veerkamp, Roel ; Kamphuis, Claudia - \ 2020
    Wageningen University & Research
    Turkey - Gait score - Accelerometers - Force plate - 3D video camera
    The selected use case was an experiment in which the gait score of turkeys was determined, here 3 animals can be used for educational purposes. This gait scoring is traditionally performed by an expert. In this experiment different type of sensors were used to explore to what extent these sensors can describe or mirror the gait score of the expert. Data & Sensors Gait score (visually by expert) Force Plate (Kistler) Accelerometers / inertial measurement units {IMUs} (Xsense MTw awinda) 3D Video camera (Intel Realsense D415)
    Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle
    Doekes, Harmen P. ; Bijma, Piter ; Veerkamp, Roel F. ; Jong, Gerben de; Wientjes, Yvonne C.J. ; Windig, Jack J. - \ 2020
    Genetics, Selection, Evolution 52 (2020)1. - ISSN 0999-193X
    Background: Inbreeding depression refers to the decrease in mean performance due to inbreeding. Inbreeding depression is caused by an increase in homozygosity and reduced expression of (on average) favourable dominance effects. Dominance effects and allele frequencies differ across loci, and consequently inbreeding depression is expected to differ along the genome. In this study, we investigated differences in inbreeding depression across the genome of Dutch Holstein Friesian cattle, by estimating dominance effects and effects of regions of homozygosity (ROH). Methods: Genotype (75 k) and phenotype data of 38,792 cows were used. For nine yield, fertility and udder health traits, GREML models were run to estimate genome-wide inbreeding depression and estimate additive, dominance and ROH variance components. For this purpose, we introduced a ROH-based relationship matrix. Additive, dominance and ROH effects per SNP were obtained through back-solving. In addition, a single SNP GWAS was performed to identify significant additive, dominance or ROH associations. Results: Genome-wide inbreeding depression was observed for all yield, fertility and udder health traits. For example, a 1% increase in genome-wide homozygosity was associated with a decrease in 305-d milk yield of approximately 99 kg. For yield traits only, including dominance and ROH effects in the GREML model resulted in a better fit (P < 0.05) than a model with only additive effects. After correcting for the effect of genome-wide homozygosity, dominance and ROH variance explained less than 1% of the phenotypic variance for all traits. Furthermore, dominance and ROH effects were distributed evenly along the genome. The most notable region with a favourable dominance effect for yield traits was on chromosome 5, but overall few regions with large favourable dominance effects and significant dominance associations were detected. No significant ROH-associations were found. Conclusions: Inbreeding depression was distributed quite equally along the genome and was well captured by genome-wide homozygosity. These findings suggest that, based on 75 k SNP data, there is little benefit of accounting for region-specific inbreeding depression in selection schemes.
    How to store and analyse animal experimental big data ?
    Schokker, D. ; Athanasiadis, I.N. ; Visser, B. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2020
    Boxmeer : CRV
    We have set-up a data lake stack to store and analyse data collected during a turkey locomotion experiment and showed the scalability of such a data lake, and the potential for machine learning pipelines to analyse these data.
    Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock
    Raymond, Biaty ; Yengo, Loic ; Costilla, Roy ; Schrooten, Chris ; Bouwman, Aniek C. ; Hayes, Ben J. ; Veerkamp, Roel F. ; Visscher, Peter M. - \ 2020
    Plos Genetics 16 (2020). - ISSN 1553-7390

    Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.

    Using short read sequencing to characterise balanced reciprocal translocations in pigs
    Bouwman, Aniek C. ; Derks, Martijn F.L. ; Broekhuijse, Marleen L.W.J. ; Harlizius, Barbara ; Veerkamp, Roel F. - \ 2020
    BMC Genomics 21 (2020)1. - ISSN 1471-2164
    Karyotype - Pig - Reciprocal translocation - Whole genome sequencing

