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Greg Keele

@grkeele.bsky.social
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Reposted by Greg Keele
Angelo D’Alessandro @dalessandrolab.bsky.social · 19/11/2025
Now out at @cellpress.bsky.social Cell Genomics, we characterized the genetic architecture of the murine red blood cell proteome using omics profiling in 350 genetically diverse mice. The goal was to understand how genetic variation shapes RBC metabolism, redox homeostasis, and storage outcomes. 1/n
cell.com
Genetic architecture of the murine red blood cell proteome reveals central role of hemoglobin beta cysteine 93 in maintaining redox balance
Keele et al. uncover the genetic architecture of the proteome of fresh and stored red blood cells using a multi-omics analysis of 350 genetically diverse mice. Their work reveals how a hemoglobin redo...
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Greg Keele @grkeele.bsky.social · 09/03/2025
See preprint for more on how genetic variation impacts the RBC proteome! Big thanks to a fantastic scientific village, including Gary Churchill @jacksonlab.bsky.social, Jim Zimring (UVA), and @dalessandrolab.bsky.social. Getting to work on this stuff sustains me in the current chaos we all face.
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Greg Keele @grkeele.bsky.social · 09/03/2025
The hemoglobin beta locus is driven by a known genetic variant that induces an additional cysteine residue that is present in 5 of the founder strains. This locus regulated glutathione levels and drove a ptmQTL hotspot, highlighting the central role of hemoglobin in RBC metabolism.
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Greg Keele @grkeele.bsky.social · 09/03/2025
Likely due to this, we observed a notable lack of cis-genetic regulation compared to other tissues previously studied in these mice. In its place, we observed strong trans pQTL hotspots, including at the hemoglobin alpha and beta. Many align with metabolite and lipid QTL (doi.org/10.1182/bloo...).
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Greg Keele @grkeele.bsky.social · 09/03/2025
We profiled the red blood cell (RBC) proteome (including PTMs) and the impacts of RBC storage in a genetically diverse mouse population. The RBC is a fascinating and unique cell system, notably lacking nuclei and the ability to respond to stress via de novo protein synthesis.
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Greg Keele @grkeele.bsky.social · 09/03/2025
Excited to share this pre-print: Genetic architecture of the red blood cell proteome in genetically diverse mice reveals central role of hemoglobin beta cysteine redox status in maintaining circulating glutathione pools www.biorxiv.org/content/10.1...
biorxiv.org
Genetic architecture of the red blood cell proteome in genetically diverse mice reveals central role of hemoglobin beta cysteine redox status in maintaining circulating glutathione pools
Red blood cells (RBCs) transport oxygen but accumulate oxidative damage over time, reducing function in vivo and during storage—critical for transfusions. To explore genetic influences on RBC resilien...
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Greg Keele @grkeele.bsky.social · 02/01/2023
Please check out heavily revised version of my manuscript comparing genetically diverse mouse populations (CC, CC-RIX, DO). I greatly expanded the simulations and now provide power curves! x.com/grkeele/status…
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Greg Keele @grkeele.bsky.social · 29/08/2022
This manuscript was a fun side project for me. I hope it can provide some tangible examples and help others better utilize these powerful populations. I also wrote an R package, musppr , that can be reused to tailor findings to others' experiments. github.com/gkeele/musppr
github.com
GitHub - gkeele/musppr: Package to evaluate genetic analysis in mouse multiparental populations (CC, CC-RIX, and DO)
Package to evaluate genetic analysis in mouse multiparental populations (CC, CC-RIX, and DO) - gkeele/musppr
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Greg Keele @grkeele.bsky.social · 29/08/2022
Other interesting findings include how CC-RIX samples with replicates can better estimate additive heritability in the presence of F1-specific genetic effects. The CC can't tease these components apart, and when trying to estimate just the additive, returns the sum.
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Greg Keele @grkeele.bsky.social · 29/08/2022
It's important to note that all populations have value for both heritability and QTL. The CC and CC-RIX had plenty of power for larger effect QTL (>=40%) -- good for eQTL, etc, and large DO samples provide meaningful, unbiased estimates of heritability.
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Greg Keele @grkeele.bsky.social · 29/08/2022
In contrast, I expected the DO to be better at QTL mapping, particularly for small effect loci in polygenic traits. The extent of this was also surprising, with 174 DO mice being better power to detect a 10% QTL in a trait that is 90% heritable than 500 CC or CC-RIX mice.
