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
Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false disco...
The prior distribution is a key ingredient in Bayesian inference. Prior information on regression coefficients may come from different sources and may or may not be in conflict with the observed data....
Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components ...
EBglmnet is an R package implementing empirical Bayesian method with both lasso (EBlasso) and elastic net (EBEN) priors for generalized linear models. In our previous studies, both EBlasso and EBEN ou...
Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it do...
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...
Recurrent implantation failure is the failure to achieve a pregnancy after multiple attempts with in vitro fertilization treatment. The reason is usually obscure. Many empirical treatments...
The effect of preoperative glycemic control measured by HbA1c on prostate cancer (PCa) outcome remains controversial. Thus, the investigators aim to examine the association of preoperative...
This study is a double-blind, randomized study of MK0991 versus liposomal amphotericin B in the empirical treatment of pediatric patients (ages 2 through 17 years) who have an absolute neu...
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...