|Piepho, Hans-peter - Hohenheim University|
Submitted to: Trends in Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/12/2011
Publication Date: 5/11/2011
Citation: Simko, I., Piepho, H. 2011. Combining phenotypic data from ordinal rating scales in multiple plant experiments. Trends in Plant Science. 16:235-237.
Interpretive Summary: While new technologies have considerably improved accuracy and throughput of genotyping, little has changed in a way of acquiring phenotypic data. Slow process of acquiring phenotypic data thus creates a bottleneck in association mapping studies and hinders research progress. One possibility for obtaining data for an association study more rapidly is to combine already available phenotypic information. For example, plant breeders, germplasm curators, and other researchers collect annually a large quantity of phenotypic data that could be combined and used for analysis. Although such datasets are available, combining data from different years, locations, and laboratories, is challenging because they were collected on a different set of accessions and frequently also with different rating scale. Combining data from different trials becomes even more complicated if some of the evaluations were performed on ordinal scales. This paper discusses methods that can be used to combine data from ordinal rating scales into a single dataset.
Technical Abstract: The explosion of genomic data is revolutionizing plant breeding and genetics research, but in most applications good phenotypic data are crucial for making efficient use of genomic data. While new technologies have considerably improved accuracy and throughput of genotyping, relative slowness of methodological advance makes phenotyping the major bottleneck for genomic studies (e.g. linkage mapping, association mapping, genomic selection, marker-assisted selection, etc.). In other applications, good phenotypic data are of interest in themselves, e.g. for assessing the value of accessions in genebanks or breeding programs. Combining data from different trials becomes complicated if some of the evaluations were performed on ordinal scales. This paper discusses methods that can be used to combine data from ordinal rating scales into a single dataset.