In order to understand how genome-enabled selection is used to improve the performance of livestock today, it is important to look back and see how we have gotten to where we are at today.
Improving livestock performance has been a goal since mankind domesticated the various species for its use. Advancements made in the animal breeding area have been designed to increase the accuracy with which we choose the correct parents to produce the next set of offspring. The traits they receive include traits contributing to producing that species profitably like number born alive, growth rate and backfat.
In the early days, animal breeders were led by Jay Lush, considered the father of modern-day animal breeding ideas and techniques. He was known as the father of modern-day animal breeding for understanding that the traits that determine commercial profitability are controlled by genes that pass from one generation to the next, and that relatives are more likely to perform similarly than animals that are not related to one another.
Lush and his student, Lanoy Hazel, are credited with developing selection index theory. The practical use of this work allowed breeders to combine all of the traits they considered important and wanted to include as selection criteria for their breeding animals. Where economics were measured, traits had appropriate economic values associated with them such that the total index value was based on a given animal’s economic value for all traits included in the index.
Breeders progressed further when C. R. Henderson developed Best Linear Unbiased Prediction, or BLUP technology. This is the most common method to estimate breeding values and rank animals based on genetic merit. It relies on measuring traits (phenotypic information) on animals and their relatives to determine the expected genetic potential for all animals in the genetic evaluation. This method only uses recorded performance values for traits considered as opposed to additional molecular information that could be used. This technology has allowed the swine industry to make tremendous genetic improvement in economically important traits. For example, the number of pigs born alive has increased tremendously since BLUP technology was implemented, improving the number born alive by approximately 3% per year.
Finally, we get to where we are at today. Most commercial swine producers may not even know much about the genome-enabled selection their genetic supplier is using other than the supplier told them they are using this technology. The reality is that this technology is set to allow swine breeders to make the next greatest leap in genetic improvement.
With the advancement of genotyping technology along with computer software and hardware technology, researchers have established ways to incorporate this new technology into breeding programs. The process of integrating genomic information into a selection strategy is known as genome-enabled selection.
How does it work?
For genome-enabled selection, a DNA sample is collected from individual animals, and the sample is assayed (genotyped) to determine the genetic information at many points along the DNA strand. The focus of DNA collection is on nucleus males and females.
Further, the number of points at which genetic information is collected can vary from small (5,000 to 6,000 base pairs) to large (50,000 to 80,000 base pairs) to complete genome sequencing. This information is used in breeding value estimation. Determining an animal’s genetic merit at the molecular level can improve the accuracy of estimated breeding values. There are two approaches to genome-enabled selection:
■ Methods that use genomic relationships (GBLUP)
■ Methods that estimate marker effects (SNP effect models)
The weighted sum of genomic information is computed by combining the genomic EBV and the EBV from traditional BLUP selection by modifying the relationship matrix that is used in traditional BLUP selection.
Typically, the relationship matrix used in genetic evaluations is based on pedigree relationships among animals in the evaluation. With GBLUP methods, the relationship matrix will include genomic relationship information. The genotyping information collected on animals in the genetic evaluations will be used to determine the portion of DNA that is common among two animals compared to average amount of DNA common among animals based on pedigree.
A weighted sum is computed by combining the GBLUP EBV and the EBV from traditional BLUP selection. After the relationship matrix has been modified, the genetic evaluation is conducted using BLUP methods.
Estimating marker effects
When genomic selection is used, an EBV is calculated for each animal based solely on genomic information.
The effect of each locus is estimated after accounting for other fixed effects such as contemporary group. The effects of genomic information at each locus genotyped are summed to estimate the genetic merit of the animal. These effects are estimated from a training population with phenotypic records.
A weighted sum is computed by combining the marker effects with the genomic EBV and the EBV from traditional BLUP selection. This sum is then used as the index value for each animal.
The information from BLUP and genomics are combined in order to ensure that all information available is used in the selection process. If only genomic information is used, the response to selection may not exceed the response to selection based on BLUP breeding values.
There are different densities of genotyping. High-density genotyping involves analyzing approximately 60,000 (or larger) loci on an animal’s DNA. Low-density genotyping analyzes as few as 300 to 400 loci. The price of low-density genotyping is less than half the cost of high-density genotyping.
Imputation is a method used to reduce the costs associated with genome-enabled selection by genotyping key ancestors using the high-density panels and genotype selection candidates using the low-density panels and then using the high-density genotypes of ancestors to infer or impute the genotypes at missing loci on the animal’s genotype using low density. The correlation between the low-density and high-density genotyping has been shown to be ~ (approximately) 0.95.
Accuracy of EBVs
The expected benefit of genome-enabled selection is improved accuracy of EBVs. Increasing EBV accuracy will proportionally increase the rate of genetic gain expected given that selection intensity and generation interval result from the direct relationship between accuracy and rate of genetic gain. This increase in accuracy will have to be high enough to recover the added costs associated with using genomic information in the selection program.
This improved accuracy comes from being able to estimate the genetic merit of an animal at an earlier age, potentially before acquiring performance information from the individual itself or its progeny when genomic selection is practiced. Traditional selection has limitations that can be improved upon using genome-enabled selection, including meat quality traits (typically measured on relatives, in this case progeny, most efficiently), traits where all animals cannot be measured or evaluated (feed efficiency), or on traits that are expressed from only one sex (milk production, semen production).
Improved accuracy occurs when GBLUP selection is practiced, giving better estimation of genetic relationships among animals rather than using expected genetic relationships based on pedigree information. Using marker-assisted selection has increased the response to selection in meat quality, net feed intake and pigs born alive compared to the response from BLUP, with the largest gap between marker-assisted selection and BLUP being for meat quality. However, less improvement has been observed for traits like growth rate (days to market, average daily gain, etc.) when comparing marker-assisted and BLUP selection methods because ADG can be measured on all selection candidates.
Commercial producers may not directly see advancement in any given trait that they can directly attribute to the newest genomic technology. However, rest assured that any improvement seen at the commercial level is a result in part of genome-enabled technology advancements made by genetic suppliers.
The greatest impact of genome-enabled selection is expected for traits that are low in heritability, hard to measure or measured late in life such as disease resistance, feed efficiency and longevity. Disease resistance is not easily defined and systematically measured. Feed efficiency is expensive to measure, especially on an individual animal basis. Sow longevity is not recorded until the sow is culled from the herd and is a trait that is only measured on females.
With traits that are not currently measured, an added cost of measuring the novel traits will be associated with genome-enabled selection if these traits are targeted.