Abstract
BackgroundPedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data.MethodologyWe have developed a Bayesian method for Linkage analysis of Ordinal and Categorical traits (LOCate) that can analyze complex genealogical structure for family groups and incorporate missing data. LOCate uses a Gibbs sampling approach to assess linkage, incorporating a simulated tempering algorithm for fast mixing. While our treatment is Bayesian, we develop a LOD (log of odds) score estimator for assessing linkage from Gibbs sampling that is highly accurate for simulated data. LOCate is applicable to linkage analysis for ordinal or nominal traits, a versatility which we demonstrate by analyzing simulated data with a nominal trait, on which LOCate outperforms LOT, an existing method which is designed for ordinal traits. We additionally demonstrate our method's versatility by analyzing a candidate locus (D2S1788) for panic disorder in humans, in a dataset with a large amount of missing data, which LOT was unable to handle.ConclusionLOCate's accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.
Highlights
Many heritable traits, from pathogen resistance in plants [1] to panic disorder in humans [2], are described using discrete categories such as color or are quantified using discrete, ordered scales such as ‘‘mildly,’’ ‘‘moderately,’’ or ‘‘severely’’ affected
Linkage analysis of Ordinal and Categorical traits (LOCate)’s accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits
The linear regression-based estimator (LinReg) estimator is faster to compute than the RLR estimator, because LinReg involves a simple linear regression, while RLR requires a complex optimization over many values of h
Summary
From pathogen resistance in plants [1] to panic disorder in humans [2], are described using discrete categories such as color or are quantified using discrete, ordered scales such as ‘‘mildly,’’ ‘‘moderately,’’ or ‘‘severely’’ affected. When performing linkage analysis of categorical traits, it is well appreciated that re-coding measurements as binary can lead to decreased power [3,4]. Most previous work done on family-based mapping of categorical traits has been restricted to particular types of pedigrees; these include backcross [9,10,11] and F2 designs [10,11,12,13], 4-way experimental crosses [1,14,15,16], and sets of independent nuclear families [17,18,19]. There is no Bayesian framework for both ordinal and nominal linkage analysis on pedigrees with inbreeding loops and missing data. Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data
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