Sheep Industry News August 2024

Blending Technology & Tradition to Achieve Breeding Goals TOM MURPHY, PH.D. U.S. Meat Animal Research Center

merit for a trait. The accuracy of phenotypic selection for a trait is equal to the square root of the trait’s heritability. Some traits such as fiber diameter and fleece weight are moderately to highly heritable (> 0.40). An animal’s own performance for more highly heritable traits serves as a reasonably accurate indicator of genetic merit for the trait (√0.40=63 percent). But many traits such as lamb survival to weaning or mastitis are lowly heritable (< 0.05). That means that an animal’s own per formance is not an accurate indicator of its genetic merit for the trait (√0.05=22 percent). This is because most of the phe notypic variability for lowly heritable traits is due to variability in non-genetic effects, which makes genetic improvement more challenging. Number of lambs born per ewe is lowly heritable (~0.13). Furthermore, we’re typically using the reproductive perfor mance of an animal’s dam – not its own performance – when practicing phenotypic selection for NLB. Since an animal shares 50 percent of its genetic variants with its dam, this cuts our accuracy in half. Therefore, if you only consider an animal’s birth type (single, twin, etc.) to infer its genetic merit for NLB, you’re only about 18 percent accurate (0.5 x √0.13=18 percent). Even if we add in more information and only keep replacements from dams that have five lambing records, the ac curacy of that individual’s genetic merit for NLB is still just 32 percent. This low accuracy likely explains why we really haven’t seen the average national lamb crop change much from 110 percent in the last 50 years, despite the “traditional method” of selecting twin (or triplet) born replacements. I was part of a long-term Rambouillet selection experiment while I was at Montana State University. It was started by Dr. Peter Burfening in 1968 and was ended by yours truly in 2017. Two lines of sheep were created: the high line selected for increased NLB and the low line selected for decreased NLB. Selection within the high and low lines was pretty much the traditional method and solely based on an individual’s dam’s average NLB. At the end of the experiment, average lamb crop was ~170 percent in the high line and ~120 percent in the low line. But that 0.5 difference in NLB took 50 years and three generations of scientists to achieve. Furthermore, virtually no other economically important trait was considered. As a result, sheep in these lines were inbred and had poor wool production and growth. Despite intentionally selecting for low NLB for 50 years, the low line was still more prolific than the average American

W e talk a lot about tradition in the American sheep industry. Traditions aren’t stagnant, they get modified from one generation to the next as new information and insights are gained. Nearly everything in modern sheep production – be that fences, milk replacer or RFID readers – was at one time a state-of-the-art technology. Whether we choose to adopt a new technology is dependent on economics and those intangibles that define our unique perspectives for raising sheep in the first place. From the dawn of sheep domestication until the last 50 years or so, the only available technologies to select replacement animals were visual appraisal and performance of the indi vidual itself. We refer to this as phenotypic selection. Evaluat ing breeding stock in this manner has taken us from the wild mouflon to the thousands of breeds we have today. At face value, phenotypic selection seems logical. The reason an animal performs or appears superior to another is because it carries a superior set of gene variants, right? If you want finer wool, heavier market weights and greater twinning rate, select the finest fibered, fastest growing, twin born rams and ewes. While this isn’t entirely illogical, it’s incomplete. The true rea son performance varies across animals is due to both genetic AND environmental (i.e., non-genetic) differences. Therefore, individuals might have better performance for a trait because they have a better set of gene variants and/or they had a better environment. We often think of an animal’s environment as just the time and place performance was recorded, but the environment is much more complex. Finding better feed, advancing animal health protocols, etc., can improve performance. But these are non-genetic improvements. If they are stopped, performance will regress back to its original state because non-genetic ef fects aren’t inherited in future generations. In contrast, an animal’s genetic merit or breeding value for a trait reflects differences in DNA, which are inherited across generations. That means genetic improvement can be perma nent. But how much are the differences in performance we observe among animals due to differences in genetic and non genetic sources? How accurate is phenotypic selection? Heritability is a statistic that describes the strength of the relationship between an animal’s performance and its genetic

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