HGG Advances最新文献

Atopic dermatitis (AD) is a chronic itchy inflammatory disease of the skin. Genetic studies have identified multiple risk factors linked to the disease; however, most of studies have been derived from European and East Asian populations. The admixed genome of African American (AA) may provide an opportunity to discovery ancestry-specific loci involved in AD susceptibility. Herein, we present joint analysis of ancestry and genotype effects followed with validation using differential gene expression analysis on AD using 710 AD cases and 1015 non-AD controls from the AA population, genotyped using MEGA followed by imputation using the CAAPA reference panel. The joint analysis identified two novel AD-susceptibility loci, rs2195989 in gene ANGPT1 (8q23.1) and rs62538818 in the intergenic region LURAP1L-MPDZ (9p23). Admixture mapping (AM) results showed potential genomic inflation, and we implemented genomic control and identified five ancestry-of-origin loci with European ancestry effects. The multi-omics functional prioritization of variants in AM signals prioritized the loci SLAIN2, RNF39, and FOXA2. GWAS identified variants significantly associated with AD in the AA population, including SGK1 (rs113357522, OR = 2.81), EFR3A (rs16904552, OR = 1.725), and MMP14 (rs911912, OR = 1.791). GWAS variants were common in the AA but rare in the European population, which suggests an African ancestry-specific risk of AD. Four genes (ANGPT1, LURAP1L, EFR3A, and SGK1) were further validated using qPCR from AD and healthy skin. This study highlighted the importance of genetic studies on admixed populations, as well as local ancestry and genotype-ancestry joint effects to identify risk loci for AD.

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 if 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 Splice AI 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 to 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 this 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 show 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 integration of splice prediction tools with RNA analysis is important to ensure accurate clinical variant classifications.

The vacuolar H⁺-ATPase (V-ATPase) is a functionally conserved multimeric complex localized at the membranes of many organelles where its proton-pumping action is required for proper lumen acidification. The V-ATPase complex is composed of several subunits, some of which have been linked to human disease. We and others previously reported pathogenic dominantly acting variants in ATP6V1B2, the gene encoding the V1B2 subunit, as underlying a clinically variable phenotypic spectrum including dominant deafness-onychodystrophy (DDOD) syndrome, Zimmermann-Laband syndrome, and deafness, onychodystrophy, osteodystrophy, intellectual disability, and seizures (DOORS) syndrome. Here, we report on an individual with features fitting DOORS syndrome caused by dysregulated ATP6V1C1 function, expand the clinical features associated with ATP6V1B2 pathogenic variants, and provide evidence that these ATP6V1C1/ATP6V1B2 amino acid substitutions result in a gain-of-function mechanism upregulating V-ATPase function that drives increased lysosomal acidification. We demonstrate a disruptive effect of these ATP6V1B2/ATP6V1C1 variants on lysosomal morphology, localization and function, resulting in a defective autophagic flux and accumulation of lysosomal substrates. We also show that the upregulated V-ATPase function affects cilium biogenesis, further documenting pleiotropy. This work identifies ATP6V1C1 as a new gene associated with a neurodevelopmental phenotype resembling DOORS syndrome, documents the occurrence of a phenotypic continuum between ZLS, and DDOD and DOORS syndromes, and classify these conditions as lysosomal disorders.

Identifying causal genes underlying genome-wide association studies (GWAS) is a fundamental problem in human genetics. Although colocalisation with gene expression quantitative trait loci (eQTLs) is often used to prioritise GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analysed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalogue 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 colocalisation methods (coloc.susie, coloc.abf and CLPP) and five Mendelian randomisation (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 colocalisation methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalisation method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalising 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 prioritising novel targets requires triangulation of evidence from multiple sources.

Artificial intelligence/deep learning (AI/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 using 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 someday predict molecular phenotypes from DNA sequences with accuracy surpassing that of regression-based approaches. Our work demonstrates the potential for large pre-trained AI/DL models to advance functional genomics.

