American journal of human genetics最新文献

By improving disease risk prediction, polygenic risk scores (PRSs) could have a significant impact on health promotion and disease prevention. Due to the historical oversampling of populations with European ancestry for genome-wide association studies, PRSs perform less well in other, understudied populations, leading to concerns that clinical use in their current forms could widen health care disparities. The PRIMED Consortium was established to develop methods to improve the performance of PRSs in global populations and individuals of diverse genetic ancestry. To this end, PRIMED is aggregating and harmonizing multiple phenotype and genotype datasets on AnVIL, an interoperable secure cloud-based platform, to perform individual- and summary-level analyses using population and statistical genetics approaches. Study sites, the coordinating center, and representatives from the NIH work alongside other NHGRI and global consortia to achieve these goals. PRIMED is also evaluating ethical and social implications of PRS implementation and investigating the joint modeling of social determinants of health and PRS in computing disease risk. The phenotypes of interest are primarily cardiometabolic diseases and cancer, the leading causes of death and disability worldwide. Early deliverables of the consortium include methods for data sharing on AnVIL, development of a common data model to harmonize phenotype and genotype data from cohort studies as well as electronic health records, adaptation of recent guidelines for population descriptors to global cohorts, and sharing of PRS methods/tools. As a multisite collaboration, PRIMED aims to foster equity in the development and use of polygenic risk assessment.

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

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.

Increasing evidence is emerging to link age-associated complex musculoskeletal diseases, including osteoarthritis (OA), to developmental factors. Multiple studies have shown a functional role for DNA methylation in the genetic mechanisms of OA risk using articular cartilage samples taken from aged individuals, yet knowledge of temporal changes to the methylome during human cartilage development is limited. We quantified DNA methylation at ∼700,000 individual CpGs across the epigenome of developing human chondrocytes in 72 samples ranging from 7 to 21 post-conception weeks. We identified significant changes in 3% of all CpGs and >8,200 developmental differentially methylated regions. We further identified 24 loci at which OA genetic variants colocalize with methylation quantitative trait loci. Through integrating developmental and mature human chondrocyte datasets, we find evidence for functional effects exerted solely in development or throughout the life course. This will have profound impacts on future approaches to translating genetic pathways for therapeutic intervention.

Individuals with obesity caused by biallelic pathogenic LEPR (leptin receptor) variants can benefit from setmelanotide, the novel MC4R agonist. An ongoing phase 3 clinical trial (NCT05093634) includes individuals with obesity who carry a heterozygous LEPR variant, although the obesogenic impact of these variants remains incompletely evaluated. The aim of this study was to functionally assess heterozygous variants in LEPR and to evaluate their effect on obesity. We sequenced LEPR in ∼10,000 participants from the French RaDiO study. We found 86 rare heterozygous variants. Each identified variant was then investigated in vitro using luciferase and western blot assays. Using the criteria of the American College of Medical Genetics and Genomics (ACMG), including the strong criterion related to functional assays, we found 12 pathogenic LEPR variants. Most heterozygotes did not present with obesity, and we found no association between these pathogenic variants and body mass index (BMI). This lack of association between pathogenic LEPR variants and obesity risk or BMI was confirmed using exome data from 200,000 individuals in the UK Biobank. In the literature, among 55 reported heterozygotes for of a rare pathogenic LEPR variant, only 27% had obesity. In conclusion, monoallelic pathogenic LEPR variants were functionally tested, and they do not elevate the risk of obesity or BMI levels. This raises questions about the use of setmelanotide, a costly drug with potential side effects, based solely on the presence of a heterozygous LEPR variant.

