Application of Causal Inference to Genomic Analysis: Advances in Methodology
The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small pro...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2018.00238/full |
| _version_ | 1831773589862350848 |
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| author | Pengfei Hu Rong Jiao Li Jin Li Jin Momiao Xiong |
| author_facet | Pengfei Hu Rong Jiao Li Jin Li Jin Momiao Xiong |
| author_sort | Pengfei Hu |
| collection | DOAJ |
| description | The current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the heritability of complex diseases. A large fraction of genetic variants is still hidden. Association analysis has limited power to unravel mechanisms of complex diseases. It is time to shift the paradigm of genomic analysis from association analysis to causal inference. Causal inference is an essential component for the discovery of mechanism of diseases. This paper will review the major platforms of the genomic analysis in the past and discuss the perspectives of causal inference as a general framework of genomic analysis. In genomic data analysis, we usually consider four types of associations: association of discrete variables (DNA variation) with continuous variables (phenotypes and gene expressions), association of continuous variables (expressions, methylations, and imaging signals) with continuous variables (gene expressions, imaging signals, phenotypes, and physiological traits), association of discrete variables (DNA variation) with binary trait (disease status) and association of continuous variables (gene expressions, methylations, phenotypes, and imaging signals) with binary trait (disease status). In this paper, we will review algorithmic information theory as a general framework for causal discovery and the recent development of statistical methods for causal inference on discrete data, and discuss the possibility of extending the association analysis of discrete variable with disease to the causal analysis for discrete variable and disease. |
| first_indexed | 2024-12-22T08:39:05Z |
| format | Article |
| id | doaj.art-b33bde915728466887fb7c28be773ec1 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T08:39:05Z |
| publishDate | 2018-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-b33bde915728466887fb7c28be773ec12022-12-21T18:32:17ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-07-01910.3389/fgene.2018.00238374509Application of Causal Inference to Genomic Analysis: Advances in MethodologyPengfei Hu0Rong Jiao1Li Jin2Li Jin3Momiao Xiong4Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, ChinaDepartment of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United StatesState Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, ChinaHuman Phenome Institute, Fudan University, Shanghai, ChinaDepartment of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, United StatesThe current paradigm of genomic studies of complex diseases is association and correlation analysis. Despite significant progress in dissecting the genetic architecture of complex diseases by genome-wide association studies (GWAS), the identified genetic variants by GWAS can only explain a small proportion of the heritability of complex diseases. A large fraction of genetic variants is still hidden. Association analysis has limited power to unravel mechanisms of complex diseases. It is time to shift the paradigm of genomic analysis from association analysis to causal inference. Causal inference is an essential component for the discovery of mechanism of diseases. This paper will review the major platforms of the genomic analysis in the past and discuss the perspectives of causal inference as a general framework of genomic analysis. In genomic data analysis, we usually consider four types of associations: association of discrete variables (DNA variation) with continuous variables (phenotypes and gene expressions), association of continuous variables (expressions, methylations, and imaging signals) with continuous variables (gene expressions, imaging signals, phenotypes, and physiological traits), association of discrete variables (DNA variation) with binary trait (disease status) and association of continuous variables (gene expressions, methylations, phenotypes, and imaging signals) with binary trait (disease status). In this paper, we will review algorithmic information theory as a general framework for causal discovery and the recent development of statistical methods for causal inference on discrete data, and discuss the possibility of extending the association analysis of discrete variable with disease to the causal analysis for discrete variable and disease.https://www.frontiersin.org/article/10.3389/fgene.2018.00238/fullcausal inferencegenomic analysisadditive noise models for discrete variablesassociation analysisentropy |
| spellingShingle | Pengfei Hu Rong Jiao Li Jin Li Jin Momiao Xiong Application of Causal Inference to Genomic Analysis: Advances in Methodology Frontiers in Genetics causal inference genomic analysis additive noise models for discrete variables association analysis entropy |
| title | Application of Causal Inference to Genomic Analysis: Advances in Methodology |
| title_full | Application of Causal Inference to Genomic Analysis: Advances in Methodology |
| title_fullStr | Application of Causal Inference to Genomic Analysis: Advances in Methodology |
| title_full_unstemmed | Application of Causal Inference to Genomic Analysis: Advances in Methodology |
| title_short | Application of Causal Inference to Genomic Analysis: Advances in Methodology |
| title_sort | application of causal inference to genomic analysis advances in methodology |
| topic | causal inference genomic analysis additive noise models for discrete variables association analysis entropy |
| url | https://www.frontiersin.org/article/10.3389/fgene.2018.00238/full |
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