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Autism spectrum disorder (ASD) displays a notable male bias in prevalence. Research into rare (<0.1) genetic variants on the X chromosome has implicated over 20 genes in ASD pathogenesis, such as MECP2, DDX3X, and DMD. The "female protective effect" in ASD suggests that females may require a higher genetic burden to manifest symptoms similar to those in males, yet the mechanisms remain unclear. Despite technological advances in genomics, the complexity of the biological nature of sex chromosomes leaves them underrepresented in genome-wide studies. Here, we conducted an X-chromosome-wide association study (XWAS) using whole-genome sequencing data from 6,873 individuals with ASD (82% males) across Autism Speaks MSSNG, Simons Simplex Collection (SSC), and Simons Powering Autism Research (SPARK), alongside 8,981 population controls (43% males). We analyzed 418,652 X chromosome variants, identifying 59 associated with ASD (p values 7.9 × 10 -6 to 1.51 × 10 -5 ), surpassing Bonferroni-corrected thresholds. Key findings include significant regions on Xp22.2 (lead SNP rs12687599, p = 3.57 × 10 -7 ) harboring ASB9/ASB11 and another encompassing DDX53 and the PTCHD1-AS long non-coding RNA (lead SNP rs5926125, p = 9.47 × 10 -6 ). When mapping genes within 10 kb of the 59 most significantly associated SNPs, 91 genes were found, 17 of which yielded association with ASD (GRPR, AP1S2, DDX53, HDAC8, PCDH19, PTCHD1, PCDH11X, PTCHD1-AS, DMD, SYAP1, CNKSR2, GLRA2, OFD1, CDKL5, GPRASP2, NXF5, and SH3KBP1). FGF13 emerged as an X-linked ASD candidate gene, highlighted by sex-specific differences in minor allele frequencies. These results reveal significant insights into X chromosome biology in ASD, confirming and nominating genes and pathways for further investigation., Competing Interests: Declaration of interests At the time of this study and its publication, S.W.S. served on the scientific advisory committee of Population Bio. Intellectual property from aspects of his research held at The Hospital for Sick Children are licensed to Athena Diagnostics and Population Bio. These relationships did not influence data interpretation or presentation during this study but are disclosed for potential future considerations., (Copyright © 2024 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Copy-number variants (CNVs) that increase the risk for neurodevelopmental disorders also affect cognitive ability. However, such CNVs remain challenging to study due to their scarcity, limiting our understanding of gene-dosage-sensitive biological processes linked to cognitive ability. We performed a genome-wide association study (GWAS) in 258,292 individuals, which identified-for the first time-a duplication at 2q12.3 associated with higher cognitive performance. We developed a functional-burden analysis, which tested the association between cognition and CNVs disrupting 6,502 gene sets biologically defined across tissues, cell types, and ontologies. Among those, 864 gene sets were associated with cognition, and effect sizes of deletion and duplication were negatively correlated. The latter suggested that functions across all biological processes were sensitive to either deletions (e.g., subcortical regions, postsynaptic) or duplications (e.g., cerebral cortex, presynaptic). Associations between non-brain tissues and cognition were driven partly by constrained genes, which may shed light on medical comorbidities in neurodevelopmental disorders., Competing Interests: Declaration of interests The authors declare that they have no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Adolescent idiopathic scoliosis (AIS) and schizophrenia (SCZ) are two distinct conditions with poorly understood aetiologies that both emerge in otherwise healthy young adolescents. One rare genetic condition associated with both phenotypic outcomes is the 22q11.2 deletion (22q11DS). This microdeletion, encompassing 47 genes, occurs in approximately 1 in 2,148 live births and confers a 20-fold higher risk for both AIS and schizophrenia compared to the general population. In the general population (non-22q11DS carriers), AIS and SCZ have also been reported to be related and genetic studies suggest the involvement of genetic variants implicated in the central nervous functioning. In this study, our objective was to further investigate genetic overlaps between these conditions in the general population. Specifically, we aimed to explore the role of genes within the 22q11.2 region, not only in terms of common variants but also their potential impact on gene networks and biopathways., Methods: We used summary statistics from three genome-wide association studies (GWAS): two focused on AIS (n = 11,210), and one on schizophrenia (n = 36,989). To explore potential overlaps between the two conditions, we conducted a comparative analysis on the significance-based ranked single nucleotide polymorphisms (SNPs) that are associated with both AIS and SCZ. Next, we employed in silico analyses to assess gene-networks enrichment for the most significant SNPs and investigate the contribution of genes within the 22q11.2 region. Post-hoc analysis was conducted to explore the biological pathways correlated with SNPs significantly associated with both AIS and SCZ., Results: The in silico analyses revealed a significant (adjusted-p < 0.05) genetic overlap between SCZ and both AIS cohorts. The top 3% of the most significant SNPs associated with both conditions exhibited a distinct enrichment cluster which is unlikely to be a result of chance (p < 3e-04). The gene-networks analyses showed a significant overlap of 26-41% with the ones involving genes in the 22q11DS region. However, there was no overlap between SNPs in this region and the most significant SNPs identified in the GWAS., Conclusion: This study revealed compelling evidence that beyond the shared association with 22q11DS as a rare genetic variant, AIS and SCZ exhibit common genetic risk variants and an overlap of important genes. The gene networks enriched by the most significant SNPs for both conditions also intersect with the ones involving genes in the 22q11DS region. However, SNPs within this region were not overrepresented among the most significant SNPs from GWAS for both conditions. Notably, gene networks linked to the risk for both conditions suggest an involvement of biopathways related to cellular signaling and neuronal development., (© 2024. The Author(s).)

