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Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure
SNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581268/ https://www.ncbi.nlm.nih.gov/pubmed/31211811 http://dx.doi.org/10.1371/journal.pone.0218306 |
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author | Gauch, Hugh G. Qian, Sheng Piepho, Hans-Peter Zhou, Linda Chen, Rui |
author_facet | Gauch, Hugh G. Qian, Sheng Piepho, Hans-Peter Zhou, Linda Chen, Rui |
author_sort | Gauch, Hugh G. |
collection | PubMed |
description | SNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. PCA is not a single method that is always done the same way, but rather requires three choices which we explore as a three-way factorial: two kinds of PCA graphs by three SNP codings by six PCA variants. Our main three recommendations are simple and easily implemented: Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA (or AMMI1 if main effects are also of interest). We also document contemporary practices by a literature survey of 125 representative articles that apply PCA to SNP data, find that virtually none implement our recommendations. The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications. |
format | Online Article Text |
id | pubmed-6581268 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65812682019-06-28 Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure Gauch, Hugh G. Qian, Sheng Piepho, Hans-Peter Zhou, Linda Chen, Rui PLoS One Research Article SNP datasets are high-dimensional, often with thousands to millions of SNPs and hundreds to thousands of samples or individuals. Accordingly, PCA graphs are frequently used to provide a low-dimensional visualization in order to display and discover patterns in SNP data from humans, animals, plants, and microbes—especially to elucidate population structure. PCA is not a single method that is always done the same way, but rather requires three choices which we explore as a three-way factorial: two kinds of PCA graphs by three SNP codings by six PCA variants. Our main three recommendations are simple and easily implemented: Use PCA biplots, SNP coding 1 for the rare allele and 0 for the common allele, and double-centered PCA (or AMMI1 if main effects are also of interest). We also document contemporary practices by a literature survey of 125 representative articles that apply PCA to SNP data, find that virtually none implement our recommendations. The ultimate benefit from informed and optimal choices of PCA graph, SNP coding, and PCA variant, is expected to be discovery of more biology, and thereby acceleration of medical, agricultural, and other vital applications. Public Library of Science 2019-06-18 /pmc/articles/PMC6581268/ /pubmed/31211811 http://dx.doi.org/10.1371/journal.pone.0218306 Text en © 2019 Gauch et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gauch, Hugh G. Qian, Sheng Piepho, Hans-Peter Zhou, Linda Chen, Rui Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title | Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title_full | Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title_fullStr | Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title_full_unstemmed | Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title_short | Consequences of PCA graphs, SNP codings, and PCA variants for elucidating population structure |
title_sort | consequences of pca graphs, snp codings, and pca variants for elucidating population structure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6581268/ https://www.ncbi.nlm.nih.gov/pubmed/31211811 http://dx.doi.org/10.1371/journal.pone.0218306 |
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