Clustering by genetic ancestry using genome-wide SNP data

Nadia Solovieff,Clinton T Baldwin,Paola Sebastiani,Stephen W Hartley,Martin H Steinberg,Thomas T Perls

doi:10.1186/1471-2156-11-108

Nadia Solovieff, Clinton T Baldwin + Show 4 more

Open Access

https://doi.org/10.1186/1471-2156-11-108

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Journal: BMC Genetics	Publication Date: Jan 1, 2010
Citations: 72	License type: cc-by

Affiliation: Boston University, Center for Human Genetics

Abstract

BackgroundPopulation stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls.ResultsWe developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations.ConclusionsIn addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.

Highlights

Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest
Algorithm The algorithm that we developed consists of the following three components: 1) selection of informative principal components (PCs) for the cluster analysis; 2) clustering to discover population substructure; 3) a novel score to automatically select the best number of clusters that satisfy a variety of criteria
Since k-means does not provide a metric for choosing the appropriate number of clusters, we developed an algorithm and scoring index to identify the optimal number of clusters

Summary

Introduction

Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. PCA [4], spectral graph theory [5], structured association analysis [6,7] and genomic control [8] can detect underlying population substructure with SNP data. Investigators typically use PCA to detect population structure and adjust the estimates of genetic effects for the top number of principal components (PCs) to control for population stratification bias [4].

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