    Background: A balanced constitutional reciprocal translocation (RT) is a mutual exchange of terminal segments of two non-homologous chromosomes without any loss or gain of DNA in germline cells. Carriers of balanced RTs are viable individuals with no apparent phenotypical consequences. These animals produce, however, unbalanced gametes and show therefore reduced fertility and offspring with congenital abnormalities. This cytogenetic abnormality is usually detected using chromosome staining techniques. The aim of this study was to test the possibilities of using paired end short read sequencing for detection of balanced RTs in boars and investigate their breakpoints and junctions. Results: Balanced RTs were recovered in a blinded analysis, using structural variant calling software DELLY, in 6 of the 7 carriers with 30 fold short read paired end sequencing. In 15 non-carriers we did not detect any RTs. Reducing the coverage to 20 fold, 15 fold and 10 fold showed that at least 20 fold coverage is required to obtain good results. One RT was not detected using the blind screening, however, a highly likely RT was discovered after unblinding. This RT was located in a repetitive region, showing the limitations of short read sequence data. The detailed analysis of the breakpoints and junctions suggested three junctions showing microhomology, three junctions with blunt-end ligation, and three micro-insertions at the breakpoint junctions. The RTs detected also showed to disrupt genes. Conclusions: We conclude that paired end short read sequence data can be used to detect and characterize balanced reciprocal translocations, if sequencing depth is at least 20 fold coverage. However, translocations in repetitive areas may require large fragments or even long read sequence data.

    Heritability estimates of the novel trait ‘suppressed in ovo virus infection’ in honey bees (Apis mellifera)
    Graaf, Dirk C. de; Laget, Dries ; Smet, Lina De; Claeys Boúúaert, David ; Brunain, Marleen ; Veerkamp, Roel F. ; Brascamp, Evert W. - \ 2020
    Scientific Reports 10 (2020)1. - ISSN 2045-2322

    Honey bees are under pressure due to abnormal high colony death rates, especially during the winter. The infestation by the Varroa destructor mite and the viruses that this ectoparasite transmits are generally considered as the bees’ most important biological threats. Almost all efforts to remedy this dual infection have so far focused on the control of the Varroa mite alone and not on the viruses it transmits. In the present study, the sanitary control of breeding queens was conducted on eggs taken from drone brood for 4 consecutive years (2015–2018). The screening was performed on the sideline of an ongoing breeding program, which allowed us to estimate the heritabilities of the virus status of the eggs. We used the term ‘suppressed in ovo virus infection’ (SOV) for this novel trait and found moderate heritabilities for the presence of several viruses simultaneously and for the presence of single viral species. Colonies that expressed the SOV trait seemed to be more resilient to virus infections as a whole with fewer and less severe Deformed wing virus infections in most developmental stages, especially in the male caste. The implementation of this novel trait into breeding programs is recommended.

    QTL detection in a pedigreed breeding population of diploid potato
    Korontzis, George ; Malosetti, Marcos ; Zheng, Chaozhi ; Maliepaard, Chris ; Mulder, Han A. ; Lindhout, Pim ; Veerkamp, Roel F. ; Eeuwijk, Fred A. van - \ 2020
    Euphytica 216 (2020)9. - ISSN 0014-2336
    Diploid potato - GWAS - Hybrid potato - Identity-by-descent - Pedigree - QTL mapping

    Diploid hybrid potato breeding is emerging as an alternative to breeding tetraploid potato clones. The development of diploid breeding varieties involves recent, shallow pedigrees with a limited number of founders. Within this context, alternative QTL detection methodologies should be considered to enable identification of relevant QTLs and characterize the founders of the pedigree. To that end, we are using a dataset of multiple diploid potato F 3 families under selection derived by a cross between an inbred Solanum chacoense and an outbred diploid Solanum tuberosum, and identify QTLs for tuber fresh weight. We used three methods for QTL detection: (1) a Genome Wide Association Study model, (2) a linkage approach tailored to the population under study and (3) a more general approach for modelling multiallelic QTLs in complex pedigrees using identity-by-descent (IBD) probabilities. We show that all three approaches enable detection of QTLs in the population under study, but the method that makes better use of IBD information has a more direct and detailed interpretation by linking QTL alleles to the founders.

    Improving predictive performance on survival in dairy cattle using an ensemble learning approach
    Heide, E.M.M. van der; Kamphuis, C. ; Veerkamp, R.F. ; Athanasiadis, I.N. ; Azzopardi, G. ; Pelt, M.L. van; Ducro, B.J. - \ 2020
    Computers and Electronics in Agriculture 177 (2020). - ISSN 0168-1699
    Dairy cow - Ensemble - Machine learning - Survival

    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.

