Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps | Nature Medicine
Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. Here, the International Common Disease Alliance’s PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.
Multivariable association discovery in population-scale meta-omics studies | PLOS Computational Biology
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Here researchers introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. This approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements.
A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments | Nature Machine Intelligence
Sequencing-based RNA structure probing can generate transcriptome-wide profiles of RNA secondary structures. Sufficient structural coverage is needed to obtain unbiased insights about RNA structures and functions, yet probing methods often yield uneven coverage, with missing structural scores across many transcripts. To overcome this barrier, researchers developed StructureImpute, a deep learning framework inspired by depth completion from computer vision that integrates an RNA sequence with available RNA structural information of neighbouring nucleotides to infer missing structure scores.