Causal modeling in a multi-omic setting: insights from GAW20
Abstract Background Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied comp...
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Genetics |
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| Online Access: | http://link.springer.com/article/10.1186/s12863-018-0645-4 |
| _version_ | 1818246725290164224 |
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| author | Jonathan Auerbach Richard Howey Lai Jiang Anne Justice Liming Li Karim Oualkacha Sergi Sayols-Baixeras Stella W. Aslibekyan |
| author_facet | Jonathan Auerbach Richard Howey Lai Jiang Anne Justice Liming Li Karim Oualkacha Sergi Sayols-Baixeras Stella W. Aslibekyan |
| author_sort | Jonathan Auerbach |
| collection | DOAJ |
| description | Abstract Background Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies. Results Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R2 measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations. Conclusions The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes. |
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| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-12-12T14:53:22Z |
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| series | BMC Genetics |
| spelling | doaj.art-481ea145afae424ebb37c1d0e3b5203f2022-12-22T00:20:58ZengBMCBMC Genetics1471-21562018-09-0119S1737910.1186/s12863-018-0645-4Causal modeling in a multi-omic setting: insights from GAW20Jonathan Auerbach0Richard Howey1Lai Jiang2Anne Justice3Liming Li4Karim Oualkacha5Sergi Sayols-Baixeras6Stella W. Aslibekyan7Department of Statistics, Columbia UniversityInstitute of Genetic Medicine, Newcastle UniversityDepartment of Epidemiology, Biostatistics and Occupational Health, McGill UniversityBiomedical and Translational Informatics, Geisinger HealthState Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan UniversityDépartement de Mathématiques, Université du Québec à MontréalCardiovascular Epidemiology and Genetics Research Group, IMIM (Hospital del Mar Medical Research Institute); Universitat Pompeu Fabra; CIBER Cardiovascular Diseases (CIBERCV)Department of Epidemiology, University of Alabama at BirminghamAbstract Background Increasingly available multilayered omics data on large populations has opened exciting analytic opportunities and posed unique challenges to robust estimation of causal effects in the setting of complex disease phenotypes. The GAW20 Causal Modeling Working Group has applied complementary approaches (eg, Mendelian randomization, structural equations modeling, Bayesian networks) to discover novel causal effects of genomic and epigenomic variation on lipid phenotypes, as well as to validate prior findings from observational studies. Results Two Mendelian randomization studies have applied novel approaches to instrumental variable selection in methylation data, identifying bidirectional causal effects of CPT1A and triglycerides, as well as of RNMT and C6orf42, on high-density lipoprotein cholesterol response to fenofibrate. The CPT1A finding also emerged in a Bayesian network study. The Mendelian randomization studies have implemented both existing and novel steps to account for pleiotropic effects, which were independently detected in the GAW20 data via a structural equation modeling approach. Two studies estimated indirect effects of genomic variation (via DNA methylation and/or correlated phenotypes) on lipid outcomes of interest. Finally, a novel weighted R2 measure was proposed to complement other causal inference efforts by controlling for the influence of outlying observations. Conclusions The GAW20 contributions illustrate the diversity of possible approaches to causal inference in the multi-omic context, highlighting the promises and assumptions of each method and the benefits of integrating both across methods and across omics layers for the most robust and comprehensive insights into disease processes.http://link.springer.com/article/10.1186/s12863-018-0645-4GenomicsEpigenomicsCausal inferenceMendelian randomizationBayesian networksStructural equation modeling |
| spellingShingle | Jonathan Auerbach Richard Howey Lai Jiang Anne Justice Liming Li Karim Oualkacha Sergi Sayols-Baixeras Stella W. Aslibekyan Causal modeling in a multi-omic setting: insights from GAW20 BMC Genetics Genomics Epigenomics Causal inference Mendelian randomization Bayesian networks Structural equation modeling |
| title | Causal modeling in a multi-omic setting: insights from GAW20 |
| title_full | Causal modeling in a multi-omic setting: insights from GAW20 |
| title_fullStr | Causal modeling in a multi-omic setting: insights from GAW20 |
| title_full_unstemmed | Causal modeling in a multi-omic setting: insights from GAW20 |
| title_short | Causal modeling in a multi-omic setting: insights from GAW20 |
| title_sort | causal modeling in a multi omic setting insights from gaw20 |
| topic | Genomics Epigenomics Causal inference Mendelian randomization Bayesian networks Structural equation modeling |
| url | http://link.springer.com/article/10.1186/s12863-018-0645-4 |
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