QTL analyses of complex traits with cross validation, bootstrapping and other biometric methods |
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Authors: | AE Melchinger HF Utz CC Schön |
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Institution: | (1) Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany;(2) State Plant Breeding Institute, University of Hohenheim, 70593 Stuttgart, Germany |
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Abstract: | With the development of molecular markers, dissection of complex quantitative traits by mapping the underlying genetic factors
has become a major research area in plant breeding. Here, we report results from a vast QTL mapping experiment in maize with
testcrosses of N= 976 F4:5 lines evaluated in E= 16 environments. Although the number of detected QTL confirmed the infinitesimal model of quantitative genetics (e.g., 30
QTL detected with LOD ≥ 2.5 for plant height, explaining p= 61% of the genetic variance), cross validation (CV) still revealed an upward bias of about 10% in p. With smaller values
of N (122, 244, 488) and E (2, 4), the number of detected QTL decreased, but the estimates of p remained almost the same due to a tremendous increase in the bias. This illustrates that QTL effects obtained from smaller
sample sizes are usually highly inflated, leading to an overly optimistic assessment of the prospects of MAS. Moreover, inferences
about the genetic architecture (number of QTL and their effects) of complex traits cannot be achieved reliably with smaller
sample sizes. Based on simulations, we conclude that CV and one method of bootstrapping (BS) performed well with regard to
yielding realistic estimates of p. In addition, we briefly review progress in new biometric methods and approaches to QTL mapping in plants including Bayesian
methods that show great promise to overcome the present limitations of QTL mapping.
This revised version was published online in July 2006 with corrections to the Cover Date. |
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Keywords: | Bayesian methods bootstrapping cross validation QTL mapping |
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