Have you ever wondered why a mating works like a charm one time, but doesn’t turn out the same the next? Or why do full siblings sometimes end up with very different EPDs (expected progeny differences)?  

It leaves many scratching their heads, for sure, but the answer lies in the natural variation within families. Just like no two rancher’s kids turn out exactly alike, cattle inherit a mix of genes that express themselves in unique ways. 

Understanding this variation is key to making smarter breeding decisions and driving genetic progress across a herd. It is also a key principle of quantitative genetics that addresses this element of chance or luck, known as Mendelian sampling; and that helps us understand how this variation occurs and why variation is essential for the genetic progress of a herd.

Genetics in full sibling differences

To begin, we know an animal’s phenotypic expression is influenced by both environmental and genetic factors, although here we will focus only on the genetic differences.  

Each parent contributes half of its genes to its offspring, and that is why the pedigree relationship between parent and offspring on a scale from 0-1 is 0.5. However, while each parent contributes half of the offspring’s genetic material, the specific genes passed on by a parent are determined randomly. This randomness in the inheritance introduces variation, or differences, between offspring. It is known as Mendelian sampling, after Gregor Mendel who is considered the father of genetics.

To fully illustrate the Mendelian sampling effect, we can define the breeding value of an animal (ai), as the sum of the half of the breeding value of the sire (as), half of the breeding value of the dam (ad) and the Mendelian sampling (mi). 

This forms the foundation of animal breeding. Based on these principles, statistical models are used to calculate an animal’s breeding value, which represents the average effects of the genes an individual inherits from both parents. Half of this breeding value represents the individual’s EPD, which reflects the average effect of the genes passed from parent to progeny. 

This formula shows that even if animals have the same sire and dam (i.e., full sibs), they can still have different breeding values and therefore different EPDs due to the Mendelian sampling, or the randomness of how each of these halves are passed down. 

On top of that, since full siblings do not receive the exact same combination of genes from their parents, some may inherit more favorable combinations, resulting in more desirable EPDs and vice versa. So, while Mendelian sampling can be frustrating when it leads to a less desirable EPD, it’s also exciting to identify and select animals within the same family that outperform their parents’ average. Again, this variation serves as the driving force behind the selection process, and as a result, genetic progress too.

Collecting data and genotyping

Even though we know variation within a family exists, when only the pedigree is available, the EPD for an animal is simply calculated as the average EPD of its parents. That is also known as the parental average. This is because no other source of information is available. In this case, all full siblings have the same EPD. While this results in lower accuracy in many cases, it is still the best prediction we can make. 

As breeders gather more data on these types of animals by submitting phenotypes and progeny data, we’ll see their EPDs change and gain more accuracy, and the variation between full siblings can be identified as we capture more sources of information. 

In addition to phenotypic data, genomic data plays an important role because by using this data, we can better quantify and understand the specific genes being inherited by the animal from its parents. In the World Angus Evaluation, the American Angus Association’s weekly genetic evaluation, the genomic information is used to calculate genomic relationships between individuals, which are more precise than the expected pedigree relationships.

To illustrate how genomics helps us more accurately capture the genomic-pedigree relationship between individuals, Figure 1 shows the genomic-pedigree relationship distribution between 85 full siblings reported to the Association database.

While the pedigree relationship between all the animals (indicated by the vertical bar) is consistent at 0.5, genomics allows us to capture more variation within families, with relationships from 0.35 to 0.68. 

With more sources of information, EPDs get more accurate and, in most cases, spread out. Figure 2 shows the Weaning Weight (WW) EPD distribution for the same full siblings. While the parental average is at 94, with genotypes and phenotypes added to the genetic evaluation, we can see a full range of EPDs. 

The Mendelian sampling that was previously unknown, now is captured and accounted for — leading to more accurate EPDs that differentiate the genetic potential between siblings. This process helps identify, sort, and select which sibling will contribute the most to genetic improvement.

Figure 2 helps us understand that substantial variation within a family exists not only in theory but also in real data. By collecting phenotypes and genotyping animals, breeders can obtain more accurate EPDs and optimize their selection decisions, making genetic progress faster than they could without this data. 

The real data example in Figure 2 shows that without data, we could pick a sire based on the parental average and leave genetic gains on the table by not knowing better animals existed within the group.

Take-home message

Genetic variation between full siblings is a natural and essential part of animal breeding, arising from the random assortment of genes passed from both parents to the progeny, known as Mendelian sampling. 

This variation is captured when phenotypic and genotypic data are incorporated into the evaluation. The more data is included, the greater the accuracy, which helps better reflect the variation between siblings. Incorporating genomics accelerates this process by defining genetic relationships more precisely and identifying sibling differences earlier and with greater accuracy. 

Understanding this variation helps breeders optimize selection strategies and achieve consistent genetic progress in their herds.