alexa Fast Computation of Significance Threshold in QTL Mappi
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Nating Wang1, Hongxiao Tian1, Yongci Li1, Rongling Wu2, Jiangtao Luo3 and Zhong Wang2*

1School of Science, Beijing Forestry University, Beijing 100083, China

2College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, PR China

3Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198-4375, USA

*Corresponding Author:
Wang Z
College of Biological Sciences and Technology
Beijing Forestry University
Beijing 100083, PR China
Tel: +86 10 6233 1279
E-mail: [email protected]

Received Date: December 05, 2016; Accepted Date: January 03, 2017; Published Date: January 09, 2017

Citation: Wang N, Tian H, Li Y, Wu R, Luo J, et al. (2017) Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits. J Biom Biostat 8: 329. doi:10.4172/2155-6180.1000329

Copyright: © 2017 Wang N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Functional Mapping is a popular statistical method in QTL mapping studies for longitudinal data. The threshold for declaring statistical significance of a QTL is commonly obtained through permutation tests, which can be time consuming. To improve the computational efficiency of a permutation test of mixture models used in Functional Mapping, we first quantified the correlation between QTL and longitudinal data, using a curve clustering method. Then, the QTLs which are highly correlated with the outcome were computed in the improved permutation tests. As a result, it reduces the amount of computation in permutation tests and speeds up the computation for Functional Mapping analysis. Simulation studies and real data analysis were conducted to demonstrate that the proposed approach can greatly improve the computational efficiency of QTL mapping without loss of accuracy.

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