The persistent HIV reservoir constitutes the main obstacle to curing HIV/AIDS disease. Our understanding of how non-productive HIV infections are established in primary human CD4+ T cells during the first round of infection remains, however, incomplete. In this study, we leveraged the HIV reporter virus pMorpheus-V5 to delineate cellular expression patterns that are upregulated in non-productively infected primary CD4+ T memory stem cells (TSCM). We found that CD4+ TSCM harboring non-productive proviruses displayed a distinct transcriptomic signature comprising 118 upregulated genes. This non-productive expression profile was distinct from that of productively infected cells as well as from negative-exposed and mock-infected cells. Among the cellular genes most upregulated in CD4+ T cells harboring non-productive proviruses were CCR4-binding migratory chemokines (CCL22, CCL17), tryptophan catabolic enzymes (IDO1, KYNU), and genes encoding cytoskeletal rearrangement proteins (BASP1, TNFAIP2). Intracellular flow cytometry-based analyses confirmed that non-productively infected CD4+ TSCM cells were enriched for CCL22 and IDO1 co-expression compared to the other CD4+ memory subsets, underscoring a clear CD4+ T cell subset specificity for the upregulation of these two immune gene sets associated with non-productive infections. These findings suggest that primary human CD4+ TSCM harboring non-productive proviruses display a distinct immunoregulatory phenotype which may facilitate immune evasion and contribute to the persistence of the HIV reservoir.

Background:Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. Methods:DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by |Δβ| > 0.10 and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. Results:We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs, mean Δβ = -0.143), an oxidative stress gene also implicated in Parkinson's disease. FTCD , a one-carbon cycle enzyme, carried 5 hypermethylated DMPs (mean Δβ = +0.210). A cluster of DMP-associated genes with established roles in innate immune and NF-κB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1 , and MSI2 ; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. Conclusions:This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-κB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-κB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

Eating disorders-including anorexia nervosa (AN), bulimia nervosa and binge-eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. Here we conducted a genomic meta-analysis of case-control studies of binge-eating behavior (BE; 39,279 cases, 1,227,436 controls), alongside analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six BE-associated loci, including loci associated with a higher body mass index and impulse-control behaviors. AN genome-wide association studies yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry studies. BE and AN exhibited similar positive genetic correlations with psychiatric disorders but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with body mass index. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

Eating disorders -including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

Genetic variants influencing gene expression have been extensively studied at the transcriptional level. How these variants affect downstream processes remains unclear. We quantitated ribosome occupancy in prefrontal cortex samples from the BrainGVEX cohort and integrated these data with transcriptomic and proteomic profiles from the same individuals. Through cis-QTL mapping, we identified genetic variants associated with transcript level (eQTLs), ribosome occupancy (rQTLs), and protein level (pQTLs). Notably, only 34% of eQTLs have their effects propagated to the protein levels, suggesting widespread post-transcriptional attenuation. Using both a gene-based approach and a variant-based approach we identified omics-specific QTLs that associated with brain disorder GWAS signals and found the majority of them to be driven predominantly by transcriptional regulation. Consistently, using a TWAS approach, we identified 74 SCZ risk genes across the three omics layers, 52 were discovered using transcriptome with 68% showing limited impact on protein expression. Our findings indicated that many disease-associated variants act through regulatory mechanisms that do not lead to an observable impact on the protein level.

