193 papers
Genetics is the fascinating study of how traits are passed down and how our DNA shapes everything from eye color to disease risk. At Gist.Science, we bring you the very latest discoveries in this dynamic field directly from bioRxiv, the leading preprint server for biology. Because these findings appear months before formal publication, staying updated requires sifting through complex data that often feels inaccessible to non-specialists.
To bridge that gap, our team processes every new genetics preprint uploaded to bioRxiv, transforming dense scientific reports into clear, plain-language explanations alongside detailed technical summaries. This dual approach ensures that whether you are a seasoned researcher or simply curious about how genes work, you can grasp the core insights without getting lost in jargon. Below are the latest papers in genetics, curated and simplified for your reading.
This study utilizes a large-scale lentiMPRA in human excitatory neurons to functionally characterize over 46,000 noncoding variants, revealing that regulatory impact is driven by local sequence context and baseline element activity rather than population frequency, while establishing a critical resource for improving predictive models of neuronal regulatory variation.
This study demonstrates that combining R10.4.1 flow cell chemistry with advanced basecalling models (specifically HYPv5.0) and quality filtering enables highly accurate autosomal and Y-STR profiling on Nanopore sequencing, achieving up to 100% concordance for single-source forensic samples.
Although myeloid-specific Pck1 deficiency reduced body weight, liver weight, and HDL-cholesterol levels in mice, it did not alter aortic root atherosclerosis, indicating that Pck1 is not an atherosclerosis modifier gene.
This study reveals a consistent, driver-independent tdTomato signal in the adult Drosophila proventriculus of the lexAop-tdTomato.nls reporter line, underscoring the critical need for driver-negative and no-antibody controls to prevent data misinterpretation in gut and proventriculus research.
This study proposes a novel framework that explicitly integrates bottleneck size estimates into selection tests for biological diversity data, thereby improving the precision of distinguishing genetic drift from selective pressure and successfully identifying novel pathogenesis-related genes in *Streptococcus pneumoniae*.
This study demonstrates that two-tower machine learning architectures, particularly TT-LASSO and TT-MLP, effectively predict binary reproductive outcomes and recover latent sex-specific fertility liabilities from genotypic data, offering a powerful framework for modeling the joint genetic contributions of mates in reproductive traits.
This paper presents a mathematical framework for modeling tunable, M-linked sex ratio distorter gene drives in *Aedes aegypti* that leverage the species' homomorphic sex chromosomes to achieve highly efficient, adjustable population suppression while minimizing impacts on neighboring populations.
This study demonstrates that strategically implementing multi-trait genomic prediction models with informative auxiliary traits and optimized sparse phenotyping significantly enhances the predictive accuracy and selection efficiency for key cassava yield traits compared to traditional single-trait approaches.
The paper introduces CATaN, an unsupervised framework that integrates transcription factor-gene regulatory networks with transcriptomes to identify shared regulatory programs significantly enriched for SNP heritability across complex diseases, offering a more comprehensive approach to prioritizing causal variants than transcriptome-based analyses alone.
This study demonstrates that congenital disorders of glycosylation and deglycosylation in zebrafish significantly impair sperm quality by reducing concentration, motility, and membrane integrity, thereby modeling the mechanisms behind human male infertility caused by these genetic defects.