HGG Advances最新文献

Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B∗15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B∗15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the United States (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B∗15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections studied, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified.

Orofacial clefts (OFCs) are common congenital birth defects with various etiologies, including genetic variants. Online Mendelian Inheritance in Man (OMIM) annotated several hundred genes involving OFCs. Furthermore, several hundreds of de novo variants (DNVs) have been identified from individuals with OFCs. Some DNVs are related to known OFC genes or pathways, but there are still many DNVs whose relevance to OFC development is unknown. To explore novel gene functions and their cellular expression profiles, we focused on DNVs in genes that were not listed in OMIM. We collected 960 DNVs in 853 genes from published studies and curated these genes, based on the DNVs' deleteriousness, into 230 and 23 genes related to cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO), respectively. For comparison, we curated 178 CL/P and 277 CPO genes from OMIM. In CL/P, the pathways enriched in DNV and OMIM genes were significantly overlapped (p = 0.002). Single-cell RNA sequencing (scRNA-seq) analysis of mouse lip development revealed that both gene sets had abundant expression in the ectoderm (DNV genes: adjusted p = 0.032, OMIM genes: adjusted p < 0.0002), while only DNV genes were enriched in the endothelium (adjusted p = 0.032). Although we did not achieve significant findings using CPO gene sets, which was mainly due to the limited number of DNV genes, scRNA-seq analysis implicated various expression patterns among DNV and OMIM genes. Our results suggest that combinatory pathway and scRNA-seq data analyses are helpful for contextualizing genes in OFC development.

Chronic inflammatory demyelinating polyneuropathy (CIDP) is a rare, immune-mediated disorder in which an aberrant immune response causes demyelination and axonal damage of the peripheral nerves. Genetic contribution to CIDP is unclear and no genome-wide association study (GWAS) has been reported so far. In this study, we aimed to identify CIDP-related risk loci, genes, and pathways. We first focused on CIDP, and 516 CIDP cases and 403,545 controls were included in the GWAS analysis. We also investigated genetic risk for inflammatory polyneuropathy (IP), in which we performed a GWAS study using FinnGen data and combined the results with GWAS from the UK Biobank using a fixed-effect meta-analysis. A total of 1,261 IP cases and 823,730 controls were included in the analysis. Stratified analyses by gender were performed. Mendelian randomization (MR), colocalization, and transcriptome-wide association study (TWAS) analyses were performed to identify associated genes. Gene-set analyses were conducted to identify associated pathways. We identified one genome-wide significant locus at 20q13.33 for CIDP risk among women, the top variant located at the intron region of gene CDH4. Sex-combined MR, colocalization, and TWAS analyses identified three candidate pathogenic genes for CIDP and five genes for IP. MAGMA gene-set analyses identified a total of 18 pathways related to IP or CIDP. Sex-stratified analyses identified three genes for IP among males and two genes for IP among females. Our study identified suggestive risk genes and pathways for CIDP and IP. Functional analyses should be conducted to further confirm these associations.

There is increasing evidence of the clinical utility of genetic and genomic testing (GT); however, factors influencing personal utility of GT, especially in diverse, multilingual populations, remain unclear. We explored these factors in a diverse cohort of parents/guardians (participants) whose children received clinical GT through the NYCKidSeq program. A total of 847 participants completed surveys at baseline, post-results disclosure, and 6 months (6m) post-results. The largest population groups were Hispanic/Latino(a) (48%), White/European American (24%), and Black/African American (16%). Personal utility was assessed using the Personal Utility (PrU) scale, adapted for pediatric populations and included on the surveys. Three PrU subscales were identified using factor analysis: practical, educational, and parental psychological utility. Overall personal utility summary score and the three subscales significantly decreased after receiving results and over time. Hispanic/Latino(a) participants identified greater overall personal utility than European American and African American participants at all time points (p < 0.001) as did participants whose children received positive/likely positive results compared with those with negative and uncertain results (post-results: p < 0.001 and p < 0.001; 6m post-results: p = 0.002 and p < 0.001, respectively). Post-results, higher subscale scores were associated with lower education levels (practical, parental psychological: p ≤ 0.02) and higher levels of trust in the healthcare system (practical, parental psychological: p ≤ 0.04). These findings help to understand the perspectives of diverse parents/guardians, which is critical to tailoring pre- and post-test counseling across a variety of populations and clinical settings.

