Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
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Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.
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Subsequently, networks were modeled based on co-expression across a series of perturbations with genes as nodes and correlations as edges.
“Subsequently, networks were modeled based on co-expression across a series of perturbations with the genes encoding the transcripts corresponding to the nodes and the correlations to the edges [67–69].”
From Multi-omics approaches to disease (2017) · cites this paper as [69]
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- Chaibub Neto E Keller MP Attie AD Yandell BS Supplement to “Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes.” 2009 DOI: 10.1214/09-AOAS288SUPP PMC3017382 21218138 · DOI
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- Authors
- Chaibub Neto, E., Keller, M. P., Attie, A. D., & Yandell, B. S.
- Affiliations
- University of Wisconsin–Madison (US)
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-03-01
- Volume
- 4
- Issue
- 1
- Pages
- 320-339
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- PMC
- PMC3017382
- OpenAlex
- W1987959788
- Cited by
- 110
- References
- 41
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 3 · 2022: 8 · 2021: 3 · 2020: 4 · 2019: 9 · 2018: 5 · 2017: 6 · 2016: 13 · 2015: 13 · 2014: 9 · 2013: 6 · 2012: 11
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (R01GM069430, R01GM074244)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK066369, DK-58037, R01DK058037)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- causal graphical models, systems genetics, quantitative trait loci, genetic architecture, correlated phenotypes, causal inference, direct and indirect effects, meiotic recombination
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