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Association mapping in an elite maize breeding population.

Wenxin Liu1, Manje Gowda, Jana Steinhoff

  • 1State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|June 18, 2011
PubMed
Summary

Comparing association mapping models in maize, Model A controlled stratification for grain yield. Model B is recommended for grain moisture to prevent false positives, while Model C revealed varied SNP effects impacting genomic selection.

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Area of Science:

  • Plant genetics
  • Quantitative trait analysis
  • Maize breeding

Background:

  • Association mapping (AM) is crucial for understanding complex genetic traits.
  • Evaluating statistical models for AM is essential for accurate genetic dissection.
  • Maize breeding programs generate valuable data for empirical model comparisons.

Purpose of the Study:

  • To compare statistical models for association mapping in an elite maize breeding program.
  • To assess QTL detection power and population stratification correction.
  • To investigate the impact of heterogeneous SNP effects on genomic selection.

Main Methods:

  • Utilized data from 930 maize testcross progenies evaluated for grain yield and moisture.
  • Employed 425 SNP markers for genetic fingerprinting.
  • Compared three statistical models: Model A (cofactors), Model B (cofactors + population effect), and Model C (nested SNP effects).

Main Results:

  • Model A effectively controlled population stratification for grain yield.
  • Model B is recommended for grain moisture to avoid false positives due to high among-population variance.
  • Model C demonstrated significant heterogeneity in SNP allele substitution effects across populations.

Conclusions:

  • The choice of association mapping model impacts QTL detection and stratification control in maize.
  • Model B is crucial for traits with substantial population structure to ensure accurate results.
  • Heterogeneous SNP effects identified by Model C have critical implications for the reliability of genomic selection in diverse breeding populations.