Female infertility is a common and complex health problem affecting millions of women worldwide. While multiple factors can contribute to this condition, the underlying cause remains elusive in up to 15%-30% of affected individuals. In our large genome-wide association study (GWAS) of 22,849 women with infertility and 198,989 control individuals from the Finnish population cohort FinnGen, we unveil a landscape of genetic factors associated with the disorder. Our recessive analysis identified a low-frequency stop-gained mutation in TATA-box binding protein-like 2 (TBPL2; c.895A>T [p.Arg299Ter]; minor-allele frequency [MAF] = 1.2%) with an impact comparable to highly penetrant monogenic mutations (odds ratio [OR] = 650, p = 4.1 × 10^-25). While previous studies have linked the orthologous gene to anovulation and sterility in knockout mice, the severe consequence of the p.Arg299Ter variant was evidenced by individuals carrying two copies of that variant having significantly fewer offspring (average of 0.16) compared to women belonging to the other genotype groups (average of 1.75 offspring, p = 1.4 × 10^-15). Notably, all homozygous women who had given birth had received infertility therapy. Moreover, our age-stratified analyses identified three additional genome-wide significant loci. Two loci were associated with early-onset infertility (diagnosed before age 30), located near CHEK2 and within the major histocompatibility complex (MHC) region. The third locus, associated with late-onset infertility, had its lead SNP located in an intron of a long non-coding RNA (lncRNA) gene. Taken together, our data highlight the significance of rare recessive alleles in shaping female infertility risk. The results further provide evidence supporting specific age-dependent mechanisms underlying this complex disorder.

Secondary structures are non-canonical arrangements of nucleic acids due to intra-strand interactions, including base pairing, stacking, or other higher-order features that deviate from the standard double-helical conformation. While these structures are extensively studied in RNA, they can also form when DNA becomes single stranded, creating topological roadblocks that can impact essential DNA-based processes such as replication, transcription, and repair, ultimately affecting genome stability. The availability of a complete linear sequence of human genomes, including repetitive loci, enables the prediction of DNA secondary structures comparing across various regions. Here, we evaluate the intrinsic properties of linear single-stranded DNA sequences derived from sampling specialized human loci such as centromeres, pericentromeres, ribosomal DNA (rDNA), and coding regions from the CHM13 genome. Our comparative analysis of predicted secondary structures across human chromosomes revealed the heightened presence, complexity, and instability of secondary structures within the centromere, which gradually decreased toward the pericentromere onto chromosomes' arms, on average lowest in coding regions. Notably, centromeric repeats exhibited the highest level of topological complexity within both the active and divergent domains, even when compared to other repetitive tandem satellites, such as rDNA in acrocentric chromosomes. Our findings provide evidence of the intrinsic self-hybridizing properties of centromere repeats, which are capable of generating complex topological structures that may functionally correlate with chromosome missegregation, especially when centromeric chromatin is disrupted. Processes such as long non-coding RNA transcription, recombination, and other mechanisms that dechromatinize and unwind stretches of linear DNA in these regions create in vivo opportunities for the DNA acrobatics hereby predicted.

Genome-sequence-based newborn screening (gNBS) has substantial potential to improve outcomes in hundreds of severe childhood genetic disorders (SCGDs). However, a major impediment to gNBS is imprecision due to variants classified as pathogenic (P) or likely pathogenic (LP) that are not SCGD causal. gNBS with 53,855 P/LP variants, 342 genes, 412 SCGDs, and 1,603 therapies was positive in 74% of UK Biobank (UKB470K) adults, suggesting 97% false positives. We used the phenomenon of purifying hyperselection, which acts to decrease the frequency of SCGD causal diplotypes, to reduce false positives. Training of gene-disease-inheritance mode-diplotype tetrads in 618,290 control and affected subjects identified 293 variants or haplotypes and seven genes with variable inheritance contributing higher positive diplotype counts than consistent with purifying hyperselection and with little or no evidence of SCGD causality. With these changes, 2.0% of UKB470K adults were positive. In contrast, gNBS was positive in 7.2% of 3,118 critically ill children with suspected SCGDs and 7.9% of 705 infant deaths. When compared with rapid diagnostic genome sequencing (RDGS), gNBS had 99.1% recall. In eight true-positive children, gNBS was projected to decrease time to diagnosis by a median of 121 days and avoid life-threatening disease presentations in four children, organ damage in six children, ∼$1.25 million in healthcare cost, and ten (1.4%) infant deaths. Federated training predicated on purifying hyperselection provides a general framework to attain high precision in population screening. Federated training across many biobanks and clinical trials can provide a privacy-preserving mechanism for qualification of gNBS in diverse genetic ancestries.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.