Semi-supervised spectral clustering with application to detect population stratification

In genetic association studies, unaccounted population stratification can cause spurious associations in a discovery process of identifying disease-associated genetic markers. In such a situation, prior information is often available for some subjects' population identities. To leverage the additional information, we propose a semi-supervised clustering approach for detecting population stratification. This approach maintains the advantages of spectral clustering, while is integrated with the additional identity information, leading to sharper clustering performance. To demonstrate utility of our approach, we analyze a whole-genome sequencing dataset from the 1000 Genomes Project, consisting of the genotypes of 607 individuals sampled from three continental groups involving 10 subpopulations. This is compared against a semi-supervised spectral clustering method, in addition to a spectral clustering method, with the known subpopulation information by the Rand index and an adjusted Rand (ARand) index. The numerical results suggest that the proposed method outperforms its competitors in detecting population stratification.