Use of Quantitative Trait Loci (QTL) in Dairy Sire Selection

1. Use of Quantitative Trait Loci (QTL) in Dairy Sire Selection Fabio Monteiro de Rezende Universidade Federal Rural de Pernambuco (UFRPE) - Brazil

2. Papers Reviewed • C. N. Costa et al. 2000. Genetic analysis of Holstein cattle populations in Brazil and the United States. • M. S. Ashwell et al. 2004. Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle.

3. My interests • Improve the profits of milk production. • Increase herd productivity. • Help improve sire selection process. • Productive aspects. • Health aspects. • Reproductive aspects. • Selection with genetic-environmental interaction. • Choose the most appropriate sires for the production environment.

4. Introduction • Quantitative Trait Loci (QTL). • Is a region of the genome that contains one or more genes affecting a quantitative trait. • Is used for traits with discrete distributions, but where one assumes that multiple loci control the phenotype. • Importance of QTL • Simple traits. • Easy to identify, few genes. • Not influenced by environmental factors. • Quantitative traits. • Difficult to identify, many genes. • Influenced by environmental factors.

5. M. S. Ashwell, D. W. Heyen, T. S. Sonstegard, C. P. Van Tassell, Y. Da, P. M. VanRaden, M. Ron, J. I. Weller, and H. A. Lewin. Detection of Quantitative Trait Loci Affecting Milk Production, Health, and Reproductive Traits in Holstein Cattle

6. Introduction • After the publication of cattle linkage maps, most groups focused on identifying QTL affecting milk production traits. • With the milk production increasing, the quality of health and reproduction are declining. • The infertility has a large impact on competitiveness and the sustainability of the dairy cattle industry.

7. Studies designed for identification of QTL are based on crosses of genetically distinct breeds or inbred lines. • In 1991 a study was initiated as a collection of semen from 35 dairy grandsire families of Holstein cattle. • The heritability for fertility has been estimated at 4%. (note: be prepared to talk about what this means: what is heritability, is 4% low or high, what does it mean for identifying the genes for this trait…)

8. Objective • To identify QTL affecting milk production traits using the merged set of Dairy Bull DNA Repository (DBDR) genotypes. • Provide the first report of QTL affecting the pregnancy.

9. Materials and Methods • Resource Population. • Semen from 10 Holstein families was selected from progeny-tested animals. • Two research groups conducted independent genome scans. • Genotyping. • For each individual genome scan, microsatellite markers were selected at approximately 20-cM intervals from published bovines maps .

10. Phenotypic Data. • Data for milk yield and composition, SCSand productivity life (PL) collected were processed in genetic evaluation procedure. • The female fertility trait is new genetic evaluation. • Pregnancy status is determined from the date of last breeding and is verified using the next calving date. • Statistical Analysis. • Data from a total of eight traits were analyzed using a regression approach originally described in 1992. • Data included daughter deviations for milk, fat, and protein yield, fat and protein percentage, SCS, and PL, weighted by their respective reliabilities.

11. Results and Discussion • Markers effects on milk production traits. • Five traits were evaluated to identify QTL affecting milk production. • Nine significant effects found on five chromosomes were identified in the cross-family analysis. • The most significant effects were located on Bos taurus autosome (BTA) 3, 6, 14 and 20. • Recently, a mutation was identified in the growth hormone receptors (GHR) gene that is associated with an effect on milk yield and milk composition.

12. Markers effects on SCS and productive life. • Four significant effects on SCS were identified in the across-family analysis. • All significant effects on this trait were detected in the independent DBDR studies. • Only one significant effect on PL was identified. • Markers effect on pregnancy rate • Seven significant effects were identified within families on six chromosomes. • No significant effect were identified across families. • Few groups have identified QTL affecting fertility traits in cattle

13. Conclusions • This study has identified putative QTL affecting female fertility, milk production, and SCS in Holstein grandsire. • These results provide additional evidence of QTL on BTA3 and 6 (affecting protein and fat percentage), BTA14 (affecting fat percentage), BTA20 (affecting protein percentage) and BTA18 (affecting pregnancy rate).

14. C. N. Costa, R. W. Blake, E. J. Pollak, P. A. Oltenacu, R. L. Quaas, and S. R. Searle. Genetic Analysis of Holstein Cattle Populations in Brazil and the United States

15. Introduction • Try to improve milk production based on the importation of germplasm of superior potential. • Potential GxE interaction can be quantified by considering as different traits the dairy performance of relatives in each country. • Small difference between daughter responses and sire breeding in sub-tropical regions.

16. Actual gain depends on the genetic value of the candidate germplasm and its performance in production environment. • About 70% of total semen of Holstein used in Brazil in 1998 was imported from the United States, Canada and Europe. • To evaluate germplasm importation options, quantifying potential interactions between US sires and Brazilian herd environments is essential.

17. Objectives • To estimate components of genetic and residual variance and covariance for yields of milk and fat between herd environment in Brazil and the United States. • To evaluate the impact of additional information from yield of milk fat on estimates of the components of (co)variance for milk and fat in Brazil and the United States. • To estimate genetic responses and correlation in Brazilian environments from indirect selection on half-sister information from alternative US herd environments.

18. Material and Methods • Data • Brazil • Data were provided by the Ministry of Agriculture and the Brazilian Agricultural Research Corporation (EMBRAPA). • United States • Data were provided by the Animal Improvement Programs Laboratory of the USDA. • Herd-year standard deviation (HYSD) classification • Data from each country were separated by HYSD into two environmental classes: Low and High HYSD. • Pedigree • Data file contained the USDA identification code, the country of origin and birth year of each bull and the origin of grandsire of a bull.

19. Results and Discussion • Severe compression of residual variance result in larger heritability estimates in low than in high HYSD. • Differentiating between breeding values is more difficult when genetic variance is restricted by environmental opportunity. • High HYSD Brazil and Low HYSD US herds were the most similar environments between these countries. • Genetic correlation estimates between Brazil and the US data sets suggest similar genetic control of yields of milk and fat in these countries.

20. Genetic expression of daughters in Brazil was similar to that of half-sisters in low HYSD US herds. • The response coefficients using information from high HYSD Brazil and low HYSD US were largest among the estimates obtained. • Management and typical herd environments in Brazil are likely to differ from those in the US because biophysical and socioeconomic condition differ. • Properly genetically tied data on daughters performance of US sires in low HYSD environments would be a better choice for an across-country evaluation.

21. Conclusions • Stratifying herds by herd-year variance was an effective criterion to distinguish herd environments and corresponding genetic gains. • Genetic correlation estimates for yield of milk and fat were large and did not suggest significant interaction in ranking of breeding values of sire between Brazil and the US. • Largest Brazilian daughter response was predicted from half-sister performance in low HYSD environment.

22. My conclusions • The QTL can be used to ranking the bulls according to the environment. • Selections of semen, should be based on results of half-sisters in similar environment. • Not only milk production should be used on selection, health and fertility are also important.