American journal of human genetics最新文献

Transcriptome-wide association studies (TWASs) have been developed to identify candidate genes associated with complex traits by integrating genome-wide association studies (GWASs) with expression quantitative trait loci (eQTL) data. However, most existing TWAS methods assess the marginal association between a single gene and a trait of interest, ignoring the influence of other genes in the same genomic region. Furthermore, false-positive gene-trait associations may arise due to correlations between eQTLs and nearby causal genetic variants. We introduce TWASKnockoff, a knockoff-based framework for detecting susceptibility genes using GWAS summary statistics and eQTL data. Unlike traditional TWAS approaches that rely on marginal testing, TWASKnockoff evaluates the conditional independence of each gene-trait pair, accounting for both cis-predicted expression correlations across genes and correlations between gene expression levels and genetic variants. TWASKnockoff estimates the correlation matrix of all genetic elements (including cis-predicted gene expression levels and genetic variant genotypes) by averaging estimations from parametric bootstrap samples, then applies knockoff-based inference to identify susceptibility genes while controlling the false discovery rate (FDR). Through simulations and an application to type 2 diabetes mellitus (T2D) data, we demonstrate that TWASKnockoff achieves superior FDR control and enhances power in detecting relevant gene-trait pairs at a fixed FDR level.

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease for which important subtypes are caused by variation in superoxide dismutase 1 (SOD1). Diagnosis based on SOD1 sequencing can not only be definitive but can also indicate specific therapies available for SOD1-associated ALS (SOD1-ALS). Unfortunately, SOD1-ALS diagnosis is limited by the fact that a substantial fraction (currently 26%) of ClinVar SOD1 missense variants are classified as "variants of uncertain significance" (VUSs). Although functional assays can provide strong evidence for clinical variant interpretation, SOD1 assay validation is challenging given the current incomplete and controversial understanding of SOD1-ALS disease mechanism. Using saturation mutagenesis and multiplexed cell-based assays, we measured the functional impact of over 2,000 SOD1 amino acid substitutions on both enzymatic function and protein abundance. The resulting "missense variant-effect maps" not only reflect prior biochemical knowledge of SOD1 but also provide sequence-structure-function insights. Importantly, our variant-abundance assay can discriminate pathogenic missense variation and provides new evidence for 41% of missense variants that had been previously reported as VUSs, offering the potential to identify additional people who would benefit from therapy approved for SOD1-ALS.

RNA sequencing has improved the diagnostic yield of individuals with rare diseases. Current analyses predominantly focus on identifying outliers in single genes that can be attributed to cis-acting variants within the gene locus. This approach overlooks causal variants with trans-acting effects on splicing transcriptome wide, such as variants impacting spliceosome function. We present a transcriptomics-first method to diagnose individuals with rare diseases by examining transcriptome-wide patterns of splicing outliers. Using splicing outlier detection methods (FRASER and FRASER2), we characterized splicing outliers from whole blood for 385 individuals from the Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) and Undiagnosed Diseases Network (UDN) consortia. We examined all individuals for excess intron retention outliers in minor intron-containing genes (MIGs). Minor introns, which account for 0.5% of all introns in the human genome, are removed by small nuclear RNAs (snRNAs) in the minor spliceosome. This approach identified five individuals with excess intron retention outliers in MIGs, all of whom were found to harbor rare, bi-allelic variants in minor spliceosome snRNAs. Four individuals had rare, compound heterozygous variants in RNU4ATAC, which aided the reclassification of four variants. Additionally, one individual had rare, highly conserved, compound heterozygous variants in RNU6ATAC that may disrupt the formation of the catalytic spliceosome, suggesting it is a gene associated with Mendelian disease. These results demonstrate that examining RNA-sequencing data for transcriptome-wide signatures can increase the diagnostic yield of individuals with rare diseases, provide variant-to-function interpretation of spliceopathies, and uncover gene-disease associations.

