Biology subjects

Maihofer, A. X.

Publications and source records attributed to Maihofer, A. X..

12 recordsLinked to original sources

bioRxiv2026

Fine mapping of PTSD GWAS reveals a role for amygdala Foxp2 in regulation of fear and threat responses

Post-traumatic Stress Disorder (PTSD) is a debilitating psychiatric condition caused by severe trauma exposure and characterized by ongoing dysregulation of fear processing, hyperarousal, and amygdala activation, but with limited effective treatments. Recent large-scale genome-wide association studies (GWAS) of PTSD have identified the transcription factor FOXP2 as a highly-significant, top putative risk gene. Both fine-mapping of the PTSD GWAS with amygdala-specific expression quantitative trait loci (eQTL) data, and transcriptome-wide association analyses, show that altered expression of FOXP2 is associated with increased PTSD risk. In vertebrates, FOXP2 mRNA is most densely expressed in the intercalated cells (ITCs) of the amygdala. ITC neurons receive excitatory input from external regulatory and sensory brain regions, as well as the basolateral amygdala, and send inhibitory projections to the central amygdala, which regulates downstream fear responses. While ITCs are critical for conditioned fear acquisition and extinction, the role of the FOXP2 gene in modulating fear-related behaviors remains unknown. Here, we used complementary bioinformatic, molecular, circuit, behavioral, and electrophysiological approaches to characterize the function of mouse (Foxp2) and human (FOXP2) orthologs in amygdala-mediated fear learning. To assess Foxp2 function in vivo, we first used shRNA-mediated knockdown (KD) of Foxp2 in ITC neurons of adult mice. Targeted Foxp2 KD robustly and significantly reduced freezing (threat/fear expression) during and after auditory fear conditioning. Whole-cell recordings from individual ITC neurons revealed that Foxp2 KD increased their intrinsic membrane excitability and action potential frequency, consistent with hypothesized enhanced inhibitory output to the central amygdala and thus reduced fear expression. This hyperexcitability was associated with reduced potassium channel conductance. Bulk RNA sequencing (RNA-seq) of mouse amygdala after ITC Foxp2 KD confirmed decreased potassium channel transcription and revealed broader Foxp2-dependent regulation of multiple genes implicated in fear learning, including Wnt, Crh (which encodes corticotropin-releasing hormone), and neurokinin signaling pathways. Consistent with these findings, bulk RNA-seq of medial amygdala postmortem tissue from humans with PTSD versus neurotypical controls showed decreased potassium channel transcription in samples with low FOXP2 expression. Downstream transcriptional changes following Foxp2 KD in the mouse amygdala also showed marked enrichment of genes identified in PTSD risk loci from the largest PTSD GWAS to date. Specifically, downregulated genes were enriched for mouse orthologs of Tier 1 PTSD GWAS risk genes. This enrichment appears to reflect subcortical Foxp2 signaling within the amygdala, driven predominantly by decreased expression of genes lacking promoter-anchored chromatin loops. This finding suggests that Foxp2 may directly bind regulatory elements of multiple top PTSD risk genes, acting as a key regulatory node for fear-related pathways in the amygdala. Collectively, our findings establish FOXP2 as a central transcriptional regulator of fear-related gene networks in the amygdala and potential regulatory hub for PTSD genetic risk.

neuroscience↗

bioRxiv2025

Tractor Workflow Pipeline: A Scalable Nextflow Framework for Local Ancestry-Aware Genome-Wide Association Studies

The routine exclusion of admixed individuals from traditional Genome-Wide Association Studies (GWAS) due to concerns about spurious associations has hindered genetic analyses involving multiple ancestries. Tractor GWAS addresses this issue by incorporating local ancestry into its analysis, empowering identification of ancestry-enriched hits and generating ancestry-specific summary statistics. However, Tractor requires accurate genomic phasing and local ancestry inference as prerequisite steps, which requires additional bioinformatics expertise and decision points regarding reference panel setup. To streamline, harmonize, and automate this process, we present a scalable Nextflow workflow that integrates all necessary steps, minimizing the need for manual intervention while remaining modular and customizable. The workflow supports multiple commonly used tools and offers flexibility in how Tractor is implemented. To demonstrate its utility, we applied this pipeline to analyze 32 blood biomarkers in 6,245 two-way AFR-EUR admixed individuals from the UK Biobank. This pipeline ran efficiently at scale, replicated known associations, and identified novel ancestry-specific loci. These novel associations were largely driven by variants present on African ancestral tracts but absent from European tracts, underscoring the value of local ancestry-aware methods in uncovering previously missed genetic signals. By enabling the efficient analysis of admixed individuals, our workflow facilitates Tractor use, paving the way for more broader genetic discovery.

