Fast polypharmacy side effect prediction using tensor factorization.

Motivation: Adverse reactions from drug combinations are increasingly common, making their accurate prediction a crucial challenge in modern medicine. Laboratory-based identification of these reactions is insufficient due to the combinatorial nature of the problem. While many computational approaches have been proposed, tensor factorization (TF) models have shown mixed results, necessitating a thorough investigation of their capabilities when properly optimized.

Triangulating evidence in health sciences with Annotated Semantic Queries.

Motivation: Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence "triangulation" presents a number of challenges for systematically identifying and integrating relevant information. These include the harmonization of heterogenous evidence with common semantic concepts and properties, as well as the priortization of the retrieved evidence for triangulation with the question of interest.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

Methods: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; n=51 929; n=39 980) and subsequent arterial ischemic stroke (AIS; n=45 120; n=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank.

DrivR-Base: a feature extraction toolkit for variant effect prediction model construction.

Motivation: Recent advancements in sequencing technologies have led to the discovery of numerous variants in the human genome. However, understanding their precise roles in diseases remains challenging due to their complex functional mechanisms. Various methodologies have emerged to predict the pathogenic significance of these genetic variants.

Design and quality control of large-scale two-sample Mendelian randomization studies.

Background: Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses.

Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke.

Methods: We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (N=51,929, N=39,980) and subsequent arterial ischemic stroke (AIS) N=45,120, N=46,789) after first incident stroke within the Million Veteran Program and UK Biobank.

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation.

Objective: Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques.

Effects of the maternal and fetal proteome on birth weight: a Mendelian randomization analysis.

Background: Fetal growth is an important indicator of survival, regulated by maternal and fetal genetic and environmental factors. However, little is known about the underlying molecular mechanisms. Proteins play a major role in a wide range of biological processes and could provide key insights into maternal and fetal molecular mechanisms regulating fetal growth.

The tumor multi-omic landscape of endometrial cancers developed on a germline genetic background of adiposity.

High body mass index (BMI) is a causal risk factor for endometrial cancer but the tumor molecular mechanisms affected by adiposity and their therapeutic relevance remain poorly understood. Here we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic exposure to elevated BMI. We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women.

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases.

Objective: To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable.

Methods: We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications.

Efficacy of metformin targets on cardiometabolic health in the general population and non-diabetic individuals: a Mendelian randomization study.

Background: Metformin shows beneficial effects on cardiometabolic health in diabetic individuals. However, the beneficial effects in the general population, especially in non-diabetic individuals are unclear. We aim to estimate the effects of perturbation of seven metformin targets on cardiometabolic health using Mendelian randomization (MR).

Triangulating evidence from observational and Mendelian randomization studies of ketone bodies for cognitive performance.

Background: Ketone bodies (KBs) are an alternative energy supply for brain functions when glucose is limited. The most abundant ketone metabolite, 3-β-hydroxybutyrate (BOHBUT), has been suggested to prevent or delay cognitive impairment, but the evidence remains unclear. We triangulated observational and Mendelian randomization (MR) studies to investigate the association and causation between KBs and cognitive function.

Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets.

Motivation: Human traits are typically represented in both the biomedical literature and large population studies as descriptive text strings. Whilst a number of ontologies exist, none of these perfectly represent the entire human phenome and exposome. Mapping trait names across large datasets is therefore time-consuming and challenging.

Linking Physical Activity to Breast Cancer via Inflammation, Part 2: The Effect of Inflammation on Breast Cancer Risk.

This review synthesized and appraised the evidence for an effect of inflammation on breast cancer risk. Systematic searches identified prospective cohort and Mendelian randomization studies relevant to this review. Meta-analysis of 13 biomarkers of inflammation were conducted to appraise the evidence for an effect breast cancer risk; we examined the dose-response of these associations.

Linking Physical Activity to Breast Cancer Risk via Inflammation, Part 1: The Effect of Physical Activity on Inflammation.

The protective effect of physical activity on breast cancer incidence may partially be mediated by inflammation. Systematic searches of Medline, EMBASE, and SPORTDiscus were performed to identify intervention studies, Mendelian randomization studies, and prospective cohort studies that examined the effects of physical activity on circulating inflammatory biomarkers in adult women. Meta-analyses were performed to generate effect estimates.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis- and trans-expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets (n ≤ 2,759). Many of the 16,169 cortex cis-eQTLs were tissue-dependent when compared with blood cis-eQTLs.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.

Biomedical consequences of elevated cholesterol-containing lipoproteins and apolipoproteins on cardiovascular and non-cardiovascular outcomes.

Background: Higher concentrations of cholesterol-containing low-density lipoprotein (LDL-C) increase the risk of cardiovascular disease (CVD). The association of LDL-C with non-CVD traits remains unclear, as are the possible independent contributions of other cholesterol-containing lipoproteins and apolipoproteins.

Methods: Nuclear magnetic resonance spectroscopy was used to measure the cholesterol content of high density (HDL-C), very low-density (VLDL-C), intermediate-density (IDL-C), as well as low-density lipoprotein fractions, the apolipoproteins Apo-A1 and Apo-B, as well as total triglycerides (TG), remnant-cholesterol (Rem-Chol) and total cholesterol (TC).

Association between genetically proxied PCSK9 inhibition and prostate cancer risk: A Mendelian randomisation study.

Background: Prostate cancer (PrCa) is the second most prevalent malignancy in men worldwide. Observational studies have linked the use of low-density lipoprotein cholesterol (LDL-c) lowering therapies with reduced risk of PrCa, which may potentially be attributable to confounding factors. In this study, we performed a drug target Mendelian randomisation (MR) analysis to evaluate the association of genetically proxied inhibition of LDL-c-lowering drug targets on risk of PrCa.