A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes
Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/14/11/1072 |
| _version_ | 1797508856910184448 |
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| author | Jan Scott Mohamed Lajnef Romain Icick Frank Bellivier Cynthia Marie-Claire Bruno Etain |
| author_facet | Jan Scott Mohamed Lajnef Romain Icick Frank Bellivier Cynthia Marie-Claire Bruno Etain |
| author_sort | Jan Scott |
| collection | DOAJ |
| description | Optimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (<i>RORA</i>, <i>TIMELESS</i> and <i>PPARGC1A</i>), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies. |
| first_indexed | 2024-03-10T05:09:49Z |
| format | Article |
| id | doaj.art-433557f612634685af2645eb7eeb9972 |
| institution | Directory Open Access Journal |
| issn | 1424-8247 |
| language | English |
| last_indexed | 2024-03-10T05:09:49Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj.art-433557f612634685af2645eb7eeb99722023-11-23T00:54:49ZengMDPI AGPharmaceuticals1424-82472021-10-011411107210.3390/ph14111072A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian GenesJan Scott0Mohamed Lajnef1Romain Icick2Frank Bellivier3Cynthia Marie-Claire4Bruno Etain5Institute of Neuroscience, Newcastle University, Newcastle NE7 6RU, UKINSERM UMR 955, IMRB, Université Paris Est Créteil, F-94000 Créteil, FranceINSERM UMR-S 1144, Université de Paris, F-75006 Paris, FranceINSERM UMR-S 1144, Université de Paris, F-75006 Paris, FranceINSERM UMR-S 1144, Université de Paris, F-75006 Paris, FranceINSERM UMR-S 1144, Université de Paris, F-75006 Paris, FranceOptimal classification of the response to lithium (Li) is crucial in genetic and biomarker research. This proof of concept study aims at exploring whether different approaches to phenotyping the response to Li may influence the likelihood of detecting associations between the response and genetic markers. We operationalized Li response phenotypes using the Retrospective Assessment of Response to Lithium Scale (i.e., the Alda scale) in a sample of 164 cases with bipolar disorder (BD). Three phenotypes were defined using the established approaches, whilst two phenotypes were generated by machine learning algorithms. We examined whether these five different Li response phenotypes showed different levels of statistically significant associations with polymorphisms of three candidate circadian genes (<i>RORA</i>, <i>TIMELESS</i> and <i>PPARGC1A</i>), which were selected for this study because they were plausibly linked with the response to Li. The three original and two revised Alda ratings showed low levels of discordance (misclassification rates: 8–12%). However, the significance of associations with circadian genes differed when examining previously recommended categorical and continuous phenotypes versus machine-learning derived phenotypes. Findings using machine learning approaches identified more putative signals of the Li response. Established approaches to Li response phenotyping are easy to use but may lead to a significant loss of data (excluding partial responders) due to recent attempts to improve the reliability of the original rating system. While machine learning approaches require additional modeling to generate Li response phenotypes, they may offer a more nuanced approach, which, in turn, would enhance the probability of identifying significant signals in genetic studies.https://www.mdpi.com/1424-8247/14/11/1072bipolar disorderlithiumresponsephenotypegeneticscircadian genes |
| spellingShingle | Jan Scott Mohamed Lajnef Romain Icick Frank Bellivier Cynthia Marie-Claire Bruno Etain A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes Pharmaceuticals bipolar disorder lithium response phenotype genetics circadian genes |
| title | A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes |
| title_full | A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes |
| title_fullStr | A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes |
| title_full_unstemmed | A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes |
| title_short | A Comparison of Different Approaches to Clinical Phenotyping of Lithium Response: A Proof of Principle Study Employing Genetic Variants of Three Candidate Circadian Genes |
| title_sort | comparison of different approaches to clinical phenotyping of lithium response a proof of principle study employing genetic variants of three candidate circadian genes |
| topic | bipolar disorder lithium response phenotype genetics circadian genes |
| url | https://www.mdpi.com/1424-8247/14/11/1072 |
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