Section: Animal Science
Topic: Agricultural sciences, Genetics/genomics, Systems biology

The future of systems genetics in farm animal sciences, a route out of the data jungle

Corresponding author(s): Devailly, Guillaume (guillaume.devailly@inrae.fr); Demars, Julie (julie.demars@inrae.fr)

10.24072/pcjournal.653 - Peer Community Journal, Volume 5 (2025), article no. e132

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Farm animal species are under intense selection on relatively small population sizes. Genetic and genomic selection has provided remarkable genetic gains in the last century. Nevertheless, current methods aiming to link genome to phenome in such populations remain limited, notably due to the difficulty to identify causal variants for complex traits. The diversity of species as well as breeds in livestock has diluted the number of genomic datasets available for each genome as compared to model organisms or human diseases. In this article, we propose a systems genetics approach as an opportunity to go beyond current limits and find a way out of the data jungle, taking advantage of novel computational development allowing integration of omics datasets from different analyses across species. A major challenge is that systems genetics requires careful but efficient data and metadata management, as well as rigorous statistical strategies on which approach to use. Here, we highlight examples of the broad contribution systems genetics can bring to farm animal sciences, particularly across species, notably in the genome-to-phenome field within the larger scope of agricultural challenges, including adaptation to environmental changes and animal welfare.

Published online:
DOI: 10.24072/pcjournal.653
Type: Opinion / perspective
Keywords: data integration, multi-omics, ontology, cross-species, FAIR principles

Devailly, Guillaume 1; Eynard, Sonia E 1; Cerutti, Chloé 1; Durante, Arthur 1; Hubert, Jean-Noël 1; Karami, Keyvan 1; Maillard, Noémien 1; Milan, Denis 1; Perret, Mathilde 1; Pitel, Frédérique 1; Robic, Annie 1; Riquet, Juliette 1; Rousse, Stacy 1; Terenina, Elena 1; Demars, Julie 1

1 Université de Toulouse, ENVT, INP, INRAE, GenPhySE, Castanet-Tolosan, France
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
@article{10_24072_pcjournal_653,
     author = {Devailly, Guillaume and Eynard, Sonia E and Cerutti, Chlo\'e and Durante, Arthur and Hubert, Jean-No\"el and Karami, Keyvan and Maillard, No\'emien and Milan, Denis and Perret, Mathilde and Pitel, Fr\'ed\'erique and Robic, Annie and Riquet, Juliette and Rousse, Stacy and Terenina, Elena and Demars, Julie},
     title = {The future of systems genetics in farm animal sciences, a route out of the data jungle
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     journal = {Peer Community Journal},
     eid = {e132},
     year = {2025},
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     volume = {5},
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     language = {en},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.653/}
}
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%A Milan, Denis
%A Perret, Mathilde
%A Pitel, Frédérique
%A Robic, Annie
%A Riquet, Juliette
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%A Demars, Julie
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Devailly, G.; Eynard, S. E.; Cerutti, C.; Durante, A.; Hubert, J.-N.; Karami, K.; Maillard, N.; Milan, D.; Perret, M.; Pitel, F.; Robic, A.; Riquet, J.; Rousse, S.; Terenina, E.; Demars, J. The future of systems genetics in farm animal sciences, a route out of the data jungle. Peer Community Journal, Volume 5 (2025), article  no. e132. https://doi.org/10.24072/pcjournal.653

PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.animsci.100353

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

[1] Aggrey, S.; Zhou, H.; Tixier-Boichard, M.; Rhoads, D. Advances in Poultry Genetics and Genomics (1st ed.), Burleigh Dodds Science Publishing, 2020 | DOI

[2] Akiyama, M. Multi-omics study for interpretation of genome-wide association study, Journal of Human Genetics, Volume 66 (2021) no. 1, pp. 3-10 | DOI

[3] Allayee, H.; Farber, C. R.; Seldin, M. M.; Williams, E. G.; James, D. E.; Lusis, A. J. Systems genetics approaches for understanding complex traits with relevance for human disease, Elife, Volume 12 (2023), p. e91004 | DOI

[4] Argelaguet, R.; Velten, B.; Arnol, D.; Dietrich, S.; Zenz, T.; Marioni, J. C.; Buettner, F.; Huber, W.; Stegle, O. Multi‐Omics Factor Analysis—a framework for unsupervised integration of multi‐omics data sets, Molecular systems biology, Volume 14 (2018) no. 6, p. e8124 | DOI

[5] Arita, M.; Karsch-Mizrachi, I.; Cochrane, G. The international nucleotide sequence database collaboration, Nucleic Acids Research, Volume 49 (2020) no. D1, p. D121-D124 | DOI

