Clustering of predicted loss-of-function variants in genes linked with monogenic disease can explain incomplete penetrance.

BACKGROUND: Genetic variants that severely alter protein products (e.g. nonsense, frameshift) are often associated with disease. For some genes, these predicted loss-of-function variants (pLoFs) are observed throughout the gene, whilst in others, they occur only at specific locations. We hypothesised that, for genes linked with monogenic diseases that display incomplete penetrance, pLoF variants present in apparently unaffected individuals may be limited to regions where pLoFs are tolerated. To test this, we investigated whether pLoF location could explain instances of incomplete penetrance of variants expected to be pathogenic for Mendelian conditions. METHODS: We used exome sequence data in 454,773 individuals in the UK Biobank (UKB) to investigate the locations of pLoFs in a population cohort. We counted numbers of unique pLoF, missense, and synonymous variants in UKB in each quintile of the coding sequence (CDS) of all protein-coding genes and clustered the variants using Gaussian mixture models. We limited the analyses to genes with ≥ 5 variants of each type (16,473 genes). We compared the locations of pLoFs in UKB with all theoretically possible pLoFs in a transcript, and pathogenic pLoFs from ClinVar, and performed simulations to estimate the false-positive rate of non-uniformly distributed variants. RESULTS: For most genes, all variant classes fell into clusters representing broadly uniform variant distributions, but genes in which haploinsufficiency causes developmental disorders were less likely to have uniform pLoF distribution than other genes (P < 2.2 × 10-6). We identified a number of genes, including ARID1B and GATA6, where pLoF variants in the first quarter of the CDS were rescued by the presence of an alternative translation start site and should not be reported as pathogenic. For other genes, such as ODC1, pLoFs were located approximately uniformly across the gene, but pathogenic pLoFs were clustered only at the end, consistent with a gain-of-function disease mechanism. CONCLUSIONS: Our results suggest the potential benefits of localised constraint metrics and that the location of pLoF variants should be considered when interpreting variants.

Genetic modifiers of rare variants in monogenic developmental disorder loci.

Rare damaging variants in a large number of genes are known to cause monogenic developmental disorders (DDs) and have also been shown to cause milder subclinical phenotypes in population cohorts. Here, we show that carrying multiple (2-5) rare damaging variants across 599 dominant DD genes has an additive adverse effect on numerous cognitive and socioeconomic traits in UK Biobank, which can be partially counterbalanced by a higher educational attainment polygenic score (EA-PGS). Phenotypic deviators from expected EA-PGS could be partly explained by the enrichment or depletion of rare DD variants. Among carriers of rare DD variants, those with a DD-related clinical diagnosis had a substantially lower EA-PGS and more severe phenotype than those without a clinical diagnosis. Our results suggest that the overall burden of both rare and common variants can modify the expressivity of a phenotype, which may then influence whether an individual reaches the threshold for clinical disease.

Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders.

Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.

Pathogenic variants associated with speech/cognitive delay and seizures affect genes with expression biases in excitatory neurons and microglia in developing human cortex.

Background & Objective: Congenital brain malformations and neurodevelopmental disorders (NDDs) are common pediatric neurological disorders and result in chronic disability. With the expansion of genetic testing, new etiologies for NDDs are continually uncovered, with as many as one third attributable to single-gene pathogenic variants. While our ability to identify pathogenic variants has continually improved, we have little understanding of the underlying cellular pathophysiology in the nervous system that results from these variants. We therefore integrated phenotypic information from subjects with monogenic diagnoses with two large, single-nucleus RNA-sequencing (snRNAseq) datasets from human cortex across developmental stages in order to investigate cell-specific biases in gene expression associated with distinct neurodevelopmental phenotypes. Methods: Phenotypic data was gathered from 1) a single-institution cohort of 84 neonates with pathogenic single-gene variants referred to Duke Pediatric Genetics, and 2) a cohort of 4,238 patients with neurodevelopmental disorders and pathogenic single-gene variants enrolled in the Deciphering Developmental Disorders (DDD) study. Pathogenic variants were grouped into genesets by neurodevelopmental phenotype and geneset expression across cortical cell subtypes was compared within snRNAseq datasets from 86 human cortex samples spanning the 2nd trimester of gestation to adulthood. Results: We find that pathogenic variants associated with speech/cognitive delay or seizures involve genes that are more highly expressed in cortical excitatory neurons than variants in genes not associated with these phenotypes (Speech/cognitive: p=2.25×10-7; Seizures: p=7.97×10-12). A separate set of primarily rare variants associated with speech/cognitive delay or seizures, distinct from those with excitatory neuron expression biases, demonstrated expression biases in microglia. We also found that variants associated with speech/cognitive delay and an excitatory neuron expression bias could be further parsed by the presence or absence of comorbid seizures. Variants associated with speech/cognitive delay without seizures tended to involve calcium regulatory pathways and showed greater expression in extratelencephalic neurons, while those associated with speech/cognitive delay with seizures tended to involve synaptic regulatory machinery and an intratelencephalic neuron expression bias (ANOVA by geneset p<2×10-16). Conclusions: By combining extensive phenotype datasets from subjects with neurodevelopmental disorders with massive human cortical snRNAseq datasets across developmental stages, we identified cell-specific expression biases for genes in which pathogenic variants are associated with speech/cognitive delay and seizures. The involvement of genes with enriched expression in excitatory neurons or microglia highlights the unique role both cell types play in proper sculpting of the developing brain. Moreover, this information begins to shed light on distinct cortical cell types that are more likely to be impacted by pathogenic variants and that may mediate the symptomatology of resulting neurodevelopmental disorders.

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.

BACKGROUND: One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes. METHODS: We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues. RESULTS: We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism. CONCLUSIONS: Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

Guidance for estimating penetrance of monogenic disease-causing variants in population cohorts.

Penetrance is the probability that an individual with a pathogenic genetic variant develops a specific disease. Knowing the penetrance of variants for monogenic disorders is important for counseling of individuals. Until recently, estimates of penetrance have largely relied on affected individuals and their at-risk family members being clinically referred for genetic testing, a 'phenotype-first' approach. This approach substantially overestimates the penetrance of variants because of ascertainment bias. The recent availability of whole-genome sequencing data in individuals from very-large-scale population-based cohorts now allows 'genotype-first' estimates of penetrance for many conditions. Although this type of population-based study can underestimate penetrance owing to recruitment biases, it provides more accurate estimates of penetrance for secondary or incidental findings. Here, we provide guidance for the conduct of penetrance studies to ensure that robust genotypes and phenotypes are used to accurately estimate penetrance of variants and groups of similarly annotated variants from population-based studies.