MixFit: Methodology for Computing Ancestry-Related Genetic Scores at the Individual Level and Its Application to the Estonian and Finnish Population Studies

Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Num...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 1; p. e0170325
Main Authors: Haller, Toomas, Leitsalu, Liis, Fischer, Krista, Nuotio, Marja-Liisa, Esko, Tõnu, Boomsma, Dorothea Irene, Kyvik, Kirsten Ohm, Spector, Tim D., Perola, Markus, Metspalu, Andres
Format: Journal Article
Language:English
Published: United States Public Library of Science 20.01.2017
Public Library of Science (PLoS)
Subjects:
Bioinformatics
Biology and Life Sciences
Cohort Studies
Computer programs
Data processing
Estonia
Estonians
Finland
Finns
Genealogy and Heraldry
Genetic aspects
Genetic diversity
Genetics, Population
Genomes
Genotypes
Haplotypes
History
Humans
Internet
Medical research
Methods
People and Places
Population
Population studies
Population-based studies
Populations
Precision medicine
Public health
Quantitative genetics
Software
Studies
Finland
Estonia
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Summary:Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Numerical ancestry component scores are assigned to individuals based on comparisons with reference populations. These comparisons are conducted with an existing analytical pipeline making use of genotype phasing, similarity matrix computation and our addition-multidimensional best fitting by MixFit. The method is demonstrated by studying Estonian and Finnish populations in geographical context. We show the main differences in the genetic composition of these otherwise close European populations and how they have influenced each other. The components of our analytical pipeline are freely available computer programs and scripts one of which was developed in house (available at: www.geenivaramu.ee/en/tools/mixfit).
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Competing Interests: The authors have declared that no competing interests exist.
Conceptualization: TH.Data curation: DIB KOK TDS MP AM TE.Formal analysis: TH KF LL.Funding acquisition: AM.Investigation: TH LL.Methodology: TH KF.Project administration: AM MP.Resources: AM TE.Software: TH.Supervision: TH.Validation: TH MLN.Visualization: TH KF.Writing – original draft: TH.Writing – review & editing: TH LL MLN.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0170325