multipleQTL

MultipleQTL refers to a class of statistical approaches for mapping and characterizing multiple quantitative trait loci (QTL) that influence a complex trait. These methods extend beyond single-QTL analysis by fitting several potential loci simultaneously, improving detection power, estimating locus effects more accurately, and identifying potential interactions among loci or with the environment.

Common implementations include multiple interval mapping and its descendants, such as MQM (multiple QTL mapping) and CIM (composite interval mapping). These approaches typically treat markers outside the target intervals as cofactors to control background genetic variation. Bayesian multi-locus methods and penalized regression frameworks (for example, Bayesian variable selection or ridge/lasso penalties) are also used, especially with high-density marker data, to handle model complexity and reduce overfitting.

The typical workflow requires a phenotypic dataset and genotypic data coordinated on the same individuals, along with a genetic linkage map. Analysts fit a multi-locus model that searches for QTL positions along the map, estimates additive and, if relevant, dominance effects, and assesses significance using LOD scores, likelihood ratios, or Bayes factors. Permutation tests are often employed to establish empirical significance thresholds. Outputs usually include QTL positions, estimated effects, the percentage of phenotypic variance explained, and information on potential QTL-by-environment interactions.

Applications of MultipleQTL methods are widespread in plant and animal breeding, where they support marker-assisted selection, identification of favorable alleles, and dissection of the genetic architecture of complex traits. Limitations include computational demands with dense marker data, sensitivity to population design, and residual risk of overfitting without proper model selection and validation.