Autism Spectrum Disorder (ASD) displays a notable male bias in prevalence. Research into rare (<0.1) genetic variants on the X chromosome has implicated over 20 genes in ASD pathogenesis, such as MECP2 , DDX3X , and DMD . The "female protective effect" in ASD suggests that females may require a higher genetic burden to manifest similar symptoms as males, yet the mechanisms remain unclear. Despite technological advances in genomics, the complexity of the biological nature of sex chromosomes leave them underrepresented in genome-wide studies. Here, we conducted an X chromosome-wide association study (XWAS) using whole-genome sequencing data from 6,873 individuals with ASD (82% males) across Autism Speaks MSSNG, Simons Simplex Cohort SSC, and Simons Foundation Powering Autism Research SPARK, alongside 8,981 population controls (43% males). We analyzed 418,652 X-chromosome variants, identifying 59 associated with ASD (p-values 7.9×10 -6 to 1.51×10 -5 ), surpassing Bonferroni-corrected thresholds. Key findings include significant regions on chrXp22.2 (lead SNP=rs12687599, p=3.57×10 -7 ) harboring ASB9 / ASB11 , and another encompassing DDX53/PTCHD1-AS long non-coding RNA (lead SNP=rs5926125, p=9.47×10 -6 ). When mapping genes within 10kb of the 59 most significantly associated SNPs, 91 genes were found, 17 of which yielded association with ASD ( GRPR , AP1S2 , DDX53 , HDAC8 , PCDH19 , PTCHD1 , PCDH11X , PTCHD1-AS , DMD , SYAP1 , CNKSR2 , GLRA2 , OFD1 , CDKL5 , GPRASP2 , NXF5 , SH3KBP1 ). FGF13 emerged as a novel X-linked ASD candidate gene, highlighted by sex-specific differences in minor allele frequencies. These results reveal significant new insights into X chromosome biology in ASD, confirming and nominating genes and pathways for further investigation., Competing Interests: At the time of this study and its publication, S.W.S. served on the Scientific Advisory Committee of Population Bio. Intellectual property from aspects of his research held at The Hospital for Sick Children are licensed to Athena Diagnostics and Population Bio. These relationships did not influence data interpretation or presentation during this study but are disclosed for potential future considerations.

We performed whole-genome sequencing (WGS) in 327 children with cerebral palsy (CP) and their biological parents. We classified 37 of 327 (11.3%) children as having pathogenic/likely pathogenic (P/LP) variants and 58 of 327 (17.7%) as having variants of uncertain significance. Multiple classes of P/LP variants included single-nucleotide variants (SNVs)/indels (6.7%), copy number variations (3.4%) and mitochondrial mutations (1.5%). The COL4A1 gene had the most P/LP SNVs. We also analyzed two pediatric control cohorts (n = 203 trios and n = 89 sib-pair families) to provide a baseline for de novo mutation rates and genetic burden analyses, the latter of which demonstrated associations between de novo deleterious variants and genes related to the nervous system. An enrichment analysis revealed previously undescribed plausible candidate CP genes (SMOC1, KDM5B, BCL11A and CYP51A1). A multifactorial CP risk profile and substantial presence of P/LP variants combine to support WGS in the diagnostic work-up across all CP and related phenotypes., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Cardiomyopathy is a clinically and genetically heterogeneous heart condition that can lead to heart failure and sudden cardiac death in childhood. While it has a strong genetic basis, the genetic aetiology for over 50% of cardiomyopathy cases remains unknown., Methods: In this study, we analyse the characteristics of tandem repeats from genome sequence data of unrelated individuals diagnosed with cardiomyopathy from Canada and the United Kingdom (n = 1216) and compare them to those found in the general population. We perform burden analysis to identify genomic and epigenomic features that are impacted by rare tandem repeat expansions (TREs), and enrichment analysis to identify functional pathways that are involved in the TRE-associated genes in cardiomyopathy. We use Oxford Nanopore targeted long-read sequencing to validate repeat size and methylation status of one of the most recurrent TREs. We also compare the TRE-associated genes to those that are dysregulated in the heart tissues of individuals with cardiomyopathy., Findings: We demonstrate that tandem repeats that are rarely expanded in the general population are predominantly expanded in cardiomyopathy. We find that rare TREs are disproportionately present in constrained genes near transcriptional start sites, have high GC content, and frequently overlap active enhancer H3K27ac marks, where expansion-related DNA methylation may reduce gene expression. We demonstrate the gene silencing effect of expanded CGG tandem repeats in DIP2B through promoter hypermethylation. We show that the enhancer-associated loci are found in genes that are highly expressed in human cardiomyocytes and are differentially expressed in the left ventricle of the heart in individuals with cardiomyopathy., Interpretation: Our findings highlight the underrecognized contribution of rare tandem repeat expansions to the risk of cardiomyopathy and suggest that rare TREs contribute to ∼4% of cardiomyopathy risk., Funding: Government of Ontario (RKCY), The Canadian Institutes of Health Research PJT 175329 (RKCY), The Azrieli Foundation (RKCY), SickKids Catalyst Scholar in Genetics (RKCY), The University of Toronto McLaughlin Centre (RKCY, SM), Ted Rogers Centre for Heart Research (SM), Data Sciences Institute at the University of Toronto (SM), The Canadian Institutes of Health Research PJT 175034 (SM), The Canadian Institutes of Health Research ENP 161429 under the frame of ERA PerMed (SM, RL), Heart and Stroke Foundation of Ontario & Robert M Freedom Chair in Cardiovascular Science (SM), Bitove Family Professorship of Adult Congenital Heart Disease (EO), Canada Foundation for Innovation (SWS, JR), Canada Research Chair (PS), Genome Canada (PS, JR), The Canadian Institutes of Health Research (PS)., Competing Interests: Declaration of interests SM holds the Heart and Stroke Foundation of Canada/Robert M Freedom Chair in Cardiovascular Science and serves on the Advisory Board for Bristol Myers Squibb and Tenaya Therapeutics. EO holds the Bitove Family Professorship of Adult Congenital Heart Disease. SWS is on the Scientific Advisory Committees of Population Bio, Inc. and Deep Genomics and serves as a Highly Cited Academic Advisor for the King Abdulaziz University. SWS and RY hold patent entitled “Genome-wide Detection of DNA Repeats Expanded in Disease” (WO2021119840A1). PM is a Senior Medical Director at Maternal Newborn Child Youth Strategic Clinical Network (Alberta Health Services) and was supported for attending Clinical Microbiology and Infectious Diseases, Long COVID and ENRICH meetings. TJM received support from CIHR, PSI and SickKids Foundation, CAMH and GSK. ES received support from COVID-19 Immune Task Force, Children's Hospital Foundation and CIHR, and is on the American Academy of Allergy, Asthma, and Immunology Credentials Committee. JR is supported by Canada Foundation for Innovation and received support for attending Ultima Biosciences and 10x Genomics meetings. Illumina, Inc. provided sequencing reagents for the CHILD cohort study. CHILD cohort study was supported by CIHR, AllerGen NCE, Alberta Health, Public Health Agency of Canada and Women's and Children's Health Research. Other authors declare that they have no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Elevated triglyceride (TG) levels are a heritable and modifiable risk factor for cardiovascular disease and have well-established associations with common genetic variation captured in a polygenic risk score (PRS). In young adulthood, the 22q11.2 microdeletion conveys a 2-fold increased risk for mild-moderate hypertriglyceridemia. This study aimed to assess the role of the TG-PRS in individuals with this elevated baseline risk for mild-moderate hypertriglyceridemia., Methods: We studied a deeply phenotyped cohort of adults (n = 157, median age 34 years) with a 22q11.2 microdeletion and available genome sequencing, lipid level, and other clinical data. The association between a previously developed TG-PRS and TG levels was assessed using a multivariable regression model adjusting for effects of sex, BMI, and other covariates. We also constructed receiver operating characteristic (ROC) curves using logistic regression models to assess the ability of TG-PRS and significant clinical variables to predict mild-moderate hypertriglyceridemia status., Results: The TG-PRS was a significant predictor of TG-levels (p = 1.52E-04), along with male sex and BMI, in a multivariable model (p model  = 7.26E-05). The effect of TG-PRS appeared to be slightly stronger in individuals with obesity (BMI ≥ 30) (beta = 0.4617) than without (beta = 0.1778), in a model unadjusted for other covariates (p-interaction = 0.045). Among ROC curves constructed, the inclusion of TG-PRS, sex, and BMI as predictor variables produced the greatest area under the curve (0.749) for classifying those with mild-moderate hypertriglyceridemia, achieving an optimal sensitivity and specificity of 0.746 and 0.707, respectively., Conclusions: These results demonstrate that in addition to significant effects of sex and BMI, genome-wide common variation captured in a PRS also contributes to the variable expression of the 22q11.2 microdeletion with respect to elevated TG levels., (© 2023. The Author(s).)

