title: A Pipeline for Classifying Relationships Using Dense SNP/SNV Data and Putative Pedigree Information.
abstract: When genome-wide association studies (GWAS) or sequencing studies are performed on family-based datasets, the genotype data can be used to check the structure of putative pedigrees. Even in datasets of putatively unrelated people, close relationships can often be detected using dense single-nucleotide polymorphism/variant (SNP/SNV) data. A number of methods for finding relationships using dense genetic data exist, but they all have certain limitations, including that they typically use average genetic sharing, which is only a subset of the available information. Here, we present a set of approaches for classifying relationships in GWAS datasets or large-scale sequencing datasets. We first propose an empirical method for detecting identity by descent segments in close relative pairs using un-phased dense SNP data and demonstrate how that information can assist in building a relationship classifier. We then develop a strategy to take advantage of putative pedigree information to enhance classification accuracy. Our methods are tested and illustrated with two datasets from two distinct populations. Finally, we propose classification pipelines for checking and identifying relationships in datasets containing a large number of small pedigrees.
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zeng
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Daniel E
authors.initials: DE
authors.lastname: Weeks
authors.affiliation: Division of Pulmonary Medicine, Allergy and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Nandita
authors.initials: N
authors.lastname: Mukhopadhyay
authors.affiliation: Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Eleanor
authors.initials: E
authors.lastname: Feingold
mesh_terms: D000465:Algorithms; D005190:Family; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D060388:Support Vector Machine
keywords: identity-by-descent; putative pedigree structures; relationship classification; support vector machine