Cited by 95Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetics and Plant Breeding

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Genetic evaluation including intermediate omics features

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Christensen, Ole F, Börner, Vinzent, Varona, Luis, Legarra, Andres·2021·Genetics

DOI 10.1093/genetics/iyab130PMC PMC8633135

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Abstract

In animal and plant breeding and genetics, there has been an increasing interest in intermediate omics traits, such as metabolomics and transcriptomics, which mediate the effect of genetics on the phenotype of interest. For inclusion of such intermediate traits into a genetic evaluation system, there is a need for a statistical model that integrates phenotypes, genotypes, pedigree, and omics traits, and a need for associated computational methods that provide estimated breeding values. In this paper, a joint model for phenotypes and omics data is presented, and a formula for the breeding values on individuals is derived. For complete omics data, three equivalent methods for best linear unbiased prediction of breeding values are presented. In all three cases, this requires solving two mixed model equation systems. Estimation of parameters using restricted maximum likelihood is also presented. For incomplete omics data, extensions of two of these methods are presented, where in both cases, the extension consists of extending an omics-related similarity matrix to incorporate individuals without omics data. The methods are illustrated using a simulated data set.

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BackgroundMethodInfluentialSelf-citation
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BackgroundMethod
Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes
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Background
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Boggio, G. M., Monteiro, H. F., de Lima, F. S., Figueiredo, C. C., Bisinotto, R. S., Santos, J. E. P., Mion, B., Schenkel, F. S., Ribeiro, E. S., WEIGEL, K. A., et al. (2024) · Journal of Dairy Science · doi:10.3168/jds.2024-24675 · Cited by 12
“The potential benefits of including omics traits (e.g., microbiome) in genetic evaluations and of splitting the genetic effect into direct and mediated effects were evaluated by Christensen et al. (2021) using a 2-joint model.”
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“However, validating the methods on real data is not straightforward, as naïve cross-validation may favor methods that predict accurate phenotypes and not accurate breeding values (Christensen et al., 2021).”
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Influential
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“Subsequently, Christensen et al. [29] developed a joint model that can effectively combine two or multiple omics data into a single-step model.”
Background
Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain
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Background
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“This reduction in h 2 and m 2 with model GM has been observed in previous studies (Déru et al., 2022; Martinez Boggio et al., 2024), and may indicate potential confounding effects between genetics and microbiota (Weishaar et al., 2020; Christensen et al., 2021).”
Self-citation
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“Thus, the model proposed by Zhao et al. (2022) is a generalized version of the model proposed by Christensen et al. (2021).”
Influential
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“Similarly, Weishaar [178] and Christensen et al. [179] developed an approach to collapse microbiota information into a single microbiome-wide effect, providing a simplified metric that can be incorporated into genetic or phenotypic models to enhance predictive power.”
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180. Strategies to find predictive variants that improve multi-breed genomic prediction in dairy cattle
van den Berg, I., Xiang, R., Khansefid, M., Goddard, M. E., Pryce, J. E., Prowse-Wilkins, C., Chamberlain, A. J., & MacLeod, I. M. (2022) · World Congress on Genetics Applied to Livestock Production (WCGALP) · doi:10.3920/978-90-8686-940-4_180 · Cited by 2
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Genetic relationships between efficiency traits and gut microbiota traits in growing pigs fed a conventional or a high fiber dietPreprint
Déru, V., Bouquet, A., Zemb, O., Blanchet, B., Almeida, M. L. D., Cauquil, L., Carillier‐Jacquin, C., & Gilbert, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.468583 · Cited by 2
“Following these results, some authors proposed to use the microbiota information to improve feed efficiency traits (Weishaar et al., 2020; Christensen et al., 2021; Pérez-Enciso et al., 2021).”
BackgroundInfluential
A Recursive Model Approach to Include Epigenetic Effects in Genetic Evaluations Using Simulated DNA Methylation Effects
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“Here, we propose an epigenetic model based on the equations proposed by Christensen et al. (2021), where the methylation frequency affects the phenotype but not the opposite.”
Influential
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504. Rumen bacteria do not provide improved genetic evaluation of dairy traits in sheep
Boggio, G. M., Christensen, O. F., Legarra, A., Allain, C., Meynadier, A., & Marie‐Etancelin, C. (2022) · World Congress on Genetics Applied to Livestock Production (WCGALP) · doi:10.3920/978-90-8686-940-4_504 · Cited by 1
“Despite this, it is interesting that with the method proposed by Christensen et al. (2021) we can decompose the heritability, and identify which part of the genetic variance was explained by the rumen microbiome, and which was directly explained by the host genetic (residual heritability).”
BackgroundMethodInfluentialSelf-citation
277. Extend mixed models to multi-layer neural networks for genomic prediction including intermediate omics data
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Zhao, T., Zeng, J., & Cheng, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.10.472187
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Hess, M. K., Zetouni, L., Hess, A. S., Budel, J. C. D. C., Dodds, K. G., Henry, H., Bräuning, R., McCulloch, A., Hickey, S. M., Johnson, P. L., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2290213/v1
306. Genomic prediction with incomplete omics data
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“…addition to these assumptions, the equilibrium of Hardy–Weinberg and linkage disequilibrium (LD) are also considered in genomic evaluations to enhance the improvement in estimations and the reliabilities of heritabilities and expected breeding values (Molina et al., 2018; Christensen et al., 2021).”
Additional file 2 of Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits
Hess, M. K., Zetouni, L., Hess, A. S., Budel, J. C. D. C., Dodds, K. G., Henry, H., Bräuning, R., McCulloch, A., Hickey, S. M., Johnson, P. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23751869
Additional file 1 of Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits
Hess, M. K., Zetouni, L., Hess, A. S., Budel, J. C. D. C., Dodds, K. G., Henry, H., Bräuning, R., McCulloch, A., Hickey, S. M., Johnson, P. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23751866
Additional file 4 of Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits
Hess, M. K., Zetouni, L., Hess, A. S., Budel, J. C. D. C., Dodds, K. G., Henry, H., Bräuning, R., McCulloch, A., Hickey, S. M., Johnson, P. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23751875
Additional file 3 of Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits
Hess, M. K., Zetouni, L., Hess, A. S., Budel, J. C. D. C., Dodds, K. G., Henry, H., Bräuning, R., McCulloch, A., Hickey, S. M., Johnson, P. L., et al. (2023) · Figshare · doi:10.6084/m9.figshare.23751872
High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictionsPreprint
Baber, A., Bertrand, H.-B., Laurent, M., François, C., Laurie, C. M., Nicolas, S., Duborjal, H., Welcker, C., Tardieu, F., Mary‐Huard, T., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.30.596567
A single-locus quantitative genetic model incorporating DNA methylation
Ayres, L., Bovenhuis, H., & Calus, M. P. L. (2025) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2025.112110
Additional file 2 of Microbiome composition as a potential predictor of longevity in rabbits
Biada, I., Ibáñez-Escriche, N., Blasco, A., Casto‐Rebollo, C., & Santacreu, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.25530366
Additional file 3 of Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience
Lim, K.-S., Cheng, J., Tuggle, C., Dyck, M. K., Canada, P., Fortin, F., Harding, J., Plastow, G., & Dekkers, J. (2023) · Figshare · doi:10.6084/m9.figshare.26652654
Additional file 8 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694066
Additional file 1 of Microbiome composition as a potential predictor of longevity in rabbits
Biada, I., Ibáñez-Escriche, N., Blasco, A., Casto‐Rebollo, C., & Santacreu, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.25530165
Additional file 1 of Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs
Juigné, C., Becker, E., & Gondret, F. (2024) · Figshare · doi:10.6084/m9.figshare.26633061
Additional file 3 of Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley
Guo, X., Sarup, P. M., Jahoor, A., Jensen, J., & Christensen, O. F. (2024) · Figshare · doi:10.6084/m9.figshare.26615296
Self-citation
Additional file 2 of The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet
Déru, V., Tiezzi, F., Carillier‐Jacquin, C., Blanchet, B., Cauquil, L., Zemb, O., Bouquet, A., Maltecca, C., & Gilbert, H. (2024) · Figshare · doi:10.6084/m9.figshare.26669565
Additional file 1 of Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience
Lim, K.-S., Cheng, J., Tuggle, C., Dyck, M. K., Canada, P., Fortin, F., Harding, J., Plastow, G., & Dekkers, J. (2023) · Figshare · doi:10.6084/m9.figshare.24797860
Additional file 1 of Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley
Guo, X., Sarup, P. M., Jahoor, A., Jensen, J., & Christensen, O. F. (2024) · Figshare · doi:10.6084/m9.figshare.26615290
Self-citation
Additional file 6 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694060
Additional file 2 of Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet
Déru, V., Tiezzi, F., Carillier‐Jacquin, C., Blanchet, B., Cauquil, L., Zemb, O., Bouquet, A., Maltecca, C., & Gilbert, H. (2022) · Figshare · doi:10.6084/m9.figshare.20390813
Additional file 2 of Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience
Lim, K.-S., Cheng, J., Tuggle, C., Dyck, M. K., Canada, P., Fortin, F., Harding, J., Plastow, G., & Dekkers, J. (2023) · Figshare · doi:10.6084/m9.figshare.24798100
Additional file 1 of Gut microbiota and host genetics contribute to the phenotypic variation of digestive and feed efficiency traits in growing pigs fed a conventional and a high fiber diet
Déru, V., Tiezzi, F., Carillier‐Jacquin, C., Blanchet, B., Cauquil, L., Zemb, O., Bouquet, A., Maltecca, C., & Gilbert, H. (2022) · Figshare · doi:10.6084/m9.figshare.20390810
Additional file 4 of Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley
Guo, X., Sarup, P. M., Jahoor, A., Jensen, J., & Christensen, O. F. (2024) · Figshare · doi:10.6084/m9.figshare.26615299
Self-citation
Additional file 1 of The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet
Déru, V., Tiezzi, F., Carillier‐Jacquin, C., Blanchet, B., Cauquil, L., Zemb, O., Bouquet, A., Maltecca, C., & Gilbert, H. (2024) · Figshare · doi:10.6084/m9.figshare.26669562
Additional file 1 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422374
Additional file 5 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694057
Additional file 2 of Metabolomic-genomic prediction can improve prediction accuracy of breeding values for malting quality traits in barley
Guo, X., Sarup, P. M., Jahoor, A., Jensen, J., & Christensen, O. F. (2024) · Figshare · doi:10.6084/m9.figshare.26615293
Self-citation
Additional file 4 of Microbiome composition as a potential predictor of longevity in rabbits
Biada, I., Ibáñez-Escriche, N., Blasco, A., Casto‐Rebollo, C., & Santacreu, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.26700574
Additional file 3 of Microbiome composition as a potential predictor of longevity in rabbits
Biada, I., Ibáñez-Escriche, N., Blasco, A., Casto‐Rebollo, C., & Santacreu, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.26700571
Additional file 4 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694054
Additional file 7 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694063
Additional file 5 of Microbiome composition as a potential predictor of longevity in rabbits
Biada, I., Ibáñez-Escriche, N., Blasco, A., Casto‐Rebollo, C., & Santacreu, M. A. (2024) · Figshare · doi:10.6084/m9.figshare.26700577
Additional file 3 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26694051
Additional file 2 of Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle
Martínez-Álvaro, M., Mattock, J., González-Recio, Ó., Saborío‐Montero, A., Weng, Z., Lima, J., Duthie, C.-A., Dewhurst, R. J., Cleveland, M. A., Watson, M., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25422422
Analysis of rumen microbiome in cattle and the practical application of the microbiome data
Yoshiaki, S. A. T. O., & HIROOKA, H. (2025) · Nihon Chikusan Gakkaiho · doi:10.2508/chikusan.96.309
Understanding the role of ruminal microbiota in phenotype expression in Lacaune dairy sheep
Boggio, G. M. (2022) · theses.fr (ABES) · doi:10.70675/1a921d6fzc059z4b59z8171z1479c934d4db
Modest contribution of metabolomic data to genomic prediction of breeding values for feed conversion ratio in pigs
Guo, X., Henryon, M., Börner, V., Ostersen, T., & Christensen, O. F. (2026) · Genetics Selection Evolution · doi:10.1186/s12711-026-01064-7
“…al. [27], heritability of the metabolomic intensities can be obtained from second step of MGB-LUP , and therefore according to Christensen et al. [27], the genomic heritability based on the MGBLUP model is: Genetic, metabolomic and residual correlations were calculated from corresponding…”
InfluentialSelf-citation
Biology‐informed neural networks learn nonlinear representations from omics data to improve genomic prediction and biological discovery
Kontolati, K., Gladstone, R. J., Davis, I., & Pickering, E. M. (2026) · The Plant Journal · doi:10.1111/tpj.71076 · Cited by 3
“…— transcripts, proteins, metabolites — as scaffolds between genotype and phenotype, enriching G2P models with mechanistic constraints (Atwell et al., 2010; Azodi et al., 2020; Christensen et al., 2021; Heffner et al., 2009; Korte & Farlow, 2013; Meu-wissen et al., 2001; Riedelsheimer et al., 2012).”
A note on a generalized single step theory for any number of hierarchical genomic matrices
Miguel Pérez-enciso, A. (2026) · Genetics Selection Evolution · doi:10.1186/s12711-026-01084-3

