Limitations of principal components in quantitative genetic association models for human studies
Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal compon...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2023-05-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/79238 |
| _version_ | 1797814093575356416 |
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| author | Yiqi Yao Alejandro Ochoa |
| author_facet | Yiqi Yao Alejandro Ochoa |
| author_sort | Yiqi Yao |
| collection | DOAJ |
| description | Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies. |
| first_indexed | 2024-03-13T08:02:28Z |
| format | Article |
| id | doaj.art-b08f660cef1740ae982d252388aeef7e |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-03-13T08:02:28Z |
| publishDate | 2023-05-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-b08f660cef1740ae982d252388aeef7e2023-06-01T13:32:40ZengeLife Sciences Publications LtdeLife2050-084X2023-05-011210.7554/eLife.79238Limitations of principal components in quantitative genetic association models for human studiesYiqi Yao0Alejandro Ochoa1https://orcid.org/0000-0003-4928-3403Department of Biostatistics and Bioinformatics, Duke University, Durham, United StatesDepartment of Biostatistics and Bioinformatics, Duke University, Durham, United States; Duke Center for Statistical Genetics and Genomics, Duke University, Durham, United StatesPrincipal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies.https://elifesciences.org/articles/79238genetic associationstatistical geneticspopulation structurecryptic relatednesscomplex quantitative traitsmultiethnic human data and simulations |
| spellingShingle | Yiqi Yao Alejandro Ochoa Limitations of principal components in quantitative genetic association models for human studies eLife genetic association statistical genetics population structure cryptic relatedness complex quantitative traits multiethnic human data and simulations |
| title | Limitations of principal components in quantitative genetic association models for human studies |
| title_full | Limitations of principal components in quantitative genetic association models for human studies |
| title_fullStr | Limitations of principal components in quantitative genetic association models for human studies |
| title_full_unstemmed | Limitations of principal components in quantitative genetic association models for human studies |
| title_short | Limitations of principal components in quantitative genetic association models for human studies |
| title_sort | limitations of principal components in quantitative genetic association models for human studies |
| topic | genetic association statistical genetics population structure cryptic relatedness complex quantitative traits multiethnic human data and simulations |
| url | https://elifesciences.org/articles/79238 |
| work_keys_str_mv | AT yiqiyao limitationsofprincipalcomponentsinquantitativegeneticassociationmodelsforhumanstudies AT alejandroochoa limitationsofprincipalcomponentsinquantitativegeneticassociationmodelsforhumanstudies |