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Genome-wide association studies (GWAS) to date have been able to leverage large sample sizes to identify genomic loci that contribute to risk for various psychiatric disorders. However, GWAS of copy number variants (CNVs) have prioritized identifying risk loci within European populations due to the lack of power in diverse ancestry groups. In this study, we called CNVs in a diverse group of samples to create CNV datasets for 2 additional ancestry groups: African/African American (AFR/AFAM) and Asian/Asian American (ASN/ASAM). SNPweights was used to infer genome-wide genetic ancestry for each sample. We were then able to boost power at specific loci by using a meta-analysis to combine EUR, AFR/AFAM, and ASN/ASAM CNV analyses (N=571,803). Rare copy number variants have been implicated in a cross-disorder European cohort (N=537,466) that includes major psychiatric disorders such as autism (ASD), schizophrenia (SCZ), major depressive disorder (MDD), bipolar disorder (BD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). This analysis was able to identify novel loci with the statistical power that comes with being the largest CNV study to date. Naturally, the inclusion of diverse samples in this analysis can further lead to novel discoveries. Additional CNV-GWAS were performed for cross-disorder datasets in AFR/AFAM (N=17,474) and ASN/ASAM (N=16,863) populations. Meta-analysis of all 3 populations used an inverse-variance weighting to account for the disparity of sample size between populations. We compared EUR CNV-GWAS and burden results with those from the meta-analysis as these were the most well-powered tests. The effect was a substantial increase in significance levels at specific loci that reached testable CNV frequencies in the diverse groups. Comparing the EUR analysis with the trans-ancestry analysis allows us to quantify the contribution of the diverse groups and provide insight into the genomic loci associated with psychiatric disorders in AFR/AFAM and ASN/ASAM populations once similar sample sizes are reached. This study highlights the importance of expanding diversity during data collection so that the genotype-phenotype relationships can benefit people worldwide. [ABSTRACT FROM AUTHOR]

Associating rare variants with disease risk has been limited by the lack of statistical power. Meta-analysis across different populations or different genetically correlated disorders have shown to successfully improve the statistical power in common variant studies. Such approaches have not been explored in the analyses of rare variants. This study investigated the association of rare CNVs aggregated at the functional level with 6 major psychiatric disorders including schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD), in 571,803 individuals of European, African, and East Asian descent. Functional gene-set burden analyses, followed by cross-ancestry meta-analysis was conducted across 16 brain regions, 32 neural cell types, and 2,453 molecular functions. This approach aimed to comprehensively capture the impact of genetic risk variants. Our preliminary findings from the European subset revealed distinct dosage sensitivity patterns across the 6 disorders. The effect sizes of DEL and DUP on SCZ were negatively correlated, while their effects on ASD risk were positively correlated. Our results suggest that multiple major psychiatric disorders share common etiologic pathways, but diagnostic outcome varies according to how gain and loss of function of neurodevelopmental and synaptic processes are spatially distributed in the developing brain. We hypothesized that even if the associated variants might differ in population frequency in different populations, the associations at functional level would agree across populations. Thus, cross-ancestry meta-analysis would strengthen the findings we had from the European subset. In this talk, we will present more on how the inclusion of African and East Asian samples influences the findings from the analysis of the European subset. We will also be discussing the dosage sensitivity patterns and genetic correlation at functional levels among the 6 major psychiatric disorders and across three populations. [ABSTRACT FROM AUTHOR]

