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Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations
Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSM...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704644/ https://www.ncbi.nlm.nih.gov/pubmed/33257650 http://dx.doi.org/10.1038/s41467-020-19588-x |
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author | Nait Saada, Juba Kalantzis, Georgios Shyr, Derek Cooper, Fergus Robinson, Martin Gusev, Alexander Palamara, Pier Francesco |
author_facet | Nait Saada, Juba Kalantzis, Georgios Shyr, Derek Cooper, Fergus Robinson, Martin Gusev, Alexander Palamara, Pier Francesco |
author_sort | Nait Saada, Juba |
collection | PubMed |
description | Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample’s birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits. |
format | Online Article Text |
id | pubmed-7704644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77046442020-12-03 Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations Nait Saada, Juba Kalantzis, Georgios Shyr, Derek Cooper, Fergus Robinson, Martin Gusev, Alexander Palamara, Pier Francesco Nat Commun Article Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of analyses. We develop FastSMC, an IBD detection algorithm that combines a fast heuristic search with accurate coalescent-based likelihood calculations. FastSMC enables biobank-scale detection and dating of IBD segments within several thousands of years in the past. We apply FastSMC to 487,409 UK Biobank samples and detect ~214 billion IBD segments transmitted by shared ancestors within the past 1500 years, obtaining a fine-grained picture of genetic relatedness in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the use of genomic data to localize a sample’s birth coordinates with a median error of 45 km. We seek evidence of recent positive selection by identifying loci with unusually strong shared ancestry and detect 12 genome-wide significant signals. We devise an IBD-based test for association between phenotype and ultra-rare loss-of-function variation, identifying 29 association signals in 7 blood-related traits. Nature Publishing Group UK 2020-11-30 /pmc/articles/PMC7704644/ /pubmed/33257650 http://dx.doi.org/10.1038/s41467-020-19588-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Nait Saada, Juba Kalantzis, Georgios Shyr, Derek Cooper, Fergus Robinson, Martin Gusev, Alexander Palamara, Pier Francesco Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title | Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title_full | Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title_fullStr | Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title_full_unstemmed | Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title_short | Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations |
title_sort | identity-by-descent detection across 487,409 british samples reveals fine scale population structure and ultra-rare variant associations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7704644/ https://www.ncbi.nlm.nih.gov/pubmed/33257650 http://dx.doi.org/10.1038/s41467-020-19588-x |
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