HGG Advances最新文献

Genome-wide association studies (GWASs) have been successful at finding associations between genetic variants and human traits, including the immune-mediated diseases (IMDs). However, the requirement of large sample sizes for discovery poses a challenge for learning about less common diseases, where increasing volunteer numbers might not be feasible. An example of this is myositis (or idiopathic inflammatory myopathies [IIM]s), a group of rare, heterogeneous autoimmune diseases affecting skeletal muscle and other organs, severely impairing life quality. Here, we applied a feature engineering method to borrow information from larger IMD GWASs to find new genetic associations with IIM and its subgroups. Combining this approach with two clustering methods, we found 17 IMDs genetically close to IIM, including some common comorbid conditions, such as systemic sclerosis and Sjögren's syndrome, as well as hypo- and hyperthyroidism. All IIM subtypes were genetically similar within this framework. Next, we colocalized IIM signals that overlapped IMD signals, and found seven potentially novel myositis associations mapped to immune-related genes, including BLK, IRF5/TNPO3, and ITK/HAVCR2, implicating a role for both B and T cells in IIM. This work proposes a new paradigm of genetic discovery in rarer diseases by leveraging information from more common IMD, and can be expanded to other conditions and traits beyond IMD.

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 with 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 of 113 (46.0%) 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. Forty-seven percent 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.

Heterotaxy is a disorder characterized by severe congenital heart defects (CHDs) and abnormal left-right patterning in other thoracic or abdominal organs. Clinical and research-based genetic testing has previously focused on evaluation of coding variants to identify causes of CHDs, leaving non-coding causes of CHDs largely unknown. Variants in the transcription factor zinc finger of the cerebellum 3 (ZIC3) cause X-linked heterotaxy. We identified an X-linked heterotaxy pedigree without a coding variant in ZIC3. Whole-genome sequencing revealed a deep intronic variant (ZIC3 c.1224+3286A>G) predicted to alter RNA splicing. An in vitro minigene splicing assay confirmed the variant acts as a cryptic splice acceptor. CRISPR-Cas9 served to introduce the ZIC3 c.1224+3286A>G variant into human embryonic stem cells demonstrating pseudoexon inclusion caused by the variant. Surprisingly, Sanger sequencing of the resulting ZIC3 c.1224+3286A>G amplicons revealed several isoforms, many of which bypass the normal coding sequence of the third exon of ZIC3, causing a disruption of a DNA-binding domain and a nuclear localization signal. Short- and long-read mRNA sequencing confirmed these initial results and identified additional splicing patterns. Assessment of four isoforms determined abnormal functions in vitro and in vivo while treatment with a splice-blocking morpholino partially rescued ZIC3. These results demonstrate that pseudoexon inclusion in ZIC3 can cause heterotaxy and provide functional validation of non-coding disease causation. Our results suggest the importance of non-coding variants in heterotaxy and the need for improved methods to identify and classify non-coding variation that may contribute to CHDs.

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.

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 the studies have been derived from European and East Asian populations. The admixed African American (AA) genome may provide an opportunity to discovery ancestry-specific loci involved in AD susceptibility. Herein, we present joint analysis of ancestry and genotype effects followed by validation using differential gene expression analysis on AD using 726 AD-affected individuals and 999 non-AD control individuals from the AA population, genotyped using Multi-Ethnic Global Array (MEGA) followed by imputation using the Consortium on Asthma among African Ancestry Populations in the Americas (CAAPA) reference panel. The joint analysis identified two novel AD-susceptibility loci, rs2195989 in gene ANGPT1 (8q23.1) and rs62538818 in the intergenic region between genes LURAP1L and 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. Genome-wide association study (GWAS) identified variants significantly associated with AD in the AA population, including SGK1 (rs113357522, odds ratio [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.

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 (ZLS), 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.

Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (<60 years) cases with no reported clinical risk factors (n = 378), compared to 0.24% of controls (odds ratio [OR] = 12.3, p = 1.27 × 10^-10). Incorporation of the results of either functional assays or protein modeling led to a pronounced increase in effect size (OR_max = 46.5, p = 1.74 × 10^-15). Association signals for the X-chromosomal gene TLR7 were also detected in the female-only subgroup, suggesting the existence of additional mechanisms beyond X-linked recessive inheritance in males. Additionally, supporting evidence was generated for a contribution to severe COVID-19 of the previously implicated genes IFNAR2, IFIH1, and TBK1. Our results refine the genetic contribution of rare TLR7 variants to severe COVID-19 and strengthen evidence for the etiological relevance of genes in the interferon signaling pathway.

The placenta, a pivotal player in the prenatal environment, holds crucial insights into early developmental pathways and future health outcomes. In this study, we explored genetic molecular regulation in chorionic villus samples (CVS) from the first trimester and placenta tissue at birth. We assessed quantitative trait locus (QTL) mapping on DNA methylation and gene expression data in a Finnish cohort of 574 individuals. We found more QTLs in birth placenta than in first-trimester placenta. Nevertheless, a substantial amount of associations overlapped in their effects and showed consistent direction in both tissues, with increasing molecular genetic effects from early pregnancy to birth placenta. The identified QTLs in birth placenta were most enriched in genes with placenta-specific expression. Conducting a phenome-wide-association study (PheWAS) on the associated SNPs, we observed numerous overlaps with genome-wide association study (GWAS) hits (spanning 57 distinct traits and 23 SNPs), with notable enrichments for immunological, skeletal, and respiratory traits. The QTL-SNP rs1737028 (chr6:29737993) presented with the highest number of GWAS hits. This SNP was related to HLA-G expression via DNA methylation and was associated with various immune, respiratory, and psychiatric traits. Our findings implicate increasing genetic molecular regulation during the course of pregnancy and support the involvement of placenta gene regulation, particularly in immunological traits. This study presents a framework for understanding placenta-specific gene regulation during pregnancy and its connection to health-related traits.

Rare genetic diseases (RGDs) affect a significant number of individuals, particularly in pediatric populations. This study investigates the efficacy of identifying RGD diagnoses through electronic health records (EHRs) and natural language processing (NLP) tools, and analyzes the prevalence of identified RGDs for potential underdiagnosis at Cincinnati Children's Hospital Medical Center (CCHMC). EHR data from 659,139 pediatric patients at CCHMC were utilized. Diagnoses corresponding to RGDs in Orphanet were identified using rule-based and machine learning-based NLP methods. Manual evaluation assessed the precision of the NLP strategies, with 100 diagnosis descriptions reviewed for each method. The rule-based method achieved a precision of 97.5% (95% CI: 91.5%, 99.4%), while the machine-learning-based method had a precision of 73.5% (95% CI: 63.6%, 81.6%). A manual chart review of 70 randomly selected patients with RGD diagnoses confirmed the diagnoses in 90.3% (95% CI: 82.0%, 95.2%) of cases. A total of 37,326 pediatric patients were identified with 977 RGD diagnoses based on the rule-based method, resulting in a prevalence of 5.66% in this population. While a majority of the disorders showed a higher prevalence at CCHMC compared with Orphanet, some diseases, such as 1p36 deletion syndrome, indicated potential underdiagnosis. Analyses further uncovered disparities in RGD prevalence and age of diagnosis across gender and racial groups. This study demonstrates the utility of employing EHR data with NLP tools to systematically investigate RGD diagnoses in large cohorts. The identified disparities underscore the need for enhanced approaches to guarantee timely and accurate diagnosis and management of pediatric RGDs.