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Greg Keele @grkeele.bsky.social · 29/08/2022
My hunch was that experiments that included replicates (CC or CC-RIX) would more efficiently estimate heritability. The extent of this surprised me. For example, five replicates per 10 CC strains (50 mice total) had greater precision than 500 DO mice in some cases!
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Greg Keele @grkeele.bsky.social · 29/08/2022
Interested in doing an genetic experiment with a cutting-edge mouse multiparental population (MPP), but don't know which best suits your needs (e.g., inbred vs outbred)? Using simulations from real genetic data, I sought to provide some answers and guidelines. x.com/biorxivpreprin…
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Greg Keele @grkeele.bsky.social · 20/07/2022
Check out this cool work from Maddie evaluating how error can influence mediation inference. Something to consider when trying to causally relate -omic data with differing measurement error properties. x.com/MS_Gastonguay/…
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Greg Keele @grkeele.bsky.social · 06/06/2022
Really want to commend @tianzhang4921 for this work. Processing all the data, performing all the analyses, finding the message and the stories, and follow up. It's so much work. But that is at least balanced by how rewarding it is to share with our scientific communities now.
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Greg Keele @grkeele.bsky.social · 06/06/2022
Other cool stories abound. Abundance of the ATP synthase in the heart appears to be stoichiometrically regulated through a low AJ allele at ATP5H. Independent of its protein abundance, phosphorylation of ATP5A1 is regulated by Pdk1.
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Greg Keele @grkeele.bsky.social · 06/06/2022
The phos regulation through Pdk1 is particularly interesting because it is primarily driven by a low NZO allele. The NZO mouse is a very distinct polygenic model for obesity and diabetes, suggesting that unique regulation of phosphorylation could contribute to its phenotype.
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Greg Keele @grkeele.bsky.social · 06/06/2022
We looked for genetic effects independent of parent protein by using a regression adjustment. This allowed us to identify distant phQTL that were mediated by plausible catalysts, such as kinases. For example, Pdk1 appears to phosphorylate 7 proteins across the tissues.
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Greg Keele @grkeele.bsky.social · 06/06/2022
The abundance of a phosphopeptide was often determined by the abundance of its protein (which we refer to as the parent protein). Using a form of mediation analysis, we see that many phQTL essentially reflect an underlying pQTL, particularly when local.
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Greg Keele @grkeele.bsky.social · 06/06/2022
This project builds from our previous work where we compared genetic effects on liver proteins among genetically diverse mouse populations. Here, we integrate expression, proteins, and phosphopeptides from three tissues of 58 inbred CC mouse strains. doi.org/10.1016/j.xgen…
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Greg Keele @grkeele.bsky.social · 06/06/2022
Very grateful to have the opportunity to work with @tianzhang4921 dissecting how genetic variation affects protein phosphorylation. Our great team included @stevemunger, @MTFerris, @GygiLab, and Gary Churchill @jacksonlab. I'll share a few insights from the manuscript. x.com/tianzhang4921/…
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Greg Keele @grkeele.bsky.social · 02/06/2022
Paper alert! Final form of work with @wescrouse, from the labs of Gary Churchill @jacksonlab and @WilliamValdar. Includes fun examples of using bmediatR for genetic mediation analysis in genetically diverse mouse and human cell line data. x.com/wescrouse/stat…
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Greg Keele @grkeele.bsky.social · 23/05/2022
I hope others find this study interesting and welcome feedback. It emphasizes the extent to which aging affects proteins post transcription. Finally, these data represent a resource from the reference mouse strain. See RShiny app to explore yourself! aging-b6-proteomics.jax.org
aging-b6-proteomics.jax.org
Aging B6 Proteomics - Home
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Greg Keele @grkeele.bsky.social · 23/05/2022
The effects of age and sex on proteins complexes can be, well, complex. We see changes in overall abundance due to age and sex, as well as changes to how tightly correlated complex members are. These differences can also be tissue-specific, as in the following ribosome complex.
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Greg Keele @grkeele.bsky.social · 23/05/2022
Tissues also exhibit unique aging changes to functionally related proteins. In the spleen, for example, proteins increase with age that are related to the ER and protein trafficking. This also highlights co-regulatory signatures we see for protein complexes.
