Kim lab journal club presentation June 6th, 2019

1. Systems genetics approaches
to understand complex traits
Civelek M et al. Nature Reviews Genetics (2014)

2. Systems genetics?
• An approach to understand the flow of biological information that
underlies complex traits across individuals in a population
• Consider both underlying genetic variation and intermediate
phenotypes (e.g. gene expression, protein, metabolite) in addition to
gene-by-gene and gene-by-environment interactions.
* Image from Systems genetics of cancer 2018

4. Analysis of systems genetics data
• Linking the variations in molecular phenotypes to clinical traits
• Correlation between molecular phenotypes and clinical traits
• Genetic mapping of molecular phenotypes and clinical traits
• Statistical modeling captures the interactions among the traits (e.g. Network
approaches to identify modules (groups) can be related to clinical trait)

5. The flow of biological information
• Genetic variants affect transcript/protein/metabolite levels (e.g.
eQTL, pQTL, mQTL)
• Comprehensive genotype–phenotype maps require analyses of
protein levels and their modifications
• Levels of many metabolites showed high heritability

6. Complexity of interactions
• Gene-by-gene (G x G), gene-by-environment (G x E) interactions
• Almost all common diseases result from a combination of genetic and
environmental factors (e.g. physical, chemical, biological, behavior
patterns or life events)
“Two different genotypes respond to environmental variation”
* Image from Wikipedia ‘Gene-environment interaction’

7. Network modelling
• Network approaches to understand how they interact with each
other and influence complex traits

8. Network modelling
1. Based on curated knowledge (e.g. metabolic pathways)
• Typically not comprehensive
2. Derived from experimental data on the basis of physical interactions
3. Inferred from high-throughput data
• Data-driven methods may uncover novel relationships and
interactions
• There is no experimental approach to create the 'true' network
structure

9. Network modelling
- No single network method
outperformed the others
- Different network connectivity
patterns by various approaches
to various levels of success
- Consensus network showed the
most robust performances
Marbach, D. et al. Wisdom of crowds for robust gene network inference. Nature Methods 9, 796–804 (2012).

10. Advanced topics for multi-omics data
• Multi-omics methods in the context
of metabolomics
• Univariate associations between
metabolites and other omics markers
• Further analyzed with networks, or
pathway enrichment analysis
• Multivariate methods exploit
covariation within / btw omics layers
using pathway/network approaches
Jan K. et al. “Computational approaches for systems metabolomics”, Current Opinion in Biotechnology (2016)

11. Advanced topics for multi-omics data
• Network approaches to
systems biology for multi-
omics data integration
• Networks as
outcomes/priors/features
• Epistasis
• Network inference
• Pathway enrichment/network
analysis
• Integrative analysis (e.g.
PARADIGM, ATHENA)
Jingwen Yan, et al. “Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data”, Briefings in Bioinformatics (2018)

12. Conclusions
• It aims to quantitate and integrate intermediate phenotypes, such as
transcript, protein or metabolite levels, in populations that vary for
traits of interest
• It provides a global view of the molecular architecture of complex
traits
• It is useful for the identification of genes, pathways and networks that
underlie common human diseases
• In the future, additional data modalities can be incorporated (e.g.
clinical imaging data, time course data, etc.)