Efficient clustering of identity-by-descent between multiple individuals
Abstract Motivation: Most existing identity-by-descent (IBD) detection methods only consider haplotype pairs; less attention has been paid to considering multiple haplotypes simultaneously, even though IBD is an equivalence relation on haplotypes that partitions a set of haplotypes into IBD clusters...
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2013
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Online Access: | http://dx.doi.org/10.1093/bioinformatics/btt734 https://academic.oup.com/bioinformatics/article-pdf/30/7/915/48921782/bioinformatics_30_7_915.pdf |
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croxfordunivpr:10.1093/bioinformatics/btt734 2024-04-28T08:32:25+00:00 Efficient clustering of identity-by-descent between multiple individuals Qian, Yu Browning, Brian L. Browning, Sharon R. 2013 http://dx.doi.org/10.1093/bioinformatics/btt734 https://academic.oup.com/bioinformatics/article-pdf/30/7/915/48921782/bioinformatics_30_7_915.pdf en eng Oxford University Press (OUP) Bioinformatics volume 30, issue 7, page 915-922 ISSN 1367-4811 1367-4803 Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability journal-article 2013 croxfordunivpr https://doi.org/10.1093/bioinformatics/btt734 2024-04-09T07:57:17Z Abstract Motivation: Most existing identity-by-descent (IBD) detection methods only consider haplotype pairs; less attention has been paid to considering multiple haplotypes simultaneously, even though IBD is an equivalence relation on haplotypes that partitions a set of haplotypes into IBD clusters. Multiple-haplotype IBD clusters may have advantages over pairwise IBD in some applications, such as IBD mapping. Existing methods for detecting multiple-haplotype IBD clusters are often computationally expensive and unable to handle large samples with thousands of haplotypes. Results: We present a clustering method, efficient multiple-IBD, which uses pairwise IBD segments to infer multiple-haplotype IBD clusters. It expands clusters from seed haplotypes by adding qualified neighbors and extends clusters across sliding windows in the genome. Our method is an order of magnitude faster than existing methods and has comparable performance with respect to the quality of clusters it uncovers. We further investigate the potential application of multiple-haplotype IBD clusters in association studies by testing for association between multiple-haplotype IBD clusters and low-density lipoprotein cholesterol in the Northern Finland Birth Cohort. Using our multiple-haplotype IBD cluster approach, we found an association with a genomic interval covering the PCSK9 gene in these data that is missed by standard single-marker association tests. Previously published studies confirm association of PCSK9 with low-density lipoprotein. Availability and implementation: Source code is available under the GNU Public License http://cs.au.dk/~qianyuxx/EMI/. Contact: qianyuxx@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. Article in Journal/Newspaper Northern Finland Oxford University Press Bioinformatics 30 7 915 922 |
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Oxford University Press |
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croxfordunivpr |
language |
English |
topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
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Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability Qian, Yu Browning, Brian L. Browning, Sharon R. Efficient clustering of identity-by-descent between multiple individuals |
topic_facet |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
description |
Abstract Motivation: Most existing identity-by-descent (IBD) detection methods only consider haplotype pairs; less attention has been paid to considering multiple haplotypes simultaneously, even though IBD is an equivalence relation on haplotypes that partitions a set of haplotypes into IBD clusters. Multiple-haplotype IBD clusters may have advantages over pairwise IBD in some applications, such as IBD mapping. Existing methods for detecting multiple-haplotype IBD clusters are often computationally expensive and unable to handle large samples with thousands of haplotypes. Results: We present a clustering method, efficient multiple-IBD, which uses pairwise IBD segments to infer multiple-haplotype IBD clusters. It expands clusters from seed haplotypes by adding qualified neighbors and extends clusters across sliding windows in the genome. Our method is an order of magnitude faster than existing methods and has comparable performance with respect to the quality of clusters it uncovers. We further investigate the potential application of multiple-haplotype IBD clusters in association studies by testing for association between multiple-haplotype IBD clusters and low-density lipoprotein cholesterol in the Northern Finland Birth Cohort. Using our multiple-haplotype IBD cluster approach, we found an association with a genomic interval covering the PCSK9 gene in these data that is missed by standard single-marker association tests. Previously published studies confirm association of PCSK9 with low-density lipoprotein. Availability and implementation: Source code is available under the GNU Public License http://cs.au.dk/~qianyuxx/EMI/. Contact: qianyuxx@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. |
format |
Article in Journal/Newspaper |
author |
Qian, Yu Browning, Brian L. Browning, Sharon R. |
author_facet |
Qian, Yu Browning, Brian L. Browning, Sharon R. |
author_sort |
Qian, Yu |
title |
Efficient clustering of identity-by-descent between multiple individuals |
title_short |
Efficient clustering of identity-by-descent between multiple individuals |
title_full |
Efficient clustering of identity-by-descent between multiple individuals |
title_fullStr |
Efficient clustering of identity-by-descent between multiple individuals |
title_full_unstemmed |
Efficient clustering of identity-by-descent between multiple individuals |
title_sort |
efficient clustering of identity-by-descent between multiple individuals |
publisher |
Oxford University Press (OUP) |
publishDate |
2013 |
url |
http://dx.doi.org/10.1093/bioinformatics/btt734 https://academic.oup.com/bioinformatics/article-pdf/30/7/915/48921782/bioinformatics_30_7_915.pdf |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
Bioinformatics volume 30, issue 7, page 915-922 ISSN 1367-4811 1367-4803 |
op_doi |
https://doi.org/10.1093/bioinformatics/btt734 |
container_title |
Bioinformatics |
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30 |
container_issue |
7 |
container_start_page |
915 |
op_container_end_page |
922 |
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1797589622790815744 |