A novel statistical framework and approach using sibling phenotype pairs allows inference of genetic architecture in the tails of complex traits to be made without genetic data.

Many published findings about population history that rely on inference of admixture graph models fitted to f-statistics are not robust since the method is generally inappropriate for extracting new information about population history.

A whole brain analysis using human neuroimaging data shows neural correlates of individual differences in conditioned fear.

A comprehensive package for pedigree-based risk modeling, with a highly optimized computational back-end, extends existing models beyond syndrome-specific approaches and incorporates data from panel studies by allowing for an arbitrary number of gene and syndrome associations.

The inclusion of the patient couple's clinical features along with blastocyst images increases live birth prediction accuracy.

The integration of pHLA binding and pHLA-TCR analysis effectively improves the precision of insilico neoantigen prioritization, potentially enhancing personalized immunotherapies for colorectal cancer.

GrimAge acceleration may increase the risk of colorectal cancer.

A new approach for fast, sparse neural circuit mapping allows circuit structure, physiology, and behavior to be quantified in the same animal and across many animals.

MorphoGraphX summarizes full 3D datasets as curved surface images (2.5D), enabling the efficient quantification of growth and gene expression data from large time-lapse volumetric datasets.

An open-source python package for phenotype analyses provides a versatile, modular and user-friendly solution to determine complementary fitness-related traits from large-scale assays of microbial colonies.