Genome research最新文献

Many African great ape chromosomes possess large subterminal heterochromatic caps at their telomeres that are conspicuously absent from the human lineage. Leveraging the complete sequences of great ape genomes, we characterize the organization of subterminal caps and reconstruct the evolutionary history of these regions in chimpanzees and gorillas. Detailed analyses of the composition of the associated terminal 32 bp satellite array from chimpanzee (termed pCht) and intervening segmental duplication (SD) spacers confirm two independent origins in the Pan and gorilla lineages. In chimpanzee and bonobo, we estimate these structures emerged ∼7.7 million years ago (MYA) in contrast to gorilla, in which they expanded more recently, ∼5.0 MYA, and now make up 8.5% of the total gorilla genome. In both lineages, the SD spacers punctuating the pCht heterochromatic satellite arrays correspond to pockets of decreased methylation, although in gorilla such regions are significantly less methylated (P < 2.2 × 10^-16) than in chimpanzee or bonobo. Allelic pairs of subterminal caps show a higher degree of sequence divergence than euchromatic sequences, with bonobo showing less divergent haplotypes and less differentially methylated spacers. In contrast, we identify virtually identical subterminal caps mapping to nonhomologous chromosomes within a species, suggesting ectopic recombination potentially mediated by SD spacers. We find that the transition regions from heterochromatic subterminal caps to euchromatin are enriched for structural variant insertions and lineage-specific duplicated genes. Our findings suggest independent evolution of subterminal caps converging on a common genetic and epigenetic structure that promoted ectopic exchange as well as the emergence of novel genes at transition regions between euchromatin and heterochromatin.

Genomic imprinting is a specialized mechanism of transcriptional regulation whereby approximately 200 mammalian genes are expressed monoallelically according to their parental origin. This crucial developmental process is primarily controlled by discrete cis-regulatory elements known as imprinting control regions (ICRs), which play essential roles in directing allele-specific gene expression across large imprinted domains. In this review, we highlight the features that define ICRs as a distinct class of cis-regulatory regions, from their ability to maintain germline-inherited DNA methylation to their multifunctional roles in transcriptional control. For each imprinted domain, we examine the diverse mechanisms by which individual ICRs integrate multiple regulatory functions to coordinate both proximal and distal imprinted gene expression. By uncovering the multifaceted roles of ICRs, this review provides a compelling framework for understanding, more broadly, the molecular basis of finely controlled gene expression.

Population structure is a well-known confounder in statistical genetics, particularly in genome-wide association studies (GWAS), where it can lead to inflated test statistics and spurious associations. Traditional methods, such as principal components (PCs), commonly used to adjust for population structure, are limited in capturing fine-scale, nonlinear patterns that arise from recent demographic events - patterns that are crucial for understanding rare variant effects. To address this challenge, we propose a novel method called SPectral Components (SPCs), which leverages identity-by-descent (IBD) graphs to capture and transform local, nonlinear fine-scale population structure into continuous representations that can be seamlessly integrated into genetic analysis pipelines. Using both simulated datasets and empirical data from the UK Biobank (N ≈ 420,000), we demonstrate that SPCs outperform PCs in adjusting for fine-scale population structure. In simulations, SPCs explained over 90% of the fine-scale population structure with fewer components, while PCs captured less than 5%. In the UK Biobank, SPCs reduced the inflation of P-values in the GWAS of an environmental-driven phenotype by 12% compared to PCs, while maintaining a similar performance to PCs in height, a highly heritable phenotype. Additionally, SPCs improved rare variant association analyses, reducing genomic inflation (e.g., from 7.6 to 1.2 in one analysis), and provided more accurate heritability estimates. Spatial autocorrelation analysis further confirmed the ability of SPCs to account for environmental effects, reducing Moran's I for both environmental and heritable phenotypes more effectively than PCs. Overall, our findings demonstrate that SPCs provide a robust, scalable adjustment for recent population structure, offering a powerful alternative or complement to PCs in large-scale biobank studies.

As sequencing techniques advance in precision, affordability, and diversity, an abundance of heterogeneous sequencing data has become available, encompassing a wide range of phenotypic features and biological perturbations. Unfortunately, increased resolution comes with the cost of increased complexity of the biological search space, even at the individual study level, as perturbations are now often examined across many dimensions simultaneously, including different donor phenotypes, anatomical regions and cell types, and time points. Furthermore, broad integration across studies promises a unique opportunity to explore the molecular underpinnings of distinct healthy and disease states, larger than the original scope of the individual study. To fully realize the promise of both individual higher resolution studies and large cross-study integrations, we need a robust methodology that can disentangle the influence of technical and nonrelevant phenotypic factors, isolating relevant condition-specific signals from shared biological information while also providing interpretable insights into the genetic effects of these conditions. Current methods typically excel in only one of these areas. To address this gap, we have developed ALPINE, a supervised nonnegative matrix factorization (NMF) framework that effectively separates both technical and nontechnical factors while simultaneously offering direct interpretability of condition-associated genes. Through simulations across four different scenarios, we demonstrate that ALPINE outperforms existing methods in both isolating the effect of different phenotypic conditions and prioritizing condition-associated genes. Furthermore, ALPINE has favorable performance in batch effect removal compared with state-of-the-art integration methods. When applied to real-world case studies, we showcase how ALPINE can be used to extract insights into the biological mechanisms that underlie differences between phenotypic conditions.

Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying therapeutic targets. Advances in sequence-based machine learning have generated diverse variant pathogenicity scores, creating opportunities to improve RVATs. However, existing methods often rely on rigid models or single annotations, limiting their ability to leverage these advances. Here, we introduce BayesRVAT, a Bayesian rare variant association test that jointly models multiple annotations. By specifying priors on annotation effects and estimating gene- and trait-specific posterior burden scores, BayesRVAT flexibly captures diverse rare-variant architectures. In simulations, BayesRVAT improves power while maintaining calibration. In UK Biobank analyses, it detects 10.2% more blood-trait associations and reveals novel gene-disease links, including PRPH2 with retinal disease. Integrating BayesRVAT within omnibus frameworks further increases discoveries, demonstrating that flexible annotation modeling captures complementary signals beyond existing burden and variance-component tests.