Identifying nineteenth century genealogical links from genotypes

Abstract

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.

Maximum-likelihood estimation of recent shared ancestry (ERSA)

“…4). For comparison, we analyzed the same relationships by applying RELPAIR (Epstein et al 2000) and GBIRP (Stankovich et al 2005) to subsets of our SNP loci (see Fig. 4; Supplemental material).…”

“…(Blue dotted line) The power of RELPAIR (Epstein et al 2000) to detect a relationship (using 9990 evenly spaced autosomal markers with minor allele frequency MAF > 0.4, default likelihood ratio LR threshold of 10 for reporting a relationship as significant). (Blue solid line) The power of GBIRP (Stankovich et al 2005) (10,028 evenly spaced autosomal markers with MAF > 0.4, LOD threshold of 2.34 for significance as in Stankovich et al 2005, corresponding to a = 0.001 with 1 d.f. ).…”

“…The likelihoods of various relationship models are then estimated from the data. Examples of these methods include RELPAIR (Boehnke and Cox 1997;Epstein et al 2000), PREST (which extended the methods in Boehnke and Cox 1997;McPeek and Sun 2000;Sun et al 2002), and GBIRP (which extended PREST to the problem of general relationship estimation) (Stankovich et al 2005). These tools were initially designed for use with hundreds of microsatellite loci spaced at intervals of several centimorgans, but they have also been applied to high-density SNP data (e.g., Berkovic et al 2008;Pemberton et al 2010).…”

Identifying genetic relatives without compromising privacy

“…While GWASs have been the most visible application of high-throughput genotyping technologies, other areas have been revolutionized as well. For example, these technologies have allowed researchers to ask fundamental questions about human history (Liu et al 2006;Reich et al 2009;Tishkoff et al 2009), to identify genetic relationships between individuals (Stankovich et al 2005;Pemberton et al 2010;Kyriazopoulou-Panagiotopoulou et al 2011), and to characterize an individual's ancestry (Royal et al 2010). Over the past few years, a personal genomics industry has been established that provides genetic sequencing, genotyping, and analysis services directly to consumers (Genetics and Public Policy Center 2011).…”

Improved maximum likelihood reconstruction of complex multi-generational pedigrees

“…However, these population samples undoubtedly contain sets of relatives and there is a growing interest in estimating such relationships for various purposes including the study of rare genes and parent-of-origin effects. Analyses that assume all individuals are unrelated can be badly biased unless hidden relationships are excluded or appropriate adjustments made (Stankovich et al, 2005;Choi et al, 2009;Thornton and McPeek, 2010;Jankovic et al, 2010;Staples et al, 2013). Even in linkage studies which require pedigree data, undeclared relationships between pedigree founders and between individuals in different families can bias the results and need to be properly accounted for (Génin and Clerget-Darpoux, 1996;Sheehan and Egeland, 2008;Glazner and Thompson, 2012).…”