[6] Ashbrook, D. G.; Cahill, S.; Hager, R. A cross-species systems genetics analysis links APBB1IP as a candidate for schizophrenia and prepulse inhibition, Frontiers in behavioral neuroscience, Volume 13 (2019), p. 266 | DOI

[7] Baud, A.; McPeek, S.; Chen, N.; Hughes, K. A. Indirect genetic effects: A cross-disciplinary perspective on empirical studies, Journal of Heredity, Volume 113 (2022) no. 1, pp. 1-15 | DOI

[8] Bellinge, R.; Liberles, D. A.; Iaschi, S.; O'brien, P.; Tay, G. Myostatin and its implications on animal breeding: a review, Animal genetics, Volume 36 (2005) no. 1, pp. 1-6 | DOI

[9] Ben Braiek, M.; Szymczak, S.; André, C.; Bardou, P.; Fidelle, F.; Granado‐Tajada, I.; Plisson‐Petit, F.; Sarry, J.; Woloszyn, F.; Moreno‐Romieux, C. A single base pair duplication in the SLC33A1 gene is associated with fetal losses and neonatal lethality in Manech Tête Rousse dairy sheep, Animal Genetics, Volume 55 (2024) no. 4, pp. 644-657 | DOI

[10] Ben Braiek, M.; Moreno-Romieux, C.; André, C.; Astruc, J.-M.; Bardou, P.; Bordes, A.; Debat, F.; Fidelle, F.; Granado-Tajada, I.; Hozé, C. Searching for homozygous haplotype deficiency in Manech Tête Rousse dairy sheep revealed a nonsense variant in the MMUT gene affecting newborn lamb viability, Genetics Selection Evolution, Volume 56 (2024) no. 1, p. 16 | DOI

[11] Ben Guebila, M.; Weighill, D.; Lopes-Ramos, C. M.; Burkholz, R.; Pop, R. T.; Palepu, K.; Shapoval, M.; Fagny, M.; Schlauch, D.; Glass, K.; others An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks, Nature methods, Volume 19 (2022) no. 5, pp. 511-513 | DOI

[12] Benegas, G.; Batra, S. S.; Song, Y. S. DNA language models are powerful predictors of genome-wide variant effects, Proceedings of the National Academy of Sciences, Volume 120 (2023) no. 44, p. e2311219120 | DOI

[13] Bidanel, J. P.; Silalahi, P.; Tribout, T.; Canario, L. L.; Ducos, A.; Garreau, H.; Gilbert, H.; Larzul, C.; Milan, D.; Riquet, J. Cinquante années d’amélioration génétique du porc en France: bilan et perspectives, INRAE Production Animales, Volume 33 (2020) no. 1 | DOI

[14] Boichard, D.; Ducrocq, V.; Fritz, S. Sustainable dairy cattle selection in the genomic era, Journal of Animal Breeding and Genetics, Volume 132 (2015) no. 2, pp. 135-143 | DOI

[15] Bujold, D.; de Lima Morais, D. A.; Gauthier, C.; Côté, C.; Caron, M.; Kwan, T.; Chen, K. C.; Laperle, J.; Markovits, A. N.; Pastinen, T. The international human epigenome consortium data portal, Cell systems, Volume 3 (2016) no. 5, pp. 496-499 | DOI

[16] Burnett, K. G.; Durica, D. S.; Mykles, D. L.; Stillman, J. H. Building Bridges from Genome to Phenome: Molecules, Methods and Models—An Introduction to the Symposium, Integrative and Comparative Biology, Volume 60 (2020) no. 2, pp. 261-266 | DOI

[17] Burnett, T. A.; Madureira, A. M.; Silper, B. F.; Tahmasbi, A.; Nadalin, A.; Veira, D. M.; Cerri, R. L. Relationship of concentrations of cortisol in hair with health, biomarkers in blood, and reproductive status in dairy cows, Journal of Dairy Science, Volume 98 (2015) no. 7, pp. 4414-4426 | DOI

[18] Calabrese, G. M.; Mesner, L. D.; Stains, J. P.; Tommasini, S. M.; Horowitz, M. C.; Rosen, C. J.; Farber, C. R. Integrating GWAS and co-expression network data identifies bone mineral density genes SPTBN1 and MARK3 and an osteoblast functional module, Cell systems, Volume 4 (2017) no. 1, pp. 46-59 | DOI

[19] Chen, L.; Fish, A. E.; Capra, J. A. Prediction of gene regulatory enhancers across species reveals evolutionarily conserved sequence properties, PLoS computational biology, Volume 14 (2018) no. 10, p. e1006484 | DOI

[20] Civelek, M.; Lusis, A. J. Systems genetics approaches to understand complex traits, Nature Reviews Genetics, Volume 15 (2014) no. 1, pp. 34-48 | DOI

[21] Clark, E. L.; Archibald, A. L.; Daetwyler, H. D.; Groenen, M. A.; Harrison, P. W.; Houston, R. D.; Kühn, C.; Lien, S.; Macqueen, D. J.; Reecy, J. M. From FAANG to fork: application of highly annotated genomes to improve farmed animal production, Genome Biology, Volume 21 (2020), pp. 1-9 | DOI

[22] Cole, J. B.; Baes, C. F.; Eaglen, S. A.; Lawlor, T. J.; Maltecca, C.; Ortega, M. S.; VanRaden, P. M. Invited review: Management of genetic defects in dairy cattle populations, Journal of Dairy Science, Volume 108 (2025), pp. 3045-3067 | DOI

[23] Dahl, A.; Thompson, M.; An, U.; Krebs, M.; Appadurai, V.; Border, R.; Bacanu, S.-A.; Werge, T.; Flint, J.; Schork, A. J. Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder, Nature Genetics, Volume 55 (2023) no. 12, pp. 2082-2093 | DOI

[24] Demars, J.; Labrune, Y.; Iannuccelli, N.; Deshayes, A.; Leroux, S.; Gilbert, H.; Aymard, P.; Benitez, F.; Riquet, J. A genome-wide epistatic network underlies the molecular architecture of continuous color variation of body extremities, Genomics, Volume 114 (2022) no. 3, p. 110361 | DOI

[25] Destoumieux-Garzón, D.; Bonnet, P.; Teplitsky, C.; Criscuolo, F.; Henry, P.-Y.; Mazurais, D.; Prunet, P.; Salvat, G.; Usseglio-Polatera, P.; Verrier, E. Animal board invited review: OneARK: Strengthening the links between animal production science and animal ecology, Animal, Volume 15 (2021) no. 1, p. 100053 | DOI

[26] Doublet, A.-C.; Croiseau, P.; Fritz, S.; Michenet, A.; Hozé, C.; Danchin-Burge, C.; Laloë, D.; Restoux, G. The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds, Genetics Selection Evolution, Volume 51 (2019) no. 1, p. 52 | DOI

[27] Duarte, D.; Newbold, C. J.; Detmann, E.; Silva, F.; Freitas, P.; Veroneze, R.; Duarte, M. d. S. Genome‐wide association studies pathway‐based meta‐analysis for residual feed intake in beef cattle, Animal genetics, Volume 50 (2019) no. 2, pp. 150-153 | DOI

[28] Durand, G.; Blanchard, G.; Neuvial, P.; Roquain, E. Post hoc false positive control for structured hypotheses, Scandinavian journal of Statistics, Volume 47 (2020) no. 4, pp. 1114-1148 | DOI

[29] Edwards, S. L.; Beesley, J.; French, J. D.; Dunning, A. M. Beyond GWASs: illuminating the dark road from association to function, The American Journal of Human Genetics, Volume 93 (2013) no. 5, pp. 779-797 | DOI

[30] Enjalbert-Courrech, N.; Neuvial, P. Powerful and interpretable control of false discoveries in two-group differential expression studies, Bioinformatics, Volume 38 (2022) no. 23, pp. 5214-5221 | DOI

[31] Eynard, S. E.; Windig, J. J.; Hiemstra, S. J.; Calus, M. P. Whole-genome sequence data uncover loss of genetic diversity due to selection, Genetics Selection Evolution, Volume 48 (2016) no. 1, p. 33 | DOI

[32] Fang, L.; Teng, J.; Lin, Q.; Bai, Z.; Liu, S.; Guan, D.; Li, B.; Gao, Y.; Hou, Y.; Gong, M. The Farm Animal Genotype–Tissue Expression (FarmGTEx) Project, Nature genetics (2025), pp. 1-11 | DOI

[33] Fisher, S. A.; Lewis, C. M. Power of genetic association studies in the presence of linkage disequilibrium and allelic heterogeneity, Human Heredity, Volume 66 (2008) no. 4, pp. 210-222 | DOI

[34] FAO Food Outlook – Biannual Report on Global Food Markets, 2022 | DOI

[35] Freking, B. A.; Murphy, S. K.; Wylie, A. A.; Rhodes, S. J.; Keele, J. W.; Leymaster, K. A.; Jirtle, R. L.; Smith, T. P. Identification of the single base change causing the callipyge muscle hypertrophy phenotype, the only known example of polar overdominance in mammals, Genome research, Volume 12 (2002) no. 10, pp. 1496-1506 | DOI

[36] Fu, Y.; Liu, H.; Dou, J.; Wang, Y.; Liao, Y.; Huang, X.; Tang, Z.; Xu, J.; Yin, D.; Zhu, S. IAnimal: a cross-species omics knowledgebase for animals, Nucleic acids research, Volume 51 (2023) no. D1, p. D1312-D1324 | DOI

