A multivariate model for ordinal trait analysis

Joint parameter estimation in the QTL mapping of ordinal traits

With the rapid development of statistical genetics, the deep researches of ordinal traits have been gradually emphasized. The data of these traits bear relatively less information than those of continuous phenotypes, therefore it is more complex to map the quantitative trait loci (QTL) of ordinal traits. In this paper, the multiple-interval mapping method is considered in the genetic mapping of ordinal traits. By combining threshold model and statistical model, we build a cumulative logistic regression model to express the relationship between the ordinal data and the QTL genotypes. In order to make the interval mapping more straightforward, we treat the recombination rates as unknown parameters, and then simultaneously obtain the estimates of QTL positions, QTL effects and threshold parameters via the EM algorithm. We perform simulation experiments to investigate and compare the proposed method. We also present a real example to test the reasonableness of the considered model and estimate both model parameters and QTL parameters. Both results of simulations and example show that the method we proposed is reasonable and effective.

Statistical Analysis in Genetic Studies of Mental Illnesses

Identifying the risk factors for mental illnesses is of significant public health importance. Diagnosis, stigma associated with mental illnesses, comorbidity, and complex etiologies, among others, make it very challenging to study mental disorders. Genetic studies of mental illnesses date back at least a century ago, beginning with descriptive studies based on Mendelian laws of inheritance. A variety of study designs including twin studies, family studies, linkage analysis, and more recently, genomewide association studies have been employed to study the genetics of mental illnesses, or complex diseases in general. In this paper, I will present the challenges and methods from a statistical perspective and focus on genetic association studies.

Bayesian linkage analysis of categorical traits for arbitrary pedigree designs

BACKGROUND

Pedigree studies of complex heritable diseases often feature nominal or ordinal phenotypic measurements and missing genetic marker or phenotype data.

METHODOLOGY

We 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.

CONCLUSION

LOCate's accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.