QTLViewer: An interactive webtool for genetic analysis in the Collaborative Cross and Diversity Outbred mouse populations

Vincent, Matthew; Gyuricza, Isabela Gerdes; Keele, Gregory R.; Churchill, Gary A.

doi:10.1101/2022.03.15.484471

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…iPSCs also enable differentiation into other cell types, 3dimensional cell models, organoids, or scaffolded arrays which can be screened across a variety of environmental conditions including other toxicants, drugs, or other culture conditions. It is important to note that while other studies mapping cmQTL were limited by lack of genetic diversity, poor adaptation of some cell types to culture, and the genetic architecture of the population being studied 2,97 , we also found that beyond large effect QTL, our study was underpowered despite previous examples showing sample sizes in this range for molecular phenotypes can detect strong QTL 12,54,98 . This is the result of experimental and residual sources of variance as described above, as well as the limited extensibility of standard dose-response models to diverse populations.…”

Section: Discussionmentioning

confidence: 57%

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

O’Connor,

Keele,

Martin

et al. 2023

Preprint

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Genetically heterogenous cell lines from laboratory mice are promising tools for population-based screening as they offer power for genetic mapping, and potentially, predictive value forin vivoexperimentation in genetically matched individuals. To explore this further, we derived a panel of fibroblast lines from a genetic reference population of laboratory mice (the Diversity Outbred, DO). We then used high-content imaging to capture hundreds of cell morphology traits in cells exposed to the oxidative stress-inducing arsenic metabolite monomethylarsonous acid (MMAIII). We employed dose-response modeling to capture latent parameters of response and we then used these parameters to identify several hundred cell morphology quantitative trait loci (cmQTL). Response cmQTL encompass genes with established associations with cellular responses to arsenic exposure, includingAbcc4andTxnrd1, as well as novel gene candidates likeXrcc2. Moreover, baseline trait cmQTL highlight the influence of natural variation on fundamental aspects of nuclear morphology. We show that the natural variants influencing response include both coding and non-coding variation, and that cmQTL haplotypes can be used to predict response in orthogonal cell lines. Our study sheds light on the major molecular initiating events of oxidative stress that are under genetic regulation, including the NRF2-mediated antioxidant response, cellular detoxification pathways, DNA damage repair response, and cell death trajectories.

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Section: Discussionmentioning

confidence: 57%

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

O’Connor,

Keele,

Martin

et al. 2023

Preprint

Self Cite

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“…We used RQTL2 software with CC genotypes imputed from qtl2qpi to identify genomic regions associated with survival after MRSA infection [59,60]. Within the CC strains we infected, survival was a highly heritable trait with a broad sense heritability score of 0.38 (Table S4).…”

Section: Correlation Of Colonization Tissue Damage and Immune Paramet...mentioning

confidence: 99%

“…This method accounts for the complex population structure in CC strains. Briefly, genotype probabilities were imputed from the QTL viewer [60]. Genome scans were performed on the transformed phenotype using the scan1 function.…”

Section: Qtl Analysismentioning

confidence: 99%

Collaborative Cross mice have diverse phenotypic responses to infection with Methicillin-resistantStaphylococcus aureusUSA300

Nagarajan

Scoggin

Gupta

et al. 2023

Preprint

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Staphylococcus aureus (S. aureus) is an opportunistic pathogen causing diseases ranging from mild skin infections to life-threatening conditions, including endocarditis, pneumonia, and sepsis. To identify host genes modulating this host-pathogen interaction, we infected 25 Collaborative Cross (CC) mouse strains with methicillin-resistant S. aureus (MRSA USA300) and monitored disease progression for seven days. We identified eight ‘susceptible’ CC strains with high bacterial load, tissue damage, and reduced survival. Among the surviving strains, six with minimal colonization were classified as ‘resistant’, while the remaining six tolerated higher organ colonization (‘tolerant’). The kidney was the most heavily colonized organ, but liver colonization was better correlated with reduced survival. We identified four CC strains with sex differences: females survived the study period while males met our euthanasia criteria earlier. In these CC strains, males had more baseline circulating monocytes and red blood cells. We identified several CC strains that may be useful as new models for endocarditis, myocarditis, pneumonia, and resistance to MRSA infection. Resistant strains had higher pre-infection circulating neutrophils than susceptible and tolerant strains. GM-CSF, IL-1-alpha, IL-15, IL-28, and IL-31 levels increased significantly in tolerant strains after infection. Using RNA seq, we identified two pathways, IL-1 and ERK1/ERK2, as potentially involved in the mechanism of tolerance. QTL analysis identified two significant loci, on Chromosomes 18 and 3, involved in early and late survival after infection. We prioritized Npc1 and Ifi44l genes as the strongest candidates influencing survival using variant analysis and mRNA expression data within these intervals.

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“…Data are also available for download and interactive analysis with the QTLViewer webtool (Vincent et al . 2022) (https://github.com/churchill-lab/qtlapi) for the DO Liver (https://churchilllab.jax.org/qtlviewer/svenson/DOHFD) and DO Kidney (https://churchilllab.jax.org/qtlviewer/JAC/DOKidney) studies.…”

Section: Data Availabilitymentioning

confidence: 99%

The Trouble with Triples: Examining the Impact of Measurement Error in Mediation Analysis

Gastonguay

Keele

Churchill

2022

Preprint

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Mediation analysis is a class of statistical methods used to determine whether the effect of an exogenous variable (X) on a target variable (Y) is mediated through an intermediate variable (M). Mediation analysis relies on propagation of variation through the variables in a causal system to produce characteristic patterns of correlation in the observed data. However, data often include measurement error that does not propagate, and in many applications of mediation analysis the potential effects of measurement error are not accounted for. In this work, we evaluate the reliability of mediation analysis in the presence of measurement error. We define a measurement error model and show that mediation analysis can infer partial mediation, i.e., X affects Y both directly and indirectly through M, even when there is no causal relationship between M and Y. This becomes more likely as sample size increases. When partial mediation is excluded from the set of possible models, the relative measurement errors of X, M, and Y play a large role in determining whether mediation analysis will infer the correct causal relationship. Using examples from genetic studies of molecular traits, we demonstrate how to assess the reliability of mediation analysis in the presence of measurement error and highlight common scenarios that can lead to incorrect inferences.

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QTLViewer: An interactive webtool for genetic analysis in the Collaborative Cross and Diversity Outbred mouse populations

Cited by 4 publications

References 31 publications

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Cell morphology QTL reveal gene by environment interactions in a genetically diverse cell population

Collaborative Cross mice have diverse phenotypic responses to infection with Methicillin-resistantStaphylococcus aureusUSA300

The Trouble with Triples: Examining the Impact of Measurement Error in Mediation Analysis

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