HGG Advances最新文献

Telomere biology disorders (TBDs) are caused by rare pathogenic variants in telomere maintenance genes and often present with variable penetrance of multi-organ system manifestations. We evaluated a family with 14 individuals heterozygous for TERT c.2591T>C (p.L864P) and 13 non-carriers. TRAP assays showed that p.L864P causes a complete loss of telomerase activity. Carriers had shorter lymphocyte telomeres than non-carriers. Carriers presented different TBD manifestations, but had similar telomere length (TL) distributions, suggesting variable penetrance and possible genetic anticipation. Somatic TERT promoter mutations were detected in four carriers aged >50 years (variant allele fractions <4% in three and 18%-19% in one). Exome sequencing did not identify other variants of interest. Although not statistically significant, polygenic scores derived from common TL-associated genetic variation were lower in c.2591T>C carriers with more TBD clinical manifestations. Alleles associated with alternative TERT splicing, VNTR6-1-Long and rs10069690-T, co-segregated with c.2591T>C. This haplotype was associated with a reduction in TL Z score (β = -1.81, p < 0.0001). Another haplotype, c.2591T, VNTR6-1-Long, and rs10069690-T, demonstrated an independent reduction of TL Z score (β = -0.84, p = 0.0111). The TBD manifestations in this family may relate to common TL-associated genetic variation and alternative TERT splicing, emphasizing the importance of investigations into TBD manifestations within and between TBD families.

Despite advancements in genomics, misconceptions about the extent to which genetics contributes to observable differences across racial groups persist. These misconceptions are often rooted in genetic essentialism, a social-cognitive bias that leads individuals to believe that most complex traits are primarily determined by genetics. This scientifically inaccurate belief overlooks the environmental and social influences on complex human outcomes, reinforcing deterministic views about human diversity. Our study examines how and for whom genetics education can reduce genetic essentialist beliefs using targeted interventions. We use data from a randomized controlled trial collected at a large US West Coast public university in 2023, including 2,061 undergraduate students. Participants were randomly assigned to one of four curriculum-based interventions, ensuring balanced characteristics across conditions. Three interventions were compared: population thinking; multifactorial causation; and a curriculum where we combined both approaches, which we call full Humane Genetics Curriculum. Results are reported relative to a control group that taught students about climate change. Using structural equation modeling, we explore the effectiveness of these interventions with our data. We find that all three interventions reduce genetic essentialist beliefs by decreasing the perception of between-group racial variation and by reducing genetic attributions for complex human traits. We also find that the three intervention curricula are highly effective across sociodemographic group characteristics such as self-reported gender, self-reported race, and cultural/political belief systems. However, the interventions were more effective among students who possessed greater baseline genetics knowledge. Using these findings, we offer evidence-based strategies for curriculum development.

Polyglutamine (polyQ) disorders, such as Huntington disease (HD) and several spinocerebellar ataxias, are severe neurological disorders caused by glutamine codon repeat expansions. These conditions lack effective treatments, with therapeutic research focused on pathogenic gene knockdown. This investigation aimed to profile these genes using diverse human genomic data to inform therapeutic strategies by identifying new biology and assessing the potential on-target effects of knocking down these genes. We conducted an unbiased phenome-wide study to identify human traits and diseases linked to polyQ disorder genes (Open Targets L2G > 0.5). Network analyses explored shared trait associations and overlapping biological processes among these genes. Lastly, we assessed the theoretical druggability of polyQ disorder genes using recently identified features predictive of clinical trial success and compared them with repeat expansion (HD) modifier genes. Our analyses identified 215 human phenotype/polyQ disorder gene associations from 3,095 studies, indicating potential adverse effects from gene knockdown. Shared trait associations among genes suggested overlapping biological processes despite distinct functions. Drug target profile analysis revealed increased safety concerns due to genomic features (i.e., constraint, molecular interactions, and tissue specificity) for polyQ disorder genes, particularly ATN1, ATXN1, ATXN7, and HTT. PolyQ disorder genes also showed significantly more safety-related risks than HD genetic modifier genes (p = 7.03 × 10^-3). In conclusion, our analyses emphasize the pleiotropic nature of polyQ disorder genes, highlighting their potential risks as drug targets. These findings reinforce the importance of exploring alternative therapeutic strategies, such as targeting genetic modifier genes, as well as allele-selective approaches, to mitigate these challenges.

