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Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds

Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds. N. P. P. Macciotta 1,* and J. B. Cole 2. 1 Dipartimento di Scienze Zootecniche, Università di Sassari, Italy 2 Animal Improvement Programs Laboratory , ARS, USDA, Beltsville, MD, USA.

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Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds

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  1. Multivariate factor analysis of genomic correlation matrices in three US dairy cattle breeds N. P. P. Macciotta1,* and J. B. Cole2 1Dipartimento di Scienze Zootecniche, Università di Sassari, Italy 2Animal Improvement Programs Laboratory, ARS, USDA, Beltsville, MD, USA Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  2. Chromosomal breeding values: CEBV Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  3. Genome-wide and chromosome-wide (BTA18) correlations can be calculated Cole et al. 2009. J. Dairy Sci. 92(6):2931–2946. Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  4. Correlations between genomic values • Correlation structures differ between chromosomes • These differences may help identify genes with small additive effects that affect several traits Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  5. Comparison of genetic correlation (covariance) matrices • Issue in population genetics • Several proposed tests • Common principal components (CPC) • PC are used to explain the variance of a system • The covariance should be investigated Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  6. The factor model A small number of latent variables can explain the covariance structure of the original data S = BB’ +  S = (Co)variance matrix of original data B = (Co)variance between original data and latent factors  = Specific variance matrix DSZ Dipartimento di Scienze Zootecniche Università degli Studi di Sassari

  7. Aim of the work • Compare correlation matrices of genomic (GEBV) and chromosome-specific (CEBV) within and across three US cattle breeds • Apply multivariate factor analysis • Can matrices for a large number of traits be compared algorithmically? Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  8. Materials and methods • GEBV from 3 breeds of US cattle Holstein 63,615 Brown Swiss 2,038 Jersey 8,084 • 23 traits (production and functional) • GEBV and CEBV from December 2010 run • Genomewide (GW) and chromosomal (CHRW) correlation matrices Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  9. Traits analyzed Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  10. Factor Analysis Genome-wide GEBV correlation matrices Breed comparison Within-breed comparison GW vs. CHRW Chromosome wide CEBV correlation matrices BTA6, BTA14, BTA18 Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  11. BS GW

  12. Summarizing Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  13. Some comparisons between breeds GW Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  14. Some comparisons between breed GW Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  15. Some considerations • Similar meaning of factors extracted in the different breeds • Some differences in their structure • More defined in the Holstein compared to the other two breeds Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  16. Some comparison within breed: BS Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  17. Some comparison within breed: JE Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  18. Some comparison within breed: BS Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  19. Some considerations • Some changes in the factor structure moving from GW to CHRW • Overlapping yield and composition factors (BTA6 in BS) • Different behaviour of protein (BTA14 in JE) • Inclusion of a fertility trait in the functional factor (BTA18 for BS) Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  20. Implications • Multivariate factor analysis can characterise the correlation structure of GEBV and CEBV • Identification of differences in genetic correlations among traits across the genome and on individual chromosomes • Observed changes in the correlation structure may be indications of genomic regions affecting multiple traits? Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

  21. Acknowledgements • Research developed within the non funded cooperative agreement 58-1265-1-033FN between Animal Improvement Programs labortory, ARS, USDA and Università di Sassari, Dipartimento di Scienze Zootecniche Dipartimento di Scienze Zootecniche DSZ Università degli Studi di Sassari

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