Faster model-based estimation of ancestry proportions
Ancestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for...
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| Language: | English |
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2024-12-01
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| Series: | Peer Community Journal |
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.503/ |
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| author | Santander, Cindy G. Refoyo Martinez, Alba Meisner, Jonas |
| author_facet | Santander, Cindy G. Refoyo Martinez, Alba Meisner, Jonas |
| author_sort | Santander, Cindy G. |
| collection | DOAJ |
| description | Ancestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for population stratification, however, it struggles with convergence issues and does not scale to modern human datasets or the large number of variants in whole-genome sequencing data. Likelihood-free approaches optimize a least square objective and have gained popularity in recent years due to their scalability. However, this comes at the cost of accuracy in the ancestry estimates in more complex admixture scenarios. We present a new model-based approach, fastmixture, which adopts aspects from likelihood-free approaches for parameter initialization, followed by a mini-batch expectation-maximization procedure to model the standard likelihood. In a simulation study, we demonstrate that the model-based approaches of fastmixture and ADMIXTURE are significantly more accurate than recent and likelihood-free approaches. We further show that fastmixture runs approximately 30$\times$ faster than ADMIXTURE on both simulated and empirical data from the 1000 Genomes Project such that our model-based approach scales to much larger sample sizes than previously possible. |
| format | Article |
| id | doaj-art-567b4c8927af4e54a17905fd6b26dcdf |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-567b4c8927af4e54a17905fd6b26dcdf2025-02-07T10:17:17ZengPeer Community InPeer Community Journal2804-38712024-12-01410.24072/pcjournal.50310.24072/pcjournal.503Faster model-based estimation of ancestry proportions Santander, Cindy G.0https://orcid.org/0000-0003-3021-6809Refoyo Martinez, Alba1https://orcid.org/0000-0002-3674-4007Meisner, Jonas2https://orcid.org/0000-0002-9540-6673Department of Biology, University of Copenhagen, DenmarkCenter for Health Data Science, University of Copenhagen, DenmarkMental Health Centre Copenhagen, Copenhagen University Hospital, Denmark; Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, DenmarkAncestry estimation from genotype data in unrelated individuals has become an essential tool in population and medical genetics to understand demographic population histories and to model or correct for population structure. The ADMIXTURE software is a widely used model-based approach to account for population stratification, however, it struggles with convergence issues and does not scale to modern human datasets or the large number of variants in whole-genome sequencing data. Likelihood-free approaches optimize a least square objective and have gained popularity in recent years due to their scalability. However, this comes at the cost of accuracy in the ancestry estimates in more complex admixture scenarios. We present a new model-based approach, fastmixture, which adopts aspects from likelihood-free approaches for parameter initialization, followed by a mini-batch expectation-maximization procedure to model the standard likelihood. In a simulation study, we demonstrate that the model-based approaches of fastmixture and ADMIXTURE are significantly more accurate than recent and likelihood-free approaches. We further show that fastmixture runs approximately 30$\times$ faster than ADMIXTURE on both simulated and empirical data from the 1000 Genomes Project such that our model-based approach scales to much larger sample sizes than previously possible.https://peercommunityjournal.org/articles/10.24072/pcjournal.503/Ancestry estimation, population structure, population genetics, evolutionary genetics, bioinformatics |
| spellingShingle | Santander, Cindy G. Refoyo Martinez, Alba Meisner, Jonas Faster model-based estimation of ancestry proportions Peer Community Journal Ancestry estimation, population structure, population genetics, evolutionary genetics, bioinformatics |
| title | Faster model-based estimation of ancestry proportions
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| title_full | Faster model-based estimation of ancestry proportions
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| title_fullStr | Faster model-based estimation of ancestry proportions
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| title_full_unstemmed | Faster model-based estimation of ancestry proportions
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| title_short | Faster model-based estimation of ancestry proportions
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| title_sort | faster model based estimation of ancestry proportions |
| topic | Ancestry estimation, population structure, population genetics, evolutionary genetics, bioinformatics |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.503/ |
| work_keys_str_mv | AT santandercindyg fastermodelbasedestimationofancestryproportions AT refoyomartinezalba fastermodelbasedestimationofancestryproportions AT meisnerjonas fastermodelbasedestimationofancestryproportions |