Genome-wide association studies (GWASs) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). Here, we identify SNPs that are associated with alternative splicing (splicing quantitative trait loci [sQTLs]) and gene expression (expression QTLs [eQTLs]) to identify functions for COPD-associated genetic variants. RNA sequencing on whole blood from 3,743 subjects in the COPDGene Study and from lung tissue of 1,241 subjects from the Lung Tissue Research Consortium (LTRC) was analyzed. Associations between all SNPs within 1,000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. We assessed colocalization with COPD-associated SNPs from a published GWAS. After adjustment for multiple statistical testing, we identified 28,110 splice sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood and 58,258 splice sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data and found that 38 genomic windows, corresponding to 33 COPD GWAS loci, had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include Nephronectin (NPNT), F box protein 38 (FBXO38), Hedgehog interacting protein (HHIP), Netrin 4 (NTN4), and Betacellulin (BTC). Overall, a total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide insights into disease mechanisms.

Aminoacyl-tRNA synthetases (ARSs) are essential, ubiquitously expressed enzymes that ligate amino acids to cognate tRNAs in the cytoplasm and mitochondria. To date, seven dimeric ARS enzymes have been implicated in dominant inherited neuropathy, suggesting that tRNA charging-exacerbated by a dominant-negative effect-is a component of the peripheral nervous system (PNS) phenotype. Interestingly, heterozygosity for missense and protein-truncating variants in the gene encoding dimeric, cytoplasmic asparaginyl-tRNA synthetase (NARS1) have been associated with distinct clinical phenotypes where patients present with either an isolated PNS neuropathy or with a complex phenotype that includes both PNS and central nervous system (CNS) features. Thus, NARS1 variants are associated with a spectrum of dominant neurological diseases. Here, we test pathogenic NARS1 variants for dominant-negative properties to determine if this mechanism is a common feature of ARS-related dominant neurological disease. Furthermore, we assess if variable dominant-negative effects explain the observed clinical heterogeneity. We performed yeast complementation assays to test NARS1 variants in isolation, which revealed loss-of-function effects. To test for dominant-negative properties, we co-expressed mutant human NARS1 with wild-type human NARS1. These studies revealed that NARS1 variants interact with the wild-type subunit and that the majority of variants repress the ability of the wild-type allele to support cellular growth, consistent with a dominant-negative effect. Furthermore, our data suggest that NARS1 variants associated with CNS and PNS phenotypes have a more severe dominant-negative effect compared with those associated with an isolated PNS phenotype.

DNA variants affecting pre-mRNA splicing are an important cause of genetic disorders and remain challenging to interpret without experimental data. Although variant classification guidelines recommend experimental characterization of variant splicing effects, the added value of routine diagnostic investigation of patient mRNA splicing has not been systematically described. Here, we assessed the utility of pre-mRNA splicing analysis in a diagnostic setting for 202 suspected splice-altering variants from individuals referred for genetic testing. Pre-mRNA splicing was assessed in patient cells by RT-PCR, followed by agarose gel electrophoresis and Sanger sequencing and/or exon trapping assays. An effect on pre-mRNA splicing was demonstrated in 63% (n = 128/202) of the tested variants. Among the 177 variants initially classified as variants of uncertain significance (VUS), 54% (n = 96/177) were reclassified based on pre-mRNA splicing analysis, including 48% (n = 85/177) that were upgraded to likely pathogenic or pathogenic. We benchmarked the splice prediction algorithms SpliceAI, SQUIRLS, SPiP, and Pangolin, the tools integrated in Alamut on this clinically relevant and experimentally validated dataset, and the CAGI6 splicing VUS dataset and found variable performance dependent on variant type and location. No single tool classified all variants equally well. We describe several examples of hard-to-predict effects and unexpected results highlighting the limitations of prediction tools, including a not previously described variant type affecting U12-splice site subtype. In summary, we provide a framework for RNA-based analysis in a molecular diagnostic setting, demonstrate the added value of routine testing of RNA from individuals with suspected splice-altering variants, and highlight the limitations of in silico prediction tools.