    Investigating the impact of preselection on subsequent single-step genomic BLUP evaluation of preselected animals
    Jibrila, Ibrahim ; Napel, Jan ten; Vandenplas, Jeremie ; Veerkamp, Roel F. ; Calus, Mario P.L. - \ 2020
    Genetics, Selection, Evolution 52 (2020). - ISSN 0999-193X

    BACKGROUND: Preselection of candidates, hereafter referred to as preselection, is a common practice in breeding programs. Preselection can cause bias and accuracy loss in subsequent pedigree-based best linear unbiased prediction (PBLUP). However, the impact of preselection on subsequent single-step genomic BLUP (ssGBLUP) is not completely clear yet. Therefore, in this study, we investigated, across different heritabilities, the impact of intensity and type of preselection on subsequent ssGBLUP evaluation of preselected animals. METHODS: We simulated a nucleus of a breeding programme, in which a recent population of 15 generations was produced with PBLUP-based selection. In generation 15 of this recent population, the parents of the next generation were preselected using several preselection scenarios. These scenarios were combinations of three intensities of preselection (no, high or very high preselection) and three types of preselection (genomic, parental average or random), across three heritabilities (0.5, 0.3 or 0.1). Following each preselection scenario, a subsequent evaluation was performed using ssGBLUP by excluding all the information from the preculled animals, and these genetic evaluations were compared in terms of accuracy and bias for the preselected animals, and in terms of realized genetic gain. RESULTS: Type of preselection affected selection accuracy at both preselection and subsequent evaluation stages. While preselection accuracy decreased, accuracy in the subsequent ssGBLUP evaluation increased, from genomic to parent average to random preselection scenarios. Bias was always negligible. Genetic gain decreased from genomic to parent average to random preselection scenarios. Genetic gain also decreased with increasing intensity of preselection, but only by a maximum of 0.1 additive genetic standard deviation from no to very high genomic preselection scenarios. CONCLUSIONS: Using ssGBLUP in subsequent evaluations prevents preselection bias, irrespective of intensity and type of preselection, and heritability. With GPS, in addition to reducing the phenotyping effort considerably, the use of ssGBLUP in subsequent evaluations realizes only a slightly lower genetic gain than that realized without preselection. This is especially the case for traits that are expensive to measure (e.g. feed intake of individual broiler chickens), and traits for which phenotypes can only be measured at advanced stages of life (e.g. litter size in pigs).

    Storing, combining and analysing turkey experimental data in the Big Data era
    Schokker, D. ; Athanasiadis, I.N. ; Visser, B. ; Veerkamp, R.F. ; Kamphuis, C. - \ 2020
    Animal 14 (2020)11. - ISSN 1751-7311 - p. 2397 - 2403.
    With the increasing availability of large amounts of data in the livestock domain, we face the challenge to store, combine and analyse these data efficiently. With this study, we explored the use of a data lake for storing and analysing data to improve scalability and interoperability. Data originated from a 2-day animal experiment in which the gait score of approximately 200 turkeys was determined through visual inspection by an expert. Additionally, inertial measurement units (IMUs), a 3D-video camera and a force plate (FP) were installed to explore the effectiveness of these sensors in automating the visual gait scoring. We deployed a data lake using the IMU and FP data of a single day of that animal experiment. This encompasses data from 84 turkeys for which we preprocessed by performing an ‘extract, transform and load’ (ETL-) procedure. To test scalability of the ETL-procedure, we simulated increasing volumes of the available data from this animal experiment and computed the ‘wall time’ (elapsed real time) for converting FP data into comma-separated files and storing these files. With a simulated data set of 30 000 turkeys, the wall time reduced from 1 h to less than 15 min, when 12 cores were used compared to 1 core. This demonstrated the ETL-procedure to be scalable. Subsequently, a machine learning (ML) pipeline was developed to test the potential of a data lake to automatically distinguish between two classses, that is, very bad gait scores v. other scores. In conclusion, we have set up a dedicated customized data lake, loaded data and developed a prediction model via the creation of an ML pipeline. A data lake appears to be a useful tool to face the challenge of storing, combining and analysing increasing volumes of data of varying nature in an effective manner.
    Impact of sub-setting the data of the main Limousin beef cattle population on the estimates of across-country genetic correlations
    Bonifazi, Renzo ; Vandenplas, Jeremie ; Napel, Jan Ten ; Matilainen, Kaarina ; Veerkamp, Roel F. ; Calus, Mario P.L. - \ 2020
    Genetics, Selection, Evolution 52 (2020)1. - ISSN 0999-193X

