Biology subjects

Renteria, M. E.

Publications and source records attributed to Renteria, M. E..

9 recordsLinked to original sources

bioRxiv2026

Distributed Genetic Effects on Human Brain Structure Emerge Across Multiple Spatial Scales

Genome-wide association studies (GWAS) have identified hundreds of common genetic variants associated with regional brain volumes, enabling the construction of polygenic scores (PGS) that summarize genetic predisposition for variation in specific neuroanatomical traits. To investigate how these genetic influences are exerted spatially throughout the brain, we computed PGS for ten brain volume phenotypes, including nine major subcortical structures and intracranial volume. Each locus was weighted by its estimated GWAS effect size on regional volume in the original GWAS. In an independent, non-overlapping sample of 2,830 UK Biobank participants, we performed whole-brain voxel-based morphometry (VBM) analyses of 3D volumetric brain MRI to reveal voxel-wise associations between each PGS and modulated gray matter volume (GMV). To probe genetic effects across multiple spatial scales, analyses were repeated across Gaussian smoothing kernels ranging from 2-mm to 12-mm full-width at half-maximum (FWHM). Several PGS demonstrated highly significant associations with GMV, including localized effects in the hippocampus, amygdala, thalamus, and basal ganglia, whereas the brainstem PGS showed more widespread associations throughout the brain. For most of the PGS, the fraction of voxels surviving the false discovery rate (FDR) correction increased with increasing FWHM. Peak voxel-wise significance was often strongest at intermediate smoothing levels. Hippocampal significance maps showed progressively larger regions of significant signal at higher smoothing levels, and subsampling showed that detectable signal remained present even with substantial reductions in sample size. These findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.

neuroscience↗

bioRxiv2026

Mapping Focal and Generalized Effects of Common Genetic Variants on Human Brain Structure

Genome-wide association studies (GWAS) have advanced the quest to understand how specific genetic variants influence human brain structure and function. Recent work has identified hundreds of common variants associated with subcortical brain volumes, sparking interest in how these genetic markers overlap across brain networks. While this can be estimated by hierarchical clustering of the genetic correlation matrix to identify modular patterns of shared architecture, no brain-wide maps of these effects are available. To address this, we computed polygenic scores (PGS) from loci associated with ten brain volume regions of interest (ROIs): nine major subcortical structures and intracranial volume, with each locus weighted by its association with regional volume. In an independent sample from the discovery GWAS, we performed large-scale segmentation of 3D volumetric T1-weighted MRI scans using voxel-based morphometry (VBM) to map 3D profile of regions where gray matter volume (GMV) was associated with each PGS. We found statistically significant, localized effects for PGS defined for the amygdala, thalamus, and basal ganglia, but PGS for brainstem volume was associated with widespread differences throughout the brain. These brain-wide maps reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.

neuroscience↗

bioRxiv2025

CD8+ T cells mediate vaccination-induced lymphatic containment of latent Mycobacterium tuberculosis infection following immunosuppression, while B cells are dispensable

It is estimated that two billion people are latently infected with Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). Latent Mtb infection (LTBI) can occur in multiple organs, including the lymphatics. The risk of LTBI reactivation increases in immunocompromised conditions, such as coinfection with human immunodeficiency virus (HIV), and during treatment of autoimmune diseases and organ transplantation. The immunological correlates of protection against TB, including against reactivation of LTBI, remain largely elusive. Here, we used a mouse model of latent lymphatic Mtb infection to dissect the immunological mechanisms underlying LTBI containment versus reactivation. We show that immunosuppression-mediated reactivation of lymphatic LTBI and the subsequent spread to non-lymphatic organs can be prevented by vaccination with multiple recombinant BCG (rBCG) strains despite the deficiency of CD4+ T cells. Using spatial transcriptomics, multi-parameter imaging, network analysis and bioinformatic integration of histopathological images, we reveal that immunosuppression is associated with a distinct repositioning of non-CD4 immune cells at the edge of TB lesions within the infection-draining cervical lymph nodes. While B cells increased in numbers, they are dispensable for the containment of LTBI. Lymphatic Mtb infection in different immune cell-deficient mouse strains, antibody-mediated cell depletion and adoptive transfer experiments into highly susceptible mice unequivocally show that vaccination-mediated prevention of LTBI reactivation is critically dependent on CD8+ T cells. These findings have profound implications for our understanding of immunity to TB and the management of LTBI. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/634479v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@151fdd6org.highwire.dtl.DTLVardef@1efed47org.highwire.dtl.DTLVardef@c2e7e0org.highwire.dtl.DTLVardef@555c7_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Pooled genetic screens identify breast cancer risk genes involved in evasion from T cell-mediated killing

