Semi-supervised spectral clustering with application to detect population stratification

Binghui Liu,Wei Pan,Xiaotong Shen

doi:10.3389/fgene.2013.00215

Abstract

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.

Highlights

With the rapid advance of high-throughput technologies, genome-wide association studies (GWAS) and whole-exome or whole-genome sequencing studies have become popular (International HapMap Consortium, 2003)
As argued in Lee et al (2009), different methods may be applicable in different situations, for instance, a combination of PCA and a clustering method may be preferable when the method is applied to preprocess in association studies
We propose two methods to detect population stratification, that is, semi-supervised clustering methods that are integrated with PCA and another clustering method, respectively

Summary

Introduction

With the rapid advance of high-throughput technologies, genome-wide association studies (GWAS) and whole-exome or whole-genome sequencing studies have become popular (International HapMap Consortium, 2003). For detecting or adjusting for population stratification, three major methods have been proposed, including genomic control (Devlin and Roeder, 1999; Devlin et al, 2004), structured association mapping and other clustering methods (Pritchard et al, 2000; Satten et al, 2001), and principal component analysis [PCA, (Patterson et al, 2006; Zhang et al, 2012)] and spectral methods (Lee et al, 2009; Zhang et al, 2009). In a situation where some subjects’ population identities are known priori, a semi-supervised approach is more suitable Towards this end, we propose two methods to detect population stratification, that is, semi-supervised clustering methods that are integrated with PCA and another clustering method, respectively. These methods are developed to (1) integrate the prior information for clustering, (2) to avoid that dense clusters are collapsed into a single group, whereas their sparser counterparts are divided into more multiple clusters, and (3) utilize the prior information to separate highly overlapped subpopulations

Methods

Results

Discussion

Conclusion