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social and communication skills, restricted interests, and repetitive behaviors. The prevalence of ASD among children in Qatar was recently estimated to be 1.1%, though the genetic architecture underlying ASD both in Qatar and the greater Middle East has been largely unexplored. Here, we describe the first genomic data release from the BARAKA-Qatar Study-a nationwide program building a broadly consented biorepository of individuals with ASD and their families available for sample and data sharing and multi-omics research., Methods: In this first release, we present a comprehensive analysis of whole-genome sequencing (WGS) data of the first 100 families (372 individuals), investigating the genetic architecture, including single-nucleotide variants (SNVs), copy number variants (CNVs), tandem repeat expansions (TREs), as well as mitochondrial DNA variants (mtDNA) segregating with ASD in local families., Results: Overall, we identify potentially pathogenic variants in known genes or regions in 27 out of 100 families (27%), of which 11 variants (40.7%) were classified as pathogenic or likely-pathogenic based on American College of Medical Genetics (ACMG) guidelines. Dominant variants, including de novo and inherited, contributed to 15 (55.6%) of these families, consisting of SNVs/indels (66.7%), CNVs (13.3%), TREs (13.3%), and mtDNA variants (6.7%). Moreover, homozygous variants were found in 7 families (25.9%), with a sixfold increase in homozygous burden in consanguineous versus non-consanguineous families (13.6% and 1.8%, respectively). Furthermore, 28 novel ASD candidate genes were identified in 20 families, 23 of which had recurrent hits in MSSNG and SSC cohorts., Conclusions: This study illustrates the value of ASD studies in under-represented populations and the importance of WGS as a comprehensive tool for establishing a molecular diagnosis for families with ASD. Moreover, it uncovers a significant role for recessive variation in ASD architecture in consanguineous settings and provides a unique resource of Middle Eastern genomes for future research to the global ASD community., (© 2023. BioMed Central Ltd., part of Springer Nature.)

