PhenoGeno
Fund: Croatian Science Foundation
Project type: Research project
Project Code: IP-2022-10-6914
Project title: Responding to innovations in phenomics and genomics in dairy cattle breeding.
Total requested grant from HRZZ (EUR): 197.989,25.
Project duration: 29.12.2023. to 28.12.2027.
Institution in which the project is carried out:
University of Zagreb Faculty of Agriculture
Prof. Ivica Kisić, PhD
Svetošimunska cesta 25, 10000 Zagreb, Croatia
Phone: +385 1 239 3779
Fax: +385 1 231 5300
E-mail: dekanat@agr.hr
Project Manager:
University of Zagreb Faculty of Agriculture
Department of Animal Science
Prof. Ino Čurik, PhD.H
Phone: +385 1 239 4008
E-mail: icurik@agr.hr
Associates from the Faculty of Agriculture:
Prof. Vlatka Čubrić Čurik, PhD
Prof. Neven Antunac, PhD
Assoc. Prof. Nataša Mikulec, PhD
Assist. Prof. Vladimir Brajković, PhD
Ivana Držaić, PhD
Assoc. Prof. Maja Ferenčaković, PhD
External associate:
Dinko Novosel, PhD, Veterinary Institute, Zagreb, Croatia
Strahil Ristov, PhD, Ruđer Bošković Institute in Zagreb
Assoc. Prof. Boris Lukić, PhD, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences
Assoc. Prof. Nikola Raguž, PhD, Josip Juraj Strossmayer University of Osijek, Faculty of Agrobiotechnical Sciences
Summary
The application of new phenotyping approaches, advanced genotyping (high-throughput genome-wide genotyping, SNP arrays), and genomic and statistical developments (genomic selection and one-step GBLUP estimation) have led to accelerated genetic progress in all dairy developing countries. This is also true for other areas of animal breeding. Unfortunately, Croatia has responded only moderately to the new developments, and our breeding programs currently include only a relatively small number of genome-wide genotyped cows and bulls. At the same time, the number of genotyped animals in developed countries exceeds 100,000 animals (> 300,000 SNP-arrayed cattle in Austria). Therefore, we decided to propose a modern project in which we will develop an exemplary innovative breeding program (with GWAS and estimation of genomic parameters in Croatian Holstein breed) using the latest technological advances in phenomics and genomics. In our proposal, we decided to go a step further than the SNP arrays routinely used today and base our genotyping strategy on the latest possibility (low-pass whole genome sequencing, lpWGS), as this might be the best strategy in our relatively small populations. At the same time, our advanced phenotyping strategy focused on introducing large-scale measurements of milk coagulation traits derived from FTIR spectra, as the development of cheese production, as in northern Italy, could be a good strategy for the Croatian dairy sector. While our project has a very strong applicable component, we have also offered a series of analyses testing the effects of neglected inheritance, more specifically the effects of X-chromosome and mito-nuclear interactions on quantitative traits (including deleterious aspects), which are quite new and ground-breaking hypotheses for animal breeding, but also for evolutionary genetics. One small aspect of the project is ''playing'' (experimenting) with detection of milk microbiota from lpWGS files (FASTQ).
Theoretical background and the scientific contribution of the project
The application of new phenotyping approaches, advanced genotyping (high-throughput genome-wide genotyping, SNP arrays), and genomic and statistical developments (genomic selection and one-step GBLUP estimation) have led to accelerated genetic progress in all dairy developing countries. This is also true for other areas of animal breeding. Unfortunately, Croatia has responded only moderately to the new developments, and our breeding programs currently include only a relatively small number of genome-wide genotyped cows and bulls. At the same time, the number of genotyped animals in developed countries exceeds 100,000 animals (> 300,000 SNP-arrayed cattle in Austria). Therefore, we decided to propose a modern project in which we will develop an exemplary innovative breeding program (with GWAS and estimation of genomic parameters in Croatian Holstein breed) using the latest technological advances in phenomics and genomics. In our proposal, we decided to go a step further than the SNP arrays routinely used today and base our genotyping strategy on the latest possibility (low-pass whole genome sequencing, lpWGS), as this might be the best strategy in our relatively small populations. At the same time, our advanced phenotyping strategy focused on introducing large-scale measurements of milk coagulation traits derived from FTIR spectra, as the development of cheese production, as in northern Italy, could be a good strategy for the Croatian dairy sector. While our project has a very strong applicable component, we have also offered a series of analyses testing the effects of neglected inheritance, more specifically the effects of X-chromosome and mito-nuclear interactions on quantitative traits (including deleterious aspects), which are quite new and ground-breaking hypotheses for animal breeding, but also for evolutionary genetics. One small aspect of the project is "playing" (experimenting) with detection of milk microbiota from lpWGS files (FASTQ).
