Sex-dependent interactions between prodromal intestinal inflammation and LRRK2 G2019S in mice promote endophenotypes of Parkinson's disease.

Gastrointestinal (GI) disruptions and inflammatory bowel disease (IBD) are commonly associated with Parkinson's disease (PD), but how they may impact risk for PD remains poorly understood. Herein, we provide evidence that prodromal intestinal inflammation expedites and exacerbates PD endophenotypes in rodent carriers of the human PD risk allele LRRK2 G2019S in a sex-dependent manner. Chronic intestinal damage in genetically predisposed male mice promotes α-synuclein aggregation in the substantia nigra, loss of dopaminergic neurons and motor impairment. This male bias is preserved in gonadectomized males, and similarly conferred by sex chromosomal complement in gonadal females expressing human LRRK2 G2019S. The early onset and heightened severity of neuropathological and behavioral outcomes in male LRRK2 G2019S mice is preceded by increases in α-synuclein in the colon, α-synuclein-positive macrophages in the colonic lamina propria, and loads of phosphorylated α-synuclein within microglia in the substantia nigra. Taken together, these data reveal that prodromal intestinal inflammation promotes the pathogenesis of PD endophenotypes in male carriers of LRRK2 G2019S, through mechanisms that depend on genotypic sex and involve early accumulation of α-synuclein in myeloid cells within the gut.

Biobank-wide association scan identifies risk factors for late-onset Alzheimer's disease and endophenotypes.

Rich data from large biobanks, coupled with increasingly accessible association statistics from genome-wide association studies (GWAS), provide great opportunities to dissect the complex relationships among human traits and diseases. We introduce BADGERS, a powerful method to perform polygenic score-based biobank-wide association scans. Compared to traditional approaches, BADGERS uses GWAS summary statistics as input and does not require multiple traits to be measured in the same cohort. We applied BADGERS to two independent datasets for late-onset Alzheimer's disease (AD; n=61,212). Among 1738 traits in the UK biobank, we identified 48 significant associations for AD. Family history, high cholesterol, and numerous traits related to intelligence and education showed strong and independent associations with AD. Furthermore, we identified 41 significant associations for a variety of AD endophenotypes. While family history and high cholesterol were strongly associated with AD subgroups and pathologies, only intelligence and education-related traits predicted pre-clinical cognitive phenotypes. These results provide novel insights into the distinct biological processes underlying various risk factors for AD.

Biomarkers for phenotype-endotype relationship in atopic dermatitis: a critical review.

Atopic dermatitis (AD) is the most common form of chronic skin inflammation with diverse clinical variants. Historically, various AD phenotypes have been grouped together without considering their heterogeneity. This approach has resulted in a lack of phenotype- and endotype-adapted therapeutic strategies. Comprehensive insights into AD pathogenesis have enabled precise medicinal approach for AD. These efforts aimed to redefine the endophenotype of AD and develop various biomarkers for diverse purposes. Among these endeavours, efforts are underway to elucidate the mechanisms (and related biomarkers) that lead to the emergence and progression of atopic diseases originating from AD (e.g., atopic march). This review focuses on diverse AD phenotypes and calls for a definition of endophenotypes. While awaiting scientific validation, these biomarkers ensure predicting disease onset and trajectory and tailoring therapeutic strategies for the future.

Association between endocrine and neuropsychological endophenotypes and gambling disorder severity.

