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

Li, Lin; Chen, Quan; Hirsch, Jeanne P.; Yoo, Seungyeul; Yeung, Ka Yee; Bumgarner, Roger E.; Schadt, Eric E.; Zhu, Jun

doi:10.1038/s41467-018-06203-3

Cited by 6 publications

(12 citation statements)

References 56 publications

(108 reference statements)

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“…Lin et al [6] proposed a Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method to detect genetic effects in temporal gene expression trajectories. The MPTGA method assumes that for each genotype j , the temporal gene expression trait y across m time points follows a multivariate normal distribution , with density function …”

Section: Methodsmentioning

confidence: 99%

“…Given the joint likelihood The maximum likelihood estimates of the parameter set can be derived as described in the Lin et al [6] paper.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we investigated temporal mouse lung gene expression traits in response to infection with influenza A/California/04/09 (H1N1), A/Wyoming/03/03 (H3N2), and A/Vietnam/1203/04 (H5N1) HALo virus (GSE98527) and aimed to identify the virus-specific effects. To compare temporal gene expression traits, we proposed a multivariate polynomial temporal genetic association (MPTGA) method [6] previously in the context of genetic association with temporal gene expression. We further extended this method to a more general setting that compares the temporal gene expression trait under different conditions, which applies to the specific aim in this study—to detect the differences in the way each virus affects temporal gene expression traits over time.…”

Section: Introductionmentioning

confidence: 99%

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Detecting virus-specific effects on post-infection temporal gene expression

Chen

Zhu

2019

BMC Bioinformatics

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Background Different types of viruses have different envelope proteins, and may have their shared or distinctive host-virus interactions which result in various post-infection effects in humans and animals. These effects often do not appear at once but take time to unfold. To characterize the virus-specific effects, we applied a Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method, previously proposed for detecting differences in temporal gene expression traits, to test for the differences in mouse lung transcriptome response to infection of different subtypes of influenza A viruses. Results We compared two methods: the Multivariate Polynomial Time-dependent Genetic Association (MPTGA) method, and the conventional modified t-test, to study the virus-specific effects on mouse lung gene expression. Both methods found H3N2 to be the most different virus among the three viruses tested, with the largest number of genes with H3N2-specific effects. However, the MPTGA method demonstrated much higher power of detection, and the detected genes with virus-specific effects showed better biological relevance. Conclusions Transcriptome response to virus infection is dynamic. MPTGA which leverages temporal gene expression traits showed increased power in detecting biologically relevant virus-specific effects comparing with conventional t-test method.

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Section: Methodsmentioning

confidence: 99%

“…Given the joint likelihood The maximum likelihood estimates of the parameter set can be derived as described in the Lin et al [6] paper.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting virus-specific effects on post-infection temporal gene expression

Chen

Zhu

2019

BMC Bioinformatics

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“…A total of 692 SNPs was determined based on the GAMMAR p-value< 5 × 10 −5 (Figure 2). We next divided the whole yeast genome into 603 20-kb bins, and then SNPs with the smallest GAMMAR p-values were selected in each bin for comparison with the previous yeast eQTL studies [21,22]. We determined that 117 trans-eQTLs had 59 eGenes on three chromosomes.…”

Section: Gammar Analysis Using the Yeast Datasetmentioning

confidence: 99%

“…Collectively, we defined the six bins as trans-regulatory hotspots and 59 eGenes as putative regulators. Of the 59 total eGenes, nine had been previously identified [21,22,24] (Table 1). In four previous studies, MATing type protein ALPHA 1; III:190000 (MATALPHA1) was reported to be a casual regulator [21,[25][26][27], and Killer toxin REsistant 33; XIV:360000 (KRE33) was recently identified as a putative causal regulator [24].…”

Section: Gammar Analysis Using the Yeast Datasetmentioning

confidence: 99%

A Fully Automated Parallel-Processing R Package for High-Dimensional Multiple-Phenotype Analysis Considering Population Structure

Lee¹,

Park²,

Jung³

et al. 2020

IJFIS

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A typical genome-wide association study is conducted through a single-phenotype analysis of the correlation between each phenotype and genotype one at a time. Alternatively, a multiple-phenotype analysis of the correlation between multiple phenotypes and a genotype often has many advantages over single-phenotype analysis. For example, statistical power in the association test may be increased in a multiple-phenotype analysis and thus may detect small effects that cannot be identified in a single-phenotype analysis. Of the several multiple-phenotype analytical methods that have been proposed, generalized analysis of molecular variance for mixed-model analysis (GAMMA) is used to analyze many phenotypes simultaneously while considering the population structure. This method shows higher accuracy than the other methods. However, GAMMA has not been widely used because no automated and user-friendly software is available; this is also the case with most other multiple-phenotype analysis methods. In addition, the lack of a parallel-processing option, which is essential in a genome-wide-association-studies analysis, is also prevalent in GAMMA. In this study, we propose an easy-to-use R package for GAMMA called GAMMA Renew (GAMMAR) that performs multiple-phenotype analysis using parallel processing. We evaluate GAMMAR using a recently published yeast dataset to locate trans-regulatory hotspots.

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Caffeine-tolerant mutations selected through an at-home yeast experimental evolution teaching lab

Moresi

Geck

Skophammer³

et al. 2023

Preprint

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yEvo is a curriculum for high school students centered around evolution experiments in S. cerevisiae. To adapt the curriculum for remote instruction, we created a new protocol to evolve non-GMO yeast in the presence of caffeine. Evolved strains had increased caffeine tolerance and distinct colony morphologies. Many possessed copy number variations, transposon insertions, and mutations affecting genes with known relationships to caffeine and TOR signaling - which is inhibited by caffeine - and in other genes not previously connected with caffeine. This demonstrates that our accessible, at-home protocol is sufficient to permit novel insights into caffeine tolerance.

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

Cited by 6 publications

References 56 publications

Detecting virus-specific effects on post-infection temporal gene expression

Detecting virus-specific effects on post-infection temporal gene expression

A Fully Automated Parallel-Processing R Package for High-Dimensional Multiple-Phenotype Analysis Considering Population Structure

Caffeine-tolerant mutations selected through an at-home yeast experimental evolution teaching lab

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