Methodology
- WP1. Modern and traditional phenomic with sampling
This work package refers to phenomics or the derivation and recording of phenotypic performance. Traditional milk production and reproduction traits and pedigrees will be sampled from routine measurements of target farms for genotyped individuals (1200) and for related individuals in the pedigree (all available information). On the other hand, milk coagulation traits (MCP) are derived as follows: RCT, k20, a30 and amax, while it is also possible to define slow (SM) or normal (NM) coagulating milk for which we need to model the coagulation function (Bittante et al., 2012). MCPs are expressed by (i) the time from rennet addition to the start of coagulation (RCT, min), (ii) the time required for the curd to reach a firmness (CF, mm) of 20 mm (k20, min), and (iii) the firmness of the curd at the end of the analysis (a30, mm). These properties are measured by optical and mechanical methods (Bittante, 2011). Our coagulation functions and properties are determined for 100 samples and cross-validated for another 100 samples. The same procedure will be repeated for 200 samples. All samples will be analysed by FTIR spectra to mathematically find functions that yield highly correlated MCP indicator variables. This procedure will allow further time-efficient phenotyping at a much lower cost. This procedure has recently been used in dairy cattle breeding (Wallen et al., 2018; Mota et al., 2021; Denholm et al., 2020; Giannuzzi et al., 2022) and allows cost-effective measurement of a large number of samples and further estimation of genetic parameters.
- WP2. Advances molecular analyses based on low-pass whole genome sequencing
DNA isolation will be performed for 1200 individuals from milk following the protocol developed by Brajkovic et al. (2018). In addition, samples will be genotyped according to NEOGEN's instructions with respect to InfiniSeek (for more information see https://www.neogen.com/en-gb/neocenter/press-releases/neogen-gencove-launch-infiniseek-first-whole-genome-genotyping-sequencing-solution-cattle-breeders/), with the additional requirement of achieving higher depth in the casein region and X chromosome. This approach is innovative in terms of a more intensive analysis of the effects of the X chromosome as well as a better study of coagulation properties. Of course, the FASTQ files along with the information on 1 million SNPs will be the final products of our sequence analyses.
- WP3. Advanced application of mitogenomics and bioinformatics
This is a rather innovative approach to the use of FASTQ files from lpWGS analyses, as it allows retrieval of mitogenome information with classification of haplotypes (for more details see Cubric-Curik et al., 2022). In addition, FASTQ files are used to retrieve information on the milk microbiota using the same principle but with alignments to expected microorganisms. A similar approach to identifying the milk microbiota from NGS was used in McHugh et al. (2021). There are additional risks associated with the success of this part of the analysis, but the potential benefits are high. In the end, the database will be archived with the FASTQ files while the mitogenome information is merged with other production traits and nuclear genotypes.
- WP4. Enhancement of computational performance and database management
This project is very computationally intensive and generates large amounts of data. For this reason, we are collaborating with a computer scientist from Ruđer Bošković's institute. His task will be to improve our pipelines and help us build a stable database with all the data.
- WP5. GWAS and estimation of quantitative genetic parameters
Estimation of genetic parameters and breeding values.
Genetic variability and heritability for the traits analysed will be assessed using pedigree and genomic data for all animals with the R programme package (R: A Language and Environment for Statistical Computing), which determines heritability by estimating the proportion of the polygenic variance component in the total phenotypic variance. A bivariate model is used to calculate genetic correlations between specific traits.