De novo variants (DNVs) analysis has proven to be a powerful approach to gene discovery in Autism Spectrum Disorder (ASD), which has not yet been shown in a Brazilian ASD cohort. The relevance of inherited rare variants has also been suggested, particularly in oligogenic models. We hypothesized that three-generation analyses of DNVs could provide new insights into the relevance of de novo and inherited variants across generations. To accomplish this goal, we performed whole-exome sequencing of 33 septet families composed of probands, parents, and grandparents (n = 231 individuals) and compared DNV rates (DNVr) between generations and those from two control cohorts. The DNVr in the probands (DNVr = 1.16) was marginally higher than in parents (DNVr = 0.60; p = 0.054), and in controls (DNVr = 0.68; p = 0.035, congenital heart disorder and DNVr = 0.70; p = 0.047, unaffected ASD siblings from Simons Simplex Collection). Moreover, most of the DNVs were found to have paternal origin in both generations (84.6%). Finally, we observed that 40% (6/15) of the DNVs in parents transmitted for probands are in ASD or ASD candidate genes, representing recently emerged risk variants to ASD in their families and suggest ZNF536, MSL2 and HDAC9 as ASD candidate genes. We did not observe an enrichment of risk variants nor sex bias of transmitted variants in the three generations, that can be due to sample size. These results further reinforce the relevance of de novo variants in ASD., (© 2023. The Author(s), under exclusive licence to European Society of Human Genetics.)

We assessed the relationship of gene copy number variation (CNV) in mental health/neurodevelopmental traits and diagnoses, physical health and cognition in a community sample of 7100 unrelated children and youth of European or East Asian ancestry (Spit for Science). Clinically significant or susceptibility CNVs were present in 3.9% of participants and were associated with elevated scores on a continuous measure of attention-deficit/hyperactivity disorder (ADHD) traits (P = 5.0 × 10-3), longer response inhibition (a cognitive deficit found in several mental health and neurodevelopmental disorders; P = 1.0 × 10-2) and increased prevalence of mental health diagnoses (P = 1.9 × 10-6, odds ratio: 3.09), specifically ADHD, autism spectrum disorder anxiety and learning problems/learning disorder (P's < 0.01). There was an increased burden of rare deletions in gene-sets related to brain function or expression in brain associated with more ADHD traits. With the current mental health crisis, our data established a baseline for delineating genetic contributors in pediatric-onset conditions., (© The Author(s) 2023. Published by Oxford University Press.)

Copy number variants (CNVs) represent major etiologic factors in rare genetic diseases. Current clinical CNV interpretation workflows require extensive back-and-forth with multiple tools and databases. This increases complexity and time burden, potentially resulting in missed genetic diagnoses. We present the Suite for CNV Interpretation and Prioritization (SCIP), a software package for the clinical interpretation of CNVs detected by whole-genome sequencing (WGS). The SCIP Visualization Module near-instantaneously displays all information necessary for CNV interpretation (variant quality, population frequency, inheritance pattern, and clinical relevance) on a single page-supported by modules providing variant filtration and prioritization. SCIP was comprehensively evaluated using WGS data from 1027 families with congenital cardiac disease and/or autism spectrum disorder, containing 187 pathogenic or likely pathogenic (P/LP) CNVs identified in previous curations. SCIP was efficient in filtration and prioritization: a median of just two CNVs per case were selected for review, yet it captured all P/LP findings (92.5% of which ranked 1st). SCIP was also able to identify one pathogenic CNV previously missed. SCIP was benchmarked against AnnotSV and a spreadsheet-based manual workflow and performed superiorly than both. In conclusion, SCIP is a novel software package for efficient clinical CNV interpretation, substantially faster and more accurate than previous tools (available at https://github.com/qd29/SCIP , a video tutorial series is available at https://bit.ly/SCIPVideos )., (© 2022. The Author(s).)

Tandem repeat expansions (TREs) are associated with over 60 monogenic disorders and have recently been implicated in complex disorders such as cancer and autism spectrum disorder. The role of TREs in schizophrenia is now emerging. In this study, we have performed a genome-wide investigation of TREs in schizophrenia. Using genome sequence data from 1154 Swedish schizophrenia cases and 934 ancestry-matched population controls, we have detected genome-wide rare (<0.1% population frequency) TREs that have motifs with a length of 2-20 base pairs. We find that the proportion of individuals carrying rare TREs is significantly higher in the schizophrenia group. There is a significantly higher burden of rare TREs in schizophrenia cases than in controls in genic regions, particularly in postsynaptic genes, in genes overlapping brain expression quantitative trait loci, and in brain-expressed genes that are differentially expressed between schizophrenia cases and controls. We demonstrate that TRE-associated genes are more constrained and primarily impact synaptic and neuronal signaling functions. These results have been replicated in an independent Canadian sample that consisted of 252 schizophrenia cases of European ancestry and 222 ancestry-matched controls. Our results support the involvement of rare TREs in schizophrenia etiology., (© 2022. The Author(s).)

Posttraumatic stress disorder (PTSD) is a heritable (h 2  = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10 -8 ). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further., (© 2022. The Author(s).)

