Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/22/2017
Publication Date: 4/1/2018
Citation: Cole, J.B., Van Raden, P.M. 2018. Symposium review: Possibilities in an age of genomics: The future of the breeding index. Journal of Dairy Science. 101(4):3686-3701. https://doi.org/10.3168/jds.2017-13335.
Interpretive Summary: Selective breeding has been practiced since animals were first domesticated, but early breeders commonly selected on appearance rather than measurements, such as milk yield. A breeding index converts information about several traits into 1 number used for selection and to predict an animal’s own performance. The first USDA selection index included only milk and fat yield, while the latest version of the lifetime net merit index includes 13 traits. It is not simple to decide how many and which traits to include in an index because traits are not independent. Indices differ from country to country, but most include yield, fertility, health, and type traits. Recording important new traits on a small fraction of cows can quickly benefit the whole population through genomics.
Technical Abstract: Selective breeding has been practiced since domestication, but early breeders commonly selected on appearance (e.g., coat color) rather than quantitative phenotypes (e.g., milk yield). A breeding index converts information about several traits into 1 number used for selection and also to predict an animal’s own performance. Calculation of selection indices is straightforward when phenotype and pedigree data are available. Prediction of economic values 3 to 10 years in the future, when the offspring of matings planned using the index will be lactating, is more challenging. The first USDA selection index included only milk and fat yield, while the latest version of the lifetime net merit (LNM) index includes 13 traits, with some traits actually composites of other traits. Selection indices are revised to reflect improved knowledge of biology, new sources of data, and changing economic conditions. Single-trait selection often suffers from antagonistic correlations with traits not in the selection objective. Multiple-trait selection avoids those problems at the cost of less-than-maximal progress for individual traits. How many and which traits to include is not simple to determine because traits are not independent. Many countries use indices that reflect the needs of different producers in different environments. While the emphasis placed on trait groups differs, most indices include yield, fertility, health, and type traits. Addition of milk composition, feed intake, and other traits is possible but are more costly to collect, and many are not yet directly rewarded in the marketplace, such as with incentives from milk processing plants. As the number of traits grows there is increasing interest in custom selection indices for closely matching genotypes to the environments in which they will perform. Traditional selection required recording lots of cows across many farms, but genomic selection favors collecting more detailed information from cooperating farms. A similar strategy may be useful in less developed countries. Recording important new traits on a small fraction of cows can quickly benefit the whole population through genomics. Gene editing may be used to increase the frequency of high-value Mendelian traits, such as polled.