Multi-Omics Selection in Animal Breeding

It is imminent to develop and utilize animal genetic breeding and germplasm resources. Apply multi-omics techniques, such as comprehensive analysis of gene expression (transcriptomics), protein production (proteomics) and metabolite production (metabolomics), etc., to explore the molecular characteristics of the excellent traits of different varieties. In-depth mining and verification of the biological regulatory functions involved in the target gene can provide a new perspective for molecular assisted breeding. BioVenic's professional knowledge in animal genetics and breeding and rich experience in animal-related research and development enable us to provide global customers with multi-omics selection services in animal genetic breeding to better meet research needs.

Genome Selection Models Integrating Multi-Omics Information

At present, various omics technologies (epigenomics, transcriptomics, proteomics, metabolomics) continue to mature, and it is relatively easy to obtain biological prior information from public data or previous research accumulation. Therefore, how to integrate the known prior information in the GS model, and then obtain additional genetic progress by improving the accuracy of GS has become an important direction of current animal breeding research.

• Site-specific Models
  The BLUP model originally used for GS is SNP-BLUP, that is, GEBV is the sum of all marker effects (Wb). Then the GBLUP method is proposed, which uses GEBV vector g instead of Wb. SNP-BLUP, GBLUP, and ssGBLUP all assume that each locus has the same effect on traits, so it is unable to integrate multiple omics information. Different loci may have different genetic contributions to the same trait. BLUP based site specificity method uses diagonal matrix D=diag (di) to weight G matrix, integrates site specific prior information, and improves the accuracy of selection.
• Class-specific Models
  Another type of GFBLUP method that integrates multi-omics information is different from the previous method that assigns different genetic weights to each SNP. The GFBLUP method divides the SNP sites into different categories according to their "genetic contribution", and by fitting two More than one G matrix assigns different genetic weights to different categories of SNPs.
• Covariate Models
  Different from previous models, some scholars directly proposed to put the highly effective loci as covariates into the mixed linear model to improve the accuracy of GS. The accuracy of GS was increased in dairy cattle breeding rate (SCR). Covariate models is applicable to traits with identified causal mutation.

Our Services

BioVenic combines its professional knowledge and rich experience in animal breeding, genetics and modern biotechnology to provide multi-omics selection services for customers around the world. Our specific services include but are not limited to:

• GBLUP model combined with the information of multi omics (epigenomics, transcriptomics, proteomics, metabolomics) was used to predict the heritability.
• Combining multi-omics data to estimate genomic prediction accuracy.
• Computation of gene-trait associations by selecting the best genomic selection model incorporating multi-omics information.

Combining multi-omics technology in genome selection can dig out important gene regulation information more quickly and accelerate customers' breeding plans. Specific advantages include: 1) improving the accuracy of GS by integrating multi-omics information; 2) additional genetic progress can be obtained in low heritability traits; 3) multi-level functional verification can be carried out and integrated network diagrams can be constructed.

Workflow of Multi-omics Selection in Animal Breeding

Want to Learn More?

With years of experience in animal breeding and genetics, BioVenic is committed to providing high-quality animal breeding and genetic research services. We are confident that we can design effective solutions to support the effective operation of your project. If you are interested in our services, please contact us and tell us more about your project.

References

1. Bouwman, A.C.; et al. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat Genet. 2018, 50: 362–367.
2. MacLeod, I.M.; et al. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits. BMC Genomics. 2016, 17: 144.