There are hundreds of risk genes associated with autism spectrum disorder (ASD), but signaling networks at the protein level remain unexplored. We use neuron-specific proximity-labeling proteomics (BioID2) to identify protein-protein interaction (PPI) networks for 41 ASD risk genes. Neuron-specific PPI networks, including synaptic transmission proteins, are disrupted by de novo missense variants. The PPI network map reveals convergent pathways, including mitochondrial/metabolic processes, Wnt signaling, and MAPK signaling. CRISPR knockout displays an association between mitochondrial activity and ASD risk genes. The PPI network shows an enrichment of 112 additional ASD risk genes and differentially expressed genes from postmortem ASD patients. Clustering of risk genes based on PPI networks identifies gene groups corresponding to clinical behavior score severity. Our data report that cell type-specific PPI networks can identify individual and convergent ASD signaling networks, provide a method to assess patient variants, and highlight biological insight into disease mechanisms and sub-cohorts of ASD., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Fully understanding autism spectrum disorder (ASD) genetics requires whole-genome sequencing (WGS). We present the latest release of the Autism Speaks MSSNG resource, which includes WGS data from 5,100 individuals with ASD and 6,212 non-ASD parents and siblings (total n = 11,312). Examining a wide variety of genetic variants in MSSNG and the Simons Simplex Collection (SSC; n = 9,205), we identified ASD-associated rare variants in 718/5,100 individuals with ASD from MSSNG (14.1%) and 350/2,419 from SSC (14.5%). Considering genomic architecture, 52% were nuclear sequence-level variants, 46% were nuclear structural variants (including copy-number variants, inversions, large insertions, uniparental isodisomies, and tandem repeat expansions), and 2% were mitochondrial variants. Our study provides a guidebook for exploring genotype-phenotype correlations in families who carry ASD-associated rare variants and serves as an entry point to the expanded studies required to dissect the etiology in the ∼85% of the ASD population that remain idiopathic., Competing Interests: Declaration of interests E. Anagnostou has received consultation fees from Roche, Quadrant, and Oron; grant funding from Roche; in-kind supports from AMO Pharma and CRR; editorial honoraria from Wiley; and book royalties from APPI and Springer. She co-holds a patent for the device Anxiety Meter (patent # US20160000365A1). S.W.S. is on the Scientific Advisory Committee of Population Bio and serves as a Highly Cited Academic Advisor for King Abdulaziz University, and intellectual property from aspects of his research held at The Hospital for Sick Children are licensed to Athena Diagnostics and Population Bio. These relationships did not influence data interpretation or presentation during this study but are disclosed for potential future considerations., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10 -3 ). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols., (© 2022. The Author(s).)

Tandem repeat expansions (TREs) can cause neurological diseases but their impact in schizophrenia is unclear. Here we analyzed genome sequences of adults with schizophrenia and found that they have a higher burden of TREs that are near exons and rare in the general population, compared with non-psychiatric controls. These TREs are disproportionately found at loci known to be associated with schizophrenia from genome-wide association studies, in individuals with clinically-relevant genetic variants at other schizophrenia loci, and in families where multiple individuals have schizophrenia. We showed that rare TREs in schizophrenia may impact synaptic functions by disrupting the splicing process of their associated genes in a loss-of-function manner. Our findings support the involvement of genome-wide rare TREs in the polygenic nature of schizophrenia., (© 2022. The Author(s).)

Ongoing inchworm-like CAG and CGG repeat expansions in brains, arising by aberrant processing of slipped DNAs, may drive Huntington's disease, fragile X syndrome, and autism. FAN1 nuclease modifies hyper-expansion rates by unknown means. We show that FAN1, through iterative cycles, binds, dimerizes, and cleaves slipped DNAs, yielding striking exo-nuclease pauses along slip-outs: 5'-C↓A↓GC↓A↓G-3' and 5'-C↓T↓G↓C↓T↓G-3'. CAG excision is slower than CTG and requires intra-strand A·A and T·T mismatches. Fully paired hairpins arrested excision, whereas disease-delaying CAA interruptions further slowed excision. Endo-nucleolytic cleavage is insensitive to slip-outs. Rare FAN1 variants are found in individuals with autism with CGG/CCG expansions, and CGG/CCG slip-outs show exo-nuclease pauses. The slip-out-specific ligand, naphthyridine-azaquinolone, which induces contractions of expanded repeats in vivo, requires FAN1 for its effect, and protects slip-outs from FAN1 exo-, but not endo-, nucleolytic digestion. FAN1's inchworm pausing of slip-out excision rates is well suited to modify inchworm expansion rates, which modify disease onset and progression., Competing Interests: Declaration of interests A patent on methods of treating diseases associated with repeat instability has been filed (application no. 16/325,066) by The Hospital for Sick Children and Osaka University. A patent on methods and compositions for interfering with FAN1 has been filed (application no. 63/184,146) by The Hospital for Sick Children and University of Zurich., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (p adj  = 6.73 × 10 -6 ). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present., (© 2020. The Author(s), under exclusive licence to Springer Nature Limited.)

Tandem DNA repeats vary in the size and sequence of each unit (motif). When expanded, these tandem DNA repeats have been associated with more than 40 monogenic disorders 1 . Their involvement in disorders with complex genetics is largely unknown, as is the extent of their heterogeneity. Here we investigated the genome-wide characteristics of tandem repeats that had motifs with a length of 2-20 base pairs in 17,231 genomes of families containing individuals with autism spectrum disorder (ASD) 2,3 and population control individuals 4 . We found extensive polymorphism in the size and sequence of motifs. Many of the tandem repeat loci that we detected correlated with cytogenetic fragile sites. At 2,588 loci, gene-associated expansions of tandem repeats that were rare among population control individuals were significantly more prevalent among individuals with ASD than their siblings without ASD, particularly in exons and near splice junctions, and in genes related to the development of the nervous system and cardiovascular system or muscle. Rare tandem repeat expansions had a prevalence of 23.3% in children with ASD compared with 20.7% in children without ASD, which suggests that tandem repeat expansions make a collective contribution to the risk of ASD of 2.6%. These rare tandem repeat expansions included previously undescribed ASD-linked expansions in DMPK and FXN, which are associated with neuromuscular conditions, and in previously unknown loci such as FGF14 and CACNB1. Rare tandem repeat expansions were associated with lower IQ and adaptive ability. Our results show that tandem DNA repeat expansions contribute strongly to the genetic aetiology and phenotypic complexity of ASD.