[37] Gallitto, G.; Englert, R.; Kincses, B.; Kotikalapudi, R.; Li, J.; Hoffschlag, K.; Bingel, U.; Spisak, T. External validation of machine learning models-registered models and adaptive sample splitting, bioRxiv (2023), pp. 1-12 | DOI

[38] Gao, L. L.; Bien, J.; Witten, D. Selective inference for hierarchical clustering, Journal of the American Statistical Association, Volume 119 (2024) no. 545, pp. 332-342 | DOI

[39] Gong, Y.; Li, Y.; Liu, X.; Ma, Y.; Jiang, L. A review of the pangenome: how it affects our understanding of genomic variation, selection and breeding in domestic animals?, Journal of Animal Science and Biotechnology, Volume 14 (2023) no. 1, p. 73 | DOI

[40] Gramates, L. S.; Agapite, J.; Attrill, H.; Calvi, B. R.; Crosby, M. A.; Dos Santos, G.; Goodman, J. L.; Goutte-Gattat, D.; Jenkins, V. K.; Kaufman, T. FlyBase: a guided tour of highlighted features, Genetics, Volume 220 (2022) no. 4, p. iyac035 | DOI

[41] Guan, D.; Bai, Z.; Zhu, X.; Zhong, C.; Hou, Y.; Zhu, D.; Li, H.; Lan, F.; Diao, S.; Yao, Y. Genetic regulation of gene expression across multiple tissues in chickens, Nature Genetics, Volume 57 (2025) no. 5, pp. 1298-1308 | DOI

[42] Han, B.; Kang, H. M.; Eskin, E. Rapid and accurate multiple testing correction and power estimation for millions of correlated markers, PLoS genetics, Volume 5 (2009) no. 4, p. e1000456 | DOI

[43] Harrison, P. W.; Sokolov, A.; Nayak, A.; Fan, J.; Zerbino, D.; Cochrane, G.; Flicek, P. The FAANG data portal: Global, open-access,“FAIR”, and richly validated genotype to phenotype data for high-quality functional annotation of animal genomes, Frontiers in Genetics, Volume 12 (2021), p. 639238 | DOI

[44] Hasin, Y.; Seldin, M.; Lusis, A. Multi-omics approaches to disease, Genome biology, Volume 18 (2017), pp. 1-15 | DOI

[45] Hayes, B. J.; Daetwyler, H. D. 1000 bull genomes project to map simple and complex genetic traits in cattle: applications and outcomes, Annual review of animal biosciences, Volume 7 (2019) no. 1, pp. 89-102 | DOI

[46] Hendriks, S. J.; Schmitt, O.; Boyle, L. Rethinking sustainability: recognizing animal welfare’s critical role, Animal Frontiers, Volume 15 (2025) no. 2, pp. 3-7 | DOI

[47] Ioannidis, J. P. Why most published research findings are false, PLoS medicine, Volume 2 (2005) no. 8, p. e124 | DOI

[48] Jagota, M.; Ye, C.; Albors, C.; Rastogi, R.; Koehl, A.; Ioannidis, N.; Song, Y. S. Cross-protein transfer learning substantially improves disease variant prediction, Genome Biology, Volume 24 (2023) no. 1, p. 182 | DOI

[49] Jin, L.; Tang, Q.; Hu, S.; Chen, Z.; Zhou, X.; Zeng, B.; Wang, Y.; He, M.; Li, Y.; Gui, L. A pig BodyMap transcriptome reveals diverse tissue physiologies and evolutionary dynamics of transcription, Nature communications, Volume 12 (2021) no. 1, p. 3715 | DOI

[50] Jourdain, J.; Barasc, H.; Faraut, T.; Calgaro, A.; Bonnet, N.; Marcuzzo, C.; Suin, A.; Barbat, A.; Hozé, C.; Besnard, F. Large-scale detection and characterization of interchromosomal rearrangements in normozoospermic bulls using massive genotype and phenotype data sets, Genome Research, Volume 33 (2023) no. 6, pp. 957-971 | DOI

[51] Jurrjens, A. W.; Seldin, M. M.; Giles, C.; Meikle, P. J.; Drew, B. G.; Calkin, A. C. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases, Elife, Volume 12 (2023), p. e86139 | DOI

[52] Kelley, D. R. Cross-species regulatory sequence activity prediction, PLoS computational biology, Volume 16 (2020) no. 7, p. e1008050 | DOI

[53] Komljenovic, A.; Li, H.; Sorrentino, V.; Kutalik, Z.; Auwerx, J.; Robinson-Rechavi, M. Cross-species functional modules link proteostasis to human normal aging, PLoS computational biology, Volume 15 (2019) no. 7, p. e1007162 | DOI

