Human Genetics最新文献

Microtia-anotia is a common congenital anomaly. In most cases, the genetic etiology remains unknown. The proper development of outer ear is closely related to cranial neural crest cells. Abnormal DNA recombination perturbing the function of long-range enhancers can lead to genomic disorder. Previously, we identified 4p16.1 duplications in microtia patients and revealed the enhancer function of an evolutionarily conserved region (ECR). Here we recruited additional patients and attempted to identify the minimal overlapping region and regulatory elements. We identified five individuals (F6-F10 probands) with 4p16.1 duplication. The duplications in F3 and F5 were refined to 192.6 kb and 96.1 kb. Precise junction breakpoints in F4 and F6-F10 were detected. The minimal overlapping region (chr4: 8,689,510-8712,827, hg19) contained conserved sequences in addition to ECR. Dual-luciferase assays detected enhancer activity in the TFAP2C binding and 1794 sequence. We present five additional cases of concha-type microtia with 4p16.1 duplication. The minimal overlapping region contains regulatory elements that function as in-cis tissue-specific modules, regulating downstream gene expression during development of cranial neural crest cell.

Many pathogenic variants implicated in Mendelian diseases impair normal protein function, often through loss-of-function effects, while loss-of-function mutations in tumor suppressor genes commonly contribute to tumorigenesis. However, many disease-causing variants act through gain-of-function or other mechanisms that do not strictly disrupt the protein. Interpreting rare and novel variants remains a major challenge in clinical genomics, highlighting the need for computational tools informed by large, well-curated clinical datasets to reliably distinguish truly deleterious mutations from neutral variation. We developed MutAnt, a mutation meta‑annotator based on machine‑learning. It is trained on a large, clinically relevant dataset of variants using multiple variant properties, including synchronised predictions from other algorithms. MutAnt models demonstrate high F1 and ROC‑AUC scores (0.88-0.99) on hold‑out datasets and provide well‑calibrated probability scores that correlate with functional assays. MutAnt's deleteriousness predictions exhibited correlations with functional scores obtained from deep mutational scanning assays for tumor suppressor proteins BRCA1, PTEN, and p53 (ρ = 0.28-0.61), and with protein stability measurements from computational models. Moreover, MutAnt prediction scores of deleteriousness improved somatic variant calling from RNA sequencing data compared to standard approaches. MutAnt's high performance in distinguishing neutral and protein-disrupting mutations highlights its potential clinical utility in variant classification.

Parkinson's disease is a progressive neurodegenerative disorder characterized by symptoms such as bradykinesia, resting tremors, and muscle rigidity. Although several disease-causing genes of juvenile Parkinson's disease have been reported, the underlying mechanism remains unclear. Here, we identified SCAMP5 as a novel disease-causing gene of Parkinson's disease in a consanguineous family with juvenile Parkinson's disease. Functional studies in PC12 cell lines revealed that SCAMP5 deficiency increased the level of α-synuclein protein and α-synuclein oligomers, leading to increased cell apoptosis and decreased dopamine secretion. SCAMP5 knockdown in SH-SY5Y cells reduces α-synuclein secretion via exosome. Expression of human wild-type SCAMP5 rescued these effects, whereas the R91W mutant SCAMP5 did not. Scamp5a knockout zebrafish showed Parkinson's disease-like phenotypes, including bradykinesia, loss of dopamine neurons and decreased dopamine content in the brain. Transcriptome analysis unveiled upregulated JNK signaling in scamp5a knockout zebrafish, contributing to neuronal apoptosis. Importantly, human SCAMP5 prevented both dopamine neuron loss and bradykinesia in scamp5a knockout zebrafish, suggesting its therapeutic potential in Parkinson's disease. Overall, our findings identify SCAMP5 as a novel disease-causing gene of Parkinson's disease and highlight its neuroprotective role, opening new avenues for Parkinson's disease treatment.

