Identifying nineteenth century genealogical links from genotypes
- Stankovich, J; Bahlo, M; Rubio, JP; Wilkinson, CR; Thomson, R; Banks, A; Ring, M; Foote, SJ; Speed, TP;
Publication Year 2005-07, Volume 117, Issue #2-3, Page 188-199
- Journal Title
- HUMAN GENETICS
- Publication Type
- Journal Article
- We have developed a likelihood method to identify moderately distant genealogical relationships from genomewide scan data. The aim is to compare the genotypes of many pairs of people and identify those pairs most likely to be related to one another. We have tested the algorithm using the genotypes of 170 Tasmanians with multiple sclerosis recruited into a haplotype association study. It is estimated from genealogical records that approximately 65% of Tasmania's current population of 470,000 are direct descendants of the 13,000 female founders living in this island state of Australia in the mid- nineteenth century. All cases and four to. five relatives of each case have been genotyped with microsatellite markers at a genomewide average density of 4 cM. Previous genealogical research has identified 51 pairwise relationships linking 56 of the 170 cases. Testing the likelihood calculation on these known relative pairs, we have good power to identify relationships up to degree eight ( e. g. third cousins once removed). Applying the algorithm to all other pairs of cases, we have identified a further 61 putative relative pairs, with an estimated false discovery rate of 10%. The power to identify genealogical links should increase when the new, denser sets of SNP markers are used. Except in populations where there is a searchable electronic database containing virtually all genealogical links in the past six generations, the algorithm should be a useful aid for genealogists working on gene- mapping projects, both linkage studies and association studies.
- GENETIC MAPS; POPULATION; INFERENCE; GENOME; INDIVIDUALS; DISEQUILIBRIUM; IDENTIFICATION
- Publisher's Version
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Creation Date: 2005-07-01 12:00:00Last Modified: 0001-01-01 12:00:00