Histopathological Image QTL Discovery of Immune Infiltration Variants

Barry, Joseph D.; Fagny, Maud; Paulson, Joseph N.; Aerts, Hugo J.W.L.; Platig, John; Quackenbush, John

doi:10.1016/j.isci.2018.07.001

Cited by 19 publications

(14 citation statements)

References 39 publications

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“…Genomic regions that underlie these complex inflammatory phenotypes associated with neutrophil variation can be identified using genetic mapping in model organisms through the use of mutational screens (Musani et al 2006;Hillhouse et al 2011;Leach et al 2012;Uddin et al 2011;Chen et al 2016;Barry et al 2018). Because of the complex interplay of genetics, microbes and environment, it is also essential to develop outbred mutant models tractable for genetic mapping of natural genetic variants influencing complex phenotypes such as inflammation (Albertson et al 2009;Gasch et al 2016).…”

mentioning

confidence: 99%

QTL Mapping of Intestinal Neutrophil Variation in Threespine Stickleback Reveals Possible Gene Targets Connecting Intestinal Inflammation and Systemic Health

Beck

Currey

Small

et al. 2020

G3 Genes|Genomes|Genetics

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Selection, via host immunity, is often required to foster beneficial microbial symbionts and suppress deleterious pathogens. In animals, the host immune system is at the center of this relationship. Failed host immune system-microbial interactions can result in a persistent inflammatory response in which the immune system indiscriminately attacks resident microbes, and at times the host cells themselves, leading to diseases such as Ulcerative Colitis, Crohn’s Disease, and Psoriasis. Host genetic variation has been linked to both microbiome diversity and to severity of such inflammatory disease states in humans. However, the microbiome and inflammatory states manifest as quantitative traits, which encompass many genes interacting with one another and the environment. The mechanistic relationships among all of these interacting components are still not clear. Developing natural genetic models of host-microbe interactions is therefore fundamental to understanding the complex genetics of these and other diseases. Threespine stickleback (Gasterosteus aculeatus) fish are a tractable model for attacking this problem because of abundant population-level genetic and phenotypic variation in the gut inflammatory response. Previous work in our laboratory identified genetically divergent stickleback populations exhibiting differences in intestinal neutrophil activity. We took advantage of this diversity to genetically map variation in an emblematic element of gut inflammation - intestinal neutrophil recruitment - using an F2-intercross mapping framework. We identified two regions of the genome associated with increased intestinal inflammation containing several promising candidate genes. Within these regions we found candidates in the Coagulation/Complement System, NFkB and MAPK pathways along with several genes associated with intestinal diseases and neurological diseases commonly accompanying intestinal inflammation as a secondary symptom. These findings highlight the utility of using naturally genetically diverse ‘evolutionary mutant models’ such as threespine stickleback to better understand interactions among host genetic diversity and microbiome variation in health and disease states.

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confidence: 99%

QTL Mapping of Intestinal Neutrophil Variation in Threespine Stickleback Reveals Possible Gene Targets Connecting Intestinal Inflammation and Systemic Health

Beck

Currey

Small

et al. 2020

G3 Genes|Genomes|Genetics

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“…helped characterize basic biological processes, including transcription, methylation, chromatin accessibility, translation, ribosomal occupancy, and expression response to stimuli. These tests can be performed on cis and trans genetic variants to search for functional quantitative trait loci [*QTL: eQTL (Montgomery et al 2010;Pickrell et al 2010), mQTL (Rakyan et al 2011), caQTL (Degner et al 2012), pQTL (Albert et al 2014), rQTL (Battle et al 2015), sQTL (Rivas et al 2015;Li et al 2016), reQTL (Fairfax et al 2014;Lee et al 2014), and iQTL (Barry et al 2017)]. Functional measurements can also be tested against nongenetic covariates with broad genomic effects, including cell type composition (Houseman et al 2012;Jaffe and Irizarry 2014;Rahmani et al 2017;Yao et al 2017), disease status [e.g., cancer (van't Veer et al 2002, autism (Parikshak et al 2016), and obesity (Horvath et al 2014)], fetal developmental stage (Colantuoni et al 2011), and ancestry (Galanter et al 2017).…”

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confidence: 99%

Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives

et al. 2019

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High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have r 2 % 10%, standard two-step methods all have . 10-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.

