Penalized regression and model selection methods for polygenic scores on summary statistics.
Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic r...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008271 |
| _version_ | 1818579044372840448 |
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| author | Jack Pattee Wei Pan |
| author_facet | Jack Pattee Wei Pan |
| author_sort | Jack Pattee |
| collection | DOAJ |
| description | Polygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer. |
| first_indexed | 2024-12-16T06:55:26Z |
| format | Article |
| id | doaj.art-dcfad8ea8fb24fba8fd05b02399ae9f2 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:55:26Z |
| publishDate | 2020-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-dcfad8ea8fb24fba8fd05b02399ae9f22022-12-21T22:40:18ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-10-011610e100827110.1371/journal.pcbi.1008271Penalized regression and model selection methods for polygenic scores on summary statistics.Jack PatteeWei PanPolygenic scores quantify the genetic risk associated with a given phenotype and are widely used to predict the risk of complex diseases. There has been recent interest in developing methods to construct polygenic risk scores using summary statistic data. We propose a method to construct polygenic risk scores via penalized regression using summary statistic data and publicly available reference data. Our method bears similarity to existing method LassoSum, extending their framework to the Truncated Lasso Penalty (TLP) and the elastic net. We show via simulation and real data application that the TLP improves predictive accuracy as compared to the LASSO while imposing additional sparsity where appropriate. To facilitate model selection in the absence of validation data, we propose methods for estimating model fitting criteria AIC and BIC. These methods approximate the AIC and BIC in the case where we have a polygenic risk score estimated on summary statistic data and no validation data. Additionally, we propose the so-called quasi-correlation metric, which quantifies the predictive accuracy of a polygenic risk score applied to out-of-sample data for which we have only summary statistic information. In total, these methods facilitate estimation and model selection of polygenic risk scores on summary statistic data, and the application of these polygenic risk scores to out-of-sample data for which we have only summary statistic information. We demonstrate the utility of these methods by applying them to GWA studies of lipids, height, and lung cancer.https://doi.org/10.1371/journal.pcbi.1008271 |
| spellingShingle | Jack Pattee Wei Pan Penalized regression and model selection methods for polygenic scores on summary statistics. PLoS Computational Biology |
| title | Penalized regression and model selection methods for polygenic scores on summary statistics. |
| title_full | Penalized regression and model selection methods for polygenic scores on summary statistics. |
| title_fullStr | Penalized regression and model selection methods for polygenic scores on summary statistics. |
| title_full_unstemmed | Penalized regression and model selection methods for polygenic scores on summary statistics. |
| title_short | Penalized regression and model selection methods for polygenic scores on summary statistics. |
| title_sort | penalized regression and model selection methods for polygenic scores on summary statistics |
| url | https://doi.org/10.1371/journal.pcbi.1008271 |
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