Cargando…
A ν-support vector regression based approach for predicting imputation quality
BACKGROUND: Decades of genome-wide association studies (GWAS) have accumulated large volumes of genomic data that can potentially be reused to increase statistical power of new studies, but different genotyping platforms with different marker sets have been used as biotechnology has evolved, prevent...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504919/ https://www.ncbi.nlm.nih.gov/pubmed/23173775 http://dx.doi.org/10.1186/1753-6561-6-S7-S3 |
_version_ | 1782250702396981248 |
---|---|
author | Huang, Yi-Hung Rice, John P Saccone, Scott F Ambite, José Luis Arens, Yigal Tischfield, Jay A Hsu, Chun-Nan |
author_facet | Huang, Yi-Hung Rice, John P Saccone, Scott F Ambite, José Luis Arens, Yigal Tischfield, Jay A Hsu, Chun-Nan |
author_sort | Huang, Yi-Hung |
collection | PubMed |
description | BACKGROUND: Decades of genome-wide association studies (GWAS) have accumulated large volumes of genomic data that can potentially be reused to increase statistical power of new studies, but different genotyping platforms with different marker sets have been used as biotechnology has evolved, preventing pooling and comparability of old and new data. For example, to pool together data collected by 550K chips with newer data collected by 900K chips, we will need to impute missing loci. Many imputation algorithms have been developed, but the posteriori probabilities estimated by those algorithms are not a reliable measure the quality of the imputation. Recently, many studies have used an imputation quality score (IQS) to measure the quality of imputation. The IQS requires to know true alleles to estimate. Only when the population and the imputation loci are identical can we reuse the estimated IQS when the true alleles are unknown. METHODS: Here, we present a regression model to estimate IQS that learns from imputation of loci with known alleles. We designed a small set of features, such as minor allele frequencies, distance to the nearest known cross-over hotspot, etc., for the prediction of IQS. We evaluated our regression models by estimating IQS of imputations by BEAGLE for a set of GWAS data from the NCBI GEO database collected from samples from different ethnic populations. RESULTS: We construct a ν-SVR based approach as our regression model. Our evaluation shows that this regression model can accomplish mean square errors of less than 0.02 and a correlation coefficient close to 0.75 in different imputation scenarios. We also show how the regression results can help remove false positives in association studies. CONCLUSION: Reliable estimation of IQS will facilitate integration and reuse of existing genomic data for meta-analysis and secondary analysis. Experiments show that it is possible to use a small number of features to regress the IQS by learning from different training examples of imputation and IQS pairs. |
format | Online Article Text |
id | pubmed-3504919 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-35049192012-11-29 A ν-support vector regression based approach for predicting imputation quality Huang, Yi-Hung Rice, John P Saccone, Scott F Ambite, José Luis Arens, Yigal Tischfield, Jay A Hsu, Chun-Nan BMC Proc Proceedings BACKGROUND: Decades of genome-wide association studies (GWAS) have accumulated large volumes of genomic data that can potentially be reused to increase statistical power of new studies, but different genotyping platforms with different marker sets have been used as biotechnology has evolved, preventing pooling and comparability of old and new data. For example, to pool together data collected by 550K chips with newer data collected by 900K chips, we will need to impute missing loci. Many imputation algorithms have been developed, but the posteriori probabilities estimated by those algorithms are not a reliable measure the quality of the imputation. Recently, many studies have used an imputation quality score (IQS) to measure the quality of imputation. The IQS requires to know true alleles to estimate. Only when the population and the imputation loci are identical can we reuse the estimated IQS when the true alleles are unknown. METHODS: Here, we present a regression model to estimate IQS that learns from imputation of loci with known alleles. We designed a small set of features, such as minor allele frequencies, distance to the nearest known cross-over hotspot, etc., for the prediction of IQS. We evaluated our regression models by estimating IQS of imputations by BEAGLE for a set of GWAS data from the NCBI GEO database collected from samples from different ethnic populations. RESULTS: We construct a ν-SVR based approach as our regression model. Our evaluation shows that this regression model can accomplish mean square errors of less than 0.02 and a correlation coefficient close to 0.75 in different imputation scenarios. We also show how the regression results can help remove false positives in association studies. CONCLUSION: Reliable estimation of IQS will facilitate integration and reuse of existing genomic data for meta-analysis and secondary analysis. Experiments show that it is possible to use a small number of features to regress the IQS by learning from different training examples of imputation and IQS pairs. BioMed Central 2012-11-13 /pmc/articles/PMC3504919/ /pubmed/23173775 http://dx.doi.org/10.1186/1753-6561-6-S7-S3 Text en Copyright ©2012 Huang 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 | Proceedings Huang, Yi-Hung Rice, John P Saccone, Scott F Ambite, José Luis Arens, Yigal Tischfield, Jay A Hsu, Chun-Nan A ν-support vector regression based approach for predicting imputation quality |
title | A ν-support vector regression based approach for predicting imputation quality |
title_full | A ν-support vector regression based approach for predicting imputation quality |
title_fullStr | A ν-support vector regression based approach for predicting imputation quality |
title_full_unstemmed | A ν-support vector regression based approach for predicting imputation quality |
title_short | A ν-support vector regression based approach for predicting imputation quality |
title_sort | ν-support vector regression based approach for predicting imputation quality |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3504919/ https://www.ncbi.nlm.nih.gov/pubmed/23173775 http://dx.doi.org/10.1186/1753-6561-6-S7-S3 |
work_keys_str_mv | AT huangyihung ansupportvectorregressionbasedapproachforpredictingimputationquality AT ricejohnp ansupportvectorregressionbasedapproachforpredictingimputationquality AT sacconescottf ansupportvectorregressionbasedapproachforpredictingimputationquality AT ambitejoseluis ansupportvectorregressionbasedapproachforpredictingimputationquality AT arensyigal ansupportvectorregressionbasedapproachforpredictingimputationquality AT tischfieldjaya ansupportvectorregressionbasedapproachforpredictingimputationquality AT hsuchunnan ansupportvectorregressionbasedapproachforpredictingimputationquality AT huangyihung nsupportvectorregressionbasedapproachforpredictingimputationquality AT ricejohnp nsupportvectorregressionbasedapproachforpredictingimputationquality AT sacconescottf nsupportvectorregressionbasedapproachforpredictingimputationquality AT ambitejoseluis nsupportvectorregressionbasedapproachforpredictingimputationquality AT arensyigal nsupportvectorregressionbasedapproachforpredictingimputationquality AT tischfieldjaya nsupportvectorregressionbasedapproachforpredictingimputationquality AT hsuchunnan nsupportvectorregressionbasedapproachforpredictingimputationquality |