genomics↗

bioRxiv2024

Characterizing features affecting local ancestry inference performance in admixed populations

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using Local Ancestry Inference (LAI). Accurate LAI is crucial to ensure downstream analyses reflect the genetic ancestry of research participants accurately. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin Americas primary continental ancestries - African (AFR), Amerindigenous (AMR), and European (EUR). Simulating LD-informed admixed haplotypes under a variety of 2 and 3-way admixture models, we implemented a standard LAI pipeline, testing three reference panel compositions to quantify their overall and ancestry-specific accuracy. We examined LAI miscall frequencies and true positive rates (TPR) across simulation models and continental ancestries. AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts in all comparisons, with TPR means for AMR ranging from 88-94%, EUR from 96-99% and AFR 98-99%. When LAI miscalls occurred, they most frequently erroneously called European ancestry in true Amerindigenous sites. Using a reference panel well-matched to the target population, even with a lower sample size, LAI produced true-positive estimates that were not statistically different from a high sample size but mismatched reference, while being more computationally efficient. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across other admixed populations. Our findings reinforce the need for inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

genomics↗

bioRxiv2022

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p<5x10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks. HIGHLIGHTSO_LIGenomic SEM can examine genetic correlation across cortical regions. C_LIO_LIWe inferred regional genetic networks of cortical thickness and surface area. C_LIO_LINetwork-associated variants have been implicated in multiple traits. C_LIO_LIThese networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence. C_LI

genomics↗

bioRxiv2020

Tractor: A framework allowing for improved inclusion of admixed individuals in large-scale association studies.

Admixed populations are routinely excluded from medical genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestryspecific effect size estimates, can boost GWAS power, and improves the resolution of association signals. Using a local ancestry aware regression model, we replicate known hits for blood lipids in admixed populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative causal variants.

genomics↗

bioRxiv2019

Genomic Influences on Self-Reported Childhood Maltreatment

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to 1) identify genetic variation associated with reported childhood maltreatment, 2) calculate the relevant SNP-based heritability estimates, and 3) quantify the genetic overlap of reported childhood maltreatment with mental and physical health-related phenotypes. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n=124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder working group (PGC-PTSD) (n=26,290). Heritability estimations for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression (LDSR). Two genome-wide significant loci associated with childhood maltreatment, located on chromosomes 3p13 (rs142346759, beta=0.015, p=4.35x10-8, FOXP1) and 7q31.1 (rs10262462, beta=-0.016, p=3.24x10-8, FOXP2), were identified in the discovery dataset but were not replicated in the PGC-PTSD sample. SNP-based heritability for childhood maltreatment was estimated to be [~]6%. Childhood maltreatment was most significantly genetically correlated with depressive symptoms (rg=0.70, p=4.65x10-40). This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. FOXP genes could influence traits such as depression and thereby be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with various psychiatric disorders, may ultimately be useful in in developing targeted treatment and prevention strategies.

genomics↗

bioRxiv2019

Meta-analysis of longitudinal epigenome-wide association studies of military cohorts reveals multiple CpG sites associated with post-traumatic stress disorder

