HGG Advances最新文献

Haploinsufficiency of JAG1 is the primary cause of Alagille syndrome (ALGS), a rare, multisystem disorder. The identification of JAG1 intronic variants outside of the canonical splice region as well as missense variants, both of which lead to uncertain associations with disease, confuses diagnostics. Strategies to determine whether these variants affect splicing include the study of patient RNA or minigene constructs, which are not always available or can be laborious to design, as well as the utilization of computational splice prediction tools. These tools, including SpliceAI and Pangolin, use algorithms to calculate the probability that a variant results in a splice alteration, expressed as a Δ score, with higher Δ scores (>0.2 on a 0-1 scale) positively correlated with aberrant splicing. We studied the consequence of 10 putative splice variants in ALGS patient samples through RNA analysis and compared this to SpliceAI and Pangolin predictions. We identified eight variants with aberrant splicing, seven of which had not been previously validated. Combining these data with non-canonical and missense splice variants reported in the literature, we identified a predictive threshold for SpliceAI and Pangolin with high sensitivity (Δ score >0.6). Moreover, we showed reduced specificity for variants with low Δ scores (<0.2), highlighting a limitation of these tools that results in the misidentification of true splice variants. These results improve genomic diagnostics for ALGS by confirming splice effects for seven variants and suggest that the integration of splice prediction tools with RNA analysis is important to ensure accurate clinical variant classifications.

Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. However, the identification of pathogenic indels from neutral variants in clinical contexts remains an understudied problem. Here, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. INDELpred was established based on key features, including allele frequency, indel length, function-based features, and gene-based features. A set of comprehensive evaluation analyses demonstrated that INDELpred exhibited superior performance over competing methods in terms of computational efficiency and prediction accuracy. Importantly, INDELpred highlighted the crucial role of function-based features in identifying pathogenic indels, with a clear interpretability of the features in understanding the disease-causing variants. We envisage INDELpred as a desirable tool for the detection of pathogenic indels within large-scale genomic datasets, thereby enhancing the precision of genetic diagnoses in clinical settings.

The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the "no horizontal pleiotropy" assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

The effective implementation of whole-exome sequencing- and whole-genome sequencing-based diagnostics in the management of children affected with genetic diseases and the rapid decrease in the cost of next-generation sequencing (NGS) enables the expansion of this method to newborn genetic screening programs. Such NGS-based screening greatly increases the number of diseases that can be detected compared to conventional newborn screening, as the latter is aimed at early detection of a limited number of inborn diseases. Moreover, genetic testing provides new possibilities for family members of the proband, as many variants responsible for adult-onset conditions are inherited from the parents. However, the idea of NGS-based screening in healthy children raises issues of medical and ethical integrity as well as technical questions, including interpretation of the observed variants. Pilot studies have shown that both parents and medical professionals have moved forward and are enthused about these new possibilities. However, either the number of participants or the number of genes studied in previous investigations thus far has been limited to a few hundred, restricting the scope of potential findings. Our current study (NCT05325749) includes 7,000 apparently healthy infants born at our center between February 2021 and May 2023, who were screened for pathogenic variants in 2,350 genes. Clinically significant variants associated with early-onset diseases that can be treated, prevented, or where symptoms can be alleviated with timely introduced symptomatic therapy, were observed in 0.9% of phenotypically normal infants, 2.1% of the screened newborns were found to carry variants associated with reduced penetrance or monogenic diseases of adult-onset and/or variable expressivity, and 0.3% had chromosomal abnormalities. Here, we report our results and address questions regarding the interpretation of variants in newborns who were presumed to be healthy.

Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.

Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by the loss of function of maternal UBE3A. The major cause of AS is a maternal deletion in 15q11.2-q13, and the minor causes are a UBE3A mutation, uniparental disomy (UPD), and imprinting defect (ID). Previous reports suggest that all patients with AS exhibit developmental delay, movement or balance disorders, behavioral characteristics, and speech impairment. In contrast, a substantial number of AS patients with a UBE3A mutation, UPD, or ID were reported not to show these consistent features and to show age-dependent changes in their features. In this study, we investigated 134 patients with AS, including 57 patients with a UBE3A mutation and 48 patients with UPD or ID. Although developmental delay was present in all patients, 20% of patients with AS caused by UPD or ID did not exhibit movement or balance disorders. Differences were also seen in hypopigmentation and seizures, depending on the causes. Moreover, patients with a UBE3A mutation, UPD, or ID tended to show fewer of the specific phenotypes depending on their age. In particular, in patients with UPD or ID, easily provoked laughter and hyperactivity tended to become more pronounced as they aged. Therefore, the clinical features of AS based on cause and age should be understood, and genetic testing should not be limited to patients with the typical clinical features of AS.

Heterozygous de novo or inherited gain-of-function mutations in the MTOR gene cause Smith-Kingsmore syndrome (SKS). SKS is a rare autosomal dominant condition, and individuals with SKS display macrocephaly/megalencephaly, developmental delay, intellectual disability, and seizures. A few dozen individuals are reported in the literature. Here, we report a cohort of 28 individuals with SKS that represent nine MTOR pathogenic variants. We conducted a detailed natural history study and found pathophysiological deficits among individuals with SKS in addition to the common neurodevelopmental symptoms. These symptoms include sleep-wake disturbance, hyperphagia, and hyperactivity, indicative of homeostatic imbalance. To characterize these variants, we developed cell models and characterized their functional consequences. We showed that these SKS variants display a range of mechanistic target of rapamycin (mTOR) activities and respond to the mTOR inhibitor, rapamycin, differently. For example, the R1480_C1483del variant we identified here and the previously known C1483F are more active than wild-type controls and less responsive to rapamycin. Further, we showed that SKS mutations dampened circadian rhythms and low-dose rapamycin improved the rhythm amplitude, suggesting that optimal mTOR activity is required for normal circadian function. As SKS is caused by gain-of-function mutations in MTOR, rapamycin was used to treat several patients. While higher doses of rapamycin caused delayed sleep-wake phase disorder in a subset of patients, optimized lower doses improved sleep. Our study expands the clinical and molecular spectrum of SKS and supports further studies for mechanism-guided treatment options to improve sleep-wake behavior and overall health.

Nuclear pore complexes (NPCs) regulate nucleocytoplasmic transport and are anchored in the nuclear envelope by the transmembrane nucleoporin NDC1. NDC1 is essential for post-mitotic NPC assembly and the recruitment of ALADIN to the nuclear envelope. While no human disorder has been associated to one of the three transmembrane nucleoporins, biallelic variants in AAAS, encoding ALADIN, cause triple A syndrome (Allgrove syndrome). Triple A syndrome, characterized by alacrima, achalasia, and adrenal insufficiency, often includes progressive demyelinating polyneuropathy and other neurological complaints. In this report, diagnostic exome and/or RNA sequencing was performed in seven individuals from four unrelated consanguineous families with AAAS-negative triple A syndrome. Molecular and clinical studies followed to elucidate the pathogenic mechanism. The affected individuals presented with intellectual disability, motor impairment, severe demyelinating with secondary axonal polyneuropathy, alacrima, and achalasia. None of the affected individuals has adrenal insufficiency. All individuals presented with biallelic NDC1 in-frame deletions or missense variants that affect amino acids and protein domains required for ALADIN binding. No other significant variants associated with the phenotypic features were reported. Skin fibroblasts derived from affected individuals show decreased recruitment of ALADIN to the NE and decreased post-mitotic NPC insertion, confirming pathogenicity of the variants. Taken together, our results implicate biallelic NDC1 variants in the pathogenesis of polyneuropathy and a triple A-like disorder without adrenal insufficiency, by interfering with physiological NDC1 functions, including the recruitment of ALADIN to the NPC.

The aim of this work was to identify the underlying genetic cause in a four-generation family segregating an unusual phenotype comprising a severe form of skeletal Class II malocclusion with gingival hyperplasia. SNP array identified a copy number gain on chromosome 1 (chr1); however, this chromosomal region did not segregate correctly in the extended family. Exome sequencing also failed to identify a candidate causative variant but highlighted co-segregating genetic markers on chr17 and chr19. Short- and long-read genome sequencing allowed us to pinpoint and characterize at nucleotide-level resolution a chromothripsis-like complex rearrangement (CR) inserted into the chr17 co-segregating region at the KCNJ2-SOX9 locus. The CR involved the gain of five different regions from chr1 that are shuffled, chained, and inserted as a single block (∼828 kb) at chr17q24.3. The inserted sequences contain craniofacial enhancers that are predicted to interact with KCNJ2/KCNJ16 through neo-topologically associating domain (TAD) formation to induce ectopic activation. Our findings suggest that the CR inserted at chr17q24.3 is the cause of the severe skeletal Class II malocclusion with gingival hyperplasia in this family and expands the panoply of phenotypes linked to variation at the KCNJ2-SOX9 locus. In addition, we highlight a previously overlooked potential role for misregulation of the KCNJ2/KCNJ16 genes in the pathomechanism of gingival hyperplasia associated with deletions and other rearrangements of the 17q24.2-q24.3 region (MIM 135400).

Artificial intelligence (AI)/deep learning (DL) models that predict molecular phenotypes like gene expression directly from DNA sequences have recently emerged. While these models have proven effective at capturing the variation across genes, their ability to explain inter-individual differences has been limited. We hypothesize that the performance gap can be narrowed through the use of pre-trained embeddings from the Nucleotide Transformer, a large foundation model trained on 3,000+ genomes. We train a transformer model using the pre-trained embeddings and compare its predictive performance to Enformer, the current state-of-the-art model, using genotype and expression data from 290 individuals. Our model significantly outperforms Enformer in terms of correlation across individuals, and narrows the performance gap with an elastic net regression approach that uses just the genetic variants as predictors. Although simple regression models have their advantages in personalized prediction tasks, DL approaches based on foundation models pre-trained on diverse genomes have unique strengths in flexibility and interpretability. With further methodological and computational improvements with more training data, these models may eventually predict molecular phenotypes from DNA sequences with an accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.