Codominant scoring of AFLP in association panels

Abstract

A study on the codominant scoring of AFLP markers in association panels without prior knowledge on genotype probabilities is described. Bands are scored codominantly by fitting normal mixture models to band intensities, illustrating and optimizing existing methodology, which employs the EM-algorithm. We study features that improve the performance of the algorithm, and the unmixing in general, like parameter initialization, restrictions on parameters, data transformation, and outlier removal. Parameter restrictions include equal component variances, equal or nearly equal distances between component means, and mixing probabilities according to Hardy-Weinberg Equilibrium. Histogram visualization of band intensities with superimposed normal densities, and optional classification scores and other grouping information, assists further in the codominant scoring. We find empirical evidence favoring the square root transformation of the band intensity, as was found in segregating populations. Our approach provides posterior genotype probabilities for marker loci. These probabilities can form the basis for association mapping and are more useful than the standard scoring categories A, H, B, C, D. They can also be used to calculate predictors for additive and dominance effects. Diagnostics for data quality of AFLP markers are described: preference for three-component mixture model, good separation between component means, and lack of singletons for the component with highest mean. Software has been developed in R, containing the models for normal mixtures with facilitating features, and visualizations. The methods are applied to an association panel in tomato, comprising 1,175 polymorphic markers on 94 tomato hybrids, as part of a larger study within the Dutch Centre for BioSystems Genomics.

Fig. 1

Histograms of band intensities of marker 1,039 with superimposed normal densities. Subplots a and b show color-coded hard classifications based on probability thresholds 0.95, and 0.98, respectively. In the last case, some observations are classified as unknown (Z)

Fig. 2

Four examples of AFLP markers from the tomato data with histograms of band intensities, and well fitting normal mixture densities

Fig. 3

Examples of features helping unmixing of marker intensities for the tomato data. Subplots 1a–b deal with starting values of parameters; 2a1–a2 restriction on σ: hetero- versus homoscedasticity; 2b1–b2 restriction on μ: equidistant component means; 2c1–c2 HWE restriction on π; 3a1–a4 transformation of band intensity; 4a1–a2 number of components of mixture model; 4b1–b3 separation of group means; 4c1–c2 outliers; five extra information in plot

Fig. 4

Histogram and fitted normal mixtures with unrestricted π_j (subplot a) and restricted π_j according to HWE (subplot b)