Temporal genetic association and temporal genetic causality methods for dissecting complex networks.
Nat Commun
; 9(1): 3980, 2018 09 28.
Article
in En
| MEDLINE
| ID: mdl-30266904
ABSTRACT
A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We apply MPTGA and TGCT to simulated data sets and a yeast F2 population in response to rapamycin, and demonstrate increased power to detect teQTLs. We identify a teQTL hotspot locus interacting with rapamycin treatment, infer putative causal regulators of the teQTL hotspot, and experimentally validate RRD1 as the causal regulator for this teQTL hotspot.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Computational Biology
/
Gene Expression Profiling
/
Gene Regulatory Networks
/
Genetic Association Studies
Type of study:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2018
Document type:
Article
Affiliation country: