Biology subjects

Webb, B. T.

Publications and source records attributed to Webb, B. T..

13 recordsLinked to original sources

bioRxiv2025

Wide-ranging behavioral dysfunction in two mouse models of pathological human variants in the GRIK2 kainate receptor gene

De novo variants in a subset of ionotropic glutamate receptor (iGluR) genes cause nonsyndromic neurodevelopmental disorders (NDDs) in individuals. Two recurrent variants in the kainate receptor (KAR) gene GRIK2 result in the gain-of-function (GoF) substitutions p.Ala657Thr and p.Thr660Lys in a critical pore-forming domain of the GluK2 subunit. Disorders in individuals with these variants manifest as intellectual disability, developmental delay, motor impairments, and, in the case of p.Thr660Lys, epilepsy. To explore their pathogenicity and phenotypic consequences in vivo, we generated knock-in mouse models harboring orthologous Grik2 mutations. Behavioral analyses revealed a range of developmental, motor, cognitive, and naturalistic behavior impairments in both lines, with T660K mice typically exhibiting more severe phenotypes, consistent with clinical observations in humans. GluK2(T660K) mice also display interictal EEG abnormalities and handling-induced seizures. These models establish the first in vivo platforms for dissecting the underlying mechanisms of NDDs caused by a GoF mutation in the GluK2 KAR subunit and represent crucial tools for therapeutic development. HighlightsO_LIDe novo variants in the GRIK2 kainate receptor gene cause developmental disorders C_LIO_LIWe studied knockin mouse models of pathogenic p.Ala657Thr and p.Thr660Lys variants C_LIO_LIGluK2(A657T) and GluK2(T660K) mice have deficits analogous to human disorder symptoms C_LIO_LILike their human counterparts, GluK2(T660K) but not GluK2(A657T) mice have seizures C_LIO_LIGrik2 knockin mice are key tools for interrogating underlying causes of dysfunction C_LI

neuroscience↗

bioRxiv2025

Transient lung eosinophilia during breakthrough influenza infection in vaccinated mice is associated with protective and balanced Type 1/2 immune responses.

Eosinophils are agile cells that participate in a multitude of homeostatic and inflammatory responses in the lung, ranging from allergic asthma to antiviral defense against respiratory viral infection. In the context of vaccination followed by viral infection, such as breakthrough infection, eosinophils have been linked to aberrant Th2 responses like vaccine-enhanced respiratory disease (VAERD). Here, we demonstrate that the lung immune cell composition, cytokine and chemokine repertoire, histopathological profile, and systemic humoral response of breakthrough influenza infection in mice is distinct from that of primary influenza infection or allergic sensitization, canonical Type 1 and 2 immune responses, respectively. Longitudinal comparison of breakthrough infection with allergic sensitization and primary influenza infection demonstrated major differences in lung immunity between treatment groups in female, BALB/c mice. Breakthrough infection mice exhibit lung eosinophil infiltration that peaks at 7-10 days post-challenge, enriched for the Siglec-Fhi subset, but in the absence of overt pro-inflammatory cytokine/chemokine signals, high viral titers, severe lung lesions, goblet cell hyperplasia, allergic levels of total IgE, or enhanced morbidity. Multiparameter fluorescence imaging corroborated findings from flow cytometry and also unveiled interactions between CD101+Siglec-F+ cells with CD3+ cells in the lung tissue space. Imaging also revealed a marked absence of eosinophil or neutrophil extracellular traps in the lungs of breakthrough infection mice, in contrast with allergic sensitization and primary influenza infection, respectively. Altogether, our findings provide a deeper understanding of the kinetics and cell-cell interplay during non-pathological, balanced Type 1/2 immune responses in vaccinated hosts during breakthrough infection.

immunology↗

bioRxiv2023

Improving the discovery of rare variants associated with alcohol problems by leveraging machine learning phenotype prediction and functional information.

Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast, ADH1C and THRA gene intervals were significant (False discovery rate (FDR) <0.05) using default and empirical weights in the predicted AUDIT-P sample, with the most significant association found using predicted AUDIT-P and empirical weights in the ADH1C gene (SKAT-O P Default= 1.06 x 10-9 and P Empirical weight = 6.25 x 10-11). These findings provide evidence for rare variant association of the ADH1C gene with the AUDIT-P and highlight the successful leveraging of ML to increase effective sample size and prior empirical functional weights based on common variant GWAS data to refine and increase the statistical significance in underpowered phenotypes.

genetics↗

bioRxiv2023

Breakthrough infections by SARS-CoV-2 variants boost cross-reactive hybrid immune responses in mRNA-vaccinated Golden Syrian Hamsters

Hybrid immunity to SARS-CoV-2 provides superior protection to re-infection. We performed immune profiling studies during breakthrough infections in mRNA-vaccinated hamsters to evaluate hybrid immunity induction. mRNA vaccine, BNT162b2, was dosed to induce binding antibody titers against ancestral spike, but inefficient serum virus neutralization of ancestral SARS-CoV-2 or variants of concern (VoCs). Vaccination reduced morbidity and controlled lung virus titers for ancestral virus and Alpha but allowed breakthrough infections in Beta, Delta and Mu-challenged hamsters. Vaccination primed T cell responses that were boosted by infection. Infection back-boosted neutralizing antibody responses against ancestral virus and VoCs. Hybrid immunity resulted in more cross-reactive sera. Transcriptomics post-infection reflects both vaccination status and disease course and suggests a role for interstitial macrophages in vaccine-mediated protection. Therefore, protection by vaccination, even in the absence of high titers of neutralizing antibodies in the serum, correlates with recall of broadly reactive B and T-cell responses.

microbiology↗

bioRxiv2022

Case-only rare variant analysis of severe alcohol dependence (AD) using a multivariate hierarchical gene clustering approach.

BackgroundVariation in genes involved in ethanol metabolism has been shown to influence risk for alcohol dependence (AD) including protective loss of function alleles in ethanol metabolizing genes. We therefore hypothesized that people with severe AD would exhibit different patterns of rare functional variation in genes with strong prior evidence for influencing ethanol metabolism and response when compared to genes not meeting these criteria. ObjectiveLeverage a novel case only design and Whole Exome Sequencing (WES) of severe AD cases from the island of Ireland to quantify differences in functional variation between genes associated with ethanol metabolism and/or response and their matched control genes. MethodsFirst, three sets of ethanol related genes were identified including those a) involved in alcohol metabolism in humans b) showing altered expression in mouse brain after alcohol exposure, and altering ethanol behavioral responses in invertebrate models. These genes of interest (GOI) sets were matched to control gene sets using multivariate hierarchical clustering of gene-level summary features from gnomAD. Using WES data from 190 individuals with severe AD, GOI were compared to matched control genes using logistic regression to detect aggregate differences in abundance of loss of function, missense, and synonymous variants, respectively. ResultsThree non-independent sets of 10, 117, and 359 genes were queried against control gene sets of 139, 1522, and 3360 matched genes, respectively. Significant differences were not detected in the number of functional variants in the primary set of ethanol-metabolizing genes. In both the mouse expression and invertebrate sets, we observed an increased number of synonymous variants in GOI over matched control genes. Post-hoc simulations showed the estimated effects sizes observed are unlikely to be under-estimated. ConclusionThe proposed method demonstrates a computationally viable and statistically appropriate approach for genetic analysis of case-only data for hypothesized gene sets supported by empirical evidence.

genetics↗

bioRxiv2021

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years.

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of genetic variation influencing BMI from midlife and beyond is unknown. By analyzing BMI data collected from 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 73 years. We then applied genomic structural equation modeling (gSEM) to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin variation in BMI from middle to early old age in men and women alike.

genetics↗

bioRxiv2021

Missingness Adapted Group Informed Clustered (MAGIC)-LASSO: A novel paradigm for prediction in data with widespread non-random missingness

The availability of large-scale biobanks linking rich phenotypes and biological measures is a powerful opportunity for scientific discovery. However, real-world collections frequently have extensive non-random missingness. While missing data prediction is possible, performance is significantly impaired by block-wise missingness inherent to many biobanks. To address this, we developed Missingness Adapted Group-wise Informed Clustered (MAGIC)-LASSO which performs hierarchical clustering of variables based on missingness followed by sequential Group LASSO within clusters. Variables are pre-filtered for missingness and balance between training and target sets with final models built using stepwise inclusion of features ranked by completeness. This research has been conducted using the UK Biobank (n>500k) to predict unmeasured Alcohol Use Disorders Identification Test (AUDIT) scores. The phenotypic correlation between measured and predicted total score was 0.67 while genetic correlations between independent subjects was high >0.86, demonstrating the method has significant accuracy and utility.

