TY - JOUR
T1 - A comparison between similarity matrices for principal component analysis to assess population stratification in sequenced genetic data sets
AU - Lee, Sanghun
AU - Hahn, Georg
AU - Hecker, Julian
AU - Lutz, Sharon M.
AU - Mullin, Kristina
AU - Hide, Winston
AU - Bertram, Lars
AU - Demeo, Dawn L.
AU - Tanzi, Rudolph E.
AU - Lange, Christoph
AU - Prokopenko, Dmitry
N1 - Funding Information:
The computations in this paper were run in part on the FASRC Cannon cluster supported by the FAS Division of Science Research Computing Group at Harvard University. The funding body has no role in the design of the study and collection, analysis and interpretation of data and in writing the manuscript. Please refer to the Supplementary Note for full acknowledgements.
Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2023/1/19
Y1 - 2023/1/19
N2 - Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.
AB - Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.
UR - http://www.scopus.com/inward/record.url?scp=85147044885&partnerID=8YFLogxK
U2 - 10.1093/bib/bbac611
DO - 10.1093/bib/bbac611
M3 - Journal articles
C2 - 36585781
AN - SCOPUS:85147044885
SN - 1467-5463
VL - 24
JO - Briefings in bioinformatics
JF - Briefings in bioinformatics
IS - 1
M1 - 1
ER -