BackgroundEpigenetic mechanisms have been suggested to play a role in the development of post-traumatic stress disorder (PTSD). Here, blood-derived DNA methylation data (HumanMethylation450 BeadChip) collected prior to and following combat exposure in three cohorts composed of male military members were combined to assess whether DNA methylation profiles are associated with the development of PTSD.\n\nMethodsA total of 123 cases and 143 trauma-exposed controls were included. The Psychiatric Genomics Consortium (PGC) PTSD EWAS QC pipeline was used on all cohorts, and results were combined using a sample size weighted meta-analysis. We first combined two cohorts in a discovery stage (N=126 and 78), sought targeted replication in the third cohort (N=62) and then performed a meta-analysis of all three datasets.\n\nResultsThe discovery stage identified four CpG sites in which, conditional on pre-deployment DNA methylation, post-deployment DNA methylation was associated with PTSD status after adjustment for multiple comparisons. The most significant CpG (p = 1.0 x 10-08) was located on 5q31 and replicated in the third cohort. When combining all cohorts, this intergenic site remained most significant along with two CpGs located in MAD1L1 and HEXDC. Interestingly, the CpG site of MAD1L1 had an underlying single nucleotide polymorphism (SNP) which was located within the same LD block as a recently identified PTSD-associated SNP. Twelve differential methylated regions (DMRs) were also identified, one of which was located in MAD1L1 and four were situated in the human leukocyte antigen (HLA) region.\n\nConclusionThis study suggests that the development of PTSD is associated with distinct methylation patterns in several genomic positions and regions. Our most prominent finding points to the involvement of MAD1L1 which was previously associated with PTSD.

genetics↗

bioRxiv2019

Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies novel methylation loci

Differences in susceptibility to posttraumatic stress disorder (PTSD) may be related to epigenetic differences between PTSD cases and trauma-exposed controls. Such epigenetic differences may provide insight into the biological processes underlying the disorder. Here we describe the results of the largest DNA methylation meta-analysis of PTSD to date with data from the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. Ten cohorts, military and civilian, contributed blood-derived DNA methylation data (HumanMethylation450 BeadChip) from 1,896 PTSD cases (42%) and trauma-exposed controls (58%). Utilizing a common QC and analysis strategy, we identified ten CpG sites associated with PTSD (9.61E-07<p<4.72E-11) after adjustment for multiple comparisons (FDR<.05). Several CpGs were located in genes previously implicated in PTSD and other psychiatric disorders. The top four CpG sites fell within the aryl-hydrocarbon receptor repressor (AHRR) locus and were associated with lower DNA methylation in PTSD cases relative to controls. Interestingly, this association appeared to uncorrelated with smoking status and was most pronounced in non-smokers with PTSD. Additional evaluation of metabolomics data supported our findings and revealed that AHRR methylation associated with kynurenine levels, which were lower among subjects with PTSD relative to controls. Overall, this study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

genomics↗

bioRxiv2019

Genomewide Analyses of Psychological Resilience in US Army Soldiers

Though a growing body of preclinical and translational research is illuminating a biological basis for resilience to stress, little is known about the genetic basis of psychological resilience in humans. We conducted genomewide association studies (GWAS) of self-assessed (by questionnaire) and outcome-based (incident mental disorders from pre- to post-deployment) resilience among European (EUR) ancestry soldiers in the Army Study To Assess Risk and Resilience in Servicemembers (STARRS). Self-assessed resilience (N=11,492) was found to have significant common-variant heritability (h2=0.162, se=0.050, p=5.37e-4), and to be significantly negatively genetically correlated with neuroticism (rg= -0.388, p=0.0092). GWAS results from the EUR soldiers revealed a genomewide significant locus (4 SNPs in LD; top SNP: rs4260523, p=5.654e-09) on an intergenic region on Chr 4 upstream from DCLK2 (Doublecortin-Like Kinase 2), a member of the doublecortin (DCX) family of kinases that promote survival and regeneration of injured neurons. A second gene, KLHL36 (Kelch Like Family Member 36) was detected at gene-wise genomewide significance (p=1.89e-06). A polygenic risk score derived from the self-assessed resilience GWAS was not significantly associated with outcome-based resilience. In very preliminary results, genomewide significant association with outcome-based resilience was found for one locus (top SNP: rs12580015) on Chr 12 downstream from SLC15A5 (solute carrier family 15 member 5) in the small group (N=581) of subjects exposed to the highest level of deployment stress. The further study of genetic determinants of resilience has the potential to illuminate the molecular bases of stress-related psychopathology and potentially point to new avenues for therapeutic intervention.

genetics↗

bioRxiv2018

Economic status mediates the relationship between educational attainment and posttraumatic stress disorder: a multivariable Mendelian randomization study

