Comprehensive whole-genome sequence analyses provide insights into the genomic architecture of cerebral palsy.

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.

A curated census of pathogenic and likely pathogenic UTR variants and evaluation of deep learning models for variant effect prediction.

Introduction: Variants in 5' and 3' untranslated regions (UTR) contribute to rare disease. While predictive algorithms to assist in classifying pathogenicity can potentially be highly valuable, the utility of these tools is often unclear, as it depends on carefully selected training and validation conditions. To address this, we developed a high confidence set of pathogenic (P) and likely pathogenic (LP) variants and assessed deep learning (DL) models for predicting their molecular effects. Methods: 3' and 5' UTR variants documented as P or LP (P/LP) were obtained from ClinVar and refined by reviewing the annotated variant effect and reassessing evidence of pathogenicity following published guidelines. Prediction scores from sequence-based DL models were compared between three groups: P/LP variants acting though the mechanism for which the model was designed (model-matched), those operating through other mechanisms (model-mismatched), and putative benign variants. PhyloP was used to compare conservation scores between P/LP and putative benign variants. Results: 295 3' and 188 5' UTR variants were obtained from ClinVar, of which 26 3' and 68 5' UTR variants were classified as P/LP. Predictions by DL models achieved statistically significant differences when comparing modelmatched P/LP variants to both putative benign variants and modelmismatched P/LP variants, as well as when comparing all P/LP variants to putative benign variants. PhyloP conservation scores were significantly higher among P/LP compared to putative benign variants for both the 3' and 5' UTR. Discussion: In conclusion, we present a high-confidence set of P/LP 3' and 5' UTR variants spanning a range of mechanisms and supported by detailed pathogenicity and molecular mechanism evidence curation. Predictions from DL models further substantiate these classifications. These datasets will support further development and validation of DL algorithms designed to predict the functional impact of variants that may be implicated in rare disease.

Gene copy number variation and pediatric mental health/neurodevelopment in a general population.

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.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder.

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.

Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge.

Genome-wide association studies have discovered hundreds of genomic loci associated with psychiatric traits, but the causal genes underlying these associations are often unclear, a research gap that has hindered clinical translation. Here, we present a Psychiatric Omnilocus Prioritization Score (PsyOPS) derived from just three binary features encapsulating high-level assumptions about psychiatric disease etiology - namely, that causal psychiatric disease genes are likely to be mutationally constrained, be specifically expressed in the brain, and overlap with known neurodevelopmental disease genes. To our knowledge, PsyOPS is the first method specifically tailored to prioritizing causal genes at psychiatric GWAS loci. We show that, despite its extreme simplicity, PsyOPS achieves state-of-the-art performance at this task, comparable to a prior domain-agnostic approach relying on tens of thousands of features. Genes prioritized by PsyOPS are substantially more likely than other genes at the same loci to have convergent evidence of direct regulation by the GWAS variant according to both DNA looping assays and expression or splicing quantitative trait locus (QTL) maps. We provide examples of genes hundreds of kilobases away from the lead variant, like GABBR1 for schizophrenia, that are prioritized by all three of PsyOPS, DNA looping and QTLs. Our results underscore the power of incorporating high-level knowledge of trait etiology into causal gene prediction at GWAS loci, and comprise a resource for researchers interested in experimentally characterizing psychiatric gene candidates.

A Regional Burden of Sequence-Level Variation in the 22q11.2 Region Influences Schizophrenia Risk and Educational Attainment.

BACKGROUND: Genomic loci where recurrent pathogenic copy number variants are associated with psychiatric phenotypes in the population may also be sensitive to the collective impact of multiple functional low-frequency single nucleotide variants (SNVs). METHODS: We examined the cumulative impact of low-frequency, functional SNVs within the 22q11.2 region on schizophrenia risk in a discovery cohort and an independent replication cohort (N = 1933 and N = 11,128, respectively), as well as the impact on educational attainment (EA) in a third, independent, general population cohort (N = 2081). In the discovery and EA cohorts, SNVs were identified using genotyping arrays; in the replication cohort, whole-exome sequencing was available. For verification, we compared the regional SNV count for schizophrenia cases in the discovery cohort with a normative count distribution derived from a large population dataset (N = 26,500) using bootstrap procedures. RESULTS: In both schizophrenia cohorts, an increased regional SNV burden (≥4 low-frequency SNVs) in the 22q11.2 region was associated with schizophrenia (discovery cohort: odds ratio = 7.48, p = .039; replication cohort: odds ratio = 1.92, p = .004). In the EA cohort, an increased regional SNV burden at 22q11.2 was associated with decreased EA (odds ratio = 4.65, p = .049). Comparing the SNV count for schizophrenia cases with a normative distribution confirmed the unique nature of the distribution for schizophrenia cases (p = .002). CONCLUSIONS: In the general population, an increased burden of low-frequency, functional SNVs in the 22q11.2 region is associated with schizophrenia risk and a decrease in EA. These findings suggest that in addition to structural variation, a cumulative regional burden of low-frequency, functional SNVs in the 22q11.2 region can also have a relevant phenotypic impact.

