A framework for conducting GWAS using repeated measures data with an application to childhood BMI.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in GWAS. Using childhood BMI as an example trait, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound.

comorbidPGS: An R Package Assessing Shared Predisposition between Phenotypes Using Polygenic Scores.

Introduction: Polygenic score (PGS) is a valuable method for assessing the estimated genetic liability to a given outcome or genetic variability contributing to a quantitative trait. While polygenic risk scores are widely used for complex traits, their application in uncovering shared genetic predisposition between phenotypes, i.e.

Mendelian Randomization Analysis Identifies Inverse Causal Relationship between External Eating and Metabolic Phenotypes.

Disordered eating contributes to weight gain, obesity, and type 2 diabetes (T2D), but the precise mechanisms underlying the development of different eating patterns and connecting them to specific metabolic phenotypes remain unclear. We aimed to identify genetic variants linked to eating behaviour and investigate its causal relationships with metabolic traits using Mendelian randomization (MR). We tested associations between 30 genetic variants and eating patterns in individuals with T2D from the Volga-Ural region and investigated causal relationships between variants associated with eating patterns and various metabolic and anthropometric traits using data from the Volga-Ural population and large international consortia.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound.

Machine Learning to Advance Human Genome-Wide Association Studies.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity.

Association of Blood MicroRNA Expression and Polymorphisms with Cognitive and Biomarker Changes in Older Adults.

Background: Identifying individuals before the onset of overt symptoms is key in the prevention of Alzheimer's disease (AD).

Objectives: Investigate the use of miRNA as early blood-biomarker of cognitive decline in older adults.

Design: Cross-sectional.

Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is characterised by pulmonary vascular remodelling causing premature death from right heart failure. Established DNA variants influence PAH risk, but susceptibility from epigenetic changes is unknown. We addressed this through epigenome-wide association study (EWAS), testing 865,848 CpG sites for association with PAH in 429 individuals with PAH and 1226 controls.

51st European Mathematical Genetics Meeting (EMGM) 2023.

NA.

GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification.

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals.

A Combined Effect of Polygenic Scores and Environmental Factors on Individual Differences in Depression Level.

The risk of depression could be evaluated through its multifactorial nature using the polygenic score (PGS) approach. Assuming a "clinical continuum" hypothesis of mental diseases, a preliminary assessment of individuals with elevated risk for developing depression in a non-clinical group is of high relevance. In turn, epidemiological studies suggest including social/lifestyle factors together with PGS to address the "missing heritability" problem.

Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes.

Objective: Depression is a common comorbidity of type 2 diabetes. We assessed the causal relationships and shared genetics between them.

Research Design And Methods: We applied two-sample, bidirectional Mendelian randomization (MR) to assess causality between type 2 diabetes and depression.

Abdominal obesity is a more important causal risk factor for pancreatic cancer than overall obesity.

Obesity and type 2 diabetes (T2D) are associated with increased risk of pancreatic cancer. Here we assessed the relationship between pancreatic cancer and two distinct measures of obesity, namely total adiposity, using BMI, versus abdominal adiposity, using BMI adjusted waist-to-hip ratio (WHRadjBMI) by utilising polygenic scores (PGS) and Mendelian randomisation (MR) analyses. We constructed z-score weighted PGS for BMI and WHRadjBMI using publicly available data and tested for their association with pancreatic cancer defined in UK biobank (UKBB).

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes.

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the rs6749704 (OR = 1.68, P = 3.

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis.

Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.

MicroRNA Processing Pathway-Based Polygenic Score for Clear Cell Renal Cell Carcinoma in the Volga-Ural Region Populations of Eurasian Continent.

The polygenic scores (PGSs) are developed to help clinicians in distinguishing individuals at high risk of developing disease outcomes from the general population. Clear cell renal cell carcinoma (ccRCC) is a complex disorder that involves numerous biological pathways, one of the most important of which is responsible for the microRNA biogenesis machinery. Here, we defined the biological-pathway-specific PGS in a case-control study of ccRCC in the Volga-Ural region of the Eurasia continent.

Structure and biological activity of particles produced from highly activated carbon adsorbent.

Over the recent years, carbon particles have gained relevance in the field of biomedical application to diminish the level of endo-/exogenous intoxication and oxidative stress products, which occur at different pathological states. However, it is very important that such carbon particles, specially developed for parenteral administration or usage, possess a high adsorption potential and can remove hazard toxic substances of the hydrophilic, hydrophobic and amphiphilic nature usually accumulated in the blood due to the disease, and be absolutely safe for normal living cells and tissues of organism. In this work, the stable monodisperse suspension containing very small-sized (D = 1125.

Is Enriched in Gut Microbiome of Kashmiri Women with Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a very common endocrine condition in women in India. Gut microbiome alterations were shown to be involved in PCOS, yet it is remarkably understudied in Indian women who have a higher incidence of PCOS as compared to other ethnic populations. During the regional PCOS screening program among young women, we recruited 19 drug naive women with PCOS and 20 control women at the Sher-i-Kashmir Institute of Medical Sciences, Kashmir, North India.