[54] Komsta, L.; Novomestky, F. moments: Moments, Cumulants, Skewness, Kurtosis and Related Tests, 2022

[55] Kreitmaier, P.; Katsoula, G.; Zeggini, E. Insights from multi-omics integration in complex disease primary tissues, Trends in Genetics, Volume 39 (2023) no. 1, pp. 46-58 | DOI

[56] Kuijjer, M. L.; Tung, M. G.; Yuan, G.; Quackenbush, J.; Glass, K. Estimating sample-specific regulatory networks, Iscience, Volume 14 (2019), pp. 226-240 | DOI

[57] Kunert-Graf, J. M.; Sakhanenko, N. A.; Galas, D. J. Optimized permutation testing for information theoretic measures of multi-gene interactions, BMC bioinformatics, Volume 22 (2021), pp. 1-11 | DOI

[58] Langer, B. E.; Amaral, A.; Baudement, M.-O.; Bonath, F.; Charles, M.; Chitneedi, P. K.; Clark, E. L.; Di Tommaso, P.; Djebali, S.; Ewels, P. A.; others Empowering bioinformatics communities with Nextflow and nf-core, Genome Biology, Volume 26 (2025) no. 1, p. 228 (Publisher: Springer) | DOI

[59] Langfelder, P.; Horvath, S. WGCNA: an R package for weighted correlation network analysis, BMC bioinformatics, Volume 9 (2008), pp. 1-13 | DOI

[60] Leal-Gutiérrez, J. D.; Rezende, F. M.; Reecy, J. M.; Kramer, L. M.; Peñagaricano, F.; Mateescu, R. G. Whole genome sequence data provides novel insights into the genetic architecture of meat quality traits in beef, Frontiers in Genetics, Volume 11 (2020), p. 538640 | DOI

[61] Li, J.; Wang, J.; Zhang, P.; Wang, R.; Mei, Y.; Sun, Z.; Fei, L.; Jiang, M.; Ma, L.; E, W. Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types, Nature Genetics, Volume 54 (2022) no. 11, pp. 1711-1720 | DOI

[62] Li, R.; Yang, P.; Dai, X.; Asadollahpour Nanaei, H.; Fang, W.; Yang, Z.; Cai, Y.; Zheng, Z.; Wang, X.; Jiang, Y. A near complete genome for goat genetic and genomic research, Genetics Selection Evolution, Volume 53 (2021), pp. 1-17 | DOI

[63] Li, Z.; Zhang, Y.; Peng, B.; Qin, S.; Zhang, Q.; Chen, Y.; Chen, C.; Bao, Y.; Zhu, Y.; Hong, Y. A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity, Nucleic Acids Research, Volume 52 (2024) no. 21, pp. 13447-13468 | DOI

[64] Lim, K.-S.; Cheng, J.; Tuggle, C.; Dyck, M.; Canada, P.; Fortin, F.; Harding, J.; Plastow, G.; Dekkers, J. Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience, Genetics Selection Evolution, Volume 55 (2023) no. 1, p. 90 | DOI

[65] Liu, S.; Gao, Y.; Canela-Xandri, O.; Wang, S.; Yu, Y.; Cai, W.; Li, B.; Xiang, R.; Chamberlain, A. J.; Pairo-Castineira, E. A multi-tissue atlas of regulatory variants in cattle, Nature Genetics, Volume 54 (2022) no. 9, pp. 1438-1447 | DOI

[66] Lloret-Villas, A.; Pausch, H.; Leonard, A. S. The size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle, Genetics Selection Evolution, Volume 55 (2023) no. 1, p. 33 | DOI

[67] Longo, D. L.; Drazen, J. M. Data sharing, New England Journal of Medicine, Volume 374 (2016) no. 3, pp. 276-277 | DOI

[68] Loy, J. D.; Klabnik, J. L.; O’Boyle, N. J. Genomics for the Modern Beef and Dairy Practitioner, Veterinary Clinics: Food Animal Practice, Volume 40 (2024) no. 3, p. ix-x | DOI

[69] Lozada, D. N.; Bosland, P. W.; Barchenger, D. W.; Haghshenas-Jaryani, M.; Sanogo, S.; Walker, S. Chile pepper (Capsicum) breeding and improvement in the “multi-omics” era, Frontiers in plant science, Volume 13 (2022), p. 879182 | DOI

[70] Lu, C. Dairy, science, society, and the environment, https://oxfordre.com/environmentalscience/view/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-316, 2017 | DOI

[71] Maurano, M. T.; Humbert, R.; Rynes, E.; Thurman, R. E.; Haugen, E.; Wang, H.; Reynolds, A. P.; Sandstrom, R.; Qu, H.; Brody, J. Systematic localization of common disease-associated variation in regulatory DNA, Science, Volume 337 (2012) no. 6099, pp. 1190-1195 | DOI

[72] Minnoye, L.; Taskiran, I. I.; Mauduit, D.; Fazio, M.; Van Aerschot, L.; Hulselmans, G.; Christiaens, V.; Makhzami, S.; Seltenhammer, M.; Karras, P. Cross-species analysis of enhancer logic using deep learning, Genome research, Volume 30 (2020) no. 12, pp. 1815-1834 | DOI

[73] Misztal, I.; Lourenco, D. Potential negative effects of genomic selection, Journal of animal science, Volume 102 (2024), p. skae155 | DOI

[74] Morris, K. M.; Hindle, M. M.; Boitard, S.; Burt, D. W.; Danner, A. F.; Eory, L.; Forrest, H. L.; Gourichon, D.; Gros, J.; Hillier, L. W. The quail genome: insights into social behaviour, seasonal biology and infectious disease response, BMC biology, Volume 18 (2020), pp. 1-18 | DOI

[75] Mourad, R. Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences, BMC bioinformatics, Volume 24 (2023) no. 1, p. 186 | DOI

[76] Neufeld, A.; Dharamshi, A.; Gao, L. L.; Witten, D. Data thinning for convolution-closed distributions, Journal of Machine Learning Research, Volume 25 (2024) no. 57, pp. 1-35

[77] Pan, Z.; Yao, Y.; Yin, H.; Cai, Z.; Wang, Y.; Bai, L.; Kern, C.; Halstead, M.; Chanthavixay, G.; Trakooljul, N. Pig genome functional annotation enhances the biological interpretation of complex traits and human disease, Nature Communications, Volume 12 (2021) no. 1, p. 5848 | DOI

[78] Peirce, J. L.; Lu, L.; Gu, J.; Silver, L. M.; Williams, R. W. A new set of BXD recombinant inbred lines from advanced intercross populations in mice, BMC genetics, Volume 5 (2004), pp. 1-17 | DOI

[79] Price, E.; Feyertag, F.; Evans, T.; Miskin, J.; Mitrophanous, K.; Dikicioglu, D. What is the real value of omics data? Enhancing research outcomes and securing long-term data excellence, Nucleic Acids Research, Volume 52 (2024) no. 20, pp. 12130-12140 | DOI

[80] Rafat, S. A novel approach for integration of omics databases from various analyses across domestic species, Peer Community in Animal Science (2025) | DOI

[81] Rice, E. S.; Alberdi, A.; Alfieri, J.; Athrey, G.; Balacco, J. R.; Bardou, P.; Blackmon, H.; Charles, M.; Cheng, H. H.; Fedrigo, O. A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants, BMC biology, Volume 21 (2023) no. 1, p. 267 | DOI

[82] Ritchie, H. Less meat is nearly always better than sustainable meat, to reduce your carbon footprint, Our world in data (2020)

[83] Roesch, E.; Greener, J. G.; MacLean, A. L.; Nassar, H.; Rackauckas, C.; Holy, T. E.; Stumpf, M. P. Julia for biologists, Nature methods, Volume 20 (2023) no. 5, pp. 655-664 | DOI

[84] Rohart, F.; Gautier, B.; Singh, A.; Lê Cao, K.-A. mixOmics: An R package for ‘omics feature selection and multiple data integration, PLoS computational biology, Volume 13 (2017) no. 11, p. e1005752 | DOI

[85] Rosen, G. D.; Chesler, E. J.; Manly, K. F.; Williams, R. W. An informatics approach to systems neurogenetics, Neuroinformatics, Volume 1 (2007), pp. 287-303 | DOI

[86] Sabik, O. L.; Calabrese, G. M.; Taleghani, E.; Ackert-Bicknell, C. L.; Farber, C. R. Identification of a core module for bone mineral density through the integration of a co-expression network and GWAS data, Cell reports, Volume 32 (2020) no. 11 | DOI

[87] Secomandi, S.; Gallo, G. R.; Rossi, R.; Rodríguez Fernandes, C.; Jarvis, E. D.; Bonisoli-Alquati, A.; Gianfranceschi, L.; Formenti, G. Pangenome graphs and their applications in biodiversity genomics, Nature Genetics (2025), pp. 1-14 | DOI

[88] Sloan, Z.; Arends, D.; Broman, K. W.; Centeno, A.; Furlotte, N.; Nijveen, H.; Yan, L.; Zhou, X.; Williams, R. W.; Prins, P. GeneNetwork: framework for web-based genetics, Journal of Open Source Software, Volume 1 (2016) no. 2, p. 25 | DOI

[89] Smith, T. P.; Bickhart, D. M.; Boichard, D.; Chamberlain, A. J.; Djikeng, A.; Jiang, Y.; Low, W. Y.; Pausch, H.; Demyda-Peyrás, S.; Prendergast, J. The Bovine Pangenome Consortium: democratizing production and accessibility of genome assemblies for global cattle breeds and other bovine species, Genome biology, Volume 24 (2023) no. 1, p. 139 | DOI

[90] Song, D.; Chen, S.; Lee, C.; Li, K.; Ge, X.; Li, J. J. Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses, International Conference on Research in Computational Molecular Biology, Cold Spring Harbor Laboratory, 2025, pp. 400-404 (Backup Publisher: Springer) | DOI

[91] Song, Y.; Miao, Z.; Brazma, A.; Papatheodorou, I. Benchmarking strategies for cross-species integration of single-cell RNA sequencing data, Nature Communications, Volume 14 (2023) no. 1, p. 6495 | DOI

[92] Stephens, M. False discovery rates: a new deal, Biostatistics, Volume 18 (2017) no. 2, pp. 275-294 | DOI

[93] Syed, H.; Otto, G. W.; Kelberman, D.; Bacchelli, C.; Beales, P. L. MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies, bioRxiv (2021), p. 2021-12 | DOI

[94] Talenti, A.; Powell, J.; Hemmink, J. D.; Cook, E. A.; Wragg, D.; Jayaraman, S.; Paxton, E.; Ezeasor, C.; Obishakin, E.; Agusi, E. A cattle graph genome incorporating global breed diversity, Nature communications, Volume 13 (2022) no. 1, p. 910 | DOI

[95] Tam, V.; Patel, N.; Turcotte, M.; Bossé, Y.; Paré, G.; Meyre, D. Benefits and limitations of genome-wide association studies, Nature Reviews Genetics, Volume 20 (2019) no. 8, pp. 467-484 | DOI

[96] Teng, J.; Gao, Y.; Yin, H.; Bai, Z.; Liu, S.; Zeng, H.; PigGTEx Consortium; Bai, L.; Cai, Z.; Zhao, B. A compendium of genetic regulatory effects across pig tissues, Nature genetics, Volume 56 (2024) no. 1, pp. 112-123 | DOI

[97] Threadgill, D. W. Meeting report for the 4th annual Complex Trait Consortium meeting: from QTLs to systems genetics, Mammalian Genome, Volume 17 (2006) no. 1, pp. 2-4 | DOI

[98] Tuggle, C. K.; Clarke, J.; Dekkers, J. C.; Ertl, D.; Lawrence-Dill, C. J.; Lyons, E.; Murdoch, B. M.; Scott, N. M.; Schnable, P. S. The Agricultural Genome to Phenome Initiative (AG2PI): creating a shared vision across crop and livestock research communities, Genome biology, Volume 23 (2022), pp. 1-11 | DOI

[99] Tuly, S. R.; Ranjbari, S.; Murat, E. A.; Arslanturk, S. From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare, Computers in Biology and Medicine, Volume 191 (2025), p. 110108 | DOI

[100] Uffelmann, E.; Huang, Q. Q.; Munung, N. S.; De Vries, J.; Okada, Y.; Martin, A. R.; Martin, H. C.; Lappalainen, T.; Posthuma, D. Genome-wide association studies, Nature Reviews Methods Primers, Volume 1 (2021) no. 1, p. 59 | DOI

[101] van Den Berg, I.; Xiang, R.; Jenko, J.; Pausch, H.; Boussaha, M.; Schrooten, C.; Tribout, T.; Gjuvsland, A. B.; Boichard, D.; Nordbø, Ø. Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds, Genetics Selection Evolution, Volume 52 (2020), pp. 1-16 | DOI

[102] Van Laere, A.-S.; Nguyen, M.; Braunschweig, M.; Nezer, C.; Collette, C.; Moreau, L.; Archibald, A. L.; Haley, C. S.; Buys, N.; Tally, M. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig, Nature, Volume 425 (2003) no. 6960, pp. 832-836 | DOI

[103] Van Zanten, H. H.; Herrero, M.; Van Hal, O.; Röös, E.; Muller, A.; Garnett, T.; Gerber, P. J.; Schader, C.; De Boer, I. J. Defining a land boundary for sustainable livestock consumption, Global change biology, Volume 24 (2018) no. 9, pp. 4185-4194 | DOI

[104] VanRaden, P.; Olson, K.; Null, D.; Hutchison, J. Harmful recessive effects on fertility detected by absence of homozygous haplotypes, Journal of dairy science, Volume 94 (2011) no. 12, pp. 6153-6161 | DOI

