Functional genomics provide key insights to improve the diagnostic yield of hereditary ataxia

Improvements in functional genomic annotation have led to a critical mass of neurogenetic discoveries. This is exemplified in hereditary ataxia, a heterogeneous group of disorders characterised by incoordination from cerebellar dysfunction. Associated pathogenic variants in more than 300 genes have been described, leading to a detailed genetic classification partitioned by age-of-onset. Despite these advances, up to 75% of patients with ataxia remain molecularly undiagnosed even following whole genome sequencing, as exemplified in the 100,000 Genomes Project. This study aimed to understand whether we can improve our knowledge of the genetic architecture of hereditary ataxia by leveraging functional genomic annotations, and as a result, generate insights and strategies that raise the diagnostic yield. To achieve these aims, we used publicly-available multi-omics data to generate 294 genic features, capturing information relating to a gene's structure, genetic variation, tissue-specific, cell-type-specific and temporal expression, as well as protein products of a gene. We studied these features across genes typically causing childhood-onset, adult-onset or both types of disease first individually, then collectively. This led to the generation of testable hypotheses which we investigated using whole genome sequencing data from up to 2,182 individuals presenting with ataxia and 6,658 non-neurological probands recruited in the 100,000 Genomes Project. Using this approach, we demonstrated a high short tandem repeat (STR) density within childhood-onset genes suggesting that we may be missing pathogenic repeat expansions within this cohort. This was verified in both childhood- and adult-onset ataxia patients from the 100,000 Genomes Project who were unexpectedly found to have a trend for higher repeat sizes even at naturally-occurring STRs within known ataxia genes, implying a role for STRs in pathogenesis. Using unsupervised analysis, we found significant similarities in genomic annotation across the gene panels, which suggested adult- and childhood-onset patients should be screened using a common diagnostic gene set. We tested this within the 100,000 Genomes Project by assessing the burden of pathogenic variants among childhood-onset genes in adult-onset patients and vice versa. This demonstrated a significantly higher burden of rare, potentially pathogenic variants in conventional childhood-onset genes among individuals with adult-onset ataxia. Our analysis has implications for the current clinical practice in genetic testing for hereditary ataxia. We suggest that the diagnostic rate for hereditary ataxia could be increased by removing the age-of-onset partition, and through a modified screening for repeat expansions in naturally-occurring STRs within known ataxia-associated genes, in effect treating these regions as candidate pathogenic loci

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery

Identifying deafness related genes from within the 100,000 Genomes Project

Hearing loss (HL) is one of the most common sensory impairments in humans. It has a prevalence of around 1/500 in new-borns and is extremely heterogeneous, with an estimated 80% of prelingual HL cases having a genetic cause. Nevertheless, the diagnostic yield remains only between 20-30% depending on methods used. This PhD thesis, therefore, investigates novel analyses designed to improve the diagnosis of HL in patients recruited to the 100,000 Genomes Project (100KGP) and enable novel gene discovery. The first step was to identify and carefully define HL cohorts within the 100KGP. Studies were then performed to identify likely causal variants, using a variety of novel genomic approaches. Family-based analyses in previously undiagnosed patients were performed using enhanced bioinformatic pipelines lead to the identification of likely causal variants in BCAP31, MAGEL2 and PUS7, together with CNVs encompassing multiple genes. Development of a CRISPR-edited mouse Bcap31 mutant was used to validate the human missense mutation. In addition, several cohort-based analyses were performed, including the use of a gene burden test on the 100KGP HL cohorts. This gene agnostic approach was designed to detect the enrichment of rare, predicted pathogenic, segregating variants in cases compared to controls. This led to the identification of 18 known and 22 novel genes with significant disease-gene associations including ADAMTS4, AIM2, LAMC1, RSPO3 and TBX2. Next, a cohort-wide gene driven approach was investigated, performing a cross-cohort and cross-species comparison for associated genetic loci, first in mice with HL phenotypes identified by the International Mouse Phenotyping Consortium. Then, using similar methods, candidate loci from a GWAS study on UK Biobank data for self-reported adult hearing difficulty identified genes including NEDD4L, NISCH, KLHDC7B and CTBP2. Collectively, the results presented in this thesis provide important new insights for patients with HL, their clinical management and future research in this field

Translational profiling of mouse dopaminoceptive neurons reveals region-specific gene expression, exon usage, and striatal PGE2 modulatory effects

International audienceForebrain dopamine-sensitive (dopaminoceptive) neurons play a key role in movement, action selection, motivation, and working memory. Their activity is altered in Parkinson's disease, addiction, schizophrenia, and other conditions, and drugs that stimulate or antagonize dopamine receptors have major therapeutic applications. Yet, similarities and differences between the various neuronal populations sensitive to dopamine have not been systematically explored. To characterize them, we compared translating mRNAs in the dorsal striatum and nucleus accumbens neurons expressing D1 or D2 dopamine receptor and prefrontal cortex neurons expressing D1 receptor. We identified genome-wide cortico-striatal, striatal D1/D2 and dorso/ventral differences in the translating mRNA and isoform landscapes, which characterize dopaminoceptive neuronal populations. Expression patterns and network analyses identified novel transcription factors with presumptive roles in these differences. Prostaglandin E2 (PGE2) was a candidate upstream regulator in the dorsal striatum. We pharmacologically explored this hypothesis and showed that misoprostol, a PGE2 receptor agonist, decreased the excitability of D2 striatal projection neurons in slices, and diminished their activity in vivo during novel environment exploration. We found that misoprostol also modulates mouse behavior including by facilitating reversal learning. Our study provides powerful resources for characterizing dopamine target neurons, new information about striatal gene expression patterns and regulation. It also reveals the unforeseen role of PGE2 in the striatum as a potential neuromodulator and an attractive therapeutic target

