title: On the use of dense SNP marker data for the identification of distant relative pairs.
abstract: There has been recent interest in the exploitation of readily available dense genome scan marker data for the identification of relatives. However, there are conflicting findings on how informative these data are in practical situations and, in particular, sets of thinned markers are often used with no concrete justification for the chosen spacing. We explore the potential usefulness of dense single nucleotide polymorphism (SNP) arrays for this application with a focus on inferring distant relative pairs. We distinguish between relationship estimation, as defined by a pedigree connecting the two individuals of interest, and estimation of general relatedness as would be provided by a kinship coefficient or a coefficient of relatedness. Since our primary interest is in the former case, we adopt a pedigree likelihood approach. We consider the effect of additional SNPs and data on an additional typed relative, together with choice of that relative, on relationship inference. We also consider the effect of linkage disequilibrium. When overall relatedness, rather than the specific relationship, would suffice, we propose an approximate approach that is easy to implement and appears to compete well with a popular moment-based estimator and a recent maximum likelihood approach based on chromosomal sharing. We conclude that denser marker data are more informative for distant relatives. However, linkage disequilibrium cannot be ignored and will be the main limiting factor for applications to real data.
authors.affiliation: Department of Health Sciences, University of Leicester, UK.
authors.forename: M
authors.initials: M
authors.lastname: Sun
authors.affiliation: Department of Genetics, University of Leicester, UK.
authors.forename: M A
authors.initials: MA
authors.lastname: Jobling
authors.affiliation: Center for Biomedicine, European Academy of Bolzano (EURAC), Bolzano, Italy; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: D
authors.initials: D
authors.lastname: Taliun
authors.affiliation: Center for Biomedicine, European Academy of Bolzano (EURAC), Bolzano, Italy.
authors.forename: P P
authors.initials: PP
authors.lastname: Pramstaller
authors.affiliation: IKBM Norwegian University of Life Sciences, Norway.
authors.forename: T
authors.initials: T
authors.lastname: Egeland
authors.affiliation: Department of Health Sciences, University of Leicester, UK; Department of Genetics, University of Leicester, UK. Electronic address: Nuala.Sheehan@leicester.ac.uk.
authors.forename: N A
authors.initials: NA
authors.lastname: Sheehan
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003241:Consanguinity; D053803:Forensic Genetics / Q000379:methods*; D005787:Gene Frequency / Q000235:genetics; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium / Q000235:genetics; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide
chemical_list: D005819:Genetic Markers
keywords: Bayes’ theorem; Likelihood; Pedigree; Relatedness; Relationship