[105] Vasseur, F.; Westgeest, A. J.; Vile, D.; Violle, C. Solving the grand challenge of phenotypic integration: allometry across scales, Genetica, Volume 150 (2022) no. 3, pp. 161-169 | DOI

[106] Von Keyserlingk, M.; Martin, N.; Kebreab, E.; Knowlton, K.; Grant, R.; Stephenson, M.; Sniffen, C.; Harner Iii, J.; Wright, A.; Smith, S. Invited review: Sustainability of the US dairy industry, Journal of dairy science, Volume 96 (2013) no. 9, pp. 5405-5425 | DOI

[107] Wang, J.; Williams, R. W.; Manly, K. F. WebQTL: web-based complex trait analysis, Neuroinformatics, Volume 1 (2003), pp. 299-308 | DOI

[108] Warr, A.; Affara, N.; Aken, B.; Beiki, H.; Bickhart, D. M.; Billis, K.; Chow, W.; Eory, L.; Finlayson, H. A.; Flicek, P. An improved pig reference genome sequence to enable pig genetics and genomics research, Gigascience, Volume 9 (2020) no. 6, p. giaa051 | DOI

[109] Weeks, E. M.; Ulirsch, J. C.; Cheng, N. Y.; Trippe, B. L.; Fine, R. S.; Miao, J.; Patwardhan, T. A.; Kanai, M.; Nasser, J.; Fulco, C. P. Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases, Nature Genetics, Volume 55 (2023) no. 8, pp. 1267-1276 | DOI

[110] Weighill, D.; Guebila, M. B.; Glass, K.; Quackenbush, J.; Platig, J. Predicting genotype-specific gene regulatory networks, Genome research, Volume 32 (2022) no. 3, pp. 524-533 | DOI

[111] Weller, J. I. Genomic selection in animals, Wiley Online Library, 2016 | DOI

[112] Wharrie, S.; Yang, Z.; Raj, V.; Monti, R.; Gupta, R.; Wang, Y.; Martin, A.; O’Connor, L. J.; Kaski, S.; Marttinen, P. HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes, Bioinformatics, Volume 39 (2023) no. 9, p. btad535 | DOI

[113] Wilkinson, M. D.; Dumontier, M.; Aalbersberg, I. J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.-W.; da Silva Santos, L. B.; Bourne, P. E. The FAIR Guiding Principles for scientific data management and stewardship, Scientific data, Volume 3 (2016) no. 1, pp. 1-9 | DOI

[114] Williams, E. G.; Auwerx, J. The convergence of systems and reductionist approaches in complex trait analysis, Cell, Volume 162 (2015) no. 1, pp. 23-32 | DOI

[115] Wragg, D.; Zhang, W.; Peterson, S.; Yerramilli, M.; Mellanby, R.; Schoenebeck, J. J.; Clements, D. N. A cautionary tale of low-pass sequencing and imputation with respect to haplotype accuracy, Genetics Selection Evolution, Volume 56 (2024) no. 1, p. 6 | DOI

[116] Xiang, R.; Berg, I. v. d.; MacLeod, I. M.; Hayes, B. J.; Prowse-Wilkins, C. P.; Wang, M.; Bolormaa, S.; Liu, Z.; Rochfort, S. J.; Reich, C. M. Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits, Proceedings of the National Academy of Sciences, Volume 116 (2019) no. 39, pp. 19398-19408 | DOI

[117] Zeng, H.; Zhang, W.; Lin, Q.; Gao, Y.; Teng, J.; Xu, Z.; Cai, X.; Zhong, Z.; Wu, J.; Liu, Y. PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs, Nucleic acids research, Volume 52 (2024) no. D1, p. D980-D989 | DOI

[118] Zhang, X.; Wang, H.; Yang, M.; Liu, R.; Zhang, X.; Jia, Z.; Li, P. Natural variation in ZmNAC087 contributes to total root length regulation in maize seedlings under salt stress, BMC Plant Biology, Volume 23 (2023) no. 1, p. 392 | DOI

[119] Zhao, B.; Luo, H.; He, J.; Huang, X.; Chen, S.; Fu, X.; Zeng, W.; Tian, Y.; Liu, S.; Li, C.-j. Comprehensive transcriptome and methylome analysis delineates the biological basis of hair follicle development and wool-related traits in Merino sheep, BMC biology, Volume 19 (2021), pp. 1-18 | DOI

[120] Zhu, M.; Zhao, S. Candidate gene identification approach: progress and challenges, International journal of biological sciences, Volume 3 (2007) no. 7, p. 420 | DOI

[121] Zuidhof, M.; Schneider, B.; Carney, V.; Korver, D.; Robinson, F. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005, Poultry science, Volume 93 (2014) no. 12, pp. 2970-2982 | DOI

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