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Journal of Applied Nonlinear Dynamics 5(1) (2016) 105--115 | DOI:10.5890/JAND.2016.03.008

Burak Karacaören

Department of Animal Science, Faculty of Agriculture, Akdeniz University Antalya, 07059, TURKEY

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Abstract

Analyzing longitudinal observations with an appropriate methodology is important for detecting significant single nucleotide polymorphisms (SNPs) for complex traits in genome wide association studies (GWAS). Here we assumed that genomic signal over time could be traced by a Bayesian random walk- Kalman filter model in state space form to obtain longitudinal residuals. The Kalman filter removes the requirement for collecting the whole data set before making estimations, thus estimates are immediately available. For example, with the functional mapping approach, whole sets of observations must to be collected in advance. This may include months (if not years) of waiting time depending on the nature of the phenotypes and the experimental design. Whereas, because of the longitudinal residuals used in association mapping, biological reasoning can be easily applied given that the signal is genuine. The advantages of our method are demonstrated by both simulated and real datasets.

Acknowledgments

The author wishes to acknowledge useful discussions with Dr Li Zitong on mouse behavioral data set. The author wishes to acknowledge useful discussions with Dr Sitki ¸Ca˘gda¸s ˙Inam on LaTeX typesettings. This work was supported by Research Fund of the Akdeniz University Project Number 106.

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