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Greg Keele @grkeele.bsky.social · 23/05/2022
Across tissues, we see aging changes in immune protein levels. Immunoglobulins notably tend to increase with age. Even in tissues where the differences don't meet statistical significance, the direction of effects are consistent. We also see matching signal in immunoproteasomes.
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Greg Keele @grkeele.bsky.social · 23/05/2022
The majority of age differences for proteins were not observed in their transcripts. This is in contrast to sex differences, which are often consistent. We compared to transcript data from a separate study of a sister strain of B6. This matches our previous findings in DO mice.
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Greg Keele @grkeele.bsky.social · 23/05/2022
We tested for differences in individual proteins' abundance due to age, sex, and age-by-sex. Tissues like kidney and liver had many proteins with sex differences, consistent with previous studies. Notably, many tissues had many proteins (approaching 500) with age differences.
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Greg Keele @grkeele.bsky.social · 23/05/2022
We used multiplexed mass-spec proteomics to quantify protein abundance across 10 tissues from 20 C57BL/6J mice, representing a balanced factorial design in terms of sex and age (8 and 18 months) within a tissue. Age groups are roughly analogous to young adult and later midlife.
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Greg Keele @grkeele.bsky.social · 23/05/2022
Excited to share this pre-print on how protein abundance changes with age across 10 tissues of B6 mice. My awesome collaborators include @dschweppe1, Gary Churchill, Ron Korstanje @jacksonlab, and Steve Gygi @GygiLab. I'll highlight some of the interesting findings below. x.com/biorxivpreprin…
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Greg Keele @grkeele.bsky.social · 31/08/2021
Big thanks to my co-authors: @stevemunger, @MTFerris and others in the Churchill lab at @jacksonlab, Gygi lab at @harvardmed, and @UNC.
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Greg Keele @grkeele.bsky.social · 23/07/2021
Also need to give a shout out for @MS_Gastonguay who performed the analysis in human cell line data (and will be applying for graduate school soon!), and my advisor Gary Churchill at @jacksonlab, co-senior author on the project.
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Greg Keele @grkeele.bsky.social · 23/07/2021
We hope this work can be helpful to others and look forward to feedback. All data and code used with the bmediatR package to produce the results in the paper are available at doi.org/10.6084/m9.fig…
doi.org
Data and code for Bayesian model selection manuscript
Data and code used for the Bayesian model selection manuscript. Data includes liver tissue protein abundance and genotypes from ~200 Diversity Outbred mice and gene expression and chromatin accessibility from human cell lines. Code for processing data, running analysis, and producing figures are included. A fixed version of the R package for Bayesian model selection, bmediatR, is also included.
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Greg Keele @grkeele.bsky.social · 23/07/2021
A key feature is flexibility in inputs for X. In genetically diverse mice, we mapped a distal pQTL for the gene Snx4, which is likely mediated by the related gene Snx7. Inputing X as haplotype data rather than single variants finds strong support for complete mediation.
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Greg Keele @grkeele.bsky.social · 23/07/2021
Consider a QTL, where genetic variation at a locus (X) controls phenotypes M and Y. Ideally, mediation analysis can elucidate the relationship between M and Y. We show how different causal relationships between simulated X, M, and Y appear as QTL and our mediation results.
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Greg Keele @grkeele.bsky.social · 23/07/2021
Great to finally show off this work with @wescrouse, the Churchill lab, and @WilliamValdar on a unique flexible approach to mediation analysis that has applications to genetics. See previous thread for a description of our framework. x.com/wescrouse/stat…
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Greg Keele @grkeele.bsky.social · 15/11/2020
Really cool study that uses Bayesian modeling of X-inactivation to map and characterize alleles in genetically diverse mice. x.com/SunKat_y/statu…
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Greg Keele @grkeele.bsky.social · 21/09/2020
These genetically diverse resource populations each have their strengths (e.g., replicable genomes in CC, high resolution mapping in DO), and together can confirm findings across each other and their differences highlight interesting biology. Comments and feedback appreciated.
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Greg Keele @grkeele.bsky.social · 21/09/2020
We leveraged the replicate CC observations to identify unique CC strain protein dynamics, some due to known genetic variation in a CC strain (such as C3 in CC026). We also identified functionally-enriched sets of proteins for CC strains, highlighting strain-specific biology.
