American journal of human genetics最新文献

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using local ancestry inference (LAI). Accurate LAI is crucial to ensure that downstream analyses accurately reflect the genetic ancestry of research participants. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries-African (AFR), Amerindigenous (AMR), and European (EUR). Simulating linkage-disequilibrium-informed admixed haplotypes under a variety of 2- and 3-way admixture models, we implemented a standard LAI pipeline, testing the impact of reference panel composition, DNA data type, demography, and software parameters to quantify ancestry-specific LAI accuracy. We observe that across all models, AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts, with true positive rate means for AMR ranging from 88% to 94%, EUR from 96% to 99%, and AFR from 98% to 99%. When LAI miscalls occurred, they most frequently erroneously called EUR ancestry in true AMR sites. Concerning reference panel curation, we find that using a reference panel well matched to the target population, even with a smaller sample size, was accurate and the most computationally efficient. Imputation did not harm LAI performance in our tests; rather, we observed that higher variant density improved accuracy. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across admixed populations. Our findings reinforce the need for the inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

The tumor suppressor CHEK2 encodes the serine/threonine protein kinase CHK2 which, upon DNA damage, is important for pausing the cell cycle, initiating DNA repair, and inducing apoptosis. CHK2 phosphorylation of the tumor suppressor BRCA1 is also important for mitotic spindle assembly and chromosomal stability. Consistent with its cell-cycle checkpoint role, both germline and somatic variants in CHEK2 have been linked to breast and other cancers. Over 90% of clinical germline CHEK2 missense variants are classified as variants of uncertain significance, complicating diagnosis of CHK2-dependent cancer. We therefore sought to test the functional impact of all possible missense variants in CHK2. Using a scalable multiplexed assay based on the ability of human CHK2 to complement DNA sensitivity of Saccharomyces cerevisiae cells lacking the CHEK2 ortholog, RAD53, we generated a systematic "missense variant effect map" for CHEK2 missense variation. The map reflects known biochemical features of CHK2 while offering new biological insights. It also provides strong evidence toward pathogenicity for some clinical missense variants and supporting evidence toward benignity for others. Overall, this comprehensive missense variant effect map contributes to understanding of both known and yet-to-be-observed CHK2 variants.

Translational efforts in osteoarthritis are hampered by a gap in our understanding of disease processes at the molecular level. Here, we present evidence of pronounced transcriptional changes in high- and low-disease-grade cartilage tissue, pointing to embryonic processes involved in disease progression. We identify shared transcriptional programs between osteoarthritis cartilage and cell populations in the human embryonic and fetal limb, pointing to increases in pre-hypertrophic chondrocytes' transcriptional programs in low-grade cartilage and increases in osteoblastic signatures in high-grade disease tissue. We find that osteoarthritis genetic risk signals are enriched in six gene co-expression modules and show that these transcriptional signatures reflect cell-type-specific expression along the endochondral ossification developmental trajectory. Using this network approach in combination with causal inference analysis, we present evidence of a causal effect on osteoarthritis risk for variants associated with the expression of ten genes that have not been previously reported as effector genes in genome-wide association studies in osteoarthritis. Our findings point to key molecular pathways as drivers of cartilage degeneration and identify high-value drug targets and repurposing opportunities.

Four main medulloblastoma (MB) molecular subtypes have been identified based on transcriptional, DNA methylation, and genetic profiles. However, it is currently not known whether 3D genome architecture differs between MB subtypes. To address this question, we performed in situ Hi-C to reconstruct the 3D genome architecture of MB subtypes. In total, we generated Hi-C and matching transcriptome data for 28 surgical specimens and Hi-C data for one patient-derived xenograft. The average resolution of the Hi-C maps was 6,833 bp. Using these data, we found that insulation scores of topologically associating domains (TADs) were effective at distinguishing MB molecular subgroups. TAD insulation score differences between subtypes were globally not associated with differential gene expression, although we identified few exceptions near genes expressed in the lineages of origin of specific MB subtypes. Our study therefore supports the notion that TAD insulation scores can distinguish MB subtypes independently of their transcriptional differences.

Normal tissues adjacent to the tumor (NATs) may harbor early breast carcinogenesis events driven by field cancerization. Although previous studies have characterized copy-number (CN) and transcriptomic alterations, the evolutionary history of NATs in breast cancer (BC) remains poorly characterized. Utilizing whole-genome sequencing (WGS), methylation profiling, and RNA sequencing (RNA-seq), we analyzed paired germline, NATs, and tumor samples from 43 individuals with BC in Hong Kong (HK). We found that single-nucleotide variants (SNVs) were common in NATs, with one-third of NAT samples exhibiting SNVs in driver genes, many of which were present in paired tumor samples. The most frequently mutated genes in both tumor and NAT samples were PIK3CA, TP53, GATA3, and AKT1. In contrast, large-scale aberrations such as somatic CN alterations (SCNAs) and structural variants (SVs) were rarely detected in NAT samples. We generated phylogenetic trees to investigate the evolutionary history of paired NAT and tumor samples. They could be categorized into tumor only, shared, and multiple-tree groups, the last of which is concordant with non-genetic field cancerization. These groups exhibited distinct genomic and epigenomic characteristics in both NAT and tumor samples. Specifically, NAT samples in the shared-tree group showed higher number of mutations, while NAT samples belonging to the multiple-tree group showed a less inflammatory tumor microenvironment (TME), characterized by a higher proportion of regulatory T cells (Tregs) and lower presence of CD14 cell populations. In summary, our findings highlight the diverse evolutionary history in BC NAT/tumor pairs and the impact of field cancerization and TME in shaping the genomic evolutionary history of tumors.