AbstractOsteogenic differentiation is essential for bone development, metabolism, and repair; however, the underlying regulatory relationships among genes remain poorly understood. To elucidate the transcriptomic changes and identify novel regulatory genes involved in osteogenic differentiation, we differentiated mesenchymal stem cells (MSCs) derived from 20 human iPSC lines into preosteoblasts (preOBs) and osteoblasts (OBs). We then performed transcriptome profiling of MSCs, preOBs and OBs. The iPSC-derived MSCs and OBs showed similar transcriptome profiles to those of primary human MSCs and OBs, respectively. Differential gene expression analysis revealed global changes in the transcriptomes from MSCs to preOBs, and then to OBs, including the differential expression of 840 genes encoding transcription factors (TFs). TF regulatory network analysis uncovered a network comprising 451 TFs, organized into five interactive modules. Multiscale embedded gene co-expression network analysis (MEGENA) identified gene co-expression modules and key network regulators (KNRs). From these analyses,KLF16emerged as an important TF in osteogenic differentiation. We demonstrate that overexpression ofKlf16in vitro inhibited osteogenic differentiation and mineralization, whileKlf16+/-mice exhibited increased bone mineral density, trabecular number, and cortical bone area. Our study underscores the complexity of osteogenic differentiation and identifies novel regulatory genes such asKLF16, which plays an inhibitory role in osteogenic differentiation both in vitro and in vivo.

Background Regulation of transcript structure generates transcript isoform and splicing diversity and plays an important role in health and disease. Differences in isoform expression account for substantial effect sizes and genetic enrichments in neuropsychiatric disorders such as ASD and schizophrenia; however, a systematic characterization of isoform expression in the human brain is lacking as most isoforms are yet to be directly profiled at the tissue or cellular level. Methods Here, we performed deep single-molecule real-time (SMRT) isoform long-read sequencing of prefrontal cortex (PFC), cerebellum (CBL), hippocampus (HIP) and dorsal anterior cingulate cortex (dACC) from >5 0 individuals and 7 pools, totaling > 60 libraries with > 100 SMRT cells and > 250 M long-reads. Results Our map of > 350k isoforms greatly expands the transcriptional landscape of brain-expressed genes with > 150k novel unannotated complete isoforms (54%), extends known coding and noncoding gene annotations, and uncovers hundreds of novel or reconstructed gene loci that can show cell-type specific expression. Orthogonal data, including, ribosome sequencing and mass-spectrometry data from > 280 prefrontal cortical samples, 5’-end TSS profiling, and nanopore long-read sequencing, were used to further characterize the structure of novel isoforms and open reading frames. We further leveraged PsychENCODE single-nuclei transcriptomic and chromatin data to annotate cell-type-specific regulatory interactions for novel isoforms, reconstructed loci, and novel genes. Multi-platform analysis of single-nuclei full-length transcripts across PFC, HIP and striatum further revealed that isoform diversity seen in brain tissues is often due to underlying cell-type-specific patterns. Finally, we leveraged our reference map of full-length isoforms to characterize medically relevant genes, assess the functional consequence of rare genetic variants from a compendium of disease studies, and to improve quantitative trait analyses and refine the prioritization of candidate risk genes. Discussion Taken together, we provide a comprehensive resource to characterize dysregulation of isoform expression in neuropsychiatric disorders. Disclosure Nothing to disclose.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

Identifying genetic risk factors for highly heterogeneous disorders such as epilepsy remains challenging. Here we present, to our knowledge, the largest whole-exome sequencing study of epilepsy to date, with more than 54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies and generalized and focal epilepsies, whereas most other gene discoveries are subtype specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single-nucleotide/short insertion and deletion variants, copy number variants and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.