Translational profiling of mouse dopaminoceptive neurons reveals region-specific gene expression, exon usage, and striatal prostaglandin E2 modulatory effects

Forebrain dopamine-sensitive (dopaminoceptive) neurons play a key role in movement, action selection, motivation, and working memory. Their activity is altered in Parkinson's disease, addiction, schizophrenia, and other conditions, and drugs that stimulate or antagonize dopamine receptors have major therapeutic applications. Yet, similarities and differences between the various neuronal populations sensitive to dopamine have not been systematically explored. To characterize them, we compared translating mRNAs in the dorsal striatum and nucleus accumbens neurons expressing D1 or D2 dopamine receptor and prefrontal cortex neurons expressing D1 receptor. We identified genome-wide cortico-striatal, striatal D1/D2 and dorso/ventral differences in the translating mRNA and isoform landscapes, which characterize dopaminoceptive neuronal populations. Expression patterns and network analyses identified novel transcription factors with presumptive roles in these differences. Prostaglandin E2 (PGE2) was a candidate upstream regulator in the dorsal striatum. We pharmacologically explored this hypothesis and showed that misoprostol, a PGE2 receptor agonist, decreased the excitability of D2 striatal projection neurons in slices, and diminished their activity in vivo during novel environment exploration. We found that misoprostol also modulates mouse behavior including by facilitating reversal learning. Our study provides powerful resources for characterizing dopamine target neurons, new information about striatal gene expression patterns and regulation. It also reveals the unforeseen role of PGE2 in the striatum as a potential neuromodulator and an attractive therapeutic target

Missense variants in RPH3A cause defects in excitatory synaptic function and are associated with a clinically variable neurodevelopmental disorder

PURPOSE: RPH3A encodes a protein involved in the stabilization of GluN2A subunit of NMDA-type glutamate receptors at the cell surface, forming a complex essential for synaptic plasticity and cognition. We investigated the effect of variants in RPH3A in patients with neurodevelopmental disorders (NDDs). METHODS: By using trio-based exome sequencing, GeneMatcher, and screening of 100,000 Genomes Project data, we identified six heterozygous variants in RPH3A. In silico and in vitro models, including rat hippocampal neuronal cultures, have been used to characterize the effect of the variants. RESULTS: Four cases had a NDD with untreatable epileptic seizures [p.(Gln73His)dn; p.(Arg209Lys); p.(Thr450Ser)dn; p.(Gln508His)], and two cases [p.(Arg235Ser); p.(Asn618Ser)dn] showed high-functioning autism spectrum disorder (ASD). Using neuronal cultures, we demonstrated that p.(Thr450Ser) and p.(Asn618Ser) reduce the synaptic localization of GluN2A; p.(Thr450Ser) also increased the surface levels of GluN2A. Electrophysiological recordings showed increased GluN2A-dependent NMDAR currents for both variants, and alteration of postsynaptic calcium levels. Finally, expression of the Rph3AThr450Ser variant in neurons affected dendritic spine morphology. CONCLUSION: Overall, we provide evidence that missense gain of function variants in RPH3A increase GluN2A-containing NMDARs at extrasynaptic sites, altering synaptic function and leading to a clinically variable neurodevelopmental presentation ranging from untreatable epilepsy to ASD

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery

Rare disease gene association discovery from burden analysis of the 100,000 Genomes Project data

To discover rare disease-gene associations, we developed a gene burden analytical framework and applied it to rare, protein-coding variants from whole genome sequencing of 35,008 cases with rare diseases and their family members recruited to the 100,000 Genomes Project (100KGP). Following in silico triaging of the results, 88 novel associations were identified including 38 with existing experimental evidence. We have published the confirmation of one of these associations, hereditary ataxia with UCHL1 , and independent confirmatory evidence has recently been published for four more. We highlight a further seven compelling associations: hypertrophic cardiomyopathy with DYSF and SLC4A3 where both genes show high/specific heart expression and existing associations to skeletal dystrophies or short QT syndrome respectively; monogenic diabetes with UNC13A with a known role in the regulation of β cells and a mouse model with impaired glucose tolerance; epilepsy with KCNQ1 where a mouse model shows seizures and the existing long QT syndrome association may be linked; early onset Parkinson's disease with RYR1 with existing links to tremor pathophysiology and a mouse model with neurological phenotypes; anterior segment ocular abnormalities associated with POMK showing expression in corneal cells and with a zebrafish model with developmental ocular abnormalities; and cystic kidney disease with COL4A3 showing high renal expression and prior evidence for a digenic or modifying role in renal disease. Confirmation of all 88 associations would lead to potential diagnoses in 456 molecularly undiagnosed cases within the 100KGP, as well as other rare disease patients worldwide, highlighting the clinical impact of a large-scale statistical approach to rare disease gene discovery. </p

100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report.

BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection. METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis. RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives. CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.)