Skip to Main navigation Skip to Left navigation Skip to Main content Skip to Footer

University of Minnesota Extension

Extension > Agriculture > Dairy Extension > Reproduction and genetics > Proper implementation of genomic evaluations

Print Icon Email Icon Share Icon

Proper implementation of genomic evaluations

Les Hansen, Ph.D.

Over the past 5 years, genomic evaluations have been the hot topic for dairy cattle genetics. Yes, the technology is exciting. However, any new technology typically receives undue hype after its launch and unrealistic predictions of impact are promoted. The initial period of unwarranted hype about a new technology is usually followed by a time of reflection, which in turn is followed by periods of disillusionment and, eventually, proper implementation of the new technology.

Definitely, genomic evaluations of dairy cattle have been the topic of considerable hype and, perhaps, unrealistic expectations have been nurtured by some. Let's face it — the potential positive impact of a new technology is likely to be emphasized and the potential downsides are likely to be glossed over. So, where are we on the hype/reflection/ disillusionment/proper adoption path in regard to genomic evaluations? Probably, somewhere between reflection and disillusionment, but proper adoption will be the next step.

Reliability versus accuracy

An overriding problem is interpretation of the "accuracy" of genomic evaluations. How is "accuracy" measured? Well, most would say by the reliability published alongside each genomic PTA (transmitting ability) for an individual trait or for an index that combines traits into a single value (such as Net Merit). However, are reliability and accuracy the same thing? No, they are not!

Accuracy has a specific statistical (scientific) meaning in addition to an intuitive meaning. Accuracy is a measure of the confidence range around a PTA — say, plus or minus 200 lb of milk or plus or minus $50 for Net Merit. Mathematically, accuracy for genetic evaluations is calculated as the square root of reliability. To put this into perspective, we will compare reliability versus accuracy alongside their corresponding confidence range for Net Merit for the four types of genetic evaluations of bulls — Parent Average (average of the PTA of his two parents), genomic test, first-crop daughters, fully proven (thousands of daughters in hundreds of herds). We will use a confidence range of 68% (which means there is about a two-thirds chance the eventual Net Merit of a bull will fall within the range).

The reliability of the genetic evaluations for Net Merit of most Holstein A.I. bulls is about 38% (Parent Average), 70% (genomic test), 85% (first-crop daughters), 99% (fully proven). These reliabilities suggest the result of a genomic test almost doubles the genetic knowledge known about a young bull without daughters. However, that is not the case. The accuracy of the genetic evaluations for Net Merit associated with those four levels of reliability are 62% (Parent Average), 84% (genomic test), 92% (first-crop daughters), and 99% (fully proven).

Use of accuracy instead of reliability demonstrates the gain in genetic knowledge when going from Parent Average to genomic testing isn't as large as reliability leads one to conclude. The truth is Parent Average is a fairly good predictor (but far from perfect) of eventual PTA for traits of bulls or females. Yes, genomic testing provides improvement in accuracy over Parent Average, but it doesn't provide the extent of improvement that some have suggested – certainly not, "a doubling of knowledge ". Most people in the dairy industry are likely unaware that reliability and accuracy are different measures and reporting of reliability exaggerates the increase of genetic knowledge when moving from Parent Average to fully proven status for bulls.

Reliability, accuracy, and 68% confidence range for Net Merit of Holstein bulls

Type of evaluation Reliability
Confidence range
($Net Merit)
Parent average 38 62 ±157
Genomic 70 84 ±110
First-crop daughters 85 92 ±77
Fully proven 99 99 ±20

Over-evaluation of highest ranking individuals

The greatest frustration has been the over-evaluation of highest-ranking young bulls by genomic testing. This upward bias of PTA for highest-ranking bulls is now well-documented by both researchers and A.I. organizations. The extent of this upward bias has been estimated by some to be at least $150 for the Net Merit index and 200 for the TPI index of Holstein USA. The upward bias is more pronounced if the PTA of the sire of a young bull is also genomic-only (no daughters contributing to the sire's PTA).

Consequently, most in the dairy industry recommend separate rankings of proven bulls (with daughters) versus genomic-only bulls (no daughters), because the genomic-only bulls are more likely to drop for PTA than bulls with daughters contributing to PTA. Definitely, when genomic-only bulls are selected for use in a herd, a group of them should be used to spread the risk over a larger number of bulls. Proven bulls can be used with more confidence in regards to potential changes of PTA.

Genomic testing of heifers

Some that provide advice to dairy producers recommend genomic testing of all heifers to determine which surplus heifers to sell or to breed to beef bulls. However, routine genomic testing of all heifers in commercial settings should be given very careful consideration prior to implementation. It's important to keep in mind environmental influences, in addition to genetics, are large on the eventual performance of heifers as cows. For example, differences of heifers due to respiratory illness that causes lung damage, the lack of growth, and structural deficiencies could easily overcome differences for genomic test results versus Parent Average. Also, daughters of genomic-only bulls are more prone to changes of their genomic test results over time compared to daughters of proven bulls with daughters contributing to their genetic evaluations.

July 2014

  • © Regents of the University of Minnesota. All rights reserved.
  • The University of Minnesota is an equal opportunity educator and employer. Privacy