Microdeletion of pseudogene chr14.232.a affects LRFN5 expression in cells of a patient with autism spectrum disorder

Systematic assessment of structural variant annotation tools for genomic interpretation

Structural variants (SVs) over 50 base pairs play a significant role in phenotypic diversity and are associated with various diseases, but their analysis is complex and resource-intensive. Numerous computational tools have been developed for SV prioritization, yet their effectiveness in biomedicine remains unclear. Here we benchmarked eight widely used SV prioritization tools, categorized into knowledge-driven (AnnotSV, ClassifyCNV) and data-driven (CADD-SV, dbCNV, StrVCTVRE, SVScore, TADA, XCNV) groups in accordance with the ACMG guidelines. We assessed their accuracy, robustness, and usability across diverse genomic contexts, biological mechanisms and computational efficiency using seven carefully curated independent datasets. Our results revealed that both groups of methods exhibit comparable effectiveness in predicting SV pathogenicity, although performance varies among tools, emphasizing the importance of selecting the appropriate tool based on specific research purposes. Furthermore, we pinpointed the potential improvement of expanding these tools for future applications. Our benchmarking framework provides a crucial evaluation method for SV analysis tools, offering practical guidance for biomedical research and facilitating the advancement of better genomic research tools.

Shared Genetic Determinants of Schizophrenia and Autism Spectrum Disorder Implicate Opposite Risk Patterns: A Genome-Wide Analysis of Common Variants

BACKGROUND AND HYPOTHESIS

The synaptic pruning hypothesis posits that schizophrenia (SCZ) and autism spectrum disorder (ASD) may represent opposite ends of neurodevelopmental disorders: individuals with ASD exhibit an overabundance of synapses and connections while SCZ was characterized by excessive pruning of synapses and a reduction. Given the strong genetic predisposition of both disorders, we propose a shared genetic component, with certain loci having differential regulatory impacts.

STUDY DESIGN

Genome-Wide single nucleotide polymorphism (SNP) data of European descent from SCZ (N cases = 53 386, N controls = 77 258) and ASD (N cases = 18 381, N controls = 27 969) were analyzed. We used genetic correlation, bivariate causal mixture model, conditional false discovery rate method, colocalization, Transcriptome-Wide Association Study (TWAS), and Phenome-Wide Association Study (PheWAS) to investigate the genetic overlap and gene expression pattern.

STUDY RESULTS

We found a positive genetic correlation between SCZ and ASD (rg = .26, SE = 0.01, P = 7.87e-14), with 11 genomic loci jointly influencing both conditions (conjFDR <0.05). Functional analysis highlights a significant enrichment of shared genes during early to mid-fetal developmental stages. A notable genetic region on chromosome 17q21.31 (lead SNP rs2696609) showed strong evidence of colocalization (PP.H4.abf = 0.85). This SNP rs2696609 is linked to many imaging-derived brain phenotypes. TWAS indicated opposing gene expression patterns (primarily pseudogenes and long noncoding RNAs [lncRNAs]) for ASD and SCZ in the 17q21.31 region and some genes (LRRC37A4P, LINC02210, and DND1P1) exhibit considerable variation in the cerebellum across the lifespan.

CONCLUSIONS

Our findings support a shared genetic basis for SCZ and ASD. A common genetic variant, rs2696609, located in the Chr17q21.31 locus, may exert differential risk regulation on SCZ and ASD by altering brain structure. Future studies should focus on the role of pseudogenes, lncRNAs, and cerebellum in synaptic pruning and neurodevelopmental disorders.

Two polygenic mouse models of major depressive disorders identify TMEM161B as a potential biomarker of disease in humans

Treatments are only partially effective in major depressive disorders (MDD) but no biomarker exists to predict symptom improvement in patients. Animal models are essential tools in the development of antidepressant medications, but while recent genetic studies have demonstrated the polygenic contribution to MDD, current models are limited to either mimic the effect of a single gene or environmental factor. We developed in the past a model of depressive-like behaviors in mice (H/Rouen), using selective breeding based on behavioral reaction after an acute mild stress in the tail suspension test. Here, we propose a new mouse model of depression (H-TST) generated from a more complex genetic background and based on the same selection process. We first demonstrated that H/Rouen and H-TST mice had similar phenotypes and were more sensitive to glutamate-related antidepressant medications than selective serotonin reuptake inhibitors. We then conducted an exome sequencing on the two mouse models and showed that they had damaging variants in 174 identical genes, which have also been associated with MDD in humans. Among these genes, we showed a higher expression level of Tmem161b in brain and blood of our two mouse models. Changes in TMEM161B expression level was also observed in blood of MDD patients when compared with controls, and after 8-week treatment with duloxetine, mainly in good responders to treatment. Altogether, our results introduce H/Rouen and H-TST as the two first polygenic animal models of MDD and demonstrate their ability to identify biomarkers of the disease and to develop rapid and effective antidepressant medications.