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“…(1) a sufficient sample size of N > 70 (matched genetic, expression, and covariate information) (similar to GTEx threshold) (2) consistent overall infiltration of immune cells in that tissue type (>50% of CIBERSORT relative deconvolutions have p < 0.50 (null hypothesis is that no immune cells from the reference are in the sample) (p = 0.50 observed previously 19 ) (3) the specific immune cell type is a substantial part of the average immune content in that tissue (> 5% mean abundance in all CIBERSORT relative deconvolutions of the tissue) (>5% cutoff observed previously) 19 (4) CIBERSORT-Absolute and xCell scores do not disagree agree with each other (no significantly negative correlation) While we were interested in studying regulatory T cell infiltration, this cell type would not pass the 3rd filter and so was removed from analysis. This leaves a total of 73 tissue x cell type combinations, which we refer to as our infiltration phenotypes.…”

Section: Filtering Infiltration Phenotypes For Statistical Analysismentioning

confidence: 94%

“…At the same time, large-scale sequencing efforts such as the GTEx project 16 have enabled a detailed exploration of the links between genomic and transcriptomic variations across different tissues. Together, these cell-type estimation methods can be utilized in synergy with massive bulk sequenced data sets to infer cellular heterogeneity and achieve statistically well-powered associations that intrinsically drive the heterogeneity [17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues

Marderstein

Uppal

Verma

et al. 2019

Preprint

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Despite infiltrating immune cells playing an essential role in human disease and the patient response to treatment, the central mechanisms influencing variability in infiltration patterns are unclear. Using bulk RNA-seq data from 53 GTEx tissues, we applied cell-type deconvolution algorithms to evaluate the immune landscape across the healthy human body. We first performed a differential expression analysis of inflamed versus non-inflamed samples to identify essential pathways and regulators of infiltration. Next, we found 21 of 73 infiltration-related phenotypes to be associated with either age or sex (FDR < 0.1). Through our genetic analysis, we discovered 13 infiltration-related phenotypes have genome-wide significant associations (iQTLs) (P < 5.0 x 10 -8 ), with a significant enrichment of tissue-specific expression quantitative trait loci in suggested iQTLs (P < 10 -5 ). We highlight an association between neutrophil content in lung tissue and a variant near the CUX1 transcription factor gene (P = 9.7 x 10 -11 ), which has been previously linked to neutrophil infiltration, inflammatory mechanisms, and the regulation of several immune response genes. Together, our results identify key factors influencing interindividual variability of specific tissue infiltration patterns, which could provide insights on therapeutic targets for shifting infiltration profiles to a more favorable one. Results Robust estimation of immune cell types in bulk RNA-seq profiles.To describe immune content from bulk RNA-seq samples, we used two central algorithms: xCell 13 and CIBERSORT 14 . xCell relies on a modification of single sample gene-set enrichment analysis to estimate cell type scores, while CIBERSORT employs a linear support vector regression model. The default reference signatures allow deconvolution of 64 immune and stroma cell types for xCell and 22 immune cell types for CIBERSORT. CIBERSORT also calculates a scaling factor that measures the degree of infiltration. We refer to the relative proportions from CIBERSORT as "CIBERSORT-Relative" and the product of the relative proportions with the scaling factor as "CIBERSORT-Absolute". We estimate three scores for each cell type to describe the immune content from the gene expression data for each tissue in each individual: xCell, CIBERSORT-Relative, and CIBERSORT-Absolute scores.We first hypothesized that the relative and absolute scores from CIBERSORT encapsulated different aspects of the single-cell deconvolution. While "CIBERSORT-Absolute" simultaneously quantifies a degree of immune infiltration, "CIBERSORT-Relative" is focused on capturing compositional changes in the immune content (Supplementary Note). We simulated synthetic mixes composed of bulk tissue "spiked" in silico with CD4+ T cells and CD8+ T cells (see Methods). We correlated the known amount of CD4+ and CD8+ T cell infiltration in these mixtures with estimated deconvolution scores under a "tissue" scenario and an "immune cell" scenario. In the "immune cell" scenario, we let the true infiltration be the prop...

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Histopathological Image QTL Discovery of Immune Infiltration Variants

Cited by 19 publications

References 39 publications

QTL Mapping of Intestinal Neutrophil Variation in Threespine Stickleback Reveals Possible Gene Targets Connecting Intestinal Inflammation and Systemic Health

QTL Mapping of Intestinal Neutrophil Variation in Threespine Stickleback Reveals Possible Gene Targets Connecting Intestinal Inflammation and Systemic Health

Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives

Age, Sex, and Genetics Influence the Abundance of Infiltrating Immune Cells in Human Tissues

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