BACKGROUND: Neurobiological characteristics have been identified regarding the severity of gambling disorder (GD). The aims of this study were: (1) to examine, through a path analysis, whether there was a relationship between neuroendocrine features, potentially mediational GD variables, and GD severity, and (2) to associate neuroendocrine variables, with GD severity-related variables according to gambling preferences. METHODS: The sample included 297 outpatients with GD. We analyzed endocrine concentrations of different appetite-related hormones (ghrelin, liver antimicrobial peptide 2 [LEAP-2], leptin, adiponectin), and neuropsychological performance (working memory, cognitive flexibility, inhibition, decision making, premorbid intelligence). Path analysis assessed mechanisms between neuroendocrine features and GD severity, including mediational GD variables (impulsivity traits and gambling-related cognitive distortions). Partial correlations evaluated the associations between neuroendocrine variables, including impulsivity traits, and variables related to GD severity (DSM-5, South Oaks Gambling Screen, illness duration, and gambling-related cognitive distortions). RESULTS: Lower adiponectin concentrations predicted greater GD severity, while higher LEAP-2 concentrations predicted more gambling-related cognitive distortions. Likewise, better neuropsychological performance directly predicted GD severity, but worse neuropsychological performance was associated with GD severity through the mediational variables of impulsivity traits and gambling-related cognitive distortions. Also, in non-strategic individuals with GD, poor working memory was associated with gambling expectancies and predictive control. In strategic individuals with GD, poor cognitive flexibility was associated with illusion of control, predictive control, and inability to stop gambling. CONCLUSIONS: These results provide updated information about the comprehension of the interaction between neuroendocrine features, clinical variables, and severity of GD. Thus, neurobiological functions seem to be strongly related to GD severity.

Multiple sclerosis endophenotypes identified by high-dimensional blood signatures are associated with distinct disease trajectories.

One of the biggest challenges in managing multiple sclerosis is the heterogeneity of clinical manifestations and progression trajectories. It still remains to be elucidated whether this heterogeneity is reflected by discrete immune signatures in the blood as a surrogate of disease pathophysiology. Accordingly, individualized treatment selection based on immunobiological principles is still not feasible. Using two independent multicentric longitudinal cohorts of patients with early multiple sclerosis (n = 309 discovery and n = 232 validation), we were able to identify three distinct peripheral blood immunological endophenotypes by a combination of high-dimensional flow cytometry and serum proteomics, followed by unsupervised clustering. Longitudinal clinical and paraclinical follow-up data collected for the cohorts revealed that these endophenotypes were associated with disease trajectories of inflammation versus early structural damage. Investigating the capacity of immunotherapies to normalize endophenotype-specific immune signatures revealed discrete effect sizes as illustrated by the limited effect of interferon-ß on endophenotype 3-related immune signatures. Accordingly, patients who fell into endophenotype 3 subsequently treated with interferon-ß exhibited higher disease progression and MRI activity over a 4-year follow-up compared with treatment with other therapies. We therefore propose that ascertaining a patient's blood immune signature before immunomodulatory treatment initiation may facilitate prediction of clinical disease trajectories and enable personalized treatment decisions based on pathobiological principles.

Large-scale animal model study uncovers altered brain pH and lactate levels as a transdiagnostic endophenotype of neuropsychiatric disorders involving cognitive impairment.

Increased levels of lactate, an end-product of glycolysis, have been proposed as a potential surrogate marker for metabolic changes during neuronal excitation. These changes in lactate levels can result in decreased brain pH, which has been implicated in patients with various neuropsychiatric disorders. We previously demonstrated that such alterations are commonly observed in five mouse models of schizophrenia, bipolar disorder, and autism, suggesting a shared endophenotype among these disorders rather than mere artifacts due to medications or agonal state. However, there is still limited research on this phenomenon in animal models, leaving its generality across other disease animal models uncertain. Moreover, the association between changes in brain lactate levels and specific behavioral abnormalities remains unclear. To address these gaps, the International Brain pH Project Consortium investigated brain pH and lactate levels in 109 strains/conditions of 2294 animals with genetic and other experimental manipulations relevant to neuropsychiatric disorders. Systematic analysis revealed that decreased brain pH and increased lactate levels were common features observed in multiple models of depression, epilepsy, Alzheimer's disease, and some additional schizophrenia models. While certain autism models also exhibited decreased pH and increased lactate levels, others showed the opposite pattern, potentially reflecting subpopulations within the autism spectrum. Furthermore, utilizing large-scale behavioral test battery, a multivariate cross-validated prediction analysis demonstrated that poor working memory performance was predominantly associated with increased brain lactate levels. Importantly, this association was confirmed in an independent cohort of animal models. Collectively, these findings suggest that altered brain pH and lactate levels, which could be attributed to dysregulated excitation/inhibition balance, may serve as transdiagnostic endophenotypes of debilitating neuropsychiatric disorders characterized by cognitive impairment, irrespective of their beneficial or detrimental nature.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network.