Before the advent of dense genomic information and accurate genomic maps, the pedigree was used to calculate additive genetic relationships, and the best animals were selected along with phenotypes. This was accomplished by estimating breeding values for each animal using the best linear unbiased prediction (BLUP methodology). In the genomic BLUP approach, the pedigree matrix was replaced by a genomic matrix or GRM (Genomic Relationship Matrix), which describes the genomic relationship between individuals and is calculated from SNP genotypes. Breeding values using pedigree information are calculated using the animal model and genomic information via a GBLUP model in the R programme package. The model for GBLUP is defined by the following equation: y = 1nμ + Zg + e, where y is a vector of phenotypes and μ is the mean, Z is a design matrix assigning records to genetic values, g is a vector of additive genetic effects of individuals assumed distributed as MVN(0, σ2 G), and e is a vector of residual σ2e assumed distributed as MVN(0, σ2 I).
GWAS analysis of the correlation between genomic region and phenotype.
In the last 10 years, modern genomic sequencing technologies have revolutionised the study of quantitative or complex traits. Large populations of domestic animals have been genotyped using large numbers of polymorphisms of a single nucleotide or SNP. With the data obtained and advanced statistical tools, genomic regions and loci associated with phenotypic traits can be determined with high statistical power and precision. Genome-wide association studies (GWAS) are an approach that analyses a large number of genomic markers and their association with traits of interest. They have been successfully used in many domestic animal species (swine, cattle, poultry, sheep, etc.) to study the association between genomic markers and many production traits, longevity, inherited diseases, etc. The main purpose of GWAS analysis is to determine which genes are responsible for the quantitative traits analysed. The association between SNPs and phenotypic values is evaluated by polygenic and mmscore functions using GenABEL software (Aulchenko et al. 2007), which incorporates population structure by introducing covariances between individuals obtained from SNP genotypes. Bonferroni-corrected thresholds of 0.05/N and 1 N (where N represents the number of SNPs used in the analysis) are used to determine significance levels, which are adjusted suggestively to 5% at the whole-genome level and as a function of the results obtained.
GBLUP at one level.
In addition to classical estimation of polygenic quantitative parameters, we will also perform single-step GBLUP analyses to analyse the potential use of a larger number of recorded measurements (measured in non-genotyped animals) (for more information on ssGBLUP models, see Lourenco et al., 2020). In analysing the X chromosome information, we will use the procedure described in Druet and Legarra (2020) to derive the X chromosome relationship matrix.
- WP6. Manging detrimental load and diversity
As we are among the pioneers in the field of genomic inbreeding and inbreeding estimation (four papers cited > 500 times in the WOS Core Collection), we will use our own already developed protocols and procedures (for more information see Ferenčaković et al., 2013a, Ferenčaković at al. 2013b, Curik et al., 2014, Curik et al., 2017, Ferenčaković et al., 2017). In addition, we recently published for the first time a pilot analysis of X-chromosome inbreeding depression in livestock populations (Curik et al., 2022). Complex modelling of pedigree-estimated inbreeding depression was demonstrated in Curik et al. (2020). Deleterious effects (Cerrier animals) will be identified using known deleterious haplotypes, while unknown deleterious genes will be searched using the missing homozygous haplotype approach (VanRaden et al., 2011; Cole 2015).
- Two packages are planned for dissemination and management of the project, as described in more detail in the project management plan
Project management plan
The research group of this project consists mainly of members of the Animal Genetics Research Group (ANGEN https://angen.agr.hr) with its external members and collaborators involved in the previous HRZZ projects MitoTAUROmics and ANAGRAMs. The goal of this group is to study the genetic diversity (current and ancient) of domestic and other animals. The leader of this project proposal, Ino Čurik, has gathered around him a team of outstanding scientists for many years and is building the foundations for the development of higher science in the Republic of Croatia. Under this project proposal, he will lead and supervise the group and manage the project by organizing monthly meetings of the project team, monitoring the implementation of individual activities, and meeting the set project goals. He will create and adjust the work plan and determine the dissemination plan and visibility of the project. He will manage the writing of scientific papers and participation in national and international scientific meetings and take care of the preparation of the descriptive and financial annual report for the Croatian Science Foundation. Through his role in all packages and as co-leader of WP6, he will contribute to the successful implementation of the project in all segments, and by leading the WP8 