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 demonstrates that natural variation in pericentromeric repeat copy number in fission yeast tunes heterochromatin dosage, where larger repeats act as sinks for limiting regulatory factors to compromise chromosome segregation under stress and alter gene expression.
This study characterizes the transcriptomic landscapes of two newly established *Rhodnius prolixus* embryo-derived cell lines (RPE/LULS53 and RPE/LULS57), revealing distinct gene expression profiles and cellular phenotypes that provide valuable resources for future genetic and functional research on this Chagas disease vector.
CRISPRtile is a cloud-based AI platform that overcomes the limitations of conventional structure-based drug design by generating high-accuracy functional and toxicity landscapes from CRISPR data to enable the systematic discovery of safe, brain-penetrant drug modulators, as demonstrated by its successful mapping of the NLRP3 inflammasome.
This study demonstrates that a modified haplotype-based likelihood framework, saltiLASSI, outperforms traditional site frequency spectrum methods in detecting selective sweeps within ancient populations with complex demographic histories, particularly when sweeps involve admixture or recent selection events.
The paper introduces SIQReg, a data-driven method that optimizes phenotype scales in large-scale biobanks to eliminate statistical artifacts and significantly enhance the power and accuracy of both additive and non-additive genetic analyses.
This study establishes the first genome-wide CRISPR-Cas9 knockout screening platform in the Lyme disease vector *Ixodes scapularis*, successfully identifying genes essential for cellular fitness and resistance to specific stressors to provide the first large-scale experimental evidence of gene function in this tick species.
This study reveals significant discordance between plasma and brain protein quantitative trait loci (pQTLs), demonstrating that while circulating pQTLs effectively capture systemic and immune pathways, tissue-specific data is crucial for accurately interpreting brain-related protein associations and prioritizing therapeutic targets.
The paper introduces MCNV2, an R package that utilizes Mendelian inheritance patterns in parent-offspring trios to compute Mendelian Precision as a standardized, reproducible metric for assessing and optimizing the quality of copy number variation calls.
This paper introduces Knowledge Inclusive Machine Learning (KIML), a novel paradigm that integrates experimental data with literature-derived and structured biomedical knowledge to significantly improve the accuracy, interpretability, and generalizability of disease gene prioritization compared to existing methods.
This study demonstrates that integrating multi-year, fine-scale genetic sampling is essential for accurately characterizing the temporally variable population structure of juvenile southern flounder, revealing distinct Gulf and Atlantic stocks while highlighting inconsistent genetic differentiation within regions that single-year or broad-scale surveys might miss.