Large biobanks, including the Million Veteran Program (MVP), the UK Biobank, and FinnGen, provide genetic association results for more than 1 million individuals for hundreds of phenotypes. To select targets for pharmaceutical development, as well as to improve the understanding of existing targets, we harmonized these studies and performed two-sample Mendelian randomization (MR) on 2,003 phenotypes using genetic variants associated with gene expression (derived from GTEx and eQTLGen) and plasma protein levels (derived from ARIC, Fenland, and deCODE) as proxies of target modulation. We found 69,669 gene-trait pairs with evidence (p ≤ 1.6 × 10^-9) for causal effects. From the selected gene-trait pairs, we observed 6,447 genes with strong causal evidence for at least one of 2,003 investigated traits. As expected, being identified as a gene-trait pair in our approach was significantly associated with higher odds of being an approved drug target and indication. We were able to rediscover 9% of approved drug targets in ChEMBL 34. Moreover, identified gene-traits were significantly associated with higher odds of being previously described as a gene-trait pair in OMIM, ClinVar, mouse knockout data, and rare variant burden studies. To enhance the translational potential of the resource, we developed a predictive ranking model trained using approved drug targets described in ChEMBL 34 as well as several different biological annotations. This model was able to accurately predict the odds of a particular significant MR result being developed into an approved drug and its clinical indication (precision-recall area under the receiver operating characteristic curve 0.79). We make our results publicly available in CIPHER.

Bi-allelic loss-of-function (LoF) variants in SLC10A7 cause short stature, amelogenesis imperfecta, and skeletal dysplasia with scoliosis (SSASKS). Here, we report findings from an individual of Chinese ancestry with SSASKS carrying compound heterozygous splice-altering SLC10A7 variants: a previously reported pathogenic variant (NM_001029998.6:c.722-16A>G, paternal) and a de novo splice site variant (NM_001029998.6:c.472-1G>T, maternal). In silico predictions, minigene assays, and analyses of RNA and protein from the affected individual revealed that c.472-1G>T causes in-frame exon 7 skipping and c.722-16A>G induces out-of-frame exon 9 skipping. RNA sequencing of blood-derived cells showed that ∼32% residual SLC10A7 function in the affected individual, consistent with the 43% protein accumulation observed by western blot analysis of muscle tissue. These findings indicate that a previously presumed complete LoF allele instead results in partial LoF, prompting a refinement of the genotype-phenotype framework for SLC10A7-related SSASKS. This study highlights the challenges of predicting partial LoF effects, the value of RNA and protein analyses from affected individual-derived tissues, and the importance of distinguishing partial from complete LoF variants in the diagnosis and counseling of recessive disorders.

To advance understanding of cellular heterogeneity in disease from single-cell sequencing data, we introduce residual principal-component analysis (ResidPCA), a robust method for identifying cell states that explicitly models cell-type heterogeneity. In simulations, ResidPCA achieved more than 4-fold higher accuracy than conventional PCA and over 3-fold higher accuracy than non-negative matrix factorization (NMF)-based methods in detecting states expressed across multiple cell types. Applied to single-cell RNA sequencing of light-stimulated mouse visual cortex cells, ResidPCA captured stimulus-driven variability with an accuracy more than 5-fold higher than NMF-based approaches. In single-nucleus datasets from an Alzheimer disease cohort, ResidPCA identified 44 chromatin accessibility-based states from single-nucleus ATAC-seq (snATAC-seq) and 42 transcriptional states from single-nucleus RNA-seq. Thirty snATAC-seq states were significantly enriched for Alzheimer disease heritability, often more so than established cell types such as microglia. The snATAC-seq state most significantly enriched for heritability further elucidates a recently implicated neuron-oligodendrocyte-microglial mechanistic axis, linking early amyloid production in neurons and oligodendrocytes with later microglial activation and immune response. These results highlight the ability of ResidPCA to uncover previously hidden biological variation in single-cell data and reveal disease-relevant cell states.