The majority of human genes maintain normal biological function when they become haploid due to a genomic deletion. However, pathogenicity may still arise when the remaining allele is affected by additional functional variation. Here, we describe analytical strategies for examining a specific type of compound heterozygosity, namely the co-occurrence of a deletion and a sequence-level variant affecting the other allele, hereafter referred to as deletion compound heterozygosity (DelCH). We report preliminary results in using these strategies to assess DelCH in Autism Spectrum Disorder (ASD). We analyzed whole-genome sequencing data from MSSNG, Simons Simplex Collection, and SPARK cohorts (collectively 11,636 autistic individuals and 22,962 family members). We developed multiple analytical strategies to examine rare (event rate < 1%) DelCH: 1) The burden analysis uses conditional logistic regression for group-level comparisons of DelCH rates between a) probands and their deletion-transmitting parents, with inherited deletion as a random effect variable, or b) probands and their family members, with family ID as a random effect variable; 2) The transmission disequilibrium test (TDT) compares the rates with which deletion-non-transmitting parents transmit sequence-level variants within genes affected by deletions to their autistic offspring. Association is indicated by transmission of non-synonymous variants at a rate higher than predicted by chance. This approach was repeated in unaffected siblings as an additional control analysis. Each strategy has different strengths and weaknesses. The first burden analysis (1a) achieves perfect matching of deleted sequence and unambiguous phasing of variants but is restricted to proband-parent pairs. The second burden analysis (1b) benefits from a larger sample size but cannot distinguish between de novo and inherited variation. In addition, unambiguous phasing is possible only for SNVs within deletion boundaries. In contrast, while the TDT (2) can include SNVs outside deletion boundaries, thereby increasing statistical power, de novo events are not analyzed. Our preliminary findings show variability in results as a function of the analytical strategy. Findings from the burden analysis suggest a modest enrichment of DelCH in ASD which was inversely proportional to the variant frequency thresholds applied. Given that the mechanism consists of two rare events at the same locus, on the population level the role of DelCH in ASD etiology is likely modest, requiring large samples sizes for sufficient statistical power. In addition to this "lightning striking twice", data preparation is demanding, as every subject has unique deletion regions in which sequence-level variants on the other allele are tallied. Variant selection metrics include allele frequency thresholds, the threshold for predicted autosomal dominant deleteriousness, and variant inclusion rules with respect to the deletion boundaries. Our findings show that altering any of these metrics affects the results, warranting careful consideration. Understanding the potential role of DelCH may contribute to a more comprehensive library of ASD-associated genetic variation. DelCHs could explain why individual probands with ASD present with a deletion inherited from a non-affected parent. Lastly, examining DelCH in genetic analyses may help identify recessive ASD genes, which otherwise escape detection. [ABSTRACT FROM AUTHOR]

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. [Display omitted] An X-chromosome-wide association study of 6,873 individuals with autism identifies 59 variants and 17 genes associated with ASD, including DMD and PTCHD1-AS. The findings highlight sex-linked genetic influences and provide key insights into X-linked mechanisms underlying the male bias in ASD prevalence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 where GAT's java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded. [ABSTRACT FROM AUTHOR]

Pathway-based microarray analysis has been found to be a powerful tool to study disease mechanisms and to identify biological markers of complex diseases like lung cancer. From previous studies, the use of pathway activity transformed from gene expression data has been shown to be more informative in disease classification. However, current works on a pathway activity transformation method are for binary-class classification. In this study, we propose a pathway activity transformation method for multi-class data termed Analysis-of-Variance-based Feature Set (AFS). The classification results of using pathway activity derived from our proposed method show high classification power in three-fold cross-validation and robustness in across dataset validation for all four lung cancer datasets used. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Polygenic risk for triglyceride levels in the presence of a high impact rare variant" published in BMC Medical Genomics. The authors reported errors in Table 1 of the original article, specifically in the heading which should read "Continuous variables" instead of "Categorical variables." The correct table is provided in this correction article. The original article has been corrected. The document also includes a table showing lipid and other phenotypic/clinical variables in adults with a 22q11.2 microdeletion. The authors of the original article are listed as Shengjie Ying, Tracy Heung, Bhooma Thiruvahindrapuram, Worrawat Engchuan, Yue Yin, Christina Blagojevic, Zhaolei Zhang, Robert A. Hegele, Ryan K. C. Yuen, and Anne S. Bassett. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • New MSSNG release contains WGS from 11,312 individuals from families with ASD • Extensive variant data available, including SNVs/indels, SVs, tandem repeats, and PRS • Annotation reveals 134 ASD-associated genes, plus SVs not detectable without WGS • Rare, dominant variation has a prominent role in multiplex ASD The latest release of the Autism Speaks MSSNG resource provides an expanded sample size and facilitates the comprehensive examination of the roles of many types of genetic variation in autism spectrum disorder. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]