Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project - Eleanor G. Seaby; N. Simon Thomas; Amy Webb; Helen Brittain; Ana Lisa Taylor Tavares; J. C. Ambrose; P Arumugam; R Bevers; M Bleda; F Boardman-Pretty; C. R. Boustred; H Brittain; et al. (Human Genetics, 2023)
Background Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient’s phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods. Methods The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes with a LOEUF score < 0.2. We compared our findings with the corresponding patient’s diagnostic reports. Results 324/332 (98%) of the variants identified using DeNovoLOEUF were diagnostic or partially diagnostic (whereby the variant was responsible for some of the phenotype). We identified 39 diagnoses that were “missed” by 100KGP standard analyses, which are now being returned to patients. Conclusion We have demonstrated a highly specific and rapid method with a 98% positive predictive value that has good concordance with standard analysis, low false-positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Modelling livelihoods and household resilience to droughts using Bayesian networks - Wendy S. Merritt; Brendan Patch; V. Ratna Reddy; Geoffrey J. Syme (Environment, Development and Sustainability, 2016)

Lead Content and Exposure from Children’s and Adult’s Jewelry Products - R. P. Maas; S. C. Patch; T. J. Pandolfo; J. L. Druhan; N. F. Gandy (Bulletin of Environmental Contamination and Toxicology, 2005)

Inhalation and Ingestion of Phthalate Compounds from Use of Synthetic Modeling Clays - R. P. Maas; S. C. Patch; T. J. Pandolfo (Bulletin of Environmental Contamination and Toxicology, 2004)

Pigment packaging and Chl a ‐specific absorption in high‐light oceanic waters - W. Paul Bissett; Jennifer S. Patch; Kendall L. Carder; Z. Ping Lee (Limnology and Oceanography, 1997)
The absorption of light by particles at a single wavelength, a ph (λ), is reduced with increased packaging of the light absorption material within these particles. This reduction can be described by the parameter : urn:x-wiley:00243590:media:lno19974250961:lno19974250961-math-0002 where a sol (λ) is the theoretical maximum light absorption of the cellular material, a cm , in a completely dissolved state. In practice, the estimations a sol (A) for living phytoplankton are hampered by the process of removing the chloroplast/thylakoid membranes from the organic matrix of the cell, the destruction of the pigment‐protein complexes when an organic solvent is used, and the interference of the excited states of the pigment chromophore by the organic solvent. What is actually being measured by many of the current methods trying to determine a sol (λ) is a om (λ), i.e. the absorption of light by the pigment material in the organic medium of the experiment (methanol, acetone, Triton‐X, etc.). The solvation factor, S, in the above equation is the ratio of the true a sol (λ) to the measured a om (λ). We have developed an internally consistent measure of a ph (λ), a om (λ), Chl a concentration, and pheopigment concentration to determine the value of . This relationship is used to determine a functional relationship for Chl a absorption for high‐light‐adapted, natural phytoplankton populations in optically clear waters. The nonlinear packaging effect in these waters is insignificant at the red end of the spectrum. Exclusion of the weight‐specific absorption of pheopigments and the assumption of a zero a ph (λ) at a zero pigment (Chl a + pheopigment) concentration produces a misleading Chl a‐ specific absorption and a false determination of pigment packaging. An algorithm is developed for predicting Chl a concentration from a p h (675).