OBJECTIVE:Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures.METHODS:This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses.RESULTS:Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors.CONCLUSIONS:This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Background The impact of genetic variants on gene expression has been intensely studied at the transcription level, yielding in valuable insights into the association between genes and the risk of complex disorders, such as schizophrenia (SCZ). However, the downstream impact of these variants and the molecular mechanisms connecting transcription variation to disease risk are not well understood. Results We quantitated ribosome occupancy in prefrontal cortex samples of the BrainGVEX cohort. Together with transcriptomics and proteomics data from the same cohort, we performed cis-Quantitative Trait Locus (QTL) mapping and identified 3,253 expression QTLs (eQTLs), 1,344 ribosome occupancy QTLs (rQTLs), and 657 protein QTLs (pQTLs) out of 7,458 genes quantitated in all three omics types from 185 samples. Of the eQTLs identified, only 34% have their effects propagated to the protein level. Further analysis on the effect size of prefrontal cortex eQTLs identified from an independent dataset showed clear post-transcriptional attenuation of eQTL effects. To investigate the biological relevance of the attenuated eQTLs, we identified 70 expression-specific QTLs (esQTLs), 51 ribosome-occupancy-specific QTLs (rsQTLs), and 107 protein-specific QTLs (psQTLs). Five of these omics-specific QTLs showed strong colocalization with SCZ GWAS signals, three of them are esQTLs. The limited number of GWAS colocalization discoveries from omics-specific QTLs and the apparent prevalence of eQTL attenuation prompted us to take a complementary approach to investigate the functional relevance of attenuated eQTLs. Using S-PrediXcan we identified 74 SCZ risk genes, 34% of which were novel, and 67% of these risk genes were replicated in a MR-Egger test. Notably, 52 out of 74 risk genes were identified using eQTL data and 70% of these SCZ-risk-gene-driving eQTLs show little to no evidence of driving corresponding variations at the protein level. Conclusion The effect of eQTLs on gene expression in the prefrontal cortex is commonly attenuated post-transcriptionally. Many of the attenuated eQTLs still correlate with SCZ GWAS signal. Further investigation is needed to elucidate a mechanistic link between attenuated eQTLs and SCZ disease risk.

Additional file 3: Table S2. Differential expression, TLE vs. TL, nuclear RNA-seq datasets for astrocyte, OPC, and neuronal FANS populations, with annotation of dysregulated genes unique to each cell type.

Additional file 5: Table S4. Functional enrichment analyses of differentially expressed genes for each nuclei cell type, TLE vs. TL, generated using HOMER.

The pathophysiology of epilepsy underlies a complex network dysfunction between neurons and glia, the molecular cell type-specific contributions of which remain poorly defined in the human disease. In this study, we validated a method that simultaneously isolates neuronal (NEUN +), astrocyte (PAX6 + NEUN–), and oligodendroglial progenitor (OPC) (OLIG2 + NEUN–) enriched nuclei populations from non-diseased, fresh-frozen human neocortex and then applied it to characterize the distinct transcriptomes of such populations isolated from electrode-mapped temporal lobe epilepsy (TLE) surgical samples. Nuclear RNA-seq confirmed cell type specificity and informed both common and distinct pathways associated with TLE in astrocytes, OPCs, and neurons. Compared to postmortem control, the transcriptome of epilepsy astrocytes showed downregulation of mature astrocyte functions and upregulation of development-related genes. To gain further insight into glial heterogeneity in TLE, we performed single cell transcriptomics (scRNA-seq) on four additional human TLE samples. Analysis of the integrated TLE dataset uncovered a prominent subpopulation of glia that express a hybrid signature of both reactive astrocyte and OPC markers, including many cells with a mixed GFAP + OLIG2 + phenotype. A further integrated analysis of this TLE scRNA-seq dataset and a previously published normal human temporal lobe scRNA-seq dataset confirmed the unique presence of hybrid glia only in TLE. Pseudotime analysis revealed cell transition trajectories stemming from this hybrid population towards both OPCs and reactive astrocytes. Immunofluorescence studies in human TLE samples confirmed the rare presence of GFAP + OLIG2 + glia, including some cells with proliferative activity, and functional analysis of cells isolated directly from these samples disclosed abnormal neurosphere formation in vitro. Overall, cell type-specific isolation of glia from surgical epilepsy samples combined with transcriptomic analyses uncovered abnormal glial subpopulations with de-differentiated phenotype, motivating further studies into the dysfunctional role of reactive glia in temporal lobe epilepsy.

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.