MOTIVATION: Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. RESULTS: Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. AVAILABILITY AND IMPLEMENTATION: The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Antisaccade error rates in first-episode psychosis, ultra-high risk for psychosis and unaffected relatives of schizophrenia: A systematic review and meta-analysis.

BACKGROUND: Antisaccade, which is described as looking at the opposite location of the target, is an eye movements paradigm used for assessing cognitive functions in schizophrenia. Initiation and sustainment of saccades in antisaccade are managed by frontal and parietal cortical areas. Antisaccade abnormalities are well-established findings in schizophrenia. However, studies in the early phases of psychotic disorders and clinical/familial risk for psychosis reported inconsistent findings. The current systematic review aimed to review the results of studies investigating antisaccade error rates in first-episode psychosis (FEP), individuals with ultra-high-risk for psychosis (UHRP), and familial-high-risk for psychosis (FHRP) compared to healthy controls. METHOD: A meta-analysis of 17 studies was conducted to quantitatively review antisaccade errors in FEP, UHR-P and FHRP. The error rate (Hedges'g) was compared between the total of 860 FEP, UHRP, FHRP, and 817 healthy controls. Hedges' g for effect size, I2 for estimating the percentage of variability, and publication bias were evaluated through the R software. RESULTS: The outcomes of this meta-analysis suggested that FEP is associated with a robust deficit in the antisaccade error rate (g = 1.16, CI = 0.95-1.38). Additionally, both the clinical and familial high-risk groups showed small but significant increases in AS errors (g = 0.26, CI = 0.02-0.52 and g = 0.34, CI = 0.13-0.55, respectively). CONCLUSION: The large effect size estimated for FEP was compatible with previously reported results in chronic schizophrenia patients. Additionally, relatives had abnormalities with small to medium effect sizes and significant differences. The current findings suggest that antisaccade errors might be a potential endophenotype for psychotic disorders.

Processing speed in first episode of psychosis and first-degree relatives: A candidate endophenotype of spectrum schizophrenia disorders.

OBJECTIVE: The processing speed (PS) is highly impacted in individuals experiencing their first episode of psychosis (FEP). Conducting family studies can help to determine whether PS can serve as an endophenotype of schizophrenia spectrum disorders (SSDs), offering valuable insights into the prevention and diagnosis of SSDs. METHOD: A comprehensive cognitive battery, encompassing tests for PS, verbal memory, visual memory, working memory, executive functions, motor dexterity, and attention, was administered to a sample consisting of 133 FEP patients, 146 parents, 98 siblings, and 202 healthy controls (HCs). Univariate analyses (analysis of covariance [ANCOVA]) were conducted to compare the different cognitive domains between groups, utilizing sex, age, and years of education as covariates and Bonferroni corrections. Effect sizes (ESs) were calculated for estimating the magnitude of differences between groups. RESULTS: Group comparisons revealed significant differences in all cognitive domains. PS was the most impaired function in patients. Parents and siblings had intermediate PS performance between FEP patients and HC. Large ES were observed in PS between FEP versus siblings, FEP versus controls, parents versus controls, and parents versus siblings. CONCLUSIONS: Despite not meeting all the necessary criteria, the PS observed in FEP patients and their first-degree relatives suggests its potential as a promising endophenotype of SSDs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aß42 ratio in ADNI cohort.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.

Causal relationships between Gut microbiota and primary open-angle Glaucoma: A Mendelian randomization and mediation analysis of Glaucoma endophenotypes.