Genome-wide association studies have identified more than 220 loci associated with breast cancer susceptibility. A major challenge is now to identify the effector genes with plausible functions in the context of breast cancer risk. We have previously performed pooled CRISPR screens to identify target genes at risk loci that drive cancer hallmarks including proliferation or modulating DNA damage response. We now extend these screens to identify genes involved in response to cytotoxic T lymphocyte (CTL) killing. We performed knockout and inhibition screens to identify genes that affect the response of the MCF7 human breast cancer cell line to CTL killing in an in vitro co-culture system. We identified 33 candidate risk genes associated with resistance or sensitisation to T cell-mediated killing. Using single gene perturbation, we showed that deletion of candidate risk genes IRF1, ATF7IP, CCDC170 and CASP8 induced resistance, while ablation of CFLAR, CREBBP, and PRMT7 sensitized cells to CTL killing. We used reporter assays to show that the risk-associated alleles at rs736801 and rs3769821 reduced transactivation of the IRF1 and CASP8 promoters, respectively. We showed that pharmacological inhibition of PRMT7 rendered breast cells sensitive to CTL killing and PRMT7 levels were negatively correlated with CD8+ infiltration and patient survival in luminal A breast cancer patient cohorts. Our results demonstrate that phenotypic pooled CRISPR screens are a useful approach for high throughput functional follow-up of GWAS findings, identifying genes which alter immune responses to breast cancer which offer opportunities to enhance immunotherapy.

genetics↗

bioRxiv2020

Evidence of genetic overlap between circadian preference and brain white matter microstructure

Study objectivePrevious neuroimaging studies have highlighted differences in white matter microstructure among individuals with different chronotypes, but it is unclear whether those differences are due to genetic or environmental factors. MethodsHere we leverage summary statistics from recent large-scale genome-wide association studies (GWAS) of chronotype and diffusion tensor imaging (DTI) measures to examine the genetic overlap and infer causal relationships between these traits. ResultsWe identified 29 significant pairwise genetic correlations, of which 13 also had evidence for a causal association. Negative genetic correlations were identified between chronotype and brain-wide mean, axial and radial diffusivities. When exploring individual tracts, ten negative genetic correlations were observed with mean diffusivities, 10 with axial diffusivities, 4 with radial diffusivities and 2 with mode of anisotropy. We found evidence for a possible causal association of chronotype with white matter microstructure in individual tracts including the posterior limb and retrolenticular part of the internal capsule; the genu and splenium of the corpus callosum and the posterior, superior and anterior regions of the corona radiata. ConclusionsOur results suggest that eveningness is associated with variation in tract-specific white matter microstructure and, for an evening person, increases in axial and / or radial diffusivities may influence a higher mean diffusivity. These findings add to our understanding of circadian preference and its relationship with the brain, providing new perspectives on the genetic neurological underpinnings of chronotypes role in health and disease. Statement of SignificanceSleep is essential for a healthy brain function, particularly for neural organization and brain structure development. Individual chronotype differences have been associated with depression, schizophrenia, diabetes and obesity, among other conditions. Investigating the shared genetic aetiology between chronotype and white matter microstructure is essential to understand the neurological basis of individual variation in chronotype. In the present study, we show that tract-specific white matter microstructure is genetically correlated and causally associated with chronotype.

neuroscience↗

bioRxiv2020

Genetic, lifestyle and environmental risk factors for chronic pain revealed through GWAS

Chronic pain (CP) is a leading cause of disability worldwide with complex aetiologies that remain elusive. Here we addressed this issue by performing a GWAS on a large UK Biobank sample (N=188,352 cases & N=69,627 controls) which identified two independent loci associated with CP near ADAMTS6 and LEMD2. Gene-based tests revealed additional CP-associated genes (DCAKD, NMT1, MLN, IP6K3). Across 1328 complex traits, 548 (41%) were genetically correlated with CP, of which 175 (13%) showed genetic causal relationships using the latent causal variable approach and Mendelian randomization. In particular, major depressive disorder, anxiety, smoking, body fat & BMI were found to increase the risk of CP, whereas diet, walking for pleasure & higher educational attainment were associated with a reduced risk (i.e., protective effect). This data-driven hypothesis-free approach has uncovered several specific risk factors that warrant further examination in longitudinal trials to help deliver effective early screening & management strategies for CP.

genetics↗

bioRxiv2019

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis.

BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messagesO_LIThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion. C_LIO_LISystematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization. C_LIO_LIUsing this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health. C_LIO_LIThe method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. C_LIO_LIWe developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries. C_LI

genetics↗

bioRxiv2019

Insights into the aetiology of snoring from observational and genetic investigations in the UK Biobank (n=408,317)

We conducted the largest study of snoring using data from the UK Biobank (n [~] 408,000; snorers [~]152,000). A genome-wide association study (GWAS) identified 42 genome-wide significant loci, with a SNP-based heritability estimate of [~]10% on the liability scale. Genetic correlations with body mass index, alcohol intake, smoking, schizophrenia, anorexia nervosa and neuroticism were observed. Gene-based associations identified 173 genes, including DLEU7, MSRB3 and POC5 highlighting genes expressed in brain, cerebellum, lungs, blood, and oesophagus tissues. We used polygenic scores (PGS) to predict recent snoring and probable obstructive sleep apnoea (OSA) in an independent Australian sample (n[~]8,000). Mendelian randomisation analyses provided evidence that larger whole body fat mass causes snoring. Altogether, our results uncover new insights into the aetiology of snoring as a complex sleep-related trait and its role in health and disease beyond being a cardinal symptom of OSA.

genomics↗

bioRxiv2017

Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide

Subcortical brain structures are integral to motion, consciousness, emotions, and learning. We identified common genetic variation related to the volumes of nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus, using genome-wide association analyses in over 40,000 individuals from CHARGE, ENIGMA and the UK-Biobank. We show that variability in subcortical volumes is heritable, and identify 25 significantly associated loci (20 novel). Annotation of these loci utilizing gene expression, methylation, and neuropathological data identified 62 candidate genes implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

genetics↗