Paper Info
Title
Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project.
Abstract
Motivation: Population stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/ next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i. e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/ sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power.
Year
DOI
Venue
2016
10.1093/bioinformatics/btv752
BIOINFORMATICS
Field
DocType
Volume
Data mining,Population,Allele frequency,Computer science,Genetic association,1000 Genomes Project,Jaccard index,DNA sequencing,Bioinformatics,Population stratification,Minor allele frequency
Journal
32
Issue
ISSN
Citations 
9
1367-4803
4
PageRank 
References 
Authors
0.50
1
8
Name
Order
Citations
PageRank
Dmitry Prokopenko141.17
Julian Hecker240.83
Edwin K. Silverman3587.75
Marcello Pagano45715.02
Markus M Nöthen5184.07
Christian Dina650.87
Christoph Lange716320.58
Heide Fier851.54