Inference of ancestry: constructing hierarchical reference populations and assigning unknown individuals
- PDF / 3,183,896 Bytes
- 24 Pages / 610 x 791 pts Page_size
- 93 Downloads / 221 Views
Inference of ancestry: Constructing hierarchical reference populations and assigning unknown individuals Jayne E. Ekins,1 Jacob B. Ekins,1 Lara Layton,1 Luke A.D. Hutchison,1 Natalie M. Myres1and Scott R. Woodward1,2* 1
Sorenson Molecular Genealogy Foundation, Salt Lake City, UT, USA Department of Molecular Biology and Microbiology, Brigham Young University, Provo, UT, USA *Correspondence to: Tel: þ1 801 461 9789; Fax: þ1 801 461 9722; Email: [email protected] 2
Date Received: 11th April 2005
Abstract
The ability to infer personal genetic ancestry is being increasingly utilised in certain medical and forensic situations. Herein, the unsupervised Bayesian clustering algorithms structure, is employed to analyse 377 autosomal short tandem repeats typed on 1,056 individuals from the Centre d’Etude du Polymorphisme Humain Human Diversity Panel. Individuals of known geographical origin were hierarchically classified into a framework of increasingly homogeneous clusters to serve as reference populations into which individuals of unknown ancestry can be assigned. The groupings were characterised by the geographical affinities of cluster members and the accuracy of these procedures was verified using several genetic indices. Fine-scale substructure was detectable beyond the broad population level classifications that previously have been explored in this dataset. Metrics indicated that within certain lines, the strongest structuring signals were detected at the leaves of the hierarchy where lineage-specific groupings were identified. The accuracy of unknown assignment was assessed at each level of the hierarchy using a ‘leave one out’ strategy in which each individual was stripped of cluster membership and then re-assigned using the supervised Bayesian clustering algorithm implemented in GeneClass2. Although most clusters at all levels of resolution experienced highly accurate assignment, a decline was observed in the finer levels due to the mixed membership characteristics of some individuals. The parameters defined by this study allowed for assignment of unknown individuals to genetically defined clusters with measured likelihood. Shared ancestry data can then be inferred for the unknown individual. Keywords: population genetics, human population structure, clustering, Bayesian inference, short tandem repeats (STRs)
Introduction Hypervariable microsatellite markers, situated across the autosomes, have been shown to produce stronger resolution for high-level differentiation of populations when compared with biallelic markers.1 Several expanded studies have demonstrated the usefulness and accuracy with which multi-locus microsatellites can define genetic groupings that correspond well with geographical and other proxy designations;2 – 7 however, the resolution of such studies has been variable. Using 377 autosomal loci and Bayesian clustering methods, Rosenberg et al.6 demonstrated genetic differentiation among major continents and the ability within certain localities to identify a subpopulation as a single genetic grouping from o
Data Loading...
