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HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411490/ https://www.ncbi.nlm.nih.gov/pubmed/27357426 http://dx.doi.org/10.1038/jhg.2016.85 |
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author | Imai, Atsuko Kohda, Masakazu Nakaya, Akihiro Sakata, Yasushi Murayama, Kei Ohtake, Akira Lathrop, Mark Okazaki, Yasushi Ott, Jurg |
author_facet | Imai, Atsuko Kohda, Masakazu Nakaya, Akihiro Sakata, Yasushi Murayama, Kei Ohtake, Akira Lathrop, Mark Okazaki, Yasushi Ott, Jurg |
author_sort | Imai, Atsuko |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5411490 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-54114902017-05-15 HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families Imai, Atsuko Kohda, Masakazu Nakaya, Akihiro Sakata, Yasushi Murayama, Kei Ohtake, Akira Lathrop, Mark Okazaki, Yasushi Ott, Jurg J Hum Genet Original Article 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. Nature Publishing Group 2016-11 2016-06-30 /pmc/articles/PMC5411490/ /pubmed/27357426 http://dx.doi.org/10.1038/jhg.2016.85 Text en Copyright © 2016 The Japan Society of Human Genetics http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
spellingShingle | Original Article Imai, Atsuko Kohda, Masakazu Nakaya, Akihiro Sakata, Yasushi Murayama, Kei Ohtake, Akira Lathrop, Mark Okazaki, Yasushi Ott, Jurg HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title | HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title_full | HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title_fullStr | HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title_full_unstemmed | HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title_short | HDR: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
title_sort | hdr: a statistical two-step approach successfully identifies disease genes in autosomal recessive families |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411490/ https://www.ncbi.nlm.nih.gov/pubmed/27357426 http://dx.doi.org/10.1038/jhg.2016.85 |
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