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Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize.

Avjinder S Kaler1, Jason D Gillman2, Timothy Beissinger3

  • 1Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR, United States.

Frontiers in Plant Science
|March 12, 2020
PubMed
Summary
This summary is machine-generated.

The Fixed and Random Model Circulating Probability Unification (FarmCPU) model effectively controls false positives and negatives in association mapping for complex traits. This advanced method outperforms traditional models in identifying quantitative trait loci (QTLs) in crops like soybean and maize.

Keywords:
association mappinggenome-wide association analysesmultiple testing correctionquantitative trait locistatistical model analysis

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Area of Science:

  • Quantitative genetics
  • Plant breeding
  • Statistical genomics

Background:

  • Association mapping (AM) identifies genetic variants for complex traits by leveraging historical recombination.
  • Controlling false positives from population structure and relatedness is crucial in AM.
  • Mixed linear models (MLM) are commonly used but can lead to false negatives due to overfitting.

Purpose of the Study:

  • To compare the performance of eight statistical models, from single-locus to multi-locus, for association mapping.
  • To evaluate model efficacy in controlling false positives and negatives in soybean and maize.
  • To assess the impact of different multiple comparison adjustments on quantitative trait loci (QTL) detection.

Main Methods:

  • Evaluated eight association mapping models, including MLM variants and the Fixed and Random Model Circulating Probability Unification (FarmCPU).
  • Utilized simulated datasets with known quantitative trait loci (QTLs) and real trait data from soybean and maize.
  • Compared model performance using Q-Q plots, identification of known QTLs, and detection accuracy under varying heritability.

Main Results:

  • FarmCPU demonstrated superior performance in controlling both false positives and false negatives compared to other models.
  • FarmCPU accurately identified known QTLs in simulated data and pinpointed significant SNPs near known genes in soybean.
  • Less conservative multiple comparison methods (FDR, pFDR) with FarmCPU yielded more accurate QTL counts than overly conservative methods with other models.

Conclusions:

  • The FarmCPU model offers a robust approach for accurate association mapping of complex traits in crops.
  • FarmCPU effectively balances the detection of true associations while minimizing false positives and negatives.
  • This study highlights FarmCPU's advantage in identifying genetic architecture underlying complex traits in soybean and maize.