HGG Advances最新文献

Gene-environment interactions may enhance our understanding of blood pressure (BP) biology. We conducted a meta-analysis of multi-population genome-wide association studies (GWASs) of BP traits accounting for gene-depressive symptomatology (DEPR) interactions. Our study included 564,680 adults from 67 cohorts and four population backgrounds: African (5%), Asian (7%), European (85%), and Hispanic (3%). We discovered seven previously unreported BP loci showing gene-DEPR interaction. These loci mapped to genes implicated in neurogenesis (TGFA and CASP3), lipid metabolism (ACSL1), neuronal apoptosis (CASP3), and synaptic activity (CNTN6 and DBI). We also showed evidence for gene-DEPR interaction at nine known BP loci, further suggesting links between mood disturbance and BP regulation. Of the 16 identified loci, 11 were derived from non-European populations. Post-GWAS analyses prioritized 36 genes, including genes involved in synaptic functions (DOCK4 and MAGI2) and neuronal signaling (CCK, UGDH, and SLC01A2). Integrative druggability analyses identified 11 druggable candidate gene targets linked to pathways involved in mood disorders as well as known anti-hypertensive drugs. Our findings emphasize the importance of considering gene-DEPR interactions on BP, particularly in non-European populations. Our prioritized genes and druggable targets highlight biological pathways connecting mood disorders and hypertension and suggest opportunities for BP drug repurposing and risk factor prevention, especially in individuals with DEPR.

Bi-allelic mutations in EEFSEC, a key factor in selenoprotein synthesis, cause a severe human selenopathy characterized by developmental delay, spasticity, and profound cerebellar atrophy. While previous studies in invertebrate models framed this condition as an early-onset neurodegenerative disorder, the contribution of primary developmental defects to the severe brain malformations in patients has remained a critical unanswered question. Here, we address this gap using a zebrafish model of EEFSEC deficiency. We discovered that loss of eefsec function does not impair global somatic growth but instead causes specific and significant hypoplasia of the midbrain and hindbrain-the embryonic precursors to the human cerebellum and brain stem. These structural defects directly correlate with robust behavioral impairments, including diminished locomotion and blunted escape responses, mirroring the severe motor dysfunction in patients. Critically, our findings provide the in vivo evidence from a vertebrate model that this disorder involves a primary neurodevelopmental defect, which underlies the severe brain malformations and creates a structurally vulnerable nervous system. This establishes a developmental basis for understanding this condition. We propose that this initial failure in brain construction, which we term a developmental selenopathy, creates a structurally vulnerable nervous system, providing a plausible mechanistic explanation for the human phenotype and proposing a framework for understanding this devastating condition.

Characterizing the ancestry of donors in single-cell transcriptomic studies is crucial to ensure genetic homogeneity, reduce biases in analyses, identify ancestry-specific regulatory mechanisms and their downstream roles in disease, and ensure that existing datasets are representative of human genetic diversity. While these datasets are now widely available, information on the ancestry of donors is often missing, hindering further analysis. Here, we propose a framework to evaluate methods for inferring genetic ancestry from genetic polymorphisms detected in single-cell sequencing reads. We demonstrate that widely used tools (e.g., ADMIXTURE) provide accurate inference of genetic ancestry and admixture proportions, despite the limited number of genetic polymorphisms identified and imperfect variant calling from sequencing reads. We infer genetic ancestry for 401 donors from 10 Human Cell Atlas datasets and report a high proportion of donors of European ancestry in this resource. For researchers generating single-cell transcriptomic datasets, we recommend reporting genetic ancestry inference for all donors and generating datasets that represent diverse ancestries.