Homozygous duplication identified by whole genome sequencing causes LRBA deficiency.

In more than one-third of primary immunodeficiency (PID) patients, extensive genetic analysis including whole-exome sequencing (WES) fails to identify the genetic defect. Whole-genome sequencing (WGS) is able to detect variants missed by other genomics platforms, enabling the molecular diagnosis of otherwise unresolved cases. Here, we report two siblings, offspring of consanguineous parents, who experienced similar severe events encompassing early onset of colitis, lymphoproliferation, and hypogammaglobulinemia, typical of lipopolysaccharide-responsive and beige-like anchor (LRBA) or cytotoxic T lymphocyte antigen 4 (CTLA4) deficiencies. Gene-panel sequencing, comparative genomic hybridization (CGH) array, and WES failed to reveal a genetic aberration in relevant genes. WGS of these patients detected a 12.3 kb homozygous tandem duplication that was absent in control cohorts and is predicted to disrupt the reading frame of the LRBA gene. The variant was validated by PCR and Sanger sequencing, demonstrating the presence of the junction between the reference and the tandem-duplicated sequence. Droplet digital PCR (ddPCR) further confirmed the copy number in the unaffected parents (CN = 3, heterozygous) and affected siblings (CN = 4, homozygous), confirming the expected segregation pattern. In cases of suspected inherited immunodeficiency, WGS may reveal a mutation when other methods such as microarray and WES analysis failed to detect an aberration.

Transcriptome-Wide Off-Target Effects of Steric-Blocking Oligonucleotides.

Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.

Genome sequencing broadens the range of contributing variants with clinical implications in schizophrenia.

The range of genetic variation with potential clinical implications in schizophrenia, beyond rare copy number variants (CNVs), remains uncertain. We therefore analyzed genome sequencing data for 259 unrelated adults with schizophrenia from a well-characterized community-based cohort previously examined with chromosomal microarray for CNVs (none with 22q11.2 deletions). We analyzed these genomes for rare high-impact variants considered causal for neurodevelopmental disorders, including single-nucleotide variants (SNVs) and small insertions/deletions (indels), for potential clinical relevance based on findings for neurodevelopmental disorders. Also, we investigated a novel variant type, tandem repeat expansions (TREs), in 45 loci known to be associated with monogenic neurological diseases. We found several of these variants in this schizophrenia population suggesting that these variants have a wider clinical spectrum than previously thought. In addition to known pathogenic CNVs, we identified 11 (4.3%) individuals with clinically relevant SNVs/indels in genes converging on schizophrenia-relevant pathways. Clinical yield was significantly enriched in females and in those with broadly defined learning/intellectual disabilities. Genome analyses also identified variants with potential clinical implications, including TREs (one in DMPK; two in ATXN8OS) and ultra-rare loss-of-function SNVs in ZMYM2 (a novel candidate gene for schizophrenia). Of the 233 individuals with no pathogenic CNVs, we identified rare high-impact variants (i.e., clinically relevant or with potential clinical implications) for 14 individuals (6.0%); some had multiple rare high-impact variants. Mean schizophrenia polygenic risk score was similar between individuals with and without clinically relevant rare genetic variation; common variants were not sufficient for clinical application. These findings broaden the individual and global picture of clinically relevant genetic risk in schizophrenia, and suggest the potential translational value of genome sequencing as a single genetic technology for schizophrenia.

Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion.

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 (padj = 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.

Combined immunodeficiency caused by a novel homozygous NFKB1 mutation.

BACKGROUND: Genetic faults in several components of the nuclear factor-κB pathway cause immunodeficiency. Most defects lead to combined immunodeficiency with a range of severity. Heterozygous mutations in NFKB1 were associated with common variable immunodeficiency, however, homozygous mutations have not been described. OBJECTIVE: We studied the molecular basis of combined immunodeficiency in a patient who presented with failure to thrive, persistent EBV viremia and hepatitis, pneumocystis jirovecii pneumonitis, and generalized lymphadenopathy. METHODS: Whole genome and exome sequencing followed by Sanger confirmation were performed to identify the genetic defect. Molecular and cellular techniques were used to assess the variant impact on the nuclear factor-κB pathway and lymphocyte function. RESULTS: Genetic analysis revealed a novel homozygous mutation in NFKB1, c.2878G>A, p.Gly960Arg (G960R). This affected p105 phosphorylation and p50 formation on antigen and cytokine stimulation, as well as attenuating nuclear signal transmission. As a result, both T- and B-cell maturation and function were perturbed. The number of memory CD4+ T cells were reduced, while CD8+ T cells consisted predominately of expanded differentiated populations. The function of T cells were diminished as shown by reduced responses to mitogens as well as diminished cytokine secretion. B-cell maturation was also affected, with decreased IgD+CD27+ memory B cells while transitional B cells were increased, likely contributing to the reduced ability to produce specific antibodies. CONCLUSION: Homozygous G960R mutation in NFKB1 leads to a severe clinical presentation of combined immunodeficiency. This was associated with blockade of nuclear factor-κB pathway signaling, resulting in aberrations in T- and B-cell maturation and function.

Genes and Pathways Implicated in Tetralogy of Fallot Revealed by Ultra-Rare Variant Burden Analysis in 231 Genome Sequences.

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.

B cell acute lymphoblastic leukemia cells mediate RANK-RANKL-dependent bone destruction.

Although most children survive B cell acute lymphoblastic leukemia (B-ALL), they frequently experience long-term, treatment-related health problems, including osteopenia and osteonecrosis. Because some children present with fractures at ALL diagnosis, we considered the possibility that leukemic B cells contribute directly to bone pathology. To identify potential mechanisms of B-ALL-driven bone destruction, we examined the p53 -/-; Rag2 -/-; Prkdcscid/scid triple mutant (TM) mice and p53 -/-; Prkdcscid/scid double mutant (DM) mouse models of spontaneous B-ALL. In contrast to DM animals, leukemic TM mice displayed brittle bones, and the TM leukemic cells overexpressed Rankl, encoding receptor activator of nuclear factor κB ligand. RANKL is a key regulator of osteoclast differentiation and bone loss. Transfer of TM leukemic cells into immunodeficient recipient mice caused trabecular bone loss. To determine whether human B-ALL can exert similar effects, we evaluated primary human B-ALL blasts isolated at diagnosis for RANKL expression and their impact on bone pathology after their transplantation into NOD.Prkdcscid/scidIl2rgtm1Wjl /SzJ (NSG) recipient mice. Primary B-ALL cells conferred bone destruction evident in increased multinucleated osteoclasts, trabecular bone loss, destruction of the metaphyseal growth plate, and reduction in adipocyte mass in these patient-derived xenografts (PDXs). Treating PDX mice with the RANKL antagonist recombinant osteoprotegerin-Fc (rOPG-Fc) protected the bone from B-ALL-induced destruction even under conditions of heavy tumor burden. Our data demonstrate a critical role of the RANK-RANKL axis in causing B-ALL-mediated bone pathology and provide preclinical support for RANKL-targeted therapy trials to reduce acute and long-term bone destruction in these patients.

ATP7B variant c.1934T > G p.Met645Arg causes Wilson disease by promoting exon 6 skipping.

Wilson disease is a recessive genetic disorder caused by pathogenic loss-of-function variants in the ATP7B gene. It is characterized by disrupted copper homeostasis resulting in liver disease and/or neurological abnormalities. The variant NM_000053.3:c.1934T > G (Met645Arg) has been reported as compound heterozygous, and is highly prevalent among Wilson disease patients of Spanish descent. Accordingly, it is classified as pathogenic by leading molecular diagnostic centers. However, functional studies suggest that the amino acid change does not alter protein function, leading one ClinVar submitter to question its pathogenicity. Here, we used a minigene system and gene-edited HepG2 cells to demonstrate that c.1934T > G causes ~70% skipping of exon 6. Exon 6 skipping results in frameshift and stop-gain, leading to loss of ATP7B function. The elucidation of the mechanistic effect for this variant resolves any doubt about its pathogenicity and enables the development of genetic medicines for restoring correct splicing.

A large data resource of genomic copy number variation across neurodevelopmental disorders.

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.

Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.

Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes; however, the principles can be applied to diverse types of omics data. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training.

Using Next-Generation Sequencing Transcriptomics To Determine Markers of Post-traumatic Symptoms: Preliminary Findings from a Post-deployment Cohort of Soldiers.

Post-traumatic stress disorder is a concerning psychobehavioral disorder thought to emerge from the complex interaction between genetic and environmental factors. For soldiers exposed to combat, the risk of developing this disorder is twofold and diagnosis is often late, when much sequela has set in. To be able to identify and diagnose in advance those at "risk" of developing post-traumatic stress disorder, would greatly taper the gap between late sequelae and treatment. Therefore, this study sought to determine whether the transcriptome can be used to track the development of post-traumatic stress disorder in this unique and susceptible cohort of individuals. Gene expression levels in peripheral blood samples from 85 Canadian infantry soldiers (n = 58 participants negative for symptoms of post-traumatic stress disorder and n = 27 participants with symptoms of post-traumatic stress disorder) following return from deployment to Afghanistan were determined using RNA sequencing technology. Count-based gene expression quantification, normalization and differential analysis (with thorough correction for confounders) revealed genes associated to PTSD; LRP8 and GOLM1 These preliminary results provide a proof-of-principle for the diagnostic utility of blood-based gene expression profiles for tracking symptoms of post-traumatic stress disorder in soldiers returning from tour. It is also the first to report transcriptome-wide expression profiles alongside a post-traumatic symptom checklist.