A B ayesian Integrative Genomic Model for Pathway Analysis of Complex Traits
Open accessAbstract
With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types.
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Fridley studied genomic effects of gemcitabine using mRNA expression and SNP high-throughput data.
“Fridley (Fridley et al., 2012) has studied the genomic effects due to the gemcitabine drug using high-throughput data from mRNA expression and SNPs.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Fridley et al., 2012
Fridley integrated mRNA expression and SNP data into one large input matrix and developed a Bayesian pathway analysis using stochastic search variable selection.
“They integrated these two datasets into one large input matrix and developed a Bayesian pathway analysis which uses a stochastic search variable selection.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Fridley et al., 2012
The Bayesian integrative model offers better performance in detecting genomic effects than conventional single-omics analysis.
“Their proposed that the Bayesian integrative model offers better performance in detecting the genomic effects in comparison to using conventional single-omics analysis.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Fridley et al., 2012
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- Authors
- Fridley, B. L., Lund, S., Jenkins, G. D., & Wang, L.
- Affiliations
- Mayo Clinic (US)
- Iowa State University (US)
- Corresponding author
- Fridley, Brooke L.
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2012-03-28
- Volume
- 36
- Issue
- 4
- Pages
- 352-359
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC3894829
- OpenAlex
- W1575126878
- Cited by
- 42
- References
- 30
- Citations by year
- 2024: 5 · 2023: 1 · 2022: 3 · 2021: 8 · 2020: 4 · 2019: 2 · 2018: 5 · 2017: 6 · 2016: 3 · 2015: 1 · 2013: 1 · 2012: 3
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Mayo Clinic
- National Institutes of Health
- National Cancer Institute (K22CA130828, R21CA140879, CA140879, CA138461, R01CA138461, CA130828)
- National Institute of General Medical Sciences (R21GM086689, U01GM061388, U19GM061388, GM86689, GM61388)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- pathway analysis, complex traits, Bayesian hierarchical model, stochastic search variable selection, path analysis, pharmacogenomics, gemcitabine, multi-omics integration, association analysis
- MeSH terms
- Gemcitabine, Algorithms, Antimetabolites, Antineoplastic, Bayes Theorem, Cell Line, Deoxycytidine, Humans, Markov Chains, Models, Theoretical, Monte Carlo Method, Pharmacogenetics, Phenotype, RNA, Messenger, Stochastic Processes, Reproducibility of Results, Models, Statistical, Polymorphism, Single Nucleotide, Genomics, Cell Line, Tumor
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