Non-protein-coding genetic variants are a major driver of the genetic risk for human disease; however, identifying which non-coding variants contribute to diseases and their mechanisms remains challenging. In silico variant prioritization methods quantify a variant's severity, but for most methods, the specific phenotype and disease context of the prediction remain poorly defined. For example, many commonly used methods provide a single, organism-wide score for each variant, while other methods summarize a variant's impact in certain tissues and/or cell types. Here, we propose a complementary disease-specific variant prioritization scheme, which is motivated by the observation that variants contributing to disease often operate through specific biological mechanisms. We combine tissue/cell-type-specific variant scores (e.g., GenoSkyline, FitCons2, DNA accessibility) into disease-specific scores with a logistic regression approach and apply it to ∼25,000 non-coding variants spanning 111 diseases. We show that this disease-specific aggregation significantly improves the association of common non-coding genetic variants with disease (average precision: 0.151, baseline = 0.09), compared with organism-wide scores (GenoCanyon, LINSIGHT, GWAVA, Eigen, CADD; average precision: 0.129, baseline = 0.09). Further on, disease similarities based on data-driven aggregation weights highlight meaningful disease groups, and it provides information about tissues and cell types that drive these similarities. We also show that so-learned similarities are complementary to genetic similarities as quantified by genetic correlation. Overall, our approach demonstrates the strengths of disease-specific variant prioritization, leads to improvement in non-coding variant prioritization, and enables interpretable models that link variants to disease via specific tissues and/or cell types.

Inherited retinal diseases (IRDs) are a group of rare monogenic diseases with high genetic heterogeneity (pathogenic variants identified in over 280 causative genes). The genetic diagnostic rate for IRDs is around 60%, mainly thanks to the routine application of next-generation sequencing (NGS) approaches such as extensive gene panels or whole exome analyses. Whole-genome sequencing (WGS) has been reported to improve this diagnostic rate by revealing elusive variants, such as structural variants (SVs) and deep intronic variants (DIVs). We performed WGS on 33 unsolved cases with suspected autosomal recessive IRD, aiming to identify causative genetic variants in non-coding regions or to detect SVs that were unexplored in the initial screening. Most of the selected cases (30 of 33, 90.9%) carried monoallelic pathogenic variants in genes associated with their clinical presentation, hence we first analyzed the non-coding regions of these candidate genes. Whenever additional pathogenic variants were not identified with this approach, we extended the search for SVs and DIVs to all IRD-associated genes. Overall, we identified the missing causative variants in 11 patients (11 of 33, 33.3%). These included three DIVs in ABCA4, CEP290 and RPGRIP1; one non-canonical splice site (NCSS) variant in PROM1 and three SVs (large deletions) in EYS, PCDH15 and USH2A. For the previously unreported DIV in CEP290 and for the NCCS variant in PROM1, we confirmed the effect on splicing by reverse transcription (RT)-PCR on patient-derived RNA. This study demonstrates the power and clinical utility of WGS as an all-in-one test to identify disease-causing variants missed by standard NGS diagnostic methodologies.

Recent collaborative genome-wide association studies (GWAS) have identified >200 independent loci contributing to risk for schizophrenia (SCZ). The genes closest to these loci have diverse functions, supporting the potential involvement of multiple relevant biological processes, yet there is no direct evidence that individual variants are functional or directly linked to specific genes. Nevertheless, overlap with certain epigenetic marks suggest that most GWAS-implicated variants are regulatory. Based on the strength of association with SCZ and the presence of regulatory epigenetic marks, we chose one such variant near TSNARE1 and ADGRB1, rs4129585, to test for functional potential and assay differences that may drive the pathogenicity of the risk allele. We observed that the variant-containing sequence drives reporter expression in relevant neuronal populations in zebrafish. Next, we introduced each allele into human induced pluripotent cells and differentiated four isogenic clones homozygous for the risk allele and five clones homozygous for the non-risk allele into neural progenitor cells. Employing RNA sequencing, we found that the two alleles yield significant transcriptional differences in the expression of 109 genes at a false discovery rate (FDR) of <0.05 and 259 genes at a FDR of <0.1. We demonstrate that these genes are highly interconnected in pathways enriched for synaptic proteins, axon guidance, and regulation of synapse assembly. Exploration of genes near rs4129585 suggests that this variant does not regulate TSNARE1 transcripts, as previously thought, but may regulate the neighboring ADGRB1, a regulator of synaptogenesis. Our results suggest that rs4129585 is a functional common variant that functions in specific pathways likely involved in SCZ risk.

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

We designed a massively parallel reporter assay (MPRA) in an Epstein-Barr virus transformed B cell line to directly characterize the potential for histone post-translational modifications, i.e., histone quantitative trait loci (hQTLs), expression QTLs (eQTLs), and variants on systemic lupus erythematosus (SLE) and autoimmune (AI) disease risk haplotypes to modulate regulatory activity in an allele-dependent manner. Our study demonstrates that hQTLs, as a group, are more likely to modulate regulatory activity in an MPRA compared with other variant classes tested, including a set of eQTLs previously shown to interact with hQTLs and tested AI risk variants. In addition, we nominate 17 variants (including 11 previously unreported) as putative causal variants for SLE and another 14 for various other AI diseases, prioritizing these variants for future functional studies in primary and immortalized B cells. Thus, we uncover important insights into the mechanistic relationships among genotype, epigenetics, and gene expression in SLE and AI disease phenotypes.