Primary open-angle glaucoma (POAG) is a widespread condition responsible for irreversible blindness, and its prevalence is expected to increase substantially in the coming decades. Despite its significance, the exact cause of POAG remains elusive, necessitating a comprehensive exploration of its pathogenesis. Emerging research suggests a potential link between alterations in gut microbiota composition and POAG. However, establishing causality in these associations remains a challenge. In this study, we employed Mendelian randomization (MR) analysis to investigate the potential causal relationships between gut microbiota (GM) and POAG. Significant bacteria taxa were further analyzed with POAG endophenotypes. We utilized data from genome-wide association studies (GWAS) for GM and POAG, as well as for glaucoma endophenotypes, including intraocular pressure (IOP), retinal nerve fiber layer (RNFL) thickness, vertical cup-to-disc ratio (VCDR), and central corneal thickness (CCT). Univariable, multivariable MR and mediation effect analysis were conducted. Our analysis revealed that certain taxa, including phylum Euryarchaeota, genus Odoribacter, Rumnicoccaceae UCG009, Ruminiclostridium9, unknown genus id.2071, and Eubacterium rectale group, were associated with an increased risk of POAG. On the other hand, family Victivallaceae, Lacchnospiraceae, genus Lachnoclostridium, Oscillospira, Ruminococcaceae UCG011, Alloprevotella, and Faecalibacterium were found to be associated with a decreased risk of POAG. Furthermore, some of these taxa were found to be connected to glaucoma endophenotypes. Through further multivariable MR analysis, it was determined that IOP, VCDR, and CCT might played mediating roles between GM and POAG. In conclusion, this study utilizes MR analysis to elucidate potential causal associations between GM and POAG, providing insights into specific GM taxa that influence POAG risk and related endophenotypes. These findings emphasize the potential role of the gut microbiota in the pathogenesis of POAG and pave the way for future research and therapeutic interventions.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

CARE4Kids Study: Endophenotypes of Persistent Post-Concussive Symptoms in Adolescents: Study Rationale and Protocol.

Treatment of youth concussion during the acute phase continues to evolve, and this has led to the emergence of guidelines to direct care. While symptoms after concussion typically resolve in 14-28 days, a portion (∼20%) of adolescents endorse persistent post-concussive symptoms (PPCS) beyond normal resolution. This report outlines a study implemented in response to the National Institute of Neurological Diseases and Stroke call for the development and initial clinical validation of objective biological measures to predict risk of PPCS in adolescents. We describe our plans for recruitment of a Development cohort of 11- to 17-year-old youth with concussion, and collection of autonomic, neurocognitive, biofluid, and imaging biomarkers. The most promising of these measures will then be validated in a separate Validation cohort of youth with concussion, and a final, clinically useful algorithm will be developed and disseminated. Upon completion of this study, we will have generated a battery of measures predictive of high risk for PPCS, which will allow for identification and testing of interventions to prevent PPCS in the most high-risk youth.

A sensory signature of unaffected biological parents predicts the risk of autism in their offspring.

AIM: Despite the emphasis on sensory dysfunction phenotypes in the revised diagnostic criteria for autism spectrum disorder (ASD), there has been limited research, particularly in the field of neurobiology, investigating the concordance in sensory features between individuals with ASD and their genetic relatives. Therefore, our objective was to examine whether neurobehavioral sensory patterns could serve as endophenotypic markers for ASD. METHODS: We combined questionnaire- and lab-based sensory evaluations with sensory fMRI measures to examine the patterns of sensory responsivity in 30 clinically diagnosed with ASD, 26 matched controls (CON), and 48 biological parents for both groups (27 parents of individuals with ASD [P-ASD] and 21 for individuals with CON [P-CON]). RESULTS: The ASD and P-ASD groups had higher sensory responsivity and rated sensory stimuli as more unpleasant than the CON and P-CON groups, respectively. They also exhibited greater hemodynamic responses within the sensory cortices. Overlapping activations were observed within these sensory cortices in the ASD and P-ASD groups. Using a machine learning approach with robust prediction models across cohorts, we demonstrated that the sensory profile of biological parents accurately predicted the likelihood of their offspring having ASD, achieving a prediction accuracy of 71.4%. CONCLUSIONS: These findings provide support for the hereditary basis of sensory alterations in ASD and suggest a potential avenue to improve ASD diagnosis by utilizing the sensory signature of biological parents, especially in families with a high risk of ASD. This approach holds promising prospects for early detection, even before the birth of the offspring.