package (project management), he will oversee the connection, work performance and motivation of all team members and convene meetings of the Project Management Committee. The project will start with sampling optimization (WP1), which will be led by Nataša Mikulec. In addition to leading this package, Nataša Mikulec will be responsible for determining the coagulation properties of milk and will coordinate the other members of the package in sampling and will also participate in the WP7 package (dissemination). Nikola Raguž (Faculty of Agricultural Sciences Osijek) will coordinate sampling by ensuring that all farms and economic entities of interest are covered by data on cattle in production, that data on milk yield control and reproductive traits are obtained, and that the results are successfully disseminated to the industry through the WP7 package of which he is the leader. Neven Antunac is involved in WP1 through tasks related to milk quality traits and milk coagulation traits. Neven Antunac is also involved in the dissemination package (WP7), is co-leader of the project management package (WP8) and member of the project board. After successful sampling, WP2 led by Vlatka Čubrić Čurik will perform genotyping of the samples, which will start with isolation of DNA from the collected samples and its control. Vlatka Čubrić Čurik will coordinate with genotyping companies to ensure successful genotyping, which will be the basis for further analysis. Their co-leader is Ivana Držaić, whose task is to isolate DNA from milk. Ivana Držaić also participates in WP1 in organizing sampling and in WP3 in sequencing pathogenic microorganisms from milk. Ivana Držaić is also involved in WP8 through financial management and is a member of the project's Board of Directors. Vladimir Brajković is the leader of the WP3 Mitogenomics and Bioinformatics package, which will provide haplotypes generated from mitogenomes and model association studies of mitogenomes and production traits, but also linking milk microbiome and production traits. Vladimir Brajković is also involved in WP1 by collecting and isolating DNA, in WP4 by creating and managing databases generated by the project, and by creating and testing algorithms and pipelines. The co-leader of WP3 is Dinko Novosel. (Croatian Veterinary Institute), who is responsible for sequencing pathogenic microorganisms and, through WP1, for determining the characteristics of the dairy microbiome. Dinko Novosel is responsible for WP8 communications and is a member of the project board. Strahil Ristov (Ruđer Bošković Institute) is the WP4 package leader ensuring efficiency of computer operation and database management, and also participates in WP1 through data acquisition and calibration activities for indirect coagulation properties, i.e. calibration of formulas based on Fourier-transformed infrared spectra and formagraph measurements. The co-leader of the WP4 package is Maja Ferenčaković, who is involved in all aspects of this package, mainly by creating databases. Maja Ferenčaković also participates in WP1 (formula calibration) and WP5 (GWAS one-step model for milk properties, milk quality properties, reproductive properties and indirect milk coagulation properties). She is also a member of the project board and responsible for risk management in WP8. He is the leader of the WP6 package, whose task is to evaluate and manage diversity at the population level, but also to evaluate the reduction in the value of milk yield and reproductive traits caused by inbreeding (inbreeding depression), which is attempted to be evaluated at the level of autosomes, sex chromosomes and regional chromosomes. Mapping of deleterious mutations also plays a special role. Boris Lukić (Faculty of Agrobiotechnical Sciences Osijek) is the leader of WP5 package responsible for GWAS analyses and genetic parameters estimation and participates in all activities of this project. He also participates in WP1 when it comes to milk sampling and in WP7 when it comes to transferring the knowledge from this project to the industry. Nikola Raguž (Faculty of Agricultural Sciences Osijek) also participates in the project through WP1 (sampling), WP5 (estimation of genetic parameters and GWAS) and WP7 (knowledge transfer to the economy and dairy industry), and he is also a member of the project board, and in WP8 he is responsible for monitoring and time management.
There are also foreign collaborators in this research group:
- Prof. Johann Soelkner, PhD (BOKU) is involved in WP1 (calibration of formulas), WP5 (all activities), WP6 (all activities) and WP7 (knowledge transfer to industry).
- Ivan Pocrnić, PhD (The Roslin Institute, Edinburgh) participates in WP3 (Modeling the influence of mitogenome and nuclear genome on production traits), WP5 (all activities), WP6 (all activities) and also in WP7 (Knowledge transfer to dairy industry).
- Prof. Gregor Gorjanc, PhD (The Roslin Institute, Edinburgh) participates in WP3 by modelling the influence of mitogenome and nuclear genome on production traits, WP5 (all activities), WP6 (all activities) and also in WP7 by knowledge transfer to the dairy industry.
- Prof. Luboš Vostry, PhD (Czech University of Life Sciences Prague) participates in WP3 by modelling the influence of mitogenome and nuclear genome on production traits, WP5 (all activities) and WP6 (all activities).
