Genetics最新文献

Mendel's law of equal segregation states that during gamete formation, the 2 alleles at a gene locus segregate such that each gamete has an equal probability of containing either allele. Transmission ratio distortion (TRD) occurs when 1 of the 2 alleles from either parent is preferentially transmitted to the offspring, resulting in a deviation from the expected 1:1 ratio. Although TRD has been observed and studied in nonhuman species, the full extent and underlying biology of TRD in humans remains poorly summarized. Here we present a systematic review to assess evidence of TRD in the human genome, tracing reports from the 1970s through 2025. Overall, 96 studies including 42 different human variants/genes/loci met inclusion criteria. These studies provided only preliminary and/or conflicting evidence of TRD. Study methods were limited by multiple recurrent biases. Experimental validation of the biological mechanism(s) underlying the putative distortion was rarely performed or possible. TRD warrants renewed attention in the field of human genetics, especially with the growing availability of very large, family-based genome-wide sequencing datasets.

Understanding gene-environment and gene-gene interactions is important for studying complex diseases. Case-only analysis has been proposed to improve power for detecting interactions. However, case-only analysis relies on key assumptions, including correct specification of the disease risk model and marginal independence between variables. In this study, we systematically investigate the challenges of case-only analysis using polygenic risk scores (PRS) as genetic variables in large biobanks. Through simulations, we demonstrate that the false positive control of PRS-based case-only analysis depends on the log-linear disease risk model and weak main effects, and that it is prone to false positives under other commonly used disease risk models. We then conduct case-only analyses for breast cancer, prostate cancer, class 3 obesity, and short stature in the UK Biobank, using PRS derived from non-overlapping chromosome sets (e.g., even-numbered and odd-numbered chromosomes) that are unlikely to interact with each other. The resulting case-only regression estimates consistently show negative shifts compared to population-based estimates, suggesting false positives driven by collider bias due to model misspecification. Furthermore, correlations between chromosome set-specific PRS, likely driven by assortative mating or population stratification, suggest additional sources of confounding. Our results underscore the challenges of applying PRS-based case-only analysis in large biobank settings and highlight the need for caution when interpreting case-only results.

Argonaute proteins are essential effectors of small RNA-mediated gene regulation, yet the extent to which their stability depends on small RNA loading remains poorly understood. In Caenorhabditis elegans, we systematically disrupted the small RNA binding capacity of multiple Argonaute proteins to assess their stability in the absence of small RNA partners. We found that while most Argonautes remain stable when unable to bind small RNAs, a subset, including PRG-1, HRDE-1, and PPW-2, exhibited markedly reduced protein levels. Focusing on the PIWI-clade Argonaute PRG-1, we show that its destabilization occurs post-translationally and is independent of mRNA expression or translational efficiency. Instead, unbound PRG-1 is targeted for degradation by the ubiquitin-proteasome system. Additionally, the failure to load piRNAs disrupts PRG-1 localization to perinuclear germ granules. We further identify the E3 ubiquitin ligase EEL-1 as a factor contributing to the degradation of unloaded PRG-1. These findings uncover a critical role for small RNA loading in maintaining the stability and localization of a subset of Argonaute proteins, and reveal a quality control mechanism that selectively eliminates unbound PRG-1 to preserve germline regulatory fidelity.

The integration of Artificial Intelligence (AI) into computational biology is changing biological research, particularly in agriculture, where large and complex datasets offer opportunities for discovery and crop improvement. Maize (Zea mays L.), a globally critical crop with extensive genomic, genetic, proteomic, and functional resources, stands to benefit from AI integration. The Maize Genetics and Genomics Database (MaizeGDB) is proactively building an AI-ready infrastructure by standardizing datasets, pre-computing complex features, developing novel interactive tools, and providing reproducible workflows. This paper details MaizeGDB's strategic initiatives to create a foundation of AI-ready data in standardized formats and generate precomputed embeddings from cutting-edge DNA and protein language models. We introduce new functionalities, including zero-shot variant effect scoring derived from biological language models (protein and DNA) and genome browser tracks for visualizing nucleotide conservation (conveying potential functional significance). Furthermore, we provide custom dataset assembly resources and reproducible workflows via GitHub. By providing access to and organization of maize data, MaizeGDB enables the maize research and breeding community to leverage AI for the accelerated discovery of gene function, variant interpretation, and the development of improved maize varieties.

PomBase is the model organism database dedicated to the fission yeast Schizosaccharomyces pombe. In this update, we outline recent progress in literature curation, the introduction of new tools, and enhancements designed to better support the research community. We highlight our recent effort to curate biological pathways and modules as causal networks using Gene Ontology - Causal Activity Modelling (GO-CAM) and describe new features that utilize these models to guide and inform hypothesis-driven research.

