Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data.

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites.

Building predictive Markov models of ion channel permeation from molecular dynamics simulations.

Molecular dynamics (MD) simulation of biological processes has always been a challenging task due to the long timescales of the processes involved and the large amount of output data to handle. Markov state models (MSMs) have been introduced as a powerful tool in this area of research, as they provide a mechanistically comprehensible synthesis of the large amount of MD data and, at the same time, can be used to rapidly estimate experimental properties of biological processes. Herein, we propose a method for building MSMs of ion channel permeation from MD trajectories, which directly evaluates the current flowing through the channel from the model's transition matrix (T), which is crucial for comparing simulations and experimental data.

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk.

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases.

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features.

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147-173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity.

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death.

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI).