Systems Genetic Approach
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There are my notes when I read the paper called System Genetic Approach.
- the causal modeling algorithms NEO
- co-expression network algorithm, wMICA
GWAS only modest success
- complex, heterogeneous nature of the disease
minimize these complexities in genetic studies of model organisms such as mice
- classical QTL linkage analyses in mice have identified a number of novel HF-related genes
previous work association analyses identified both known and novel genes contributing to hypertrophy
an extension of this study though the modeling of biological networks
an improved version of the Maximal Information Component Analysis(MICA) algorithms
several modules that showed significant association to HF-related phenotypes were identified
NEO algorithm to develop a directed network with predicted casual interactions among the module genes.
using an in vitro model we validated several of these casual links
Gene Network Analysis Using Weighted MICA
- HDMP: Hybrid Mouse Diversity Panel
- prior research using HMDP to generate mRNA co-expression networks.
MICA: an unbiased gene network construction algorithm
- several conceptual improvement over traditional co-expression methods.
- captures both linear and nonlinear interactions within the data
- allow genes to be spread proportionally across multiple modules
- horvath: weighted network construction algorithms, in which all edges are included in the analysis, have greater versatility and power than unweighted algorithms, in which edges are included or exclude based on a hard threshold.
- improve upon origin algorithm and develop a modified, weighted, form of MICA, called wMICA.
- the application of wMICA to the analysis of HF, using gene expression data across inbred strains of mice from the HMDP HF study.
- Filter probes for transcripts that were significantly expressed in at least 25% of samples and had a coefficient of variation of at least 5%. Final a set of 8126 probes, representing 31.6% of the total probes on the array.
- Three gene networks, 20 modules each.
- one based only on transcripts from the untreated hearts,
- one based only on the treated
- a third based on the change in gene expression between these two conditions two measures.
- Two measures were used for the preliminary analyses of these networks.
- calculate significant GO enrichments within each of these modules at several module membership cutoffs.
- use principal component analysis(PCA) to identify the first principal component of each module.