During transcription initiation in Saccharomyces cerevisiae, RNA polymerase II (Pol II) and general transcription factors (GTFs) assemble upstream of transcription start sites (TSSs) to form the pre-initiation complex (PIC). In this model organism, yeast, the PIC selects TSSs through a unidirectional scanning mechanism referred to as promoter scanning. Previous studies have shown that the TFIIH subunit Tfb3 connects TFIIH to the rest of the PIC through interactions with Pol II and the GTF TFIIE. Activities within the PIC that influence TSS selection can do so by control of initiation efficiency at individual TSSs or by control of TSS scanning (either rate of scanning or scanning processivity). To understand how this critical interface withing the PIC participates in scanning, we used genetic screens to identify tfb3 and tfa1 mutants that alter initiation using initiation-linked phenotypes. We found mutations within the TFIIH-Pol II-TFIIE interface able to alter promoter scanning in either upstream or downstream directions, suggesting that changes to this interface can fine-tune scanning. Subsets of alleles were analyzed using TSS sequencing approaches, showing that tested tfb3 and tfa1 alleles shift TSS distributions across most genomic promoters. Genetic interaction and genomic analysis revealed that the Tfb3 interfaces with Rpb7 and Tfa1 separately contribute to promoter scanning, and that tfb3 alleles exhibit additive effects with scanning processivity mutants in, consistent with Tfb3-PIC interactions modulating scanning processivity. The ability of this interface to easily modulate scanning in both directions is consistent with the types of changes that might incrementally allow promoter scanning to have evolved.

Understanding how adult stem cells generate neurons is critical for advancing regenerative medicine. However, few in vivo models enable studying how stem cell fates are specified as neurons in an adult body. The planarian Schmidtea mediterranea provides a powerful system for investigating these mechanisms, owing to its abundant adult pluripotent stem cells, termed neoblasts, and its capacity to regenerate a molecularly complex nervous system. The SoxB1 family of transcription factors is broadly implicated in ectodermal lineage commitment. In planarians, the SoxB1 homolog soxB1-2 has been shown to promote neural and epidermal differentiation. However, the mechanisms by which soxB1-2 influences chromatin dynamics and transcriptional programs during adult neurogenesis remain unknown. To address this, we performed ATAC-seq and RNA-seq on neural-rich head tissues to assess how soxB1-2 RNAi knockdown alters chromatin accessibility and gene expression. Disrupting soxB1-2 resulted in reduced chromatin accessibility and transcriptional downregulation at neural and epidermal loci, consistent with a pioneer-like role in chromatin priming. We identified 31 candidate downstream targets with concordant accessibility and expression changes, including the transcription factors castor and mecom, which regulate mechanosensory and ion transport genes. Head tissue sampling enabled the detection of soxB1-2-responsive genes within rare neural subtypes that were missed in our previous whole-body RNA-seq experiments. These findings offer mechanistic insight into adult ectodermal lineage specification and establish a framework for understanding chromatin-mediated neurogenesis in regenerative systems.

Metazoan species possess multiple Wnt ligands and receptor genes that regulate diverse developmental processes. Because these genes often act redundantly, analysis of single-gene mutants does not necessarily reveal the full roles of Wnt signaling. In C. elegans, three Wnt genes (cwn-1, egl-20, and cwn-2) and three receptor genes (lin-17/Fzd, mom-5/Fzd, and cam-1/Ror) redundantly regulate the polarity of asymmetrically dividing seam cells. Here, we comprehensively analyzed genetic interactions among these Wnt and receptor genes. In mom-5 mutant backgrounds, additional mutations in Wnt genes disrupted cell polarization. In contrast, in cam-1 mutant backgrounds, Wnt mutations frequently caused abnormal polarity orientation. These findings indicate that MOM-5 and CAM-1 play distinct roles in establishing cell polarization and determining its orientation, respectively. lin-17 mutations suppressed polarity reversal in multiple Wnt compound mutants, suggesting that LIN-17 may function as a molecular switch for polarity orientation. Although all three Wnt genes regulate polarity orientation in a gradient-independent manner in the absence of receptor mutations, in lin-17 mutant backgrounds, reversing the expression gradients of cwn-1 and egl-20, but not cwn-2, enhanced polarity reversal. This suggests that cwn-1 and egl-20 act not only permissively but also instructively to regulate polarity orientation. Together, our results reveal distinct and cooperative functions of multiple Wnt ligands and receptors that ensure robust control of cell polarity.

Standard methods for estimating the population recombination parameter, ρ, are dependent on sampling individual genotypes and calculating various types of disequilibria. However, recent machine learning (ML) approaches to estimating recombination have used pooled sequencing data, which does not sample individual genotypes and cannot be used to calculate disequilibria beyond the length of a single sequence read. Motivated by these results, this study examines the "black box" of such ML methods to understand what signals are being used to infer recombination rates. We find that it is indeed possible to estimate recombination solely using the allele frequency spectrum, and we provide a genealogical interpretation of these results. We further show that even a simplified representation of the allele frequency spectrum can be used to estimate recombination. We demonstrate the accuracy of such inferences using both simulations and data from humans. These results offer a new way to understand the effects of recombination on patterns of sequence data, as well as providing an example of how the internal workings of ML methods can give insight into biological processes.

Recombination is central to genetics and to evolution of sexually reproducing organisms. However, obtaining accurate estimates of recombination rates, and of how they vary along chromosomes, continues to be challenging. To advance our ability to estimate recombination rates, we present Hi-reComb, a new method and software for estimation of recombination maps from bulk gamete chromosome conformation capture sequencing (Hi-C). Simulations show that Hi-reComb produces robust, accurate recombination landscapes. With empirical data from sperm of five fish species we show the advantages of this approach, including joint assessment of recombination maps and large structural variants, map comparisons using bootstrap, and workflows with trio phasing vs. Hi-C phasing. With off-the-shelf library construction and a straightforward rapid workflow, our approach will facilitate routine recombination landscape estimation for a broad range of studies and model organisms in genetics and evolutionary biology. Hi-reComb is open-source and freely available at https://github.com/millanek/Hi-reComb.