Is “Phenotype King” in the era of genomics?

{The following is an extract from the Canadian Dairy Network’s extension article entitled, “Value of Type Classification in the Era of Genomics” published April 30, 2019. To access the full article, click here:  https://www.cdn.ca/document.php?id=524.}

CowsEating_2017.jpgIt has been said by many researchers around the world that “Phenotype is King!” in this era of genomics. What does this really mean? In Canada, there has been a recent surge of discussion on this topic, especially as it relates to the value of type classification data.  The Canadian Dairy Network took a closer look at the key questions being asked by breeders to help clarify the value of genotypes versus phenotypes (i.e.: performance data) in today’s environment of dairy cattle selection.

…If a bull dam’s classification data has such a minor impact on the accuracy of their son’s genomic evaluation, why is type classification important at all?  Why do researchers claim that “Phenotype is King!”? 

This question can be answered in two ways.

First, in a general way, the accuracy of any genomic evaluation system is dependent upon the continued collection of good quality data (i.e.: phenotypes) on an ongoing basis.  Even once a genomic evaluation system is built and established, such phenotypic data is required year after year to keep the genomic predictions relevant.

 Secondly, the reason to collect phenotypes is more specific to each breeder at their herd level.  Every heifer calf born on a farm starts with a Parent Average as the first estimate of its genetic potential.  This estimate of an animal’s genetic merit serves as a predictor of those that are expected to have the highest level of performance in the milking herd.  After birth, there are two ways to improve the accuracy of this first estimate.  By genomic testing a heifer calf, the Parent Average (PA) can be replaced by its Genomic Parent Average (GPA).  Later in life, however, measuring each animal’s own performance also contributes to their estimate of genetic merit, whether they have been genotyped or not.  As an example, classifying all first lactation animals in your herd results in changes to their Conformation index as they go from being a Parent Average (PA) to an Estimated Breeding Value (EBV).  The figure below shows the distribution of changes that occur for Conformation when a heifer PA becomes an EBV after being classified in the first lactation.  Half of all heifers change by at least 1 point up or down once they are classified with some changing as much as ±8 points for their Conformation genetic evaluation. Classifying cows in your own herd will re-rank your cows and cow families, which can have a significant impact on your heifer replacement and culling decisions.

Distribution of the Change in Conformation Index by Adding an Animal’s Own Classification (Without Genomics)

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As expected, if heifer calves are genotyped and the PA is replaced by a GPA, then adding their classification phenotype in the first lactation has less impact than the distribution shown in the Figure.  That said, there are still 20% of heifers that experience a change in their Conformation index that is 2 points or more.

Genomics has changed so many things associated with dairy cattle selection schemes. Genetics offered by A.I. companies through their genomic young sires has reached incredible heights resulting in (a) a focus on reduced generation intervals, (b) doubling of the semen market share occupied by young bulls, and (c) more than doubling the annual rate of genetic progress.  These significant changes have also led to less complete information being available on young sire pedigrees compared to a decade ago, especially the performance data on bull dams. While this trend is undesirable from the perspective of pedigree completeness for the resulting daughters, the impact on the accuracy of selection decision is minor.  On the other hand, herd owners must realize the benefits and value of a continued collection of performance data, such as production and classification, for their milking herd.  Such data serves to validate and/or improve the genetic evaluation predictions used to make important selection and mating decisions.

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