Authors: Christensen, O. F., Börner, V., Varona, L., & Legarra, A.
Affiliations: Aarhus University (DK)
Universidad de Zaragoza (ES)
GenPhySE (Génétique, Physiologie et Systèmes d’Elevage), INRA, 31326 Castanet-Tolosan, France
Corresponding author: Christensen, Ole F
ORCID: Christensen, Ole F
Varona, Luis
Legarra, Andres
Venue: Genetics
Publisher: Oxford University Press (OUP)
Published: 2021-08-07
Volume: 219
Issue: 2
Pages: 14 p;
ISSN: 0016-6731 (ISSN-L)
1943-2631
DOI: 10.1093/genetics/iyab130
PMC: PMC8633135
OpenAlex: W3198274722
Cited by: 81
References: 29
Citations by year: 2026: 6 · 2025: 15 · 2024: 23 · 2023: 15 · 2022: 20 · 2021: 2
FWCI: 10.45
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Genetics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: Green Development and Demonstration Programme
Ministry of Food, Agriculture and Fisheries of Denmark (34009-19-1586)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Agricultural and Biological Sciences › Plant Science › Genetics and Plant Breeding
Keywords: genetic evaluation, metabolomics, transcriptomics, breeding value estimation, BLUP, mixed model equations, restricted maximum likelihood, animal breeding, plant breeding
MeSH terms: Plant Breeding, Animals, Breeding, Models, Genetic, Plants, Genomics, Genetic Fitness

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Genetic evaluation including intermediate omics features

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