Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants
A long-standing goal in clinical genomics is to map individual genetic variants to clinical outcomes. Typically, variants which lead to loss of function (e.g. nonsense or stop-codon inducing variants, frameshifts, or deletions) are more easily classified as pathogenic in an established disease gene....
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| Other Authors: | |
| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/139502 |
| _version_ | 1811095610543046656 |
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| author | Bernatchez, Jackson |
| author2 | Cassa, Christopher |
| author_facet | Cassa, Christopher Bernatchez, Jackson |
| author_sort | Bernatchez, Jackson |
| collection | MIT |
| description | A long-standing goal in clinical genomics is to map individual genetic variants to clinical outcomes. Typically, variants which lead to loss of function (e.g. nonsense or stop-codon inducing variants, frameshifts, or deletions) are more easily classified as pathogenic in an established disease gene. However, there are many other missense variants identified in established disease genes which are more challenging to classify. Improving predictions of such variants has the potential to lead to clinically actionable solutions for individual patients. In this paper, we develop and evaluate several new clustering-based approaches for predicting the clinical risk of rare missense variants. We find that our results are comparable to existing methods, and offer several opportunities to significantly improve clinical risk predictions for missense variants. |
| first_indexed | 2024-09-23T16:20:53Z |
| format | Thesis |
| id | mit-1721.1/139502 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:20:53Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1395022022-01-15T03:04:07Z Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants Bernatchez, Jackson Cassa, Christopher Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science A long-standing goal in clinical genomics is to map individual genetic variants to clinical outcomes. Typically, variants which lead to loss of function (e.g. nonsense or stop-codon inducing variants, frameshifts, or deletions) are more easily classified as pathogenic in an established disease gene. However, there are many other missense variants identified in established disease genes which are more challenging to classify. Improving predictions of such variants has the potential to lead to clinically actionable solutions for individual patients. In this paper, we develop and evaluate several new clustering-based approaches for predicting the clinical risk of rare missense variants. We find that our results are comparable to existing methods, and offer several opportunities to significantly improve clinical risk predictions for missense variants. M.Eng. 2022-01-14T15:15:57Z 2022-01-14T15:15:57Z 2021-06 2021-06-17T20:12:51.041Z Thesis https://hdl.handle.net/1721.1/139502 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Bernatchez, Jackson Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title | Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title_full | Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title_fullStr | Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title_full_unstemmed | Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title_short | Clustering-Based Methods for Clinical Risk Prediction of Rare Missense Variants |
| title_sort | clustering based methods for clinical risk prediction of rare missense variants |
| url | https://hdl.handle.net/1721.1/139502 |
| work_keys_str_mv | AT bernatchezjackson clusteringbasedmethodsforclinicalriskpredictionofraremissensevariants |