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 reveals that epigenetic aging in solid tumors is not uniformly accelerated but exhibits bidirectional, tissue-specific shifts, with some cancers showing age acceleration and others deceleration, driven primarily by subclonal methylation events that disproportionately affect the X chromosome.
This study validates a low-input RNA extraction and transcriptomic profiling protocol using Deep Brain Stimulation microelectrodes to successfully recover and characterize brain-specific, subcortical gene expression from post-mortem human tissue, establishing a methodological framework for future in vivo molecular profiling of the basal ganglia in Parkinson's disease patients.
This study estimates Residual Variation Intolerance Scores for protein-coding genes and domains in cattle, revealing patterns of variant tolerance consistent with other species and providing valuable resources for analyzing deleterious variants and population genomics in cattle.
Using long-read nanopore sequencing, researchers identified and characterized a previously undescribed Tyr::CreERT2 allele with a unique Chromosome 1 integration and genomic deletion in a widely used melanoma mouse stock, providing essential genotyping tools to ensure the accuracy of future genetic studies.
This paper demonstrates that the CERIS-JGRA method's reliance on environmental indices highly correlated with mean performance inherently suppresses the statistical power to detect genuine phenotypic plasticity loci by minimizing the orthogonal "residual channel" necessary for such discovery, thereby often misidentifying mean-performance loci as plasticity drivers.
This study identifies NPR-14, a G-protein-coupled receptor expressed in specific sensory and motor neurons of *C. elegans*, as a wake-promoting regulator that inhibits sleep-like quiescence by suppressing the EGL-4/PKG signaling pathway, thereby linking neuropeptide signaling to arousal and metabolic homeostasis.
This study demonstrates that CRISPR-mediated fission of the *Arabidopsis thaliana* centromere generates stable neo-chromosomes that, when crossed with wild type, form meiotic trivalents whose segregation and distorted inheritance are governed by crossover position and centromere strength.
This study demonstrates that under the diffuse polygenic architecture and long-range linkage disequilibrium typical of livestock populations, widely used GWAS methods often generate misleadingly strong, localized associations that reflect accumulated tiny effects rather than true causal variants, whereas full-genomic relationship matrix mixed models like SLEMM effectively suppress this spillover to enable more accurate biological localization.
This study evaluates four decades of genomic selection in Brazilian cassava breeding, demonstrating significant realized genetic gains for yield traits and improved model calibration over time, while highlighting the need to manage unfavorable correlations with quality traits and maintain genetic diversity for long-term sustainability.
This paper introduces a simple, closed-form mathematical method using standard linear algebra to directly estimate the fitness of individual genotypes from noisy time-series data without requiring assumptions about epistasis or iterative optimization, demonstrating its effectiveness across diverse simulation models and real-world datasets ranging from laboratory evolution to global pandemics.