    Background: Cattle international genetic evaluations allow the comparison of estimated breeding values (EBV) across different environments, i.e. countries. For international evaluations, across-country genetic correlations (r g ) need to be estimated. However, lack of convergence of the estimated parameters and high standard errors of the r g are often experienced for beef cattle populations due to limited across-country genetic connections. Furthermore, using all available genetic connections to estimate r g is prohibitive due to computational constraints, thus sub-setting the data is necessary. Our objective was to investigate and compare the impact of strategies of data sub-setting on estimated across-country r g and their computational requirements. Methods: Phenotype and pedigree information for age-adjusted weaning weight was available for ten European countries and 3,128,338 Limousin beef cattle males and females. Using a Monte Carlo based expectation-maximization restricted maximum likelihood (MC EM REML) methodology, we estimated across-country r g by using a multi-trait animal model where countries are modelled as different correlated traits. Values of r g were estimated using the full data and four different sub-setting strategies that aimed at selecting the most connected herds from the largest population. Results: Using all available data, direct and maternal r g (standard errors in parentheses) were on average equal to 0.79 (0.14) and 0.71 (0.19), respectively. Direct-maternal within-country and between-country r g were on average equal to - 0.12 (0.09) and 0.00 (0.14), respectively. Data sub-setting scenarios gave similar results: on average, estimated r g were smaller compared to using all data for direct (0.02) and maternal (0.05) genetic effects. The largest differences were obtained for the direct-maternal within-country and between-country r g, which were, on average 0.13 and 0.12 smaller compared to values obtained by using all data. Standard errors always increased when reducing the data, by 0.02 to 0.06, on average. The proposed sub-setting strategies reduced the required computing time up to 22% compared to using all data. Conclusions: Estimating all 120 across-country r g that are required for beef cattle international evaluations, using a multi-trait MC EM REML approach, is feasible but involves long computing time. We propose four strategies to reduce computational requirements while keeping a multi-trait estimation approach. In all scenarios with data sub-setting, the estimated r g were consistently smaller (mainly for direct-maternal r g ) and had larger standard errors.

    A deterministic equation to predict the accuracy of multi-population genomic prediction with multiple genomic relationship matrices
    Raymond, Biaty ; Wientjes, Yvonne C.J. ; Bouwman, Aniek C. ; Schrooten, Chris ; Veerkamp, Roel F. - \ 2020
    Genetics, Selection, Evolution 52 (2020)1. - ISSN 0999-193X - 22 p.

    BACKGROUND: A multi-population genomic prediction (GP) model in which important pre-selected single nucleotide polymorphisms (SNPs) are differentially weighted (MPMG) has been shown to result in better prediction accuracy than a multi-population, single genomic relationship matrix ([Formula: see text]) GP model (MPSG) in which all SNPs are weighted equally. Our objective was to underpin theoretically the advantages and limits of the MPMG model over the MPSG model, by deriving and validating a deterministic prediction equation for its accuracy. METHODS: Using selection index theory, we derived an equation to predict the accuracy of estimated total genomic values of selection candidates from population [Formula: see text] ([Formula: see text]), when individuals from two populations, [Formula: see text] and [Formula: see text], are combined in the training population and two [Formula: see text], made respectively from pre-selected and remaining SNPs, are fitted simultaneously in MPMG. We used simulations to validate the prediction equation in scenarios that differed in the level of genetic correlation between populations, heritability, and proportion of genetic variance explained by the pre-selected SNPs. Empirical accuracy of the MPMG model in each scenario was calculated and compared to the predicted accuracy from the equation. RESULTS: In general, the derived prediction equation resulted in accurate predictions of [Formula: see text] for the scenarios evaluated. Using the prediction equation, we showed that an important advantage of the MPMG model over the MPSG model is its ability to benefit from the small number of independent chromosome segments ([Formula: see text]) due to the pre-selected SNPs, both within and across populations, whereas for the MPSG model, there is only a single value for [Formula: see text], calculated based on all SNPs, which is very large. However, this advantage is dependent on the pre-selected SNPs that explain some proportion of the total genetic variance for the trait. CONCLUSIONS: We developed an equation that gives insight into why, and under which conditions the MPMG outperforms the MPSG model for GP. The equation can be used as a deterministic tool to assess the potential benefit of combining information from different populations, e.g., different breeds or lines for GP in livestock or plants, or different groups of people based on their ethnic background for prediction of disease risk scores.