Inherited retinal diseases (IRDs) are rare disorders, typically presenting as Mendelian traits, that result in stationary or progressive visual impairment. They are characterized by extensive genetic heterogeneity, possibly the highest among all human genetic diseases, as well as diverse inheritance patterns. Despite advances in gene discovery, limited understanding of gene function and challenges in accurately interpreting variants continue to hinder both molecular diagnosis and genetic research in IRDs. One key problem is the absence of a comprehensive and widely accepted catalog of disease-associated genes, which would ensure consistent genetic testing and reliable molecular diagnoses. With the rapid pace of IRD gene discovery, gene catalogs require frequent validation and updates to remain clinically and scientifically useful. To address these gaps, we developed RetiGene, an expert-curated gene atlas that integrates variant data, bulk and single-cell RNA sequencing, and functional annotations. Through the integration of diverse data sources, RetiGene supports candidate gene prioritization, functional studies, and therapeutic development in IRDs.

Mosaic loss of the Y chromosome (mLOY) is the most common somatic event in men, strongly associated with aging and various health conditions. Current methods for detecting mLOY primarily rely on DNA genotyping arrays. Here, we present MosCoverY, a method for estimating mLOY from exome or whole-genome sequencing data. MosCoverY addresses the challenges posed by the structure of the Y chromosome by focusing on single-copy genes and normalizing their coverage against autosomal exons matched by length and GC content. We validated it using data from 212,062 male participants in the UK Biobank, comparing the results to those obtained using genotyping- or whole-genome-sequencing-based methods. MosCoverY identified mLOY in 5.6% of men, demonstrating performance that was comparable to the other methods. We validated our approach by replicating known mLOY associations with age, smoking, all-cause mortality, and germline genetic loci. We further confirmed the robustness of our method at lower sequencing depth and demonstrated its applicability in single-sample analysis. Finally, we used data from The Cancer Genome Atlas to demonstrate that MosCoverY can also reliably detect variable mLOY in tumoral genomes. MosCoverY offers a valuable tool for detecting mLOY from exome or genome data in population-scale studies.

Van der Woude syndrome (VWS) is an autosomal dominant disorder characterized by lower lip pits and orofacial clefts (OFCs). With a prevalence of ∼1 in 35,000 live births, it is the most common form of syndromic clefting. Most VWS is attributed to variants in IRF6 (∼70%) or GRHL3 (∼5%), leaving up to 25% of individuals without a molecular diagnosis. Both IRF6 and GRHL3 function in a transcriptional regulatory network (TRN) governing differentiation of periderm, a single epithelial cell layer preventing pathological adhesions during palatogenesis. Periderm disruption can elicit a spectrum of phenotypes, including lip pits and OFCs, pterygia, and severe or fatal congenital anomalies. Understanding these mechanisms is vital in improving health outcomes for individuals with peridermopathies. We hypothesized genes encoding members of the periderm TRN, including kinases such as atypical protein kinase C (aPKC) acting upstream of IRF6, could harbor variants resulting in VWS. Consistent with this hypothesis, we identified 7 de novo variants (DNs) and 11 rare variants in PRKCI in 18 individuals with clinical features of syndromic OFCs and peridermopathies. Among the identified DNs, c.1148A>G (p.Asn383Ser) was found in five unrelated individuals, indicating a hotspot mutation. We functionally tested 12 proband-specific alleles in a zebrafish model. Three alleles, c.389G>A (p.Arg130His), c.1148A>G (p.Asn383Ser), and c.1155A>C (p.Leu385Phe), were confirmed loss-of-function variants. We also show that phosphomimetic Irf6 can rescue the effects of aPKC inhibition, supporting placement of PRKCI within this TRN. In summary, we identified PRKCI variants as causative for VWS and syndromic OFC with other features of peridermopathies.

