Larry Kuehn
USDA-MARC
When we go from less than a thousand animals to several thousands of animals, genomic predictions can explain about 50% of the genetic variance for important traits. Genomic prediction is working and providing tremendous benefits to seedstock and commercial producers.
But, we still struggle with genomic predictions with very little data recording and genomic predictions that work well across breeds.
Two methods are used to use genomics in national cattle evaluation. With the genomic pedigree method you track genetic effects more accurately than with pedigree data. With the second method you are relying on linkage on chromosomes between the DNA markers and the variants responsible for the differences (causal mutations).
The linkage signal between DNA markers and causal variants breaks down over generations due to recombination (switching) between paternal and maternal chromosomes. Because this linkage breaks down over time is part of the reason genomic predicitons don't work well across breeds.
When we train a prediction in Angus and use the predictions in Red Angus, the predictive ability of the genomic prediction goes down significantly.
Table 1. Correlations from genomic predictions trained in Angus and used in Angus or Red Angus.
| Breed |
Weaning Weight |
Yearling Weight |
| Train in Angus, Predict in Angus |
0.36 |
0.51 |
| Train in Angus, Predict in Red Angus |
0.16 |
0.08 |
As we have additional whole genome (entire DNA) sequencing data, we will discover more DNA variants that affect the composition (sequence) or length of proteins. Many of these broken genes will likely affect fertility.
As we discover variants that appear to affect the function of proteins or are causal variants, we will need to use different methods to fully utilize this information. These DNA variants will need to be weighted differently in the genomic prediction, or genomic predictions will need to fit multiple classes of variants.
USDA has started a selection experiment in which they are selecting against variants that cause the protein coded by the gene not to function properly. The first calves from this experiment will be born in the spring.
Kuehn highlighted several research needs including:
- Continued annotation (identification of genes and regulatory elements) of the reference genome
- New sequence assemblies
- Improvement of functional variant panels (DNA tests)
- Improved imputation method and strategies (infer DNA variants not testing using the patterns of genotyped variants)
- Continued detail oriented phenotyping
- Improved methods to incorporate into national cattle evaluation
- Gene expression difference (the amount of RNA produced by the same gene in different animals)
- Evaluating cellular expression
Kuehn and coworkers believe that functional variants offers new opportunities for national cattle evaluation.
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