Risk Ratio as parameter for the genetic characterization of new defined traits for mastitis of cattle

The identification of economic trait loci promises new opportunities for genetic improvement in animal breeding. Additional gains are expected by including molecular genetic information in breeding programs. This is especially true for traits, for which the achievement of genetic progress by classical selection procedures is limited, like mastitis of cattle. One important prerequisite for successful mapping experiments is a precise trait definition, which reduces the environmental influence on the trait and increases its genetic determination. The risk ratio (R), the recurrence risk for a relative of an affected individual compared to the prevalence of the trait in the general population, is a well suited parameter for the genetic characterization of binary traits even for comparatively small datasets. Risk ratios were estimated for newly defined traits for mastitis based on available bacteriological datasets in cattle. Dividing a general trait “bacteriological finding” into subtraits for the pathogens “Staphylococcus aureus” and “non agalactiae Streptococcus” resulted in increased risk ratios. A more precise definition of the trait was achieved through the elimination of cows which were negative for the trait causing pathogen, but were infected with another pathogen instead, which led to an additional increase of R in most situations. The splitting of the traits due to different parities influenced the risk ratios as well. The estimates of R for the trait “S. aureus” were higher in the first parity as well as the traits “bacteriological finding” and “non agalactiae Streptococcus” in the second lactation and in all parities together. Restrictions of the lactation period influenced the estimates of R for the different traits in a different manner. The highest estimates of R were 1.09 for “bacteriological finding”, 1.19 for “S. aureus” and 1.17 for “non agalactiae Streptococcus”. To conclude, higher risk ratios were obtained by applying restrictions on the bacteriological datasets to define more precise traits of mastitis. Such traits are more adequate for mapping experiments aimed to detect genes involved in outcome of mastitis.

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