Interval mapping for loci affecting unordered categorical traits

Abstract

Many traits including shapes and colors of flowers, fruits and seeds in plants, as well as coat colors and some behavioral properties in animals, are recorded in discrete categories. If categories are ordered, genetic analyses of the categorical traits are often performed using the threshold model, which considers a latent continuous variable, called the liability, underlying a trait and assumes the monotonic relationship between the phenotype and the liability. In some categorical traits, however, descriptions of phenotypes are purely nominal and the phenotypic scores cannot be ordered. The threshold model is unreasonable for the analyses of such unordered categorical traits. In this study, we developed a method for interval mapping of loci affecting unordered categorical traits with more than two categories. The probability of the phenotype of an individual falling in each of the categories was expressed by a polychotomous logistic model, in which the log-odds for each category relative to the reference category were assumed to follow a linear model including genotype at a locus affecting a trait as covariate. Based on the model, the interval mapping using a maximum likelihood method was devised for the analysis of complex categorical traits described with unordered categories. We confined ourselves to the case of F2 populations derived from a cross between two inbred lines, although this approach can easily be extended to the analyses for other populations of general structures. As results of analyses of simulated data show, the method showed high efficiency in detecting the loci affecting unordered categorical traits.

The genetic architecture of floral traits in the woody plant Prunus mume

“…In this GWAS study 63 , 64 , we describe the measured value y i of a trait for individual i using a regression model where μ is the overall mean, X i refers to the subpopulation of individual i , β is the effect for subpopulation, a and d are the additive and dominant effect of SNPs, respectively, ξ i and ζ i are the indicator vectors of the additive and dominant effects of SNPs for individual i , and ɛ i is the residual error. The J -th elements of ξ i and ζ i are defined as The regression coefficients can be fitted using maximum likelihood method.…”

Bayesian Linkage Analysis of Categorical Traits for Arbitrary Pedigree Designs

“…Most previous work done on family-based mapping of categorical traits has been restricted to particular types of pedigrees; these include backcross [9]–[11] and F2 designs [10]–[13], 4-way experimental crosses [1], [14]–[16], and sets of independent nuclear families [17]–[19]. Recent methods by Zhang et al [20], Dupuis et al [21], and Diao and Lin [22] allow linkage analysis for ordinal traits on arbitrary pedigrees.…”