Humans with pathogenic loss of function alanyl-tRNA synthetase 1 (AARS1) variants have a range of congenital brain phenotypes, including a high prevalence of microcephaly. The molecular mechanisms for this are unclear but zebrafish mutants in aars1 have reduced neurogenesis and increased apoptosis. Here, we report two individuals with compound heterozygous AARS1 variants. We created two mouse models to study the role of Aars1 in embryonic brain development. We provide evidence from these mouse models and in vitro splicing assays that both human AARS1 alleles are pathogenic. Mice homozygous for either a missense allele or an indel allele are both lethal very early in embryonic development. Aars1^G80S/WT heterozygous animals show reduced Purkinje cell immunoreactivity at 8 months of age but no gross morphological cerebellar phenotypes or impaired performance in a motor coordination assay. We conclude these are pathogenic alleles in AARS1 but lethality in mice preclude a detailed study of neural development.

Unsupervised genome-wide ancestry estimation in unrelated individuals has been a staple in population and medical genetics for decades, and its importance continues to grow with the increasing number of large genetic cohorts of mixed ancestries. We propose an extension to the hapla framework that scales model-based ancestry estimation to unprecedented sample sizes by leveraging inferred haplotype clusters from phased genotype data. Our haplotype cluster-based approach is approximately 5× and 20× faster than the fastest model-free and model-based SNP-based approaches, respectively, for unsupervised genome-wide ancestry estimation on the harmonized Human Genome Diversity Project and 1000 Genomes Project dataset. Furthermore, we demonstrate that it is the most accurate method to date in an extensive simulation study, across a range of sample sizes from thousands to hundreds of thousands of individuals. Our accurate ancestry estimates can help reduce health disparities and accelerate precision medicine efforts in the growing number of biobanks globally.

Transcriptome-wide association studies (TWASs) are powerful for identifying gene-trait associations by integrating gene expression and genome-wide association data, but findings may be impacted by the choice of gene expression reference. We performed TWAS of cardiovascular outcomes using multi-tissue and ancestry-matched gene expression references. We used data from the Chronic Renal Insufficiency Cohort Study for participants of African (AFR, n = 1,512) and European (EUR, n = 2,067) ancestry and three outcomes: all-cause stroke, coronary heart disease, and heart failure. We performed TWASs using EUR and AFR predicted gene expression reference panels and multi-tissue TWAS by integrating gene expression from 10 GTEx selected tissues. TWAS identified KDELR2 associated with heart failure in AFR participants using matched AFR reference panel (p = 4.7 × 10^-6), although findings were near significant using the EUR mismatched reference panel (p = 5.6 × 10^-6). PSMC1 was associated with coronary heart disease in TWAS of CRIC EUR using AFR reference panel, and this gene was not present in the EUR-trained gene expression model. Multi-tissue TWASs identified KHDRBS2 significantly associated with all-cause stroke in CRIC AFR participants (p = 4.0 × 10^-6). Variants near KDELR2 have been associated with coronary artery disease, which is a main cause of heart failure, while KHDRBS2 has been associated with cardiovascular risk factors in genome-wide association studies. Our findings highlight differences in gene discovery for TWAS of cardiovascular disease applied to high-risk participants based on participant ancestry and gene expression reference panels, and gains to identify genes compared with traditional genome-wide association approaches.

Loose anagen hair syndrome is a form of childhood-onset non-scarring alopecia marked by easily and painlessly plucking terminal hair during its active growth, or anagen, phase. It is believed to result from poor hair shaft anchoring within the follicle due to premature keratinization. Our study identified a plausibly pathogenic variant in KRT32 (c.296C>T; p.Thr99Ile) that co-segregates with the phenotype in a large family. This study aimed to explore the role of KRT32, previously unassociated with loose anagen hair, in hair anchorage and assess the functional impact of its p.Thr99Ile variant. We hypothesized that the p.Thr99Ile variant reduces the binding affinity of KRT32 to KRT82, disrupting the intermediate filament structure in the hair shaft cuticle and leading to weak anagen hair anchorage. To test this hypothesis, we conducted a protein-protein interaction assay using far-western blotting and performed in silico intermediate filament network segmentation analysis on high-resolution fluorescent microscopy images. Our results showed a decreased binding affinity of KRT32^Thr99Ile to KRT82 when compared to KRT32^WT. There were significant differences in segment count and filament thickness, as measured by brightness, between the KRT32^Thr99Ile and the KRT32^WT. We conclude that the c.296C>T variant of KRT32 is associated with loose anagen hair phenotype.

Lamins A/C, coded by LMNA gene, are crucial for nuclear architecture preservation. Pathogenic LMNA variants cause a wide range of inherited diseases called "laminopathies". A subgroup is referred to "progeroid syndromes" characterized by premature aging and other manifestations including cardiac valve abnormalities. Atypical phenotypes, generally less severe, have also been reported. We report the case of a 26-year-old male with calcific tricuspid aortic and mitral valve diseases. His father was diagnosed with severe aortic valve stenosis and mitral annulus calcification at the age of 38. The goal of this study was to identify the putative variant causing this non-syndromic multivalvular disease. Known disease-causing variants in NOTCH1, FLNA, and DCHS1 were first excluded by Sanger sequencing. Whole-exome sequencing was then performed in five family members. A LMNA variant (p.Glu262Val) was identified with in silico evidences of pathogenicity (CADD [combined annotation dependent depletion] = 33). Cells transfected with the cDNA construct harboring p.Glu262Val were characterized by abnormal nuclear morphology. Along with a literature review, the variant was classified as likely pathogenic. Elucidating the mechanism by which LMNA p.Glu262Val specifically affects cardiac heart valves is likely to provide insight about the pathogenesis of Mendelian forms of valvular heart diseases and may help guide the development of therapies.

Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic risk scores (PRSs) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the variational autoencoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, while being computationally efficient. Through comprehensive experiments, we found that the VAE-PRS model offers the ability to capture interaction effects in high-dimensional data and shows robust performance across different pre-screened variant sets. Furthermore, VAE-PRS is easily interpretable via assessing the contribution of each individual marker to the final prediction score through the Shapley additive explanations method, providing potential new insights in identifying trait-associated genetic variants. In summary, VAE-PRS presents a measure to genetic risk prediction for blood cell traits by harnessing the power of deep learning methods given appropriate training sample size, which could further facilitate the development of personalized medicine and genetic research.

Genome-wide association studies (GWASs) from multiple ancestral populations are increasingly available, offering opportunities to improve the accuracy and equity of polygenic scores (PGSs). Several methods now aim to leverage multiple GWAS sources, but predictive performance and computational efficiency remain unclear, particularly when individual-level tuning data are unavailable. This study evaluates a comprehensive set of PGS methods across African (AFR), East Asian (EAS), and European (EUR) ancestries for 10 complex traits, using summary statistics from the Ugandan Genome Resource, Biobank Japan, UK Biobank, and the Million Veteran Program. Single-source PGSs were derived using methods including DBSLMM, lassosum, LDpred2, MegaPRS, pT + clump, PRS-CS, QuickPRS, and SBayesRC. Multi-source approaches included PRS-CSx, TL-PRS, X-Wing, and combinations of independently optimized single-source scores. All methods were restricted to HapMap3 variants and used linkage disequilibrium reference panels matching the GWAS super population. A key contribution is a novel application of the LEOPARD method to estimate optimal linear combinations of population-specific PGSs using only summary statistics. Analyses were implemented using the open-source GenoPred pipeline. In AFR and EAS populations, PGS combining ancestry-aligned and European GWASs outperformed single-source models. Linear combinations of independently optimized scores consistently outperformed current jointly optimized multi-source methods, while being substantially more computationally efficient. The LEOPARD extension offered a practical solution for tuning these combinations when only summary statistics were available, achieving performance comparable to tuning with individual-level data. These findings highlight a flexible and generalizable framework for multi-source PGS construction. The GenoPred pipeline supports more equitable, accurate, and accessible polygenic prediction.