genetics↗

bioRxiv2019

DNA Methylation Associated with Postpartum Depressive Symptoms Overlaps Findings from a Genome-wide Association Meta-Analysis of Depression

BackgroundPerinatal depressive symptoms have been linked to adverse maternal and infant health outcomes. The etiology associated with perinatal depressive psychopathology is poorly understood, but accumulating evidence suggests that understanding inter-individual differences in DNA methylation (DNAm) patterning may provide insight regarding the genomic regions salient to the risk liability of perinatal depressive psychopathology.\n\nResultsGenome-wide DNAm was measured in maternal peripheral blood using the Infinium MethylationEPIC microarray. Ninety-two participants (46% African-American) had DNAm samples that passed all quality control metrics, and all participants were within seven months of delivery. Linear models were constructed to identify differentially methylated sites and regions, and permutation testing was utilized to assess significance. Differentially methylated regions (DMRs) were defined as genomic regions of consistent DNAm change with at least two probes within 1kb of each other. Maternal age, current smoking status, estimated cell-type proportions, ancestry-relevant principal components, days since delivery, and chip position served as covariates to adjust for technical and biological factors. Current postpartum depressive symptoms were measured using the Edinburgh Postnatal Depression Scale. Ninety-eight DMRs were significant (False Discovery Rate < 5%) and overlapped 92 genes. Synaptic signaling, neural development, and platelet formation were the most represented biological processes in gene set analysis, and comparison to the 44 loci discovered in the latest Psychiatric Genomics Consortium meta-analysis of depression revealed 3 overlapping regions and significant enrichment (p<0.03).\n\nConclusionsMany of the genes identified in this analysis corroborate previous allelic, transcriptomic, and DNAm association results related to depressive phenotypes. Future work should integrate data from multi-omic platforms to understand the functional relevance of these DMRs and refine DNAm association results by limiting phenotypic heterogeneity and clarifying if DNAm differences relate to the timing of onset, severity, duration of perinatal mental health outcomes of the current pregnancy or to previous history of depressive psychopathology.

genetics↗

bioRxiv2018

DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

bioinformatics↗

bioRxiv2018

Twin Study of Early-Onset Major Depression Finds DNA Methylation Enrichment for Neurodevelopmental Genes

Major depression (MD) is a debilitating mental health condition with peak prevalence occurring early in life. Genome-wide examination of DNA methylation (DNAm) offers an attractive comple ment to studies of allelic risk given it can reflect the combined influence of genes and environment. The current study used a co-twin control design to identify differentially and variably methylated regions of the genome that distinguish monozygotic (MZ) twins with and without a lifetime history of early-onset MD. The sample included 150 Caucasian monozygotic twins (73% female; Mage=17.52 SD=1.28) assessed during a developmental stage characterized by relatively dis tinct neurophysiological changes. All twins were generally healthy and currently free of medica tions with psychotropic effects. DNAm was measured in peripheral blood cells using the Infinium Human BeadChip 450K Array. MD associations were detected at 760 differentially and variably methylated probes/regions that mapped to 428 genes. Results indicated an association between early-onset MD and many genes and genomic regions involved in neural circuitry formation, pro jection, functioning, and plasticity. Gene enrichment analyses implicated genes related to neuron structures and neurodevelopmental processes including cell-cell adhesion genes (e.g., CDHs, PCDHAs, PCDHA1C/2C). Genes previously implicated in mood and psychiatric disorders as well as chronic stress (e.g., HDAC4, NRG1) also were identified. DNAm regions associated with MD where found to overlap genetic loci observed in the latest Psychiatric Genomics Consortium meta- analysis of depression. Understanding the time course of epigenetic influences during emerging adulthood may clarify developmental phases where genes modulate individual differences in MD risk.

genetics↗

bioRxiv2017

GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3-7, but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimers dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

genetics↗

bioRxiv2017

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

MotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.\n\nResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of p-values.\n\nContactchris.chatzinakos@vcuhealth.org\n\nSupplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Polygenic prediction of the phenome, across ancestry, in emerging adulthood

BackgroundIdentifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on clinical disorder or cognitive abilities rather than normal-range behavioral outcomes.\n\nMethodsThis study examined a sample of emerging adults 18-22 years of age (N = 5,947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. Twenty-eight hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes.\n\nResultsPolygenic risk for schizophrenia in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease.\n\nConclusionsThese results demonstrate the extensive impact of genetic risk for schizophrenia, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.

genomics↗