HGG Advances最新文献

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.

Copy-number variants (CNVs) have been implicated in a variety of neuropsychiatric and cognitive phenotypes. We found that deleterious CNVs are less prevalent in non-European ancestry groups than they are in European ancestry groups of both the UK Biobank (UKBB) and a US replication cohort (SPARK). We also identified specific recurrent CNVs that consistently differ in frequency across ancestry groups in both the UKBB and SPARK. These ancestry-related differences in CNV prevalence present in both an unselected community population and a family cohort enriched with individuals diagnosed with autism spectrum disorder (ASD) strongly suggest that genetic ancestry should be considered when probing associations between CNVs and health outcomes.

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

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.

Heterozygous mutations in SLC40A1, encoding a multi-pass membrane protein of the major facilitator superfamily known as ferroportin 1 (FPN1), are responsible for two distinct hereditary iron-overload diseases: ferroportin disease, which is associated with reduced FPN1 activity (i.e., decrease in cellular iron export), and SLC40A1-related hemochromatosis, which is associated with abnormally high FPN1 activity (i.e., resistance to hepcidin). Here, we report three SLC40A1 missense variants with opposite functional consequences. In cultured cells, the p.Arg40Gln and p.Ser47Phe substitutions partially reduced the ability of FPN1 to export iron and also partially reduced its sensitivity to hepcidin. The p.Ala350Val substitution had more profound effects, resulting in low FPN1 iron egress and weak FPN1/hepcidin interaction. Structural analyses helped to differentiate the first two substitutions, which are predicted to cause local instabilities, and the third, which is thought to prevent critical rigid-body movements that are essential to the iron transport cycle. The phenotypic traits observed in a total of 12 affected individuals are highly suggestive of ferroportin disease. Our findings dismantle the classical dualism of FPN1 loss versus gain of function, highlight some specific and unexpected functions of FPN1 transmembrane helices in the molecular mechanism of iron export and its regulation by hepcidin, and extend the spectrum of rare genetic variants that may cause ferroportin disease.

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.

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.

Approximately 20% of breast cancer cases are attributed to increased family risk, yet variation in BRCA1/2 can only explain 20%-25% of cases. Historically, only single gene or single variant testing were common in at-risk family members, and further sequencing studies were rarely offered after negative results. In this study, we applied an efficient and inexpensive targeted sequencing approach to provide molecular diagnoses in 245 human samples representing 134 BRCA mutation-negative (BRCAX) hereditary breast and ovarian cancer (HBOC) families recruited from 1973 to 2019 by Dr. Henry Lynch. Sequencing identified 391 variants, which were functionally annotated and ranked based on their predicted clinical impact. Known pathogenic CHEK2 breast cancer variants were identified in five BRCAX families in this study. While BRCAX was an inclusion criterion for this study, we still identified a pathogenic BRCA2 variant (p.Met192ValfsTer13) in one family. A portion of BRCAX families could be explained by other hereditary cancer syndromes that increase HBOC risk: Li-Fraumeni syndrome (gene: TP53) and Lynch syndrome (gene: MSH6). Interestingly, many families carried additional variants of undetermined significance (VOUSs) that may further modify phenotypes of syndromic family members. Ten families carried more than one potential VOUS, suggesting the presence of complex multi-variant families. Overall, nine BRCAX HBOC families in our study may be explained by known likely pathogenic/pathogenic variants, and six families carried potential VOUSs, which require further functional testing. To address this, we developed a functional assay where we successfully re-classified one family's PMS2 VOUS as benign.

Aminoacyl-tRNA synthetases (ARSs) are ubiquitously expressed, essential enzymes that complete the first step of protein translation: ligation of amino acids to cognate tRNAs. Genes encoding ARSs have been implicated in myriad dominant and recessive phenotypes, the latter often affecting multiple tissues but with frequent involvement of the central and peripheral nervous systems, liver, and lungs. Threonyl-tRNA synthetase (TARS1) encodes the enzyme that ligates threonine to tRNA^THR in the cytoplasm. To date, TARS1 variants have been implicated in a recessive brittle hair phenotype. To better understand TARS1-related recessive phenotypes, we engineered three TARS1 missense variants at conserved residues and studied these variants in Saccharomyces cerevisiae and Caenorhabditis elegans models. This revealed two loss-of-function variants, including one hypomorphic allele (R433H). We next used R433H to study the effects of partial loss of TARS1 function in a compound heterozygous mouse model (R432H/null). This model presents with phenotypes reminiscent of patients with TARS1 variants and with distinct lung and skin defects. This study expands the potential clinical heterogeneity of TARS1-related recessive disease, which should guide future clinical and genetic evaluations of patient populations.