Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach

Algorithms

Ahmadreza Ghaffarizadeh³  Mehdi Eftekhari¹  Ali K. Esmailizadeh² 

[1] Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran; E-Mail:;Department of Animal Science, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran; E-Mail:;Department of Computer Science, Utah State University, Logan, UT 84341, USA; E-Mail:

关键词: QTL;    quantitative trait loci mapping;    multi-objective evolutionary algorithm;    partial least squares;    extinction-based EAs;   

DOI  :  10.3390/a6030546

来源: mdpi

【 摘 要 】

The Quantitative Trait Loci (QTL) mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA) method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS) regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.