Recent genome-wide studies of rare genetic variants have begun to implicate novel mechanisms for tetralogy of Fallot (TOF), a severe congenital heart defect (CHD). To provide statistical support for case-only data without parental genomes, we re-analyzed genome sequences of 231 individuals with TOF ( n = 175) or related CHD. We adapted a burden test originally developed for de novo variants to assess ultra-rare variant burden in individual genes, and in gene-sets corresponding to functional pathways and mouse phenotypes, accounting for highly correlated gene-sets and for multiple testing. For truncating variants, the gene burden test confirmed significant burden in FLT4 (Bonferroni corrected p -value < 0.01). For missense variants, burden in NOTCH1 achieved genome-wide significance only when restricted to constrained genes (i.e., under negative selection, Bonferroni corrected p -value = 0.004), and showed enrichment for variants affecting the extracellular domain, especially those disrupting cysteine residues forming disulfide bonds (OR = 39.8 vs. gnomAD). Individuals with NOTCH1 ultra-rare missense variants, all with TOF, were enriched for positive family history of CHD. Other genes not previously implicated in CHD had more modest statistical support in gene burden tests. Gene-set burden tests for truncating variants identified a cluster of pathways corresponding to VEGF signaling ( FDR = 0%), and of mouse phenotypes corresponding to abnormal vasculature ( FDR = 0.8%); these suggested additional candidate genes not previously identified (e.g., WNT5A and ZFAND5 ). Results for the most promising genes were driven by the TOF subset of the cohort. The findings support the importance of ultra-rare variants disrupting genes involved in VEGF and NOTCH signaling in the genetic architecture of TOF, accounting for 11-14% of individuals in the TOF cohort. These proof-of-principle data indicate that this statistical methodology could assist in analyzing case-only sequencing data in which ultra-rare variants, whether de novo or inherited, contribute to the genetic etiopathogenesis of a complex disorder., (Copyright © 2020 Manshaei, Merico, Reuter, Engchuan, Mojarad, Chaturvedi, Heung, Pellecchia, Zarrei, Nalpathamkalam, Khan, Okello, Liston, Curtis, Yuen, Marshall, Jobling, Oechslin, Wald, Silversides, Scherer, Kim and Bassett.)

Mutations that cause an error in the splicing of a messenger RNA (mRNA) can lead to diseases in humans. Various computational models have been developed to recognize the sequence pattern of the splice sites. In recent studies, Convolutional Neural Network (CNN) architectures were shown to outperform other existing models in predicting the splice sites. However, an insufficient effort has been put into extending the CNN model to predict the effect of the genomic variants on the splicing of mRNAs. This study proposes a framework to elaborate on the utility of CNNs to assess the effect of splice variants on the identification of potential disease-causing variants that disrupt the RNA splicing process. Five models, including three CNN-based and two non-CNN machine learning based, were trained and compared using two existing splice site datasets, Genome Wide Human splice sites (GWH) and a dataset provided at the Deep Learning and Artificial Intelligence winter school 2018 (DLAI). The donor sites were also used to test on the HSplice tool to evaluate the predictive models. To improve the effectiveness of predictive models, two datasets were combined. The CNN model with four convolutional layers showed the best splice site prediction performance with an AUPRC of 93.4% and 88.8% for donor and acceptor sites, respectively. The effects of variants on splicing were estimated by applying the best model on variant data from the ClinVar database. Based on the estimation, the framework could effectively differentiate pathogenic variants from the benign variants ( p  = 5.9 × 10 -7 ). These promising results support that the proposed framework could be applied in future genetic studies to identify disease causing loci involving the splicing mechanism. The datasets and Python scripts used in this study are available on the GitHub repository at https://github.com/smiile8888/rna-splice-sites-recognition., Competing Interests: The authors declare there are no competing interests., (©2020 Thanapattheerakul et al.)

Copy number variations (CNVs) are implicated across many neurodevelopmental disorders (NDDs) and contribute to their shared genetic etiology. Multiple studies have attempted to identify shared etiology among NDDs, but this is the first genome-wide CNV analysis across autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), schizophrenia (SCZ), and obsessive-compulsive disorder (OCD) at once. Using microarray (Affymetrix CytoScan HD), we genotyped 2,691 subjects diagnosed with an NDD (204 SCZ, 1,838 ASD, 427 ADHD and 222 OCD) and 1,769 family members, mainly parents. We identified rare CNVs, defined as those found in <0.1% of 10,851 population control samples. We found clinically relevant CNVs (broadly defined) in 284 (10.5%) of total subjects, including 22 (10.8%) among subjects with SCZ, 209 (11.4%) with ASD, 40 (9.4%) with ADHD, and 13 (5.6%) with OCD. Among all NDD subjects, we identified 17 (0.63%) with aneuploidies and 115 (4.3%) with known genomic disorder variants. We searched further for genes impacted by different CNVs in multiple disorders. Examples of NDD-associated genes linked across more than one disorder (listed in order of occurrence frequency) are NRXN1 , SEH1L , LDLRAD4 , GNAL , GNG13 , MKRN1 , DCTN2, KNDC1 , PCMTD2 , KIF5A , SYNM , and long non-coding RNAs: AK127244 and PTCHD1-AS . We demonstrated that CNVs impacting the same genes could potentially contribute to the etiology of multiple NDDs. The CNVs identified will serve as a useful resource for both research and diagnostic laboratories for prioritization of variants., Competing Interests: Competing interestsS.W.S. serves on the Scientific Advisory Committees of Population Bio and Deep Genomics; intellectual property originating from his research and held at the Hospital for Sick Children is licensed to Lineagen, and separately Athena Diagnostics. D.M. is a full-time employee of Deep Genomics and is entitled to a stock option. R.J.S., P.D.A., and J.C. consult for Highland Therapeutics. Intellectual property from ADHD research at the Hospital for Sick Children is licensed to Ehave and the National Research Council of Canada. Other authors declare no competing interests for the data and interpretation presented in this study. R.J.S., P.D.A., and J.C. consults for Highland Therapeutics. Intellectual property from their research at the Hospital for Sick Children is licensed to Ehave and the National Research Council. D.M. is a full-time employee of Deep Genomics and is entitled to stock options. S.W.S. is on the Scientific Advisory Committees of Population Bio and Deep Genomics; intellectual property from his research held at the Hospital for Sick Children is licensed to Athena Diagnostics, and separately to Lineagen. These relationships did not influence data interpretation or presentation during this study, but are disclosed for potential future consideration., (© The Author(s) 2019.)