The aforementioned scientists have so far carried out significant research in their fields (calculation of inbreeding depression; simulation of quantitative models; development of fattening programs of animals-plants-insects; genomic characterization of autochthonous breeds of animals; phylogenetic studies; influence of genome and mitogenome polymorphisms on production properties of animals; genetic diversity of viruses; analysis of milk and dairy products, biochemistry of cheese ripening) which can be seen from scientific publications and completed European projects, and as a group, with their competences and scientific research activities, they can achieve the set goals of this project. Management of the group is planned in coordination with package managers and regular meetings so that the sequence of analyzes follows the work plan of the project. Special attention and regular meetings will be provided for the coordination of the mentioned packages. Dissemination of project results will be carried out through active participation in international conferences and publication of scientific publications according to the planned work plan. The project provides for the employment of a postdoctoral fellow and two doctoral students. The postdoctoral fellow is involved in activities WP1 (determination of milk quality traits, coagulation, and indirect coagulation), WP4 (creation of databases, efficiency of computer operations), WP5 (GWAS for indirect coagulation traits), and WP7 (knowledge transfer to the dairy industry) during the first two years of the project). The first PhD student is involved in the project through WP3 (all activities), WP4 (all activities) and WP6 (all activities). Another PhD student is involved in the project through WP5 (all activities). All members of the research group participate in the production of scientific publications and the dissemination of results through scientific contributions and conferences through WP7. The research group will use all the research equipment provided by the University of Zagreb, Faculty of Agriculture. For the purposes of the project, the DNA isolation and quality control equipment of the Laboratory of Conservation Genetics of the Animal Scinece department and the equipment of the Milk Reference Laboratory of the Dairy Science department will be used. The equipment of the Conservation Genetics Laboratory, which has a value of more than 5,000.00 EUR, is an instrument for precise measurement of DNA concentration Implen Nanophotometer P330. Other Conservation Genetics Laboratory instruments used are: Eppendorf 5810R centrifuge, BOECO M-6 mini centrifuge, BIO RAD Sub Cell GT agarose gel electrophoresis, UVITEC Gel Imaging System, OT012 autoclave, Eppenforf research micropipette sets, Gilson micropipette sets. Milk reference laboratory equipment with a value of more than EUR 5,000.00 is: Milkoscan FT 120 (Foss), Bactoscan FC (Foss), Fossomatic Minor (Foss), pH meter SevenMulti (Mettler Toledo). The faculty provides support and creates the conditions for the implementation of the project in the form of project management through administrative, legal and accounting support, work space, support for the dissemination of results and access to the infrastructure of the faculty, which includes the IT service, licensed programs and the use of the Central Agronomy Library.
References
Zeder, M. A., Emshwiller, E., Smith, B. D., & Bradley, D. G. (2006). Documenting domestication: the intersection of genetics and archaeology. TRENDS in Genetics, 22(3), 139-155.
Larson, G., Piperno, D. R., Allaby, R. G., Purugganan, M. D., Andersson, L., Arroyo-Kalin, M., ... & Fuller, D. Q. (2014). Current perspectives and the future of domestication studies. Proceedings of the National Academy of Sciences, 111(17), 6139-6146.
Cubric‐Curik, V., Novosel, D., Brajkovic, V., Rota Stabelli, O., Krebs, S., Sölkner, J., ... & Medugorac, I. (2022). Large‐scale mitogenome sequencing reveals consecutive expansions of domestic taurine cattle and supports sporadic aurochs introgression. Evolutionary applications, 15(4), 663-678.
Miglior, F., Fleming, A., Malchiodi, F., Brito, L. F., Martin, P., & Baes, C. F. (2017). A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle. Journal of dairy science, 100(12), 10251-10271.
Brito, L. F., Bédère, N., Douhard, F., Oliveira, H. R., Arnal, M., Peñagaricano, F., ... & Miglior, F. (2021). Genetic selection of high-yielding dairy cattle toward sustainable farming systems in a rapidly changing world. Animal, 100292.
Brotherstone, S., & Goddard, M. (2005). Artificial selection and maintenance of genetic variance in the global dairy cow population. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1459), 1479-1488.