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Greg Keele @grkeele.bsky.social · 21/09/2020
Differences due occur between the CC and DO, notably at the level of emergent phenotypes, like protein complexes. For the exosome complex in CC, genetic variation at one member regulates the entire complex. This is absent in the DO, where the homozygous genotype is not observed.
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Greg Keele @grkeele.bsky.social · 21/09/2020
We quantified abundance for >6000 proteins from the livers of male/female pairs of 58 Collaborative Cross (CC) mouse strains, using multiplexed mass-spec. We then characterized the effects of sex and genetics on proteins and protein complexes.
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Greg Keele @grkeele.bsky.social · 30/08/2020
Excited to be a part of this awesome paper led by Isabela Gyuricza! Lots of cool biology, including changes to key protein complexes with age. Genome-wide transcript and protein analysis reveals distinct features of aging in the mouse heart biorxiv.org/content/10.110…
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Greg Keele @grkeele.bsky.social · 29/03/2020
Fun collaboration with @ahepperla and the @DrBrianStrahl lab. Studied rapid H3K36me dynamics genome-wide in yeast using optogenetic switch. We also used Bayesian modeling to detect genes with consistent patterns, accounting for non-linear data and replicate observations.
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Greg Keele @grkeele.bsky.social · 29/03/2020
An optogenetic switch for the Set2 methyltransferase provides evidence for rapid transcription-dependent and independent dynamics of H3K36 methylation biorxiv.org/content/10.110…
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Greg Keele @grkeele.bsky.social · 05/03/2020
Great work from @kwangbom on the challenge of modeling zeros, which is becoming increasingly relevant with explosion of scRNA-Seq experiments x.com/kwangbom/statu…
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Greg Keele @grkeele.bsky.social · 10/02/2020
Excited to see (and share) the final version of this work with @bryancquach! From the labs of @WilliamValdar, Ivan Rusyn, and Terry Furey. #PLOSGenetics: Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissu ... dx.plos.org/10.1371/journa…
dx.plos.org
Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation
Author summary Genetic variation can drive alterations in gene expression levels and chromatin accessibility, the latter of which defines gene regulatory elements genome-wide. The same genetic variants may associate with both molecular events, and these may be connected within the same causal path: a variant that reduces promoter region chromatin accessibility, potentially by affecting transcription factor binding, may lead to reduced expression of that gene. Moreover, these causal regulatory paths can differ between tissues depending on functions and cellular activity specific to each tissue. We identify cross-tissue and tissue-selective genetic regulators of gene expression and chromatin accessibility in liver, lung, and kidney tissues using a panel of genetically diverse inbred mouse strains. Further, we identify a number of candidate causal mediators of the genetic regulation of gene expression, including a zinc finger protein that helps silence the Akr1e1 gene. Our analyses are consistent with chromatin accessibility playing a role in the regulation of transcription. Our study demonstrates the power of genetically diverse, multi-parental mouse populations, such as the Collaborative Cross, for large-scale studies of genetic drivers of gene regulation that underlie complex phenotypes, as well as identifying causal intermediates that drive variable activity of specific genes and pathways.
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Greg Keele @grkeele.bsky.social · 28/03/2019
Excited to finally show off fun work with @bryancquach (from Crawford, @WilliamValdar, Rusyn, and Furey labs). Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation biorxiv.org/content/10.110…
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Greg Keele @grkeele.bsky.social · 27/03/2019
My paper with @wescrouse (from the labs of @samir_kelada,@WilliamValdar) is now out in G3. Interested in the Collaborative Cross? Read it. Improved from the preprint with big ol' heaping of Beavis effect! If that likely makes no sense, read it. g3journal.org/content/early/…
g3journal.org
Determinants of QTL Mapping Power in the Realized Collaborative Cross
Abstract. The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping.
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Greg Keele @grkeele.bsky.social · 09/12/2018
Got a bunch of inbred strains? Want to pick out a few for crosses? Use DIDACT! Glad to share a project I've been working on for quite a while. Work out of @WilliamValdar lab with @TweetNTD and @Dgoreper. Check it out at biorxiv.org/content/early/…
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Greg Keele @grkeele.bsky.social · 12/11/2018
Designing a Collaborative Cross (CC) experiment or just need a power trip? Check out my preprint with @wescrouse. Work out of the @samir_kelada and @WilliamValdar labs. Determinants of QTL mapping power in the realized Collaborative Cross biorxiv.org/content/early/…
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