Interpretation of genetic variants is most accurate when gene- and disease-specific considerations are considered. The 2015 ACMG/AMP guidelines form the basis for the application of variant interpretation criteria for Mendelian disorders. The Hereditary Breast, Ovarian, and Pancreatic Cancer Variant Curation Expert Panel (HBOP VCEP) has undertaken the process for creating gene- and disease-specific specifications for the interpretation of PALB2 germline sequence variants. The HBOP VCEP is comprised of experts in the fields of clinical and molecular genetics, epidemiology, functional assays, and variant interpretation. The group met regularly to consider each of the codes from the 2015 ACMG/AMP guidelines to determine their relevance for PALB2. After criteria were created using database analysis, literature review, and expert opinion, they were vetted against a diverse set of pilot variants and ultimately finalized. The HBOP VCEP advised against using 13 codes, limited the use of six codes, and tailored nine codes to create the final PALB2 variant interpretation guidelines. Among the 39 pilot variants, 37 were in ClinVar, and using the new specifications concordant classifications resulted for 31 of the variants (84%). Of the 14 variants of uncertain significance/conflicting variants in ClinVar, four were classified by the VCEP, likely due to code combination modifications and refined population frequency cutoffs. The PALB2-specific guidelines put forward by the HBOP VCEP represent a conservative approach to classifying variants in PALB2 and lead to improved classifications relative to current ClinVar entries. Adoption of these specifications will help to harmonize classifications deposited in the public domain.

The increasing availability of diverse biobanks has enabled multi-ancestry genome-wide association studies (GWASs) to enhance the discovery of genetic variants across traits and diseases. However, the choice of an optimal method remains debated, due to challenges in statistical power differences across ancestral groups and approaches to account for population structure. Two primary strategies exist: (1) pooled analysis, which combines individuals from all genetic backgrounds into a single dataset while adjusting for population stratification using principal components, increasing the sample size and statistical power but requiring careful control of population stratification; and (2) meta-analysis, which performs ancestry-group-specific GWASs and subsequently combines summary statistics, potentially capturing fine-scale population structure but facing limitations in handling admixed individuals. Using large-scale simulations with varying sample sizes and ancestry compositions, we compare these methods alongside real data analyses of eight continuous and five binary traits from the UK Biobank (N ≈ 324,000) and the All of Us Research Program (N ≈ 207,000). Our results demonstrate that pooled analysis generally exhibits better statistical power while effectively adjusting for population stratification. We further present a theoretical framework linking power differences to allele-frequency variations across populations. These findings, validated across both biobanks, highlight pooled analysis as a powerful and scalable strategy for multi-ancestry GWASs, improving genetic discovery while maintaining rigorous population structure control.

Smith-Magenis syndrome (SMS) is a genomic disorder caused by the deletion of a chromosomal region at 17p11.2. Individuals with SMS are frequently diagnosed with autism and have profound cortical deficits, including reduced cortex volume, mild ventriculomegaly, and epilepsy. Here, we developed human induced pluripotent stem cell (hiPSC)-derived neuronal models to understand how del(17)p11.2 affects cortical development. Hi-C experiments identified local fusion and global reorganization of topological domains, as well as genome-wide miswiring of chromatin three-dimensional (3D) interactions in SMS hiPSCs and 3D cortical organoids. Single-nucleus RNA sequencing of SMS cortical organoids identified neuropsychiatric disease-enriched transcriptional signatures and dysregulation of genes involved in catabolic and biosynthetic pathways, cell-cycle processes, and neuronal signaling. SMS cortical organoids displayed reduced growth, enlarged ventricles, impaired cell-cycle progression, and accelerated neuronal maturation. Through the use of a complementary hiPSC-derived 2D cortical neuronal model, we report that SMS cortical neurons exhibited accelerated dendritic growth, followed by neuronal hyperexcitability associated with reduced potassium conductance. Our study demonstrates that del(17)p11.2 disrupts multiple steps of human cortical development, from chromatin wiring, transcriptional regulation, cell-cycle progression, and morphological maturation to neurophysiological properties, and hiPSC-derived models recapitulate key neuroanatomical and neurophysiological features of SMS.