Weigel, K. A., VanRaden, P. M., Norman, H. D., & Grosu, H. (2017). A 100-Year Review: Methods and impact of genetic selection in dairy cattle—From daughter–dam comparisons to deep learning algorithms. Journal of dairy science, 100(12), 10234-10250
Meuwissen, T. H., Hayes, B. J., & Goddard, M. (2001). Prediction of total genetic value using genome-wide dense marker maps. genetics, 157(4), 1819-1829.
Meuwissen, T., Hayes, B., & Goddard, M. (2013). Accelerating improvement of livestock with genomic selection. Annu. Rev. Anim. Biosci., 1(1), 221-237.
Meuwissen, T., Hayes, B., & Goddard, M. (2016). Genomic selection: A paradigm shift in animal breeding. Animal frontiers, 6(1), 6-14.
García-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2016). Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Sciences, 113(28), E3995-E4004.
Ferenčaković, M., Sölkner, J., & Curik, I. (2013). Estimating autozygosity from high-throughput information: effects of SNP density and genotyping errors. Genetics Selection Evolution, 45(1), 1-9.
Curik, I., Ferenčaković, M., & Sölkner, J. (2014). Inbreeding and runs of homozygosity: a possible solution to an old problem. Livestock Science, 166, 26-34.
Ferenčaković, M., Sölkner, J., Kapš, M., & Curik, I. (2017). Genome-wide mapping and estimation of inbreeding depression of semen quality traits in a cattle population. Journal of Dairy Science, 100(6), 4721-4730.
Curik, I., Ferenčaković, M., & Sölkner, J. (2017). Genomic dissection of inbreeding depression: a gate to new opportunities. Revista Brasileira de Zootecnia, 46, 773-782.
Doekes, Harmen P., Roel F. Veerkamp, Piter Bijma, Gerben de Jong, Sipke J. Hiemstra, and Jack J. Windig. "Inbreeding depression due to recent and ancient inbreeding in Dutch Holstein–Friesian dairy cattle." Genetics Selection Evolution51, no. 1 (2019): 1-16.
Doekes, H. P., Bijma, P., Veerkamp, R. F., de Jong, G., Wientjes, Y. C., & Windig, J. J. (2020). Inbreeding depression across the genome of Dutch Holstein Friesian dairy cattle. Genetics Selection Evolution, 52(1), 1-18.
VanRaden, P. M., Olson, K. M., Null, D. J., & Hutchison, J. L. (2011). Harmful recessive effects on fertility detected by absence of homozygous haplotypes. Journal of dairy science, 94(12), 6153-6161.
Cole, J. B. (2015). A simple strategy for managing many recessive disorders in a dairy cattle breeding program. Genetics Selection Evolution, 47(1), 1-13.
Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of dairy science, 92(9), 4656-4663.
Hickey, J. M., Gorjanc, G., Cleveland, M. A., Kranis, A., Jenko, J., & Mésázros, G. (2014). Sequencing millions of animals for genomic selection 2.0. In 12th World Congress on Genetics Applied to Livestock Production.
Li, J. H., Mazur, C. A., Berisa, T., & Pickrell, J. K. (2021). Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome research, 31(4), 529-537.
Wasik, K., Berisa, T., Pickrell, J. K., Li, J. H., Fraser, D. J., King, K., & Cox, C. (2021). Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics. BMC genomics, 22(1), 1-7.
Chat, V., Ferguson, R., Morales, L., & Kirchhoff, T. (2021). Ultra low-coverage whole genome sequencing (ulcWGS) as an alternative to genotyping arrays in genome-wide association studies (GWASs). Frontiers in genetics, 2712.
Aguilar, I., Misztal, I., Johnson, D. L., Legarra, A., Tsuruta, S., & Lawlor, T. J. (2010). Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of dairy science, 93(2), 743-752.
Snelling, W. M., Hoff, J. L., Li, J. H., Kuehn, L. A., Keel, B. N., Lindholm-Perry, A. K., & Pickrell, J. K. (2020). Assessment of imputation from low-pass sequencing to predict merit of beef steers. Genes, 11(11), 1312.
Martin, A. R., Atkinson, E. G., Chapman, S. B., Stevenson, A., Stroud, R. E., Abebe, T., ... & NeuroGAP-Psychosis Study Team. (2021). Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. The American Journal of Human Genetics, 108(4), 656-668.
