Keratin 16 spatially inhibits type I interferon responses in stressed skin.

The stress-induced keratin intermediate filament gene/protein (K16) is spatially restricted to the suprabasal compartment of the epidermis and extensively used as a biomarker for psoriasis, hidradenitis suppurativa, atopic dermatitis and other inflammatory disorders. However, its role in these conditions remains poorly defined. Here we show that K16 negatively regulates type-I interferon (IFN) signaling and innate immune responses.

Polygenic risk scores in the clinic: a systematic review of stakeholders' perspectives, attitudes, and experiences.

Polygenic Risk Scores (PRS) are statistical methods estimating part of an individual's genetic susceptibility to various disease phenotypes. Their potential clinical applications to enhance the prediction, prevention, and risk management of complex conditions motivate current research efforts worldwide. While a growing body of literature has highlighted the scientific and ethical limitations of PRS, the technology's clinical translation will present both opportunities and challenges for the stakeholders involved.

Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping.

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.

Genetic ancestry in population pharmacogenomics unravels distinct geographical patterns related to drug toxicity.

Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals from publicly available datasets to assess the risk proximity of experiencing drug-related adverse events. Our findings indicate that Admixed Americans and Europeans have demonstrated a higher risk of experiencing drug toxicity, whereas individuals with East Asian ancestry and, to a lesser extent, Oceanians displayed a lower risk proximity.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs.