Theoretical and Applied Genetics

, Volume 110, Issue 6, pp 1061–1067

Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program

Original Paper

DOI: 10.1007/s00122-005-1926-7

Cite this article as:
Yu, J., Arbelbide, M. & Bernardo, R. Theor Appl Genet (2005) 110: 1061. doi:10.1007/s00122-005-1926-7


Most quantitative trait locus (QTL) mapping studies in plants have used designed mapping populations. As an alternative to traditional QTL mapping, in silico mapping via a mixed-model approach simultaneously exploits phenotypic, genotypic, and pedigree data already available in breeding programs. The statistical power of this in silico mapping method, however, remains unknown. Our objective was to evaluate the power of in silico mapping via a mixed-model approach in hybrid crops. We used maize (Zea mays L.) as a model species to study, by computer simulation, the influence of number of QTLs (20 or 80), heritability (0.40 or 0.70), number of markers (200 or 400), and sample size (600 or 2,400 hybrids). We found that the average power to detect QTLs ranged from 0.11 to 0.59 for a significance level of α=0.01, and from 0.01 to 0.47 for α=0.0001. The false discovery rate ranged from 0.22 to 0.74 for α=0.01, and from 0.05 to 0.46 for α=0.0001. As with designed mapping experiments, a large sample size, high marker density, high heritability, and small number of QTLs led to the highest power for in silico mapping via a mixed-model approach. The power to detect QTLs with large effects was greater than the power to detect QTL with small effects. We conclude that gene discovery in hybrid crops can be initiated by in silico mapping. Finding an acceptable compromise, however, between the power to detect QTL and the proportion of false QTL would be necessary.

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Department of Agronomy and Plant GeneticsUniversity of Minnesota St. PaulUSA
  2. 2.Institute for Genomic DiversityCornell UniversityIthacaUSA