    Deep learning approach to classify false and true positive chromosomal translocations
    Hulsegge, Ina ; Bouwman, Aniek ; Veerkamp, Roel ; Kamphuis, Claudia - \ 2020
    Impact of preselection varies across genetic evaluation models
    Jibrila, I. ; Vandenplas, J. ; Napel, J. ten; Veerkamp, R.F. ; Calus, M.P.L. - \ 2020
    In: Wias Annual Conference 2020. - WIAS - p. 47 - 47.
    Accurate and unbiased evaluation of genetic merits of animals is of utmost importance in animal breeding. This is mainly to identify the animals that resemble the breeding goal the most, so that these animals are selected to become parents of the next generation. There are two categories of genetic evaluation models: pedigree-based and genome-based models. Pedigree-based models use pedigree information, and genome-based models use genomic information, for estimating genetic relationships among animals.Historically,an animal is genetically evaluated shortly before selection to become a parent, based on its performance record supplemented with performance records of its relatives. Because it is expensive to raise all selection candidates to this age, breeding companies, based on some information available at young ages, preselect a proportion of young selection candidates and raise them until selection of future parents. This practice is called preselection.Genetic evaluations assume that the animals to be evaluated are a true representation of the entire population of the animals at birth. Preselection clearly violates this assumption. It is known that evaluations produced by genome-based models are less affected by preselection than evaluations produced by pedigree-based models. However, the reason for this is not fully understood. In order to be able to adapt the models to better handle the impact of preselection, it is important to understand this reason through understanding the impact of preselection on components of genetic evaluation models. This reason is most likely associated with a component that is different between pedigree-based and genome-based models: the genetic relationship matrix. We hypothesise that in genome-based models,average relationships among preselected selection candidates are higher than average relationships among selection candidates without preselection. Therefore, the aim of this study was to investigate the impact of preselection on average pedigree and genomic relationships among selection candidates. A breeding programme was then simulated, with features of pig and poultry breeding programmes. Impact of various combinations of forms and intensities of preselection on average pedigree and genomic relationships among selection candidates were investigated. Preliminary results show that average genomic relationships among preselected selection candidates are indeed higher than average genomic relationships among unselected and randomly preselected selection candidates.Further results will be presented and discussed at the conference.
    Between-herd variation in cow resilience and relations to management
    Poppe, H.W.M. ; Kamphuis, C. ; Veerkamp, R.F. ; Mulder, H.A. - \ 2020
    In: WIAS Annual Conference 2020. - WIAS - p. 23 - 23.
    Resilient cows are minimally affected in their functioning by disturbances, such as diseases or heat waves, and when affected they quickly recover. Low variance of daily deviations from expected milk yield (LnVar) indicates few fluctuations in milk yield due to disturbances and thus good resilience. Genetic variation in LnVar has been shown, and therefore we can breed for lower LnVar and thus improve resilience. However, it is unknown to what extent resilience of cows differs between herds, and how cow resilience is related to herd management. Therefore, the objectives of this study were (1) to estimate herd-year effects for the resilience indicator LnVar using a mixed animal model, and (2) to determine associations between these herd-year effects and herd parameters derived from milk production registration (MPR) data. Herd-year effects were estimated for 9,917 herd-year classes based on the LnVar of 227,615 primiparous cows. For these same herd-year classes,also herd parameters were derived from MPR data, such as average somatic cell count,proportion of cows with a rumen acidosis indication, and herd size. Correlations between these herd-year parameters and the herd-year effects on LnVar were then calculated. Ln-Var differed considerably between herd-years; the LnVar in the herd-year with the largest effect was more than 6 times as large as the LnVar in the herd-year with the smallest effect.The correlation between herd-year effects of subsequent years within the same farm was on average 0.69, indicating that within farms LnVar was quite consistent between years.The correlations between the herd-year parameters and the herd-year effects on LnVar showed that a high LnVar was associated with a high proportion of cows with a rumen acidosis indication (r = 0.31), a high average somatic cell score (r = 0.19), a low average fat content (r = -0.18), a long calving interval (r = 0.14), and a low survival to second lactation(r = -0.13). These correlations indicate that herds with a high LnVar have suboptimal management with regard to resilience. In conclusion, large differences in LnVar exist between herds, and herd-year effects on LnVar are a promising tool to inform farmers about the resilience of their cows.
    Genomic Regions Associated With Skeletal Type Traits in Beef and Dairy Cattle Are Common to Regions Associated With Carcass Traits, Feed Intake and Calving Difficulty
    Doyle, Jennifer L. ; Berry, Donagh P. ; Veerkamp, Roel F. ; Carthy, Tara R. ; Walsh, Siobhan W. ; Evans, Ross D. ; Purfield, Deirdre C. - \ 2020
    Frontiers in Genetics Livestock Genomics 11 (2020). - ISSN 1664-8021
    cattle - genome-wide association study - linear type traits - sequence - single nucleotide polymorphism - skeletal