Research participants report interest in receiving genetic research results. How best to return results remains unclear. In this randomized pilot study, we sought to assess the feasibility of returning actionable research results through a two-step process including a patient-centered digital intervention as compared to a genetic counselor (GC) in the Penn Medicine biobank. In Step 1, participants with an actionable result and procedural controls (no actionable result) were invited to digital pre-disclosure education and provided options for opting out of results. In Step 2, those with actionable results who had not opted out were randomized to receive results via a digital disclosure intervention or with a GC. Five participants (2%) opted out of results after Step 1. After both steps, 52/113 (46.0%) of eligible cases received results, 5 (4.4%) actively declined results, 34 (30.1%) passively declined and 22 (19.5%) could not be reached. Receiving results was associated with younger age (p<0.001), completing pre-disclosure education (p<0.001) and being in the GC arm (p=0.06). Being older, female, and of Black race were associated with being unable to reach. Older age and Black race were associated with passively declining. 47% of those who received results did not have personal or family history to suggest the mutation, and 55.1% completed clinical confirmation testing. The use of digital tools may be acceptable to participants and could reduce costs of returning results. Low uptake, disparities in uptake, and barriers to confirmation testing will be important to address to realize the benefit of returning actionable research results.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder (NDD) that affects approximately 4% of males and 1% of females in the United States. While causes of ASD are multi-factorial, single rare genetic variants contribute to around 20% of cases. Here we report a case series of seven unrelated probands (6 males, 1 female) with ASD or another variable NDD phenotype attributed to de novo heterozygous loss of function or missense variants in the gene LARP1. LARP1 encodes an RNA binding protein that post-transcriptionally regulates the stability and translation of thousands of mRNAs, including those regulating cellular metabolism and metabolic plasticity. Using lymphocytes collected and immortalized from an index proband who carries a truncating variant in one allele of LARP1, we demonstrated lower cellular levels of LARP1 protein causing reduced rates of aerobic respiration and glycolysis. As expression of LARP1 increases during neurodevelopment, with higher levels in neurons and astrocytes, we propose that LARP1 haploinsufficiency contributes to ASD or related NDDs through attenuated metabolic activity in the developing fetal brain.

A novel algorithm, AlphaMissense, has been shown to have an improved ability to predict the pathogenicity of rare missense genetic variants. However, it is not known whether AlphaMissense improves the ability of gene-based testing to identify disease-influencing genes. Using whole-exome sequencing data from the UK Biobank, we compared gene-based association analysis strategies including sets of deleterious variants: predicted loss-of-function (pLoF) variants only, pLoF plus AlphaMissense pathogenic variants, pLoF with missense variants predicted to be deleterious by any of five commonly utilized annotation methods (Missense (1/5)) or only variants predicted to be deleterious by all five methods (Missense (5/5)). We measured performance to identify 519 previously identified positive control genes, which can lead to Mendelian diseases, or are the targets of successfully developed medicines. These strategies identified 0.85 million pLoF variants and 5 million deleterious missense variants, including 22,131 likely pathogenic missense variants identified exclusively by AlphaMissense. The gene-based association tests found 608 significant gene associations (at p < 1.25 × 10^-7) across 24 common traits and diseases. Compared with pLoFs plus Missense (5/5), tests using pLoFs and AlphaMissense variants found slightly more significant gene-disease and gene-trait associations, albeit with a marginally lower proportion of positive control genes. Nevertheless, their overall performance was similar. Merging AlphaMissense with Missense (5/5), whether through their intersection or union, did not yield any further enhancement in performance. In summary, employing AlphaMissense to select deleterious variants for gene-based testing did not improve the ability to identify genes that are known to influence disease.

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.

The majority of human genomic research studies have been conducted in European-ancestry cohorts, reducing the likelihood of detecting potentially novel and globally impactful findings. Here, we present mid-pass whole-genome sequencing data and a genome-wide association study in a cohort of 264 self-reported Malagasy individuals from three locations on the island of Madagascar. We describe genetic variation in this Malagasy cohort, providing insight into the shared and unique patterns of genetic variation across the island. We observe phenotypic variation by location, and find high rates of hypertension particularly in the Southern Highlands sampling site as well as elevated self-reported malaria prevalence in the West Coast site relative to other sites. After filtering to a subset of 214 minimally-related individuals, we find a number of genetic associations with body composition traits, including many variants that are only observed in African populations or populations with admixed African ancestry from the 1000 Genomes Project. This study highlights the importance of including diverse populations in genomic research for the potential to gain novel insights, even with small cohort sizes. This project was conducted in partnership and consultation with local stakeholders in Madagascar and serves as an example of genomic research that prioritizes community engagement and that has potential impacts on our understanding of human health and disease.