BACKGROUND Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. MATERIAL AND METHODS The health status of 6,209 adults, age <65 years (n=1,585), 65-79 years (n=3,267), and >80 years (n=1,357) were measured using an established health metric (0-100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model. RESULTS Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006). CONCLUSIONS Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling.

Background: The use of Cardiovascular Disease (CVD) risk estimation scores in primary prevention has long been established. However, their performance still remains a matter of concern. The aim of this study was to explore the potential of using ML methodologies on CVD prediction, especially compared to established risk tool, the HellenicSCORE., Methods: Data from the ATTICA prospective study (n = 2020 adults), enrolled during 2001-02 and followed-up in 2011-12 were used. Three different machine-learning classifiers (k-NN, random forest, and decision tree) were trained and evaluated against 10-year CVD incidence, in comparison with the HellenicSCORE tool (a calibration of the ESC SCORE). Training datasets, consisting from 16 variables to only 5 variables, were chosen, with or without bootstrapping, in an attempt to achieve the best overall performance for the machine learning classifiers., Results: Depending on the classifier and the training dataset the outcome varied in efficiency but was comparable between the two methodological approaches. In particular, the HellenicSCORE showed accuracy 85%, specificity 20%, sensitivity 97%, positive predictive value 87%, and negative predictive value 58%, whereas for the machine learning methodologies, accuracy ranged from 65 to 84%, specificity from 46 to 56%, sensitivity from 67 to 89%, positive predictive value from 89 to 91%, and negative predictive value from 24 to 45%; random forest gave the best results, while the k-NN gave the poorest results., Conclusions: The alternative approach of machine learning classification produced results comparable to that of risk prediction scores and, thus, it can be used as a method of CVD prediction, taking into consideration the advantages that machine learning methodologies may offer.

Fetal alcohol spectrum disorder (FASD) is characterized by a combination of neurological, developmental, and congenital defects that may occur as a consequence of prenatal alcohol exposure. Earlier reports showed that large chromosomal anomalies may link to FASD. Here, we examined the prevalence and types of copy number variations (CNVs) in FASD cases previously diagnosed by a multidisciplinary FASD team in sites across Canada. We genotyped 95 children with FASD and 87 age-matched, typically developing controls on the Illumina Human Omni2.5 SNP (single nucleotide polymorphisms) array platform. We compared their CNVs with those of 10 851 population controls to identify rare CNVs (<0.1% frequency), which may include large unbalanced chromosomal abnormalities, that might be relevant to FASD. In 12/95 (13%) of the FASD cases, rare CNVs were found that impact potentially clinically relevant developmental genes, including the CACNA1H involved in epilepsy and autism, the 3q29 deletion disorder, and others. Our results show that a subset of children diagnosed with FASD have chromosomal deletions and duplications that may co-occur or explain the neurodevelopmental impairments in a diagnosed cohort of FASD individuals. Children suspected to have FASD with or without sentinel facial features of fetal alcohol syndrome and neurodevelopmental delays should potentially be evaluated by a clinical geneticist and possibly have genetic investigations as appropriate to exclude other etiologies.

We present an 18-year-old boy with cerebral palsy, intellectual disability, speech delay, and seizures. He carries a likely pathogenic 1.3 Mb de novo heterozygous deletion in the 4q21.22 microdeletion syndrome region. He also carries a 436 kb maternally-inherited duplication impacting the first three exons of CHRNA7. The majority of previously published cases with 4q21.22 syndrome shared common features including growth restriction, muscular hypotonia, and absent or severely delayed speech. Using copy number variation (CNV) data available for other subjects, we defined a minimal critical region of 170.8 kb within the syndromic region, encompassing HNRNPD. We also identified a larger 2 Mb critical region encompassing ten protein-coding genes, of which six (PRKG2, RASGEF1B, HNRNPDL, HNRNPD, LIN54, COPS4) have a significantly low number of truncating loss-of-function mutations. Long-range chromatin interaction data suggest that this deletion may alter chromatin interactions at the 4q21.22 microdeletion region. We suggest that the deletion or misregulation of these genes is likely to contribute to the neurodevelopmental and neuromuscular abnormalities in 4q21.22 syndrome., (© 2017 Wiley Periodicals, Inc.)

We are performing whole-genome sequencing of families with autism spectrum disorder (ASD) to build a resource (MSSNG) for subcategorizing the phenotypes and underlying genetic factors involved. Here we report sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal. We found an average of 73.8 de novo single nucleotide variants and 12.6 de novo insertions and deletions or copy number variations per ASD subject. We identified 18 new candidate ASD-risk genes and found that participants bearing mutations in susceptibility genes had significantly lower adaptive ability (P = 6 × 10 -4 ). In 294 of 2,620 (11.2%) of ASD cases, a molecular basis could be determined and 7.2% of these carried copy number variations and/or chromosomal abnormalities, emphasizing the importance of detecting all forms of genetic variation as diagnostic and therapeutic targets in ASD.