Large prospective clinical trials are underway or planned that examine the clinical utility and cost effectiveness of genome-based newborn screening (gNBS). One gNBS platform, BeginNGS, currently screens 53,575 variants for 412 severe childhood genetic diseases with 1,603 efficacious therapies. Retrospective evaluation of BeginNGS in 618,290 subjects suggests adequate sensitivity and positive predictive value (PPV) to proceed to prospective studies. To inform pivotal clinical trial design, we undertook a pilot clinical trial. We enrolled 120 infants in a regional neonatal intensive care unit (NICU) who were not under consideration for rapid diagnostic genome sequencing (RDGS). Each enrollee received BeginNGS and two index tests (California state NBS and RDGS). California NBS identified 4 of 4 true positive (TP) findings (TP rate 3.6%, sensitivity 100%) and 11 false positive (FP) findings (PPV 27%). RDGS identified 41 diagnostic findings in 36 neonates (diagnostic rate 30%). BeginNGS identified 5 of 6 on-target TP disorders (TP rate 4.2%, 95% confidence interval 1%-8%, sensitivity 83%) and no FPs (PPV 100%). Changes in management were anticipated following the return of 27 RDGS results in 25 enrollees (clinical utility [CU] 21%), 3 of 4 NBS TPs (CU 2.7%), and all BeginNGS TPs (CU 4.2%). The incidence of actionable genetic diseases in NICU infants not being considered for RDGS suggests (1) performance of RDGS in ∼20% of admissions misses many genetic diagnoses, (2) NICU enrollment in gNBS trials will greatly increase power to test endpoints, and (3) NICUs may be attractive for early implementation of consented BeginNGS screening.

Polycystic ovarian syndrome (PCOS) is an endocrine syndrome that affects a large portion of women worldwide. This proteogenomic and functional study aimed to uncover candidate therapeutic targets for PCOS. We comprehensively investigated the causal association between circulating proteins and PCOS using two-sample Mendelian randomization analysis. Cis-protein quantitative trait loci were derived from six genome-wide association studies (GWASs) on plasma proteome. Genetic associations with PCOS were obtained from a large-scale GWAS meta-analysis, FinnGen cohort, and UK Biobank. Colocalization analyses were performed to prioritize the causal role of candidate proteins. Protein-protein interaction (PPI) and druggability evaluation assessed the druggability of candidate proteins. We evaluated the enrichment of tier 1 and 2 candidate proteins in individuals with PCOS and a mouse model and explored the potential application of the identified drug target. Genetically predicted levels of 65 proteins exhibited associations with PCOS risk, with 30 proteins showing elevated levels and 35 proteins showing decreased levels linked to higher susceptibility. PPI analyses revealed that FSHB, POSTN, CCN2, and CXCL11 interacted with targets of current PCOS medications. Eighty medications targeting 20 proteins showed their potential for repurposing as therapeutic targets for PCOS. EGLN1 levels were elevated in granulosa cells and the plasma of individuals with PCOS and in the plasma and ovaries of dehydroepiandrosterone (DHEA)-induced PCOS mouse model. As an EGLN1 inhibitor, administration of roxadustat in the PCOS mouse model elucidated the EGLN1-HIF1α-ferroptosis axis in inducing PCOS and validated its therapeutic effect in PCOS. Our study identifies candidate proteins causally associated with PCOS risk and suggests that targeting EGLN1 provides a promising treatment strategy.

Delineation of structural variants (SVs) at sequence resolution in highly repetitive genomic regions has long been intractable. The sequence properties, origins, and functional effects of classes of genomic rearrangements such as ring chromosomes and Robertsonian translocations thus remain unknown. To resolve these complex structures, we leveraged several recent milestones in the field, including (1) the emergence of long-read sequencing, (2) the gapless telomere-to-telomere (T2T) assembly, and (3) a tool (BigClipper) to discover chromosomal rearrangements from long reads. We applied these technologies across 13 cases with ring chromosomes, Robertsonian translocations, and complex SVs that were unresolved by short reads, followed by validation using optical genome mapping (OGM). Our analyses resolved 10 of 13 cases, including a Robertsonian translocation and all ring chromosomes. Multiple breakpoints were localized to genomic regions previously recalcitrant to sequencing such as acrocentric p-arms, ribosomal DNA arrays, and telomeric repeats, and involved complex structures such as a deletion-inversion and interchromosomal dispersed duplications. We further performed methylation profiling from long-read data to discover phased differential methylation in a gene promoter proximal to a ring fusion, suggesting a long-range position effect (LRPE) with heterochromatin spreading. Breakpoint sequences suggested mechanisms of SV formation such as microhomology-mediated and non-homologous end-joining, as well as non-allelic homologous recombination. These methods provide some of the first glimpses into the sequence resolution of Robertsonian translocations and illuminate the structural diversity of ring chromosomes and complex chromosomal rearrangements with implications for genome biology, prediction of LRPEs from integrated multi-omics technologies, and molecular diagnostics in rare disease cases.

There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to enable clinical variant annotation on a large scale and hence increase the impact of patient sequencing in guiding diagnosis and treatment. To realize this potential, it is essential to provide reliable assessments of model performance, scope of applicability, and robustness. As a response to this need, the ClinGen Sequence Variant Interpretation Working Group, Pejaver et al., recently proposed a strategy for validation and calibration of in-silico predictions in the context of guidelines for variant annotation. While this work marks an important step forward, the strategy presented still has important limitations. We propose core principles and recommendations to overcome these limitations that can enable both more reliable and more impactful use of variant effect prediction models in the future.