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Evolving hard problems: Generating human genetics datasets with a complex etiology
BACKGROUND: A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154150/ https://www.ncbi.nlm.nih.gov/pubmed/21736753 http://dx.doi.org/10.1186/1756-0381-4-21 |
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author | Himmelstein, Daniel S Greene, Casey S Moore, Jason H |
author_facet | Himmelstein, Daniel S Greene, Casey S Moore, Jason H |
author_sort | Himmelstein, Daniel S |
collection | PubMed |
description | BACKGROUND: A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. RESULTS: Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. CONCLUSIONS: This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/. |
format | Online Article Text |
id | pubmed-3154150 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31541502011-08-11 Evolving hard problems: Generating human genetics datasets with a complex etiology Himmelstein, Daniel S Greene, Casey S Moore, Jason H BioData Min Methodology BACKGROUND: A goal of human genetics is to discover genetic factors that influence individuals' susceptibility to common diseases. Most common diseases are thought to result from the joint failure of two or more interacting components instead of single component failures. This greatly complicates both the task of selecting informative genetic variants and the task of modeling interactions between them. We and others have previously developed algorithms to detect and model the relationships between these genetic factors and disease. Previously these methods have been evaluated with datasets simulated according to pre-defined genetic models. RESULTS: Here we develop and evaluate a model free evolution strategy to generate datasets which display a complex relationship between individual genotype and disease susceptibility. We show that this model free approach is capable of generating a diverse array of datasets with distinct gene-disease relationships for an arbitrary interaction order and sample size. We specifically generate eight-hundred Pareto fronts; one for each independent run of our algorithm. In each run the predictiveness of single genetic variation and pairs of genetic variants have been minimized, while the predictiveness of third, fourth, or fifth-order combinations is maximized. Two hundred runs of the algorithm are further dedicated to creating datasets with predictive four or five order interactions and minimized lower-level effects. CONCLUSIONS: This method and the resulting datasets will allow the capabilities of novel methods to be tested without pre-specified genetic models. This allows researchers to evaluate which methods will succeed on human genetics problems where the model is not known in advance. We further make freely available to the community the entire Pareto-optimal front of datasets from each run so that novel methods may be rigorously evaluated. These 76,600 datasets are available from http://discovery.dartmouth.edu/model_free_data/. BioMed Central 2011-07-07 /pmc/articles/PMC3154150/ /pubmed/21736753 http://dx.doi.org/10.1186/1756-0381-4-21 Text en Copyright ©2011 Himmelstein et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Himmelstein, Daniel S Greene, Casey S Moore, Jason H Evolving hard problems: Generating human genetics datasets with a complex etiology |
title | Evolving hard problems: Generating human genetics datasets with a complex etiology |
title_full | Evolving hard problems: Generating human genetics datasets with a complex etiology |
title_fullStr | Evolving hard problems: Generating human genetics datasets with a complex etiology |
title_full_unstemmed | Evolving hard problems: Generating human genetics datasets with a complex etiology |
title_short | Evolving hard problems: Generating human genetics datasets with a complex etiology |
title_sort | evolving hard problems: generating human genetics datasets with a complex etiology |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154150/ https://www.ncbi.nlm.nih.gov/pubmed/21736753 http://dx.doi.org/10.1186/1756-0381-4-21 |
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