Identifying genetic loci and phenomic associations of substance use traits: A multi-trait analysis of GWAS (MTAG) study

BACKGROUND AND AIMS

Genome-wide association studies (GWAS) of opioid use disorder (OUD) and cannabis use disorder (CUD) have lagged behind those of alcohol use disorder (AUD) and smoking, where many more loci have been identified. We sought to identify novel loci for substance use traits (SUTs) in both African- (AFR) and European- (EUR) ancestry individuals to enhance our understanding of the traits' genetic architecture.

DESIGN

We used multi-trait analysis of GWAS (MTAG) to analyze four SUTs in EUR subjects (OUD, CUD, AUD and smoking initiation [SMKinitiation]), and three SUTs in AFR subjects (OUD, AUD and smoking trajectory [SMKtrajectory]). We conducted gene-set and protein-protein interaction analyses and calculated polygenic risk scores (PRS) in two independent samples.

SETTING

This study was conducted in the United States.

PARTICIPANTS

A total of 5692 EUR and 4918 AFR individuals in the Yale-Penn sample and 29 054 EUR and 10 265 AFR individuals in the Penn Medicine BioBank sample.

FINDINGS

MTAG identified genome-wide significant (GWS) single nucleotide polymorphisms (SNPs) for all four traits in EUR: 41 SNPs in 36 loci for OUD; 74 SNPs in 60 loci for CUD; 63 SNPs in 52 loci for AUD; and 183 SNPs in 144 loci for SMKinitiation. MTAG also identified GWS SNPs in AFR: 2 SNPs in 2 loci for OUD; 3 SNPs in 3 loci for AUD; and 1 SNP in 1 locus for SMKtrajectory. In the Yale-Penn sample, the MTAG-derived PRS consistently yielded more significant associations with both the corresponding substance use disorder diagnosis and multiple related phenotypes than the GWAS-derived PRS.

CONCLUSIONS

Multi-trait analysis of genome-wide association studies boosted the number of loci found for substance use traits, identifying genes not previously linked to any substance, and increased the power of polygenic risk scores. Multi-trait analysis of genome-wide association studies can be used to identify novel associations for substance use, especially those for which the samples are smaller than those for historically legal substances.

Identify novel, shared and disorder-specific genetic architecture of major depressive disorder, insomnia and chronic pain

Major depressive disorder (MDD), insomnia (INS) and chronic pain (CP) often have high comorbidity and show high genetic correlation. Here we aimed to better characterize their novel, shared and disorder-specific genetic architecture. Based on genome-wide association study (GWAS) summary data, we applied the conditional false discovery rate (condFDR) and conjunctional FDR (conjFDR) approach to investigate the novel and overlapped genetic loci for MDD, INS and CP. In addition, putative disorder-specific SNP associations were analyzed by conditioning the other two traits. The functions of the identified genomic loci were explored by performing gene set enrichment analysis (GSEA) for the loci mapped genes. We identified 22, 43 and 91 novel risk loci for MDD, INS and CP. GSEA for the loci mapped genes highlighted olfactory signaling pathway for MDD novel loci, breast cancer related gene set for both INS and CP novel loci, and nervous system related development, structure and activity for CP. Furthermore, we identified three loci jointly associated with the three disorders, including 13q14.3 locus with nearby gene OLFM4, 14q21.1 locus with nearby gene LRFN5 and 5q21.2 locus located in intergenic region. In addition, we identified one specific loci for MDD, 7 for INS and 11 for CP respectively by conditioning the other two traits, which were mapped to 68 genes for MDD, 85 for INS and 100 for CP. The MDD specific genes are enriched in immune system related pathways. This study increases understanding of the genetic architectures underlying MDD, INS and CP. The shared underlying genetic risk may help to explain the high comorbidity rates of the disorders.