Morrill, K., Hekman, J., Li, X., McClure, J., Logan, B., Goodman, L., ... & Karlsson, E. K. (2022). Ancestry-inclusive dog genomics challenges popular breed stereotypes. Science, 376(6592), eabk0639.
Boettcher, P. J., Jairath, L. K., & VanRaden, P. M. (2001). Evaluation of sire predicted transmitting abilities for evidence of X-chromosomal inheritance in North American sire families. Journal of dairy science, 84(1), 256-265.
Pacheco, H. A., Rezende, F. M., & Peñagaricano, F. (2020). Gene mapping and genomic prediction of bull fertility using sex chromosome markers. Journal of dairy science, 103(4), 3304-3311.
Sanchez, M. P., Escouflaire, C., Baur, A., Hozé, C., Capitan, A., Fritz, S., & Boichard, D. (2022, July). Sequence-based association analyses on X chromosome in six dairy cattle breeds. In 12th World Congress on Genetics Applied to Livestock Production.
Curik, I., Vostra-Vydrova, H., Shihabi, M., Sölkner, J., Vostry, L. (2022) Estimation of sex chromosome inbreeding depression on milk production in cattle. In 12th World Congress on Genetics Applied to Livestock Production.
Albuquerque, L. G., Keown, J. F., & Van Vleck, L. D. (1998). Variances of direct genetic effects, maternal genetic effects, and cytoplasmic inheritance effects for milk yield, fat yield, and fat percentage. Journal of dairy science, 81(2), 544-549.
Bell, B. R., McDaniel, B. T., & Robison, O. W. (1985). Effects of cytoplasmic inheritance on production traits of dairy cattle. Journal of Dairy Science, 68(8), 2038-2051.
Boettcher, P. J., & Gibson, J. P. (1997). Estimation of variance of maternal lineage effects among Canadian Holsteins. Journal of dairy science, 80(9), 2167-2176.
Kennedy, B. W. (1986). A further look at evidence for cytoplasmic inheritance of production traits in dairy cattle. Journal of Dairy Science, 69(12), 3100-3105.
Roughsedge, T., Brotherstone, S., & Visscher, P. M. (1999). Estimation of variance of maternal lineage effects at the Langhill dairy herd. Animal Science, 68(1), 79-86.
Schutz, M. M., Freeman, A. E., Beitz, D. C., & Mayfield, J. E. (1992). The importance of maternal lineage on milk yield traits of dairy cattle. Journal of dairy science, 75(5), 1331-1341.
Brajković, V. (2019). Utjecaj mitogenoma na svojstva mliječnosti goveda (Doctoral dissertation, University of Zagreb. Faculty of Agriculture).
Selle, M. L., Steinsland, I., Lindgren, F., Brajkovic, V., Cubric-Curik, V., & Gorjanc, G. (2021). Hierarchical Modelling of Haplotype Effects on a Phylogeny. Frontiers in genetics, 11, 531218.
Novosel, D., Brajković, V., Simčič, M., Zorc, M., Svara, T., Cakanic, K. B., ... & Curik, I. (2022). The Consequences of Mitochondrial T10432C Mutation in Cika Cattle: A “Potential” Model for Leber’s Hereditary Optic Neuropathy. International Journal of Molecular Sciences, 23(11), 6335.
Dorji, J., Vander Jagt, C. J., Garner, J. B., Marett, L. C., Mason, B. A., Reich, C. M., ... & Daetwyler, H. D. (2020). Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle. BMC genomics, 21(1), 1-17.
Dorji, J., MacLeod, I. M., Chamberlain, A. J., Vander Jagt, C. J., Ho, P. N., Khansefid, M., ... & Daetwyler, H. D. (2021). Mitochondrial protein gene expression and the oxidative phosphorylation pathway associated with feed efficiency and energy balance in dairy cattle. Journal of Dairy Science, 104(1), 575-587.
Kwon, T., Kim, K., Caetano-Anolles, K., Sung, S., Cho, S., Jeong, C., ... & Kim, H. (2022). Mitonuclear incompatibility as a hidden driver behind the genome ancestry of African admixed cattle. BMC biology, 20(1), 1-20.
Ward, J. A., McHugo, G. P., Dover, M. J., Hall, T. J., Ismael Ng'ang'a, S., Sonstegard, T. S., ... & MacHugh, D. E. (2022). Genome-wide local ancestry and evidence for mitonuclear coadaptation in African hybrid cattle populations. iScience, 25(7), 104672.