Altered HCAR3 expression may underlying the blunted niacin responses of the psychiatric disorders and the risk of schizophrenia.

AIM: Blunted niacin response (BNR) was an endophenotype of schizophrenia, but the underlying mechanism remains unclarified. The objective of this study was to verify whether genes associated with BNR pathway constitute the genetic basis and the pathological mechanism of BNR phenotypic psychiatric patients. METHODS: Two independent sample sets consisting of 971 subjects were enrolled in this study. A total of 62 variants were genotyped in the discovery set, then the related variants were verified in the verification set. The published PGC GWAS data were used to validate the associations between the variants and psychiatry disorders. RT-PCR analysis, eQTL data, and Dual-Luciferase Reporter experiment were used to investigate the potential molecular mechanisms of the variants underlying BNR. RESULTS: The results showed that two SNPs, rs56959712 in HCAR2 and rs2454721 in HCAR3 were significantly associated with niacin response. The risk allele T of rs2454721 could affect the niacin responses of psychiatric patients through elevated HCAR3 gene expression. These two genes, especially HCAR3, were significantly associated with the risk of schizophrenia, as identified in this study and verified using the published GWAS data. CONCLUSION: HCAR3 is a novel schizophrenia susceptibility gene which is significantly associated with blunted niacin response in schizophrenia. In-depth investigation of HCAR3 is of great significance for uncovering the pathogenesis and propose new therapeutic targets for psychiatric disorders, especially for the BNR subgroup patients.

Longitudinal change in memory performance as a strong endophenotype for Alzheimer's disease.

INTRODUCTION: Although large-scale genome-wide association studies (GWAS) have been conducted on AD, few have been conducted on continuous measures of memory performance and memory decline. METHODS: We conducted a cross-ancestry GWAS on memory performance (in 27,633 participants) and memory decline (in 22,365 participants; 129,201 observations) by leveraging harmonized cognitive data from four aging cohorts. RESULTS: We found high heritability for two ancestry backgrounds. Further, we found a novel ancestry locus for memory decline on chromosome 4 (rs6848524) and three loci in the non-Hispanic Black ancestry group for memory performance on chromosomes 2 (rs111471504), 7 (rs4142249), and 15 (rs74381744). In our gene-level analysis, we found novel genes for memory decline on chromosomes 1 (SLC25A44), 11 (BSX), and 15 (DPP8). Memory performance and memory decline shared genetic architecture with AD-related traits, neuropsychiatric traits, and autoimmune traits. DISCUSSION: We discovered several novel loci, genes, and genetic correlations associated with late-life memory performance and decline. HIGHLIGHTS: Late-life memory has high heritability that is similar across ancestries. We discovered four novel variants associated with late-life memory. We identified four novel genes associated with late-life memory. Late-life memory shares genetic architecture with psychiatric/autoimmune traits.

Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.

INTRODUCTION: The molecular mechanisms that contribute to sex differences, in particular female predominance, in Alzheimer's disease (AD) prevalence, symptomology, and pathology, are incompletely understood. METHODS: To address this problem, we investigated cellular metabolism and immune responses ("immunometabolism endophenotype") across AD individuals as a function of sex with diverse clinical diagnosis of cognitive status at death (cogdx), Braak staging, and Consortium to Establish a Registry for AD (CERAD) scores using human cortex metabolomics and transcriptomics data from the Religious Orders Study / Memory and Aging Project (ROSMAP) cohort. RESULTS: We identified sex-specific metabolites, immune and metabolic genes, and pathways associated with the AD diagnosis and progression. We identified female-specific elevation in glycerophosphorylcholine and N-acetylglutamate, which are AD inflammatory metabolites involved in interleukin (IL)-17 signaling, C-type lectin receptor, interferon signaling, and Toll-like receptor pathways. We pinpointed distinct microglia-specific immunometabolism endophenotypes (i.e., lipid- and amino acid-specific IL-10 and IL-17 signaling pathways) between female and male AD subjects. In addition, female AD subjects showed evidence of diminished excitatory neuron and microglia communications via glutamate-mediated immunometabolism. DISCUSSION: Our results point to new understanding of the molecular basis for female predominance in AD, and warrant future independent validations with ethnically diverse patient cohorts to establish a likely causal relationship of microglial immunometabolism in the sex differences in AD. HIGHLIGHTS: Sex-specific immune metabolites, gene networks and pathways, are associated with Alzheimer's disease pathogenesis and disease progression. Female AD subjects exhibit microglial immunometabolism endophenotypes characterized by decreased glutamate metabolism and elevated interleukin-10 pathway activity. Female AD subjects showed a shift in glutamate-mediated cell-cell communications between excitatory neurons to microglia and astrocyte.