ObjectivesTo investigate the genetic overlap and causal relationship between posttraumatic stress disorder (PTSD) and traits related to educational attainment.\n\nDesignGenetic correlation, polygenic risk scoring, and causal inference via multivariable Mendelian randomization (MR).\n\nSettingPsychiatric Genomics Consortium for PTSD, UK Biobank, 23andMe, and Social Science Genetic Association Consortium.\n\nParticipants23,185 PTSD cases and 151,309 controls; up to 1,131,881 individuals assessed for educational attainment and related traits.\n\nMain outcome measuresGenetic correlation obtained from linkage disequilibrium score regression, phenotypic variance explained by polygenic risk scores, and casual effects (beta values) estimated with MR\n\nResultsPTSD showed strong negative genetic correlations with educational attainment (EdAtt; rg=-0.26, p=4.6x10-8). PRS based on genome-wide significant variants associated with EdAtt significantly predicted PTSD (p=6.16x10-4), but PRS based on variants associated with PTSD did not predict EdAtt (p>0.05). MR analysis indicated that EdAtt has negative causal effects on PTSD (beta=-0.23, p=0.004). Investigating potential mediators of the EdAtt-PTSD relationship, we observed that propensity for trauma exposure and risk-taking behaviors are risk factors for PTSD independently from EdAtt (beta = 0.36, p = 2.57x10-5 and beta = 0.76, p = 6.75x10-4, respectively), while income fully mediates the causal effect of EdAtt on PSTD (MR: Income - beta = -0.18, p =0.001; EdAtt - beta =-0.23, p=0.004; multivariable MR: Income - beta = -0.32, p = 0.017; EdAtt - beta = -0.04, p = 0.786).\n\nConclusionsWe report novel findings based on large-scale datasets regarding the relationship between educational attainment and PTSD, supporting the role of economic status as the key mediator in the causal relationship observed.\n\nWhat is already known on this topicThere is a well-established negative association of educational attainment and other traits related to cognitive ability with posttraumatic stress disorders (PTSD). However, the findings of these previous studies support various possible causal explanations: 1) individuals with high educational attainment are more resilient with respect to developing PTSD, 2) PTSD negatively impacts cognitive ability, or 3) PTSD and educational attainment share some underlying determinants, including relevant molecular mechanisms.\n\nA key obstacle to disentangling the complex association between educational attainment and PTSD is reverse causation, i.e. the situation in which the outcome precedes and causes the exposure instead of the other way around.\n\nWhat this study addsWe conducted a causal-inference investigation based on large-scale information from the investigation of more than one million individuals. Our main assumption is that genetic information can strongly minimize the bias of reverse causation, because genetic variants are determined at conception and do not change throughout life.\n\nOur findings indicate 1) the effect of traits related to educational attainment on PTSD, 2) no reverse effect of PTSD on educational attainment, and 3) economic status mediates the relationship between educational attainment and PTSD, independently from the brain mechanisms related to educational attainment.

genetics↗

bioRxiv2018

Largest genome-wide association study for PTSD identifies genetic risk loci in European and African ancestries and implicates novel biological pathways

Post-traumatic stress disorder (PTSD) is a common and debilitating disorder. The risk of PTSD following trauma is heritable, but robust common variants have yet to be identified by genome-wide association studies (GWAS). We have collected a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls. We first demonstrate significant genetic correlations across 60 PTSD cohorts to evaluate the comparability of these phenotypically heterogeneous studies. In this largest GWAS meta-analysis of PTSD to date we identify a total of 6 genome-wide significant loci, 4 in European and 2 in African-ancestry analyses. Follow-up analyses incorporated local ancestry and sex-specific effects, and functional studies. Along with other novel genes, a non-coding RNA (ncRNA) and a Parkinsons Disease gene, PARK2, were associated with PTSD. Consistent with previous reports, SNP-based heritability estimates for PTSD range between 10-20%. Despite a significant shared liability between PTSD and major depressive disorder, we show evidence that some of our loci may be specific to PTSD. These results demonstrate the role of genetic variation contributing to the biology of differential risk for PTSD and the necessity of expanding GWAS beyond European ancestry.

genetics↗

bioRxiv2017

PTSD Blood Transcriptome Mega-Analysis: Shared Inflammatory Pathways Across Genders And Modes Of Traumas

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module down-regulated in men exposed to combat traumas, one IL12-mediated signaling module up-regulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and MAPK-activity up-regulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

neuroscience↗