Halachmi, I., Guarino, M., Bewley, J., & Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annu. Rev. Anim. Biosci, 7(1), 403-425.
Cole, J. B., Eaglen, S. A., Maltecca, C., Mulder, H. A., & Pryce, J. E. (2020). The future of phenomics in dairy cattle breeding. Animal Frontiers, 10(2), 37-44.
Pérez-Enciso, M., & Steibel, J. P. (2021). Phenomes: the current frontier in animal breeding. Genetics Selection Evolution, 53(1), 1-10.
Dal Zotto, R., De Marchi, M., Cecchinato, A., Penasa, M., Cassandro, M., Carnier, P., ... & Bittante, G. (2008). Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. Journal of dairy science, 91(10), 4103-4112.
Bittante, G., Penasa, M., & Cecchinato, A. (2012). Invited review: Genetics and modeling of milk coagulation properties. Journal of dairy science, 95(12), 6843-6870.
Cecchinato, A., De Marchi, M., Gallo, L., Bittante, G., & Carnier, P. (2009). Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk. Journal of Dairy Science, 92(10), 5304-5313.
Bittante, G., & Cecchinato, A. (2013). Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds. Journal of Dairy Science, 96(9), 5991-6006.
Bittante, G., Ferragina, A., Cipolat-Gotet, C., & Cecchinato, A. (2014). Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy. Journal of Dairy Science, 97(10), 6560-6572.
Soyeurt, H., Gillon, A., Vanderick, S., Mayeres, P., Bertozzi, C., & Gengler, N. (2007). Estimation of heritability and genetic correlations for the major fatty acids in bovine milk. Journal of Dairy Science, 90(9), 4435-4442.
Soyeurt, H., Dehareng, F., Mayeres, P., Bertozzi, C., & Gengler, N. (2008). Variation of Δ9-desaturase activity in dairy cattle. Journal of Dairy Science, 91(8), 3211-3224.
Wallén, S. E., Prestløkken, E., Meuwissen, T. H. E., McParland, S., & Berry, D. P. (2018). Milk mid-infrared spectral data as a tool to predict feed intake in lactating Norwegian Red dairy cows. Journal of dairy science, 101(7), 6232-6243.
Mota, L. F., Pegolo, S., Baba, T., Peñagaricano, F., Morota, G., Bittante, G., & Cecchinato, A. (2021). Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data. Journal of Dairy Science, 104(7), 8107-8121.
Denholm, S. J., Brand, W., Mitchell, A. P., Wells, A. T., Krzyzelewski, T., Smith, S. L., ... & Coffey, M. P. (2020). Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning. Journal of dairy science, 103(10), 9355-9367.
Giannuzzi, D., Toscano, A., Pegolo, S., Gallo, L., Tagliapietra, F., Mele, M., ... & Cecchinato, A. (2022). Associations between Milk Fatty Acid Profile and Body Condition Score, Ultrasound Hepatic Measurements and Blood Metabolites in Holstein Cows. Animals, 12(9), 1202.
Ristov, S., Brajkovic, V., Cubric-Curik, V., Michieli, I., & Curik, I. (2016). MaGelLAn 1.0: a software to facilitate quantitative and population genetic analysis of maternal inheritance by combination of molecular and pedigree information. Genetics Selection Evolution, 48(1), 1-10.
Brajkovic, V., Bradic, L., Turkalj, K., Novosel, D., Ristov, S., Ajmone-Marsan, P., Colli, L., Cubric-Curik, V., Sölkner, J., Curik, I. (2022, July). Selection, validation, and utilization of mitogenome SNP array information in cattlebreeding. In 12th World Congress on Genetics Applied to Livestock Production.
Hršak, D., Ristov, S., Cubric-Curik, V., Novosel, D., Curik, I., Brajkovic, V. (2022) Magellan 2.0: Extending the capabilities for the population genetic analysis. In 30th International Symposium Animal Science Days 2022.
Brajković, V., Duvnjak, I., Ferenčaković, M., Špehar, M., Raguž, N., Lukić, B., ... & Cubric-Curik, V. (2018). The effect of DNA quality on the sequencing success of cattle. Journal of Central European Agriculture, 19(4), 804-809.