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Evolutionary triangulation: informing genetic association studies with evolutionary evidence
Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has s...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818851/ https://www.ncbi.nlm.nih.gov/pubmed/27042214 http://dx.doi.org/10.1186/s13040-016-0091-7 |
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author | Huang, Minjun Graham, Britney E. Zhang, Ge Harder, Reed Kodaman, Nuri Moore, Jason H. Muglia, Louis Williams, Scott M. |
author_facet | Huang, Minjun Graham, Britney E. Zhang, Ge Harder, Reed Kodaman, Nuri Moore, Jason H. Muglia, Louis Williams, Scott M. |
author_sort | Huang, Minjun |
collection | PubMed |
description | Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0091-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4818851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48188512016-04-04 Evolutionary triangulation: informing genetic association studies with evolutionary evidence Huang, Minjun Graham, Britney E. Zhang, Ge Harder, Reed Kodaman, Nuri Moore, Jason H. Muglia, Louis Williams, Scott M. BioData Min Methodology Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13040-016-0091-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-02 /pmc/articles/PMC4818851/ /pubmed/27042214 http://dx.doi.org/10.1186/s13040-016-0091-7 Text en © Huang et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Huang, Minjun Graham, Britney E. Zhang, Ge Harder, Reed Kodaman, Nuri Moore, Jason H. Muglia, Louis Williams, Scott M. Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title | Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title_full | Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title_fullStr | Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title_full_unstemmed | Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title_short | Evolutionary triangulation: informing genetic association studies with evolutionary evidence |
title_sort | evolutionary triangulation: informing genetic association studies with evolutionary evidence |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818851/ https://www.ncbi.nlm.nih.gov/pubmed/27042214 http://dx.doi.org/10.1186/s13040-016-0091-7 |
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