Track topics on Twitter Track topics that are important to you
Complex binary traits are influenced by many factors including the main effects of many quantitative trait loci (QTLs), the epistatic effects involving more than one QTLs, environmental effects and the effects of gene-environment interactions. Although a number of QTL mapping methods for binary traits have been developed, there still lacks an efficient and powerful method that can handle both main and epistatic effects of a relatively large number of possible QTLs.
In this paper, we use a Bayesian logistic regression model as the QTL model for binary traits that includes both main and epistatic effects. Our logistic regression model employs hierarchical priors for regression coefficients similar to the ones used in the Bayesian LASSO linear model for multiple QTL mapping for continuous traits. We develop efficient empirical Bayesian algorithms to infer the logistic regression model. Our simulation study shows that our algorithms can easily handle a QTL model with a large number of main and epistatic effects on a personal computer, and outperform five other methods examined including the LASSO, HyperLasso, BhGLM, RVM and the single-QTL mapping method based on logistic regression in terms of power of detection and false positive rate. The utility of our algorithms is also demonstrated through analysis of a real data set. A software package implementing the empirical Bayesian algorithms in this paper is freely available upon request.
The EBLASSO logistic regression method can handle a large number of effects possibly including the main and epistatic QTL effects, environmental effects and the effects of gene-environment interactions. It will be a very useful tool for multiple QTLs mapping for complex binary traits.
This article was published in the following journal.
Name: BMC genetics
We proposed multiple-trait random regression models (MTRRM) combining different functions to describe milk yield (MY) and fat (FP) and protein (PP) percentage in dairy goat genetic evaluation by using...
Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of ...
Understanding dose-response relationship is a crucial step in drug development. There are a few parametric methods to estimate dose-response curves, such as the Emax model and the logistic model. Thes...
Many prediction, decision-making, and control architectures rely on online learned Gaussian process (GP) models. However, most existing GP regression algorithms assume a single generative model, leadi...
We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized exp...
This is a phase Ib multi-center, open-label study: escalation part followed by expansion part. The primary purpose of the Phase Ib CBCL201X2102C study is to characterize the safety and tol...
To enhance statistical methods for epidemiological studies by extending the Disturbed Highest Derivative Polynomial (DHDP) to models for binary-logistic and Poisson data and by including r...
Patients and physicians often differ in their perceptions of rheumatoid arthritis (RA) disease activity, as quantified by the patient's global assessment (PGA) and by the evaluator's globa...
The aim was to determine the knowledge attitudes, beliefs, and practices regarding HIV in a population of sex workers in French Guiana and in the Brazilian border town of Oiapoque. A stand...
The purpose of this first-in-human (FIH) study of BLZ945 given as a single agent or in combination with PDR001 is to characterize the safety, tolerability, pharmacokinetics (PK), pharmacod...
Procedures for finding the mathematical function which best describes the relationship between a dependent variable and one or more independent variables. In linear regression (see LINEAR MODELS) the relationship is constrained to be a straight line and LEAST-SQUARES ANALYSIS is used to determine the best fit. In logistic regression (see LOGISTIC MODELS) the dependent variable is qualitative rather than continuously variable and LIKELIHOOD FUNCTIONS are used to find the best relationship. In multiple regression, the dependent variable is considered to depend on more than a single independent variable.
Locations, on the GENOME, of GENES or other genetic elements that encode or control the expression of a quantitative trait (QUANTITATIVE TRAIT, HERITABLE).
The record of descent or ancestry, particularly of a particular condition or trait, indicating individual family members, their relationships, and their status with respect to the trait or condition.
A syndrome of multiple abnormalities characterized by the absence or hypoplasia of the PATELLA and congenital nail dystrophy. It is a genetically determined autosomal dominant trait.
Detailed account or statement or formal record of data resulting from empirical inquiry.
Bioinformatics is the application of computer software and hardware to the management of biological data to create useful information. Computers are used to gather, store, analyze and integrate biological and genetic information which can then be applied...