121 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 stochastic agent-based model to demonstrate that the population structure of *Plasmodium falciparum* and the reliability of genomic surveillance metrics are determined not by transmission intensity alone, but by its complex, nonlinear interaction with standing genetic diversity across a transmission gradient.
Heterologous expression of the human cohesin complex in *Saccharomyces cerevisiae* fails to rescue yeast cohesin mutants and instead induces a dominant-negative phenotype by forming dysfunctional hybrid complexes with endogenous yeast cohesin rings, leading to cohesion dysregulation and DNA damage sensitivity.
The paper introduces ARGMix, a graph transformer that leverages ancient DNA within an ancestral recombination graph framework to significantly improve the accuracy and robustness of local ancestry inference, particularly for older admixture events where traditional segment-based methods struggle.
This study proposes reclassifying glycopeptide antibiotics into the broader class of xyclopeptides, subdivided into dalabactins (classical types I–IV) and murobactins (type V), based on structural and evolutionary analyses that reveal a fundamental divergence between these two groups.
This paper introduces and validates a novel statistical model using the expectation-maximization algorithm to accurately estimate population-level frequencies of *Plasmodium falciparum* haplotypes with *Pfhrp2/3* deletions, overcoming the limitations of standard molecular assays in detecting these deletions within mixed-clone infections.
This paper demonstrates that omitting heritable, mortality-relevant extrinsic risk factors from survival models causes the latent intrinsic frailty variance to absorb this omitted structure, thereby systematically inflating inferred intrinsic lifespan heritability estimates by approximately 9 percentage points.
This study demonstrates that adult-derived polygenic g scores effectively chart the cognitive development of children, showing minimal predictive power in toddlerhood that increases substantially to explain 12% of variance in early adulthood while also correlating with educational achievement and faster cognitive growth.
This study presents a preliminary 16S rRNA metagenomic analysis of six soil samples from North-Central Argentine agroecosystems, revealing a dominance of copiotrophic bacteria alongside specific oligotrophic taxa and characterizing the abundance of agronomically relevant genera to expand understanding of microbial diversity beyond the Pampas region.
This study presents a unified multi-kernel mixed model that integrates oligogenic and polygenic indirect genetic effects to effectively dissect the genetic architecture of group performance, revealing evidence of intergenotypic competition in woody plants and identifying specific competitive variants in apple trees.
This paper introduces a Bayesian Varying Coefficient Model (BVCM) that successfully deciphers the genetic architecture of time-dependent phenotypic plasticity across diverse species by integrating temporal and genetic multivariate structures, thereby detecting dynamic QTLs and reducing missing heritability compared to traditional time-by-time association methods.