Within-family genome-wide association studies (GWAS) can separate direct genetic effects from non-direct genetic biases introduced by analyses based on unrelated individuals, yet evidence regarding metabolic phenotypes remains sparse. Here, we aim to uncover non-direct genetic effects for metabolic traits and the role of diet in the non-direct genetic mechanism. We conducted family-based GWAS studies on six metabolic traits using data from full siblings (N = 777) and parent-offspring trios (N = 386). We calculated and compared within-family and population-based polygenic score (PGS) associations to identify non-direct genetic effects. Additionally, we assessed the parental indirect genetic effects of diet on offspring's metabolic traits. Within-sibship GWAS analyses were also conducted to evaluate the impact of non-direct genetic effects at the individual variant level. On average, the magnitudes of within-family PGS associations for metabolic traits showed a 35.2% reduction compared to population-based estimates, suggesting the presence of non-direct genetic effects. This discrepancy diminished after accounting for dietary score, indicating that diet is a major source of non-direct genetic effects. Additionally, parental indirect genetic effects of diet were revealed in parent-offspring models. For instance, PGS of parental fat consumption was positively related to the child's blood glucose levels (β: 0.44, 95% CI 0.21-0.67). After excluding non-direct genetic effects, within-sibship GWAS models are more effective at identifying functional genes associated with metabolic traits. Our study showed significant contributions of non-direct genetic effects on metabolic traits and also identified diet as a major source of non-direct genetic effects. These findings underlined the importance of family-based GWAS data in disentangling the genetic effects and gene-environment correlations underlying metabolic traits.

Genomic resources from Tibeto-Burman (TB)-speaking populations are underrepresented in human genome research, limiting the understanding of their evolutionary history and health-related genetic influences. We genotyped 95 individuals, including Baima and Amdo Tibetans from Jiuzhaigou on the eastern Qinghai-Xizang Plateau and Qiang from Mianyang Prefecture in Sichuan Province. These data were jointly analyzed with 1722 genomes from modern and ancient East Asian populations. Clustering patterns revealed by principal component analysis suggested that the Tibetan and Qiang populations formed three distinct genetic clines, which were supported by model-based ADMIXTURE and fineSTRUCTURE analyses, highlighting complex population histories and unique genetic clusters among the Qiang and Tibetan people. Shared genetic drift estimated via f₃/f₄-statistics revealed significant gene flow between the Qiang and Han groups, suggesting that interactions with geographically proximate Han populations likely drove genomic affinity. Comparisons among TB groups (Amdo, Baima, Ü-Tsang, and Qiang) revealed varying levels of genetic affinity with ancient populations, particularly those from the Qinghai-Xizang Plateau and Yellow River Basin. Identity-by-descent and runs of homozygosity analyses indicated the persistence of stable population structures over approximately 2700 years and revealed relative demographic similarities among culturally different Tibetan groups, characterized by smaller effective population sizes than Han groups. Twenty-five high-confidence regions under selection were identified in Tibetans through XP-EHH, PBS, and Fisher score statistics, whereas 28 regions were detected in Qiangs, most of which were first identified here. The Tibetan-specific selection signals included genes related to hypoxia adaptation (e.g., TNNI3K), whereas the Qiang populations presented selection related to skin pigmentation (e.g., SLC44A5) and alcohol metabolism. The results of functional enrichment analyses suggested that the shared and distinct adaptations among these populations involved cardiovascular, metabolic, and immune processes. Overall, our findings reveal the complex genetic structure, population history, and evolutionary adaptations of Tibetan and Qiang populations in northern Sichuan. The results emphasize the role of geographic and historical factors in shaping genetic diversity and adaptive traits, contributing to our understanding of human adaptation to high-altitude environments and UV radiation in East Asia.

Despite two decades since the completion of the human genome, many genes remain poorly understood, with their functions largely unknown. Among these, AHDC1 stands out as a top-ranking gene in the SFARI database due to its role in the rare and likely underestimated neurodevelopmental disorder, Xia-Gibbs syndrome (XIGIS). First identified in 2014 by Prof. Richard A. Gibbs and his team at Baylor College of Medicine, AHDC1 has historically been understudied. Until July 2023, it was classified as a Tdark gene in the Pharos database, reflecting minimal knowledge of its biological function and the lack of molecular tools for its investigation. However, interest in AHDC1 has grown significantly recently as researchers have strived to uncover the mechanisms underlying XIGIS-associated phenotypes. Recognizing these advances, the Pharos database reclassified AHDC1 as a Tbio gene in 2023, acknowledging its rising significance and the expanding body of research surrounding it. This review consolidates the latest findings on AHDC1, providing an in-depth examination of its genetic structure, regulatory mechanisms, and protein functions while exploring its potential roles in nervous system development and beyond. By compiling existing literature and integrating publicly available data, this review aims to illuminate the broader biological relevance of AHDC1 and its implications for human health and disease.

This study aims to assess the genetic burden of fetal congenital diaphragmatic hernia (CDH) and identify prenatal, perinatal, and postnatal predictors to improve early diagnosis, monitoring, and intervention. This study included 130 CDH fetuses who underwent invasive prenatal diagnosis, with fetal prognosis evaluated using imaging parameters such as observed-to-expected lung-to-head ratio (o/e LHR), observed-to-expected total lung volume (o/e TLV), and percent predicted lung volume (PPLV). Clinical outcomes included neonatal outcomes, extracorporeal membrane oxygenation (ECMO) requirement, and post-neonatal prognosis. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to evaluate prognostic indicators and construct predictive models. Chromosomal microarray analysis (CMA) and exome sequencing (ES) yielded diagnostic rates of 7.7% and 8.7%, respectively, identifying a wide spectrum of pathogenic variants and highlighting the genetic heterogeneity of CDH. Among imaging parameters, o/e LHR, o/e TLV, and PPLV were significantly associated with neonatal outcomes, ECMO requirement, and post-neonatal prognosis. Multivariable models incorporating these parameters achieved high predictive accuracy (AUCs > 0.85), with the neonatal outcomes model reaching an AUC of 0.929, sensitivity of 93.2%, and specificity of 78.6%. By integrating genetic, imaging and clinical outcome data, this study identified CMA and ES as key tools for detecting genetic burden in CDH fetuses, and confirmed o/e LHR, o/e TLV, PPLV, and liver herniation as reliable prognostic indicators. Multivariable models based on these parameters showed strong predictive performance. A combined genetic-imaging approach is recommended to support individualized risk assessment and guide perinatal management.

Connexin 26, the protein encoded by the GJB2 (Gap junction protein beta 2) gene, is expressed in different tissues, including the cochlea and skin. Pathogenic DNA alterations in GJB2 cause autosomal recessive nonsyndromic hearing loss, whereas some GJB2 variants may lead to deafness-associated skin disorders. Genes encoding proteins of the Connexin26 molecular complex may fit as candidates to explain genetic hearing loss of yet unknown etiology. In search for Connexin26 direct protein partners, 120 million clones of a human fetal brain cDNA library were screened for interaction with full-length Cx26 in a membrane yeast two-hybrid assay. Each Connexin26-interacting protein was submitted to a pipeline of in-silico characterization yielding a total of 40 direct interactors. It was disclosed that the mouse Gjb2 gene orthologue is coexpressed with 38 (95%) and 28 (70%) of the genes encoding Connexin26 interactors, respectively in specific cochlea cell types and embryonic keratinocytes. Interactors expressed in the organ of Corti supporting cells are significantly enriched in the gene ontology class of proteins with transporter activity (N = 10; 26%), seven of which are ion transporters. Nine interactor-encoding genes are either associated with deafness and/or skin disorders or have chromosomal mapping overlapping non-syndromic hearing loss-related loci. Altogether, the Connexin26 membrane interaction network highlights proteins with biological relevance to the physiology of cochlea and skin.