Biology subjects

Logue, M. W.

Publications and source records attributed to Logue, M. W..

6 recordsLinked to original sources

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↗

bioRxiv2021

The genetic etiology of longitudinal measures of predicted brain ageing in a population-based sample of mid to late-age males

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimers Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 52 to 72 years. Biometrical genetic analyses twin models revealed significant and highly correlated estimates of additive genetic heritability ranging from 59% to 75%. Multivariate longitudinal modelling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences. HighlightsWe explored the genetic and environmental etiology of MRI-based predicted brain age (PBA) in a longitudinal sample of males starting in midlife. Genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and largely explained by common genetic influences.

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↗

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↗

bioRxiv2018

Genetic risk for coronary heart disease alters the influence of Alzheimer’s genetic risk on mild cognitive impairment

BACKGROUNDAlzheimers disease (AD) is under considerable genetic influence. However, known susceptibility loci only explain a modest proportion of variance in disease outcomes. This small proportion could occur if the etiology of AD is heterogeneous. We previously found that an AD polygenic risk score (PRS) was significantly associated with mild cognitive impairment (MCI), an early stage of AD. Poor cardiovascular health is also associated with increased risk for AD and has been found to interact with AD pathology. Conditions such as coronary artery disease (CAD) are also heritable, and may contribute to heterogeneity if there are interactions of genetic risk for these conditions as there is phenotypically. However, case-control designs based on prevalent cases of a disease with relatively high case-fatality rate such as CAD may be biased toward individuals who have long post-event survival times and may therefore also identify loci with protective effects.\n\nMETHODSWe compared interactions between an AD-PRS and two CAD-PRSs, one based on a GWAS of incident cases and one on prevalent cases, on MCI status in 1,209 individuals.\n\nRESULTSAs expected, the incidence-based CAD-PRS interacts with the AD-PRS to further increase MCI risk. Conversely, higher prevalence-based CAD-PRSs reduced the effect of AD genetic risk on MCI status.\n\nCONCLUSIONSThese results demonstrate: i) the utility of including multiple PRSs and their interaction effects; ii) how genetic risk for one disease may modify the impact of genetic risk for another; and iii) the importance of considering ascertainment procedures of GWAS being used for genetic risk prediction.

genomics↗