Neural Endophenotype Assessment in Zebrafish Larvae Using Optomotor and ZebraBox Locomotion Assessment.

Due to the highly conserved genetics across the central nervous system, the easily probed visual system can act as an endophenotype for assessing neurological function. Here, we describe a psychophysics approach to assess visually driven swimming behavior in the high-throughput zebrafish genetic model system. We use the optomotor response test together with general locomotion behavior to assess neural processing while excluding motor defects related to muscle function.

Genome-Wide Association Analysis across Endophenotypes in Alzheimer's Disease: Main Effects and Disease Stage-Specific Interactions.

The underlying genetic susceptibility for Alzheimer's disease (AD) is not yet fully understood. The heterogeneous nature of the disease challenges genetic association studies. Endophenotype approaches can help to address this challenge by more direct interrogation of biological traits related to the disease. AD endophenotypes based on amyloid-ß, tau, and neurodegeneration (A/T/N) biomarkers and cognitive performance were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). A genome-wide association study (GWAS) of quantitative phenotypes was performed using an SNP main effect and an SNP by Diagnosis interaction (SNP × DX) model to identify disease stage-specific genetic effects. Nine loci were identified as study-wide significant with one or more A/T/N endophenotypes in the main effect model, as well as additional findings significantly associated with cognitive measures. These nine loci include SNPs in or near the genes APOE, SRSF10, HLA-DQB1, XKR3, and KIAA1671. The SNP × DX model identified three study-wide significant genetic loci (BACH2, EP300, and PACRG-AS1) with a neuroprotective effect in later AD stage endophenotypes. An endophenotype approach identified novel genetic associations and provided insight into the molecular mechanisms underlying the genetic associations that may otherwise be missed using conventional case-control study designs.

Endophenotype trait domains for advancing gene discovery in autism spectrum disorder.

Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For a plurality of individuals with ASD, it is likely that the primary causes involve multiple common inherited variants that individually account for only small levels of variation in phenotypic outcomes. This genetic landscape creates a major challenge for detecting small but important pathogenic effects associated with ASD. To address similar challenges, separate fields of medicine have identified endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition and leveraged the study of these traits to map polygenic mechanisms and advance more personalized therapeutic strategies for complex diseases. Endophenotypes represent a distinct class of biomarkers useful for understanding genetic contributions to psychiatric and developmental disorders because they are embedded within the causal chain between genotype and clinical phenotype, and they are more proximal to the action of the gene(s) than behavioral traits. Despite their demonstrated power for guiding new understanding of complex genetic structures of clinical conditions, few endophenotypes associated with ASD have been identified and integrated into family genetic studies. In this review, we argue that advancing knowledge of the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs with measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. We also emphasize that clarification of endophenotypic traits necessarily will involve integration of transdiagnostic approaches as candidate traits likely reflect liability for multiple clinical conditions and often are agnostic to diagnostic boundaries. Multiple candidate endophenotypes associated with ASD likelihood are described, and we propose a new focus on the analysis of "endophenotype trait domains" (ETDs), or traits measured across multiple levels (e.g., molecular, cellular, neural system, neuropsychological) along the causal pathway from genes to behavior. To inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We also present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical autistic traits, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes associated with ASD as well as overlapping neurodevelopmental disorders.