title: HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families.
abstract: In the search for sequence variants underlying disease, commonly applied filtering steps usually result in a number of candidate variants that cannot further be narrowed down. In autosomal recessive families, disease usually occurs only in one generation so that genetic linkage analysis is unlikely to help. Because homozygous recessive mutations tend to be inherited together with flanking homozygous variants, we developed a statistical method to detect pathogenic variants in autosomal recessive families: We look for differences in patterns of homozygosity around candidate variants between patients and control individuals and expect that such differences are greater for pathogenic variants than random candidate variants. In six autosomal recessive mitochondrial disease families, in which pathogenic homozygous variants have already been identified, our approach succeeded in prioritizing pathogenic mutations. Our method is applicable to single patients from recessive families with at least a few dozen control individuals from the same population; it is easy to use and is highly effective for detecting causative mutations in autosomal recessive families.
authors.affiliation: Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Osaka, Japan.|Department of Genome Informatics, Osaka University Graduate School of Medicine, Osaka, Japan.|Laboratory of Statistical Genetics, Rockefeller University, New York, NY, USA.|McGill University and Genome Québec Innovation Centre, Montréal, Québec, Canada.
authors.forename: Atsuko
authors.initials: A
authors.lastname: Imai
authors.affiliation: Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical University, Saitama, Japan.
authors.forename: Masakazu
authors.identifier: http://orcid.org/0000-0001-5970-2224
authors.initials: M
authors.lastname: Kohda
authors.affiliation: Department of Genome Informatics, Osaka University Graduate School of Medicine, Osaka, Japan.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Nakaya
authors.affiliation: Department of Cardiovascular Medicine, Osaka University Graduate School of Medicine, Osaka, Japan.
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Sakata
authors.affiliation: Department of Metabolism, Chiba Children's Hospital, Chiba, Japan.
authors.forename: Kei
authors.initials: K
authors.lastname: Murayama
authors.affiliation: Department of Pediatrics, Saitama Medical University, Saitama, Japan.
authors.forename: Akira
authors.initials: A
authors.lastname: Ohtake
authors.affiliation: McGill University and Genome Québec Innovation Centre, Montréal, Québec, Canada.
authors.forename: Mark
authors.initials: M
authors.lastname: Lathrop
authors.affiliation: Division of Translational Research, Research Center for Genomic Medicine, Saitama Medical University, Saitama, Japan.|Division of Functional Genomics & Systems Medicine, Research Center for Genomic Medicine, Saitama Medical University, Saitama, Japan.
authors.forename: Yasushi
authors.initials: Y
authors.lastname: Okazaki
authors.affiliation: Laboratory of Statistical Genetics, Rockefeller University, New York, NY, USA.|Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
authors.forename: Jurg
authors.initials: J
authors.lastname: Ott
mesh_terms: D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods*; D005808:Genes, Recessive; D056726:Genetic Association Studies / Q000379:methods*; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D010375:Pedigree; D012984:Software