ArticleNature communications2018
Temporal genetic association and temporal genetic causality methods for dissecting complex networks.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Nonlinear dynamic genetic regulation identifies peripheral drivers of neurodegenerative disease progression.medRxiv : the preprint server for health sciences · 2026Article
- Caffeine-tolerant mutations selected through an at-home yeast experimental evolution teaching lab.bioRxiv : the preprint server for biology · 2023Article
- Caffeine-tolerant mutations selected through an at-home yeast experimental evolution teaching lab.microPublication biology · 2023Article
- Fully automated web-based tool for identifying regulatory hotspots.BMC genomics · 2020Article
- Detecting virus-specific effects on post-infection temporal gene expression.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.