    Linear type traits describing the skeletal characteristics of an animal are moderately to strongly genetically correlated with a range of other performance traits in cattle including feed intake, reproduction traits and carcass merit; thus, type traits could also provide useful insights into the morphological differences among animals underpinning phenotypic differences in these complex traits. The objective of the present study was to identify genomic regions associated with five subjectively scored skeletal linear traits, to determine if these associated regions are common in multiple beef and dairy breeds, and also to determine if these regions overlap with those proposed elsewhere to be associated with correlated performance traits. Analyses were carried out using linear mixed models on imputed whole genome sequence data separately in 1,444 Angus, 1,129 Hereford, 6,433 Charolais, 8,745 Limousin, 1,698 Simmental, and 4,494 Holstein-Friesian cattle, all scored for the linear type traits. There was, on average, 18 months difference in age at assessment of the beef versus the dairy animals. While the majority of the identified quantitative trait loci (QTL), and thus genes, were both trait-specific and breed-specific, a large-effect pleiotropic QTL on BTA6 containing the NCAPG and LCORL genes was associated with all skeletal traits in the Limousin population and with wither height in the Angus. Other than that, little overlap existed in detected QTLs for the skeletal type traits in the other breeds. Only two QTLs overlapped the beef and dairy breeds; both QTLs were located on BTA5 and were associated with height in both the Angus and the Holstein-Friesian, despite the difference in age at assessment. Several detected QTLs in the present study overlapped with QTLs documented elsewhere that are associated with carcass traits, feed intake, and calving difficulty. While most breeding programs select for the macro-traits like carcass weight, carcass conformation, and feed intake, the higher degree of granularity with selection on the individual linear type traits in a multi-trait index underpinning the macro-level goal traits, presents an opportunity to help resolve genetic antagonisms among morphological traits in the pursuit of the animal with optimum performance metrics.

    Can greenhouse gases in breath be used to genetically improve feed efficiency of dairy cows?
    Difford, G.F. ; Løvendahl, P. ; Veerkamp, R.F. ; Bovenhuis, H. ; Visker, M.H.P.W. ; Lassen, J. ; Haas, Y. de - \ 2020
    Journal of Dairy Science 103 (2020)3. - ISSN 0022-0302 - p. 2442 - 2459.
    breath gas measurement - carbon dioxide - feed efficiency - methane - residual feed intake

    There is considerable interest in improving feed utilization of dairy cattle while limiting losses to the environment (i.e., greenhouse gases, GHG). To breed for feed-efficient or climate-friendly cattle, it is first necessary to obtain accurate estimates of genetic parameters and correlations of feed intake, greenhouse gases, and production traits. Reducing dry matter take (DMI) requirements while maintaining production has high economic value to farmers, but DMI is costly to record and thus limited to small research or nucleus herds. Conversely, enteric methane (CH4) currently has no economic value, is also costly to record, and is limited to small experimental trials. However, breath gas concentrations of methane (CH4c) and carbon dioxide (CO2c) are relatively cheap to measure at high throughput under commercial conditions by installing sniffers in automated milking stations. The objective of this study was to assess the genetic correlations between DMI, body weight (BW), fat- and protein-corrected milk yield (FPCM), and GHG-related traits: CH4c and CO2c from Denmark (DNK) and the Netherlands (NLD). A second objective was to assess the genetic potential for improving feed efficiency and the added benefits of using CH4c and CO2c as indicators. Feed intake data were available on 703 primiparous cows in DNK and 524 in NLD; CH4c and CO2c records were available on 434 primiparous cows in DNK and 656 in NLD. The GHG-related traits were heritable (e.g., CH4c h2: DNK = 0.26, NLD = 0.15) but were differentially genetically correlated with DMI and feed efficiency in both magnitude and sign, depending on the population and the definition of feed efficiency. Across feed efficiency traits and DMI, having bulls with 100 daughters with FPCM, BW, and GHG traits resulted in sufficiently high accuracy to almost negate the need for DMI records. Despite differences in genetic correlation structure, the relatively cheap GHG-related traits showed considerable potential for improving the accuracy of breeding values of highly valuable feed intake and feed efficiency traits.

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