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 QTL s 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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