LRFN5 locus structure is associated with autism and influenced by the sex of the individual and locus conversions

LRFN5 is a regulator of synaptic development and the only gene in a 5.4 Mb mammalian‐specific conserved topologically associating domain (TAD); the LRFN5 locus. An association between locus structural changes and developmental delay (DD) and/or autism was suggested by several cases in DECIPHER and own records. More significantly, we found that maternal inheritance of a specific LRFN5 locus haplotype segregated with an identical type of autism in distantly related males. This autism‐susceptibility haplotype had a specific TAD pattern. We also found a male/female quantitative difference in the amount histone‐3‐lysine‐9‐associated chromatin around the LRFN5 gene itself (p < 0.01), possibly related to the male‐restricted autism susceptibility. To better understand locus behavior, the prevalence of a 60 kb deletion polymorphism was investigated. Surprisingly, in three cohorts of individuals with DD (n = 8757), the number of deletion heterozygotes was 20%–26% lower than expected from Hardy–Weinberg equilibrium. This suggests allelic interaction, also because the conversions from heterozygosity to wild‐type or deletion homozygosity were of equal magnitudes. Remarkably, in a control group of medical students (n = 1416), such conversions were three times more common (p = 0.00001), suggesting a regulatory role of this allelic interaction. Taken together, LRFN5 regulation appears unusually complex, and LRFN5 dysregulation could be an epigenetic cause of autism.

Autism risk gene POGZ promotes chromatin accessibility and expression of clustered synaptic genes

Deleterious genetic variants in POGZ, which encodes the chromatin regulator Pogo Transposable Element with ZNF Domain protein, are strongly associated with autism spectrum disorder (ASD). Although it is a high-confidence ASD risk gene, the neurodevelopmental functions of POGZ remain unclear. Here we reveal the genomic binding of POGZ in the developing forebrain at euchromatic loci and gene regulatory elements (REs). We profile chromatin accessibility and gene expression in Pogz-/- mice and show that POGZ promotes the active chromatin state and transcription of clustered synaptic genes. We further demonstrate that POGZ forms a nuclear complex and co-occupies loci with ADNP, another high-confidence ASD risk gene, and provide evidence that POGZ regulates other neurodevelopmental disorder risk genes as well. Our results reveal a neurodevelopmental function of an ASD risk gene and identify molecular targets that may elucidate its function in ASD.

Microdeletion of 9q22.3: A patient with minimal deletion size associated with a severe phenotype

Basal cell nevus syndrome (also known as Gorlin Syndrome; MIM109400) is an autosomal dominant disorder characterized by recurrent pathological features such as basal cell carcinomas and odontogenic keratocysts as well as skeletal abnormalities. Most affected individuals have point mutations or small insertions or deletions within the PTCH1 gene on human chromosome 9, but there are some cases with more extensive deletion of the region, usually including the neighboring FANCC and/or ERCC6L2 genes. We report a 16‐year‐old patient with a deletion of approximately 400,000 bases which removes only PTCH1 and some non‐coding RNA genes but leaves FANCC and ERCC6L2 intact. In spite of the small amount of DNA for which he is haploid, his phenotype is more extreme than many individuals with longer deletions in the region. This includes early presentation with a large number of basal cell nevi and other skin lesions, multiple jaw keratocysts, and macrosomia. We found that the deletion was in the paternal chromosome, in common with other macrosomia cases. Using public databases, we have examined possible interactions between sequences within and outside the deletion and speculate that a regulatory relationship exists with flanking genes, which is unbalanced by the deletion, resulting in abnormal activation or repression of the target genes and hence the severity of the phenotype.

Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics

Depression is a heterogeneous and etiologically complex psychiatric syndrome, not a unitary disease entity, encompassing a broad spectrum of psychopathology arising from distinct pathophysiological mechanisms. Motivated by a need to advance our understanding of these mechanisms and develop new treatment strategies, there is a renewed interest in investigating the neurobiological basis of heterogeneity in depression and rethinking our approach to diagnosis for research purposes. Large-scale genome-wide association studies have now identified multiple genetic risk variants implicating excitatory neurotransmission and synapse function and underscoring a highly polygenic inheritance pattern that may be another important contributor to heterogeneity in depression. Here, we review various sources of phenotypic heterogeneity and approaches to defining and studying depression subtypes, including symptom-based subtypes and biology-based approaches to decomposing the depression syndrome. We review "dimensional," "categorical," and "hybrid" approaches to parsing phenotypic heterogeneity in depression and defining subtypes using functional neuroimaging. Next, we review recent progress in neuroimaging genetics (correlating neuroimaging patterns of brain function with genetic data) and its potential utility for generating testable hypotheses concerning molecular and circuit-level mechanisms. We discuss how genetic variants and transcriptomic profiles may confer risk for depression by modulating brain structure and function. We conclude by highlighting several promising areas for future research into the neurobiological underpinnings of heterogeneity, including efforts to understand sexually dimorphic mechanisms, the longitudinal dynamics of depressive episodes, and strategies for developing personalized treatments and facilitating clinical decision-making.