A most challenging task for scientists that are involved in the study of ageing is the development of a measure to quantify health status across populations and over time. In the present study, a Bayesian multilevel Item Response Theory approach is used to create a health score that can be compared across different waves in a longitudinal study, using anchor items and items that vary across waves. The same approach can be applied to compare health scores across different longitudinal studies, using items that vary across studies. Data from the English Longitudinal Study of Ageing (ELSA) are employed. Mixed-effects multilevel regression and Machine Learning methods were used to identify relationships between socio-demographics and the health score created. The metric of health was created for 17,886 subjects (54.6% of women) participating in at least one of the first six ELSA waves and correlated well with already known conditions that affect health. Future efforts will implement this approach in a harmonised data set comprising several longitudinal studies of ageing. This will enable valid comparisons between clinical and community dwelling populations and help to generate norms that could be useful in day-to-day clinical practice.

Background: Gene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results., Methods: The two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three subnetwork identification algorithms., Results: The two searching algorithms resulted in better classification and gene/gene-set agreement compared to the original greedy search of the GNFS method. The identified lung cancer subnetwork using the proposed searching algorithm resulted in an improvement of the cross-dataset validation and an increase in the consistency of findings between two independent datasets. The homogeneity measurement of the datasets was conducted to assess dataset compatibility in cross-dataset validation. The lung cancer dataset with higher homogeneity showed a better result when using the GS search while the dataset with low homogeneity showed a better result when using the PN search. The 10-fold cross-dataset validation on the independent lung cancer datasets showed higher classification performance of the proposed algorithms when compared with the greedy search in the original GNFS method., Conclusions: The proposed searching algorithms provide a higher number of genes in the subnetwork expansion step than the greedy algorithm. As a result, the performance of the subnetworks identified from the GSNFS method was improved in terms of classification performance and gene/gene-set level agreement depending on the homogeneity of the datasets used in the analysis. Some common genes obtained from the four datasets using different searching algorithms are genes known to play a role in lung cancer. The improvement of classification performance and the gene/gene-set level agreement, and the biological relevance indicated the effectiveness of the GSNFS method for gene subnetwork identification using expression data.

Cancer is a complex disease that cannot be diagnosed reliably using only single gene expression analysis. Using gene-set analysis on high throughput gene expression profiling controlled by various environmental factors is a commonly adopted technique used by the cancer research community. This work develops a comprehensive gene expression analysis tool (gene-set activity toolbox: (GAT)) that is implemented with data retriever, traditional data pre-processing, several gene-set analysis methods, network visualization and data mining tools. The gene-set analysis methods are used to identify subsets of phenotype-relevant genes that will be used to build a classification model. To evaluate GAT performance, we performed a cross-dataset validation study on three common cancers namely colorectal, breast and lung cancers. The results show that GAT can be used to build a reasonable disease diagnostic model and the predicted markers have biological relevance. GAT can be accessed from http://gat.sit.kmutt.ac.th where GAT's java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded.

Background: A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry., Results: We analyzed 1,892 Caucasian ASD subjects and 2,342 matched controls. Rare CNVs (frequency 1% or less) were detected using Illumina 1M and 1M-Duo BeadChips. Conditional Inference Forest (CF) typically performed as well as or better than other classification methods. We found a maximum AUC (area under the ROC curve) of 0.533 when considering all ASD subjects with rare genic CNVs, corresponding to 7.9% correctly classified ASD subjects and less than 3% incorrectly classified controls; performance was significantly higher when considering only subjects harboring de novo or pathogenic CNVs. We also found rare losses to be more predictive than gains and that curated neurally-relevant annotations (brain expression, synaptic components and neurodevelopmental phenotypes) outperform Gene Ontology and pathway-based annotations., Conclusions: CF is an optimal classification approach for case-control rare CNV data and it can be used to prioritize subjects with variants potentially contributing to ASD risk not yet recognized. The neurally-relevant annotations used in this study could be successfully applied to rare CNV case-control data-sets for other neuropsychiatric disorders.

Background: Plants respond to external stimuli through fine regulation of gene expression partially ensured by small RNAs. Of these, microRNAs (miRNAs) play a crucial role. They negatively regulate gene expression by targeting the cleavage or translational inhibition of target messenger RNAs (mRNAs). In Hevea brasiliensis, environmental and harvesting stresses are known to affect natural rubber production. This study set out to identify abiotic stress-related miRNAs in Hevea using next-generation sequencing and bioinformatic analysis., Results: Deep sequencing of small RNAs was carried out on plantlets subjected to severe abiotic stress using the Solexa technique. By combining the LeARN pipeline, data from the Plant microRNA database (PMRD) and Hevea EST sequences, we identified 48 conserved miRNA families already characterized in other plant species, and 10 putatively novel miRNA families. The results showed the most abundant size for miRNAs to be 24 nucleotides, except for seven families. Several MIR genes produced both 20-22 nucleotides and 23-27 nucleotides. The two miRNA class sizes were detected for both conserved and putative novel miRNA families, suggesting their functional duality. The EST databases were scanned with conserved and novel miRNA sequences. MiRNA targets were computationally predicted and analysed. The predicted targets involved in "responses to stimuli" and to "antioxidant" and "transcription activities" are presented., Conclusions: Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs when the complete genome is not yet available. Our study provided additional information for evolutionary studies and revealed potentially specific regulation of the control of redox status in Hevea.