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Identifying the causal genetic markers responsible for certain phenotypes is a main aim in human genetics. In the context of complex diseases, which are believed to have multiple causal loci of largely unknown effects and positions, it is essential to formulate general yet accurate methods for gene mapping. In this direction of research, a new algorithm for gene mapping is proposed which treats the data using partial least squares regression and then locates the causal markers by cross model validation. Results obtained show their compliance with the ones obtained by standard techniques, yet more accuracy is achieved; hence, showing another application of multi-variate data analysis to the problem of human genetics.