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ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Dairy Forage Research » Research » Publications at this Location » Publication #327845
Title: The use of marker-data transformations to account for linkage disequilibrium in genomic selection: a case study in switchgrass (Panicum virgatum L.)

Author
item RAMSTEIN, GUILLAUME - University Of Wisconsin
item Casler, Michael

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/16/2016
Publication Date: 6/12/2016
Citation: Ramstein, G., Casler, M.D. 2016. The use of marker-data transformations to account for linkage disequilibrium in genomic selection: a case study in switchgrass (Panicum virgatum L.) [abstract]. 5th International Conference on Quantitative Genetics. Paper No. 11.

Interpretive Summary:

Technical Abstract: Genomic selection (GS) is an attractive technology to generate rapid genetic gains, particularly in perennial grass species like switchgrass, where phenotyping generally requires at least two years of field trial. In this study, we empirically assessed prediction procedures for GS in two different populations consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height and heading date. Marker data was produced for the families’ parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data was preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Furthermore, our analyses indicate that the cases in which marker-data transformations seem advantageous are those where the causal loci are underrepresented in the marker data, thereby bringing insight into why and when accounting for linkage disequilibrium might be useful in genomic prediction.