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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

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Temporal genetic association and temporal genetic causality methods for dissecting complex networks.

Luan Lin^1,2, Quan Chen^1,2, Jeanne P Hirsch³

¹Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

Nature Communications

|September 30, 2018

Summary

We developed new methods, multivariate polynomial temporal genetic association (MPTGA) and temporal genetic causality test (TGCT), to analyze complex biological data over time. These approaches enhance the detection of genetic influences on traits and infer causal relationships, validated in yeast.

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Area of Science:

Genetics
Systems Biology
Computational Biology

Background:

Understanding complex biological systems requires analyzing large-scale population data.
Existing genetic and temporal models have limitations in establishing causal relationships among traits.
High-dimensional temporal and genetic data integration is crucial for biological discovery.

Purpose of the Study:

To develop novel computational approaches for analyzing high-dimensional temporal and genetic data.
To detect temporal genetic loci (teQTLs) associated with quantitative traits over time.
To infer causal relationships between traits linked to identified genetic loci.

Main Methods:

Developed a multivariate polynomial temporal genetic association (MPTGA) method for teQTL detection.
Developed a temporal genetic causality test (TGCT) for inferring causal relationships.
Applied MPTGA and TGCT to simulated data and a yeast F2 population under rapamycin treatment.

Main Results:

Demonstrated increased statistical power to detect teQTLs using MPTGA and TGCT.
Identified a teQTL hotspot locus exhibiting interaction with rapamycin treatment.
Inferred and experimentally validated RRD1 as a causal regulator for the identified teQTL hotspot.

Conclusions:

MPTGA and TGCT are powerful tools for dissecting genetic architecture in time-series data.
The study identified a novel rapamycin-responsive genetic locus and its causal regulator in yeast.
This work advances the understanding of genotype-phenotype relationships in dynamic biological systems.