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The genome‐wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type‐I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective‐risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption‐free model built upon data‐consistent inversion (DCI), which is a recently developed measure‐theoretic framework utilized for uncertainty quantification. This proposed DCI‐derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI‐derived model to cover all genetic variants without emphasizing on additivity of the classic‐GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type‐I error rate at least as well as the classic‐GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

 

Point-of-care (POC) tests for the diagnosis of diseases are critical to the improvement of the standard of living, especially for resource-limited areas or countries. In recent years, nanobiosensors based on noble metal nanoparticles (NM NPs) have emerged as a class of effective and versatile POC testing technology. The unique features of NM NPs ensure great performance of associated POC nanobiosensors. In particular, NM NPs offer various signal transduction principles, such as plasmonics, catalysis, photothermal effect, and so on. Significantly, the detectable signal from NM NPs can be tuned and optimized by controlling the physicochemical parameters (e.g., size, shape, and elemental composition) of NPs. In this article, we introduce the inherent merits of NM NPs that make them attractive for POC testing, discuss recent advancement of NM NPs-based POC tests, highlight their social impacts, and provide perspectives on challenges and opportunities in the field. We hope the review and insights provided in this article can inspire new fundamental and applied research in this emerging field. 

The ability to detect pathogens specifically and sensitively is critical to combat infectious diseases outbreaks and pandemics. Colorimetric assays involving loop‐mediated isothermal amplification (LAMP) provide simple readouts yet suffer from the intrinsic non‐template amplification. Herein, a highly specific and sensitive assay relying on plasmonic sensing of LAMP amplicons via DNA hybridization, termed as plasmonic LAMP, is developed for the severe acute respiratory syndrome‐related coronavirus 2 (SARS‐CoV‐2) RNA detection. This work has two important advances. First, gold and silver (Au–Ag) alloy nanoshells are developed as plasmonic sensors that have 4‐times stronger extinction in the visible wavelengths and give a 20‐times lower detection limit for oligonucleotides over Au counterparts. Second, the integrated method allows cutting the complex LAMP amplicons into short repeats that are amendable for hybridization with oligonucleotide‐functionalized Au–Ag nanoshells. In the SARS‐CoV‐2 RNA detection, plasmonic LAMP takes ≈75 min assay time, achieves a detection limit of 10 copies per reaction, and eliminates the contamination from non‐template amplification. It also shows better detection specificity and sensitivity over commercially available LAMP kits due to the additional sequence identification. This work opens a new route for LAMP amplicon detection and provides a method for virus testing at its early representation.