Interpreting the impact of noncoding structural variation in neurodevelopmental disorders

The emergence of novel sequencing technologies has greatly improved the identification of structural variation, revealing that a human genome harbors tens of thousands of structural variants (SVs). Since these SVs primarily impact noncoding DNA sequences, the next challenge is one of interpretation, not least to improve our understanding of human disease etiology. However, this task is severely complicated by the intricacy of the gene regulatory landscapes embedded within these noncoding regions, their incomplete annotation, as well as their dependence on the three-dimensional (3D) conformation of the genome. Also in the context of neurodevelopmental disorders (NDDs), reports of putatively causal, noncoding SVs are accumulating and understanding their impact on transcriptional regulation is presenting itself as the next step toward improved genetic diagnosis.

Identification of a robust non-coding RNA signature in diagnosing autism spectrum disorder by cross-validation of microarray data from peripheral blood samples

Novel molecular signatures are needed to improve the early and accurate diagnosis of autism spectrum disorder (ASD), and indicate physicians to provide timely intervention. This study aimed to identify a robust blood non-coding RNA (ncRNA) signature in diagnosing ASD. One hundred eighty six blood samples in the microarray dataset were randomly divided into the training set (n = 112) and validation set (n = 72). Then, the microarray probe expression profile was re-annotated into the expression profile of 4143 ncRNAs though probe sequence mapping. In the training set, least absolute shrinkage and selection operator (LASSO) penalized generalized linear model was adopted to identify the 20-ncRNA signature, and a diagnostic score was calculated for each sample according to the ncRNA expression levels and the model coefficients. The score demonstrated an excellent diagnostic ability for ASD in the training set (area under receiver operating characteristic curve [AUC] = 0.96), validation set (AUC = 0.97) and the overall (AUC = 0.96). Moreover, the blood samples of 23 ASD patients and 23 age- and gender-matched controls were collected as the external validation set, in which the signature also showed a good diagnostic ability for ASD (AUC = 0.96). In subgroup analysis, the signature was also robust when considering the potential confounders of sex, age, and disease subtypes. In comparison with a 55-gene signature deriving from the same dataset, the ncRNA signature showed an obviously better diagnostic ability (AUC: 0.96 vs 0.68, P < .001). In conclusion, this study identified a robust blood ncRNA signature in diagnosing ASD, which might help improve the diagnostic accuracy for ASD in clinical practice.

Re-recognition of pseudogenes: From molecular to clinical applications

Pseudogenes were initially regarded as "nonfunctional" genomic elements that did not have protein-coding abilities due to several endogenous inactivating mutations. Although pseudogenes are widely expressed in prokaryotes and eukaryotes, for decades, they have been largely ignored and classified as gene "junk" or "relics". With the widespread availability of high-throughput sequencing analysis, especially omics technologies, knowledge concerning pseudogenes has substantially increased. Pseudogenes are evolutionarily conserved and derive primarily from a mutation or retrotransposon, conferring the pseudogene with a "gene repository" role to store and expand genetic information. In contrast to previous notions, pseudogenes have a variety of functions at the DNA, RNA and protein levels for broadly participating in gene regulation to influence the development and progression of certain diseases, especially cancer. Indeed, some pseudogenes have been proven to encode proteins, strongly contradicting their "trash" identification, and have been confirmed to have tissue-specific and disease subtype-specific expression, indicating their own value in disease diagnosis. Moreover, pseudogenes have been correlated with the life expectancy of patients and exhibit great potential for future use in disease treatment, suggesting that they are promising biomarkers and therapeutic targets for clinical applications. In this review, we summarize the natural properties, functions, disease involvement and clinical value of pseudogenes. Although our knowledge of pseudogenes remains nascent, this field deserves more attention and deeper exploration.