Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #301590

Research Project: Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information

Location: Animal Genomics and Improvement Laboratory

Title: Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis

Author
item Macciotta, Nicolo - University Of Sassari
item Dimauro, Corrado - University Of Sassari
item Null, Daniel
item Gaspa, Giustino - University Of Sassari
item Castellini, Mirko - University Of Sassari
item Cole, John

Submitted to: Journal of Animal Breeding and Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/3/2014
Publication Date: 2/1/2015
Publication URL: http://handle.nal.usda.gov/10113/60895
Citation: Macciotta, N., Dimauro, C., Null, D.J., Gaspa, G., Castellini, M., Cole, J.B. 2015. Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis. Journal of Animal Breeding and Genetics. 132(1):9-20.

Interpretive Summary: Multivariate factor analysis was used to compare the correlation structure between direct genomic and chromosomal predictions for 31 production, fitness and conformation traits on US Holstein bulls. Differences in the factor pattern between genomic and chromosomal correlation matrices were found for those groups of traits for which the chromosome was known to harbour genes or QTL with a relevant effect. The method could be used for performing a genome wide scan for flagging chromosomes of interest for a specific set of traits by using estimates that are currently produced in genomic evaluation routines.

Technical Abstract: Aim of the study was to compare correlation matrices between direct genomic predictions for 31 production, fitness and conformation traits both at genomic and chromosomal level in US Holstein bulls. Multivariate factor analysis was used to quantify basic features of correlation matrices. Factor extraction carried out at genome level identified seven new variables associated to conformation, longevity, yield, feet and legs, fat and protein content traits, respectively. Results obtained on four different chromosomes (BTA 6, 14, 18 and 20) showed some variation in comparison with the genome-wide structure, that were interpreted as indications of differences in the genetic control of groups of traits, due to existence of a segregating QTL in that specific chromosome. For example, milk yield and composition were associated to distinct factors at genome-wide level, whereas they tended to join in a single factor on BTA 14, which is known to harbour the DGAT1 locus that ìt is known to affect milk production traits. Another example is the variation detected for BTA18, that showed a factor strongly correlated with sire calving and conformation traits, respectively. It is known that in US Holstein there is a segregating QTL on BTA18 that affects these traits. The methodology proposed in this study could be used to performe a scan for flagging chromosomes of interest for a specific set of traits by using estimates that are currently produced in genomic evaluation routines.