Recent research has shown expression of clock genes in peripheral tissue explants, targeting multiple pathways leading to the entrainment of circadian rhythms. Temporal variations are not solely regulated by photoperiod, but factors such as maternal feed availability can entrain fetal circadian clock. Currently, a paucity of information exists for clock gene expression and short-term temporal transcript abundance in the bovine placenta, which is essential for proper offspring development. Therefore, the objective of this study was to determine the effect of early to mid-gestational nutrient restriction on clock genes, angiogenic factors, and nutrient sensing genes mRNA transcript abundance in placental explants during a 24 h period. Placentomes from adequately fed and nutrient restricted heifers were collected via Cesarean section at day 180 of gestation; separated into caruncular and cotyledonary tissue and placed in culture media for a 24 h period. The mRNA transcript abundance of clock genes (ARNTL, CRY1, and PER2), angiogenic factors (HIF1A and VEGFA), and nutrient sensing genes (NAMPT and NR3C1) was determined every 4 h. Clock genes were expressed in caruncular and cotyledonary explant tissue. The caruncular explant transcript abundance of the clock genes was not influenced by time (P > 0.05); while ARNTL abundance decreased over time in the cotyledon explant (P < 0.05). A main effect of time was observed for HIF1A, VEGFA, and NR3C1 in the caruncular tissue (P < 0.05). Although, angiogenic factors and nutrient sensing genes in cotyledonary tissue displayed evident temporal variation in transcript abundance (P < 0.05). Nutrient restriction did not alter (P > 0.15) mRNA transcript abundance of clock genes, angiogenic factors, or nutrient sensing genes in either caruncular or cotyledonary tissue. Interestingly, these data may indicate limited transmission and synchronization of maternal and fetal temporal variations in transcript abundance. These findings demonstrate that multiple timepoint collections are needed in future studies due to the innate existence of temporal oscillations observed in the bovine placenta.

The diaphragm muscle (DIAm) has a large reserve capacity for force generation such that in rats, the transdiaphragmatic pressure (Pdi) generated during ventilatory behaviors is less than 50% of maximal Pdi (Pdimax) elicited by bilateral phrenic nerve stimulation. Accordingly, we hypothesized that following unilateral denervation (DNV), the ability of the contralateral DIAm to generate sufficient Pdi to accomplish ventilatory behaviors will not be compromised and normal ventilation (as determined by arterial blood gas measurements) will not be impacted, although neural drive to the DIAm increases. In contrast, we hypothesized that higher force, non-ventilatory behaviors requiring Pdi generation greater than 50% of Pdimax will be compromised following DIAm hemiparalysis, i.e., increased neural drive cannot fully compensate for lack of force generating capacity. Pdi generated during ventilatory behaviors (eupnea and hypoxia (10% O2)–hypercapnia (5% CO2)) did not change after DNV and arterial blood gases were unaffected by DNV. However, neural drive to the contralateral DIAm, assessed by the rate of rise of root mean squared (RMS) EMG at 75 ms after onset of inspiratory activity (RMS75), increased after DNV (p < 0.05). In contrast, Pdi generated during higher force, non-ventilatory behaviors was significantly reduced after DNV (p < 0.01), while RMS75 was unchanged. These findings support our hypothesis that only non-ventilatory behaviors requiring Pdi generation greater than 50% of Pdimax are impacted after DNV. Clinically, these results indicate that an evaluation of DIAm weakness requires examination of Pdi across multiple motor behaviors, not just ventilation.

BACKGROUND: Genetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.METHODS: A secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (,13 years), and typical-onset AN, and genetic correlation, genetic risk score, and Mendelian randomization analyses.RESULTS: Two loci were genome-wide significant in the typical-onset AN GWAS. Heritability estimates (single nucleotide polymorphism-h2) were 0.01-0.04 for age of onset, 0.16-0.25 for early-onset AN, and 0.17-0.25 for typical-onset AN. Early-and typical-onset AN showed distinct genetic correlation patterns with putative risk factors for AN. Specifically, early-onset AN was significantly genetically correlated with younger age at menarche, and typical-onset AN was significantly negatively genetically correlated with anthropometric traits. Genetic risk scores for age of onset and early-onset AN estimated from independent GWASs significantly predicted age of onset. Mendelian randomization analysis suggested a causal link between younger age at menarche and early -onset AN.CONCLUSIONS: Our results provide evidence consistent with a common variant genetic basis for age of onset and implicate biological pathways regulating menarche and reproduction.