Cross-validation in association mapping and its relevance for the estimation of QTL
parameters of complex traits |
| |
Authors: | T Würschum T Kraft |
| |
Institution: | 1.State Plant Breeding Institute, University of
Hohenheim, Stuttgart, Germany;2.Syngenta Seeds AB, Landskrona, Sweden |
| |
Abstract: | Association mapping has become a widely applied genomic approach to identify quantitative
trait loci (QTL) and dissect the genetic architecture of complex traits. However,
approaches to assess the quality of the obtained QTL results are lacking. We therefore
evaluated the potential of cross-validation in association mapping based on a large sugar
beet data set. Our results show that the proportion of the population that should be used
as estimation and validation sets, respectively, depends on the size of the mapping
population. Generally, a fivefold cross-validation, that is, 20% of the lines as
independent validation set, appears appropriate for commonly used population sizes. The
predictive power for the proportion of genotypic variance explained by QTL was
overestimated by on average 38% indicating a strong bias in the estimated QTL
effects. The cross-validated predictive power ranged between 4 and 50%, which are
more realistic estimates of this parameter for complex traits. In addition, QTL frequency
distributions can be used to assess the precision of QTL position estimates and the
robustness of the detected QTL. In summary, cross-validation can be a valuable tool to
assess the quality of QTL parameters in association mapping. |
| |
Keywords: | association mapping cross-validation predictive power QTL position sugar beet |
|
|