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Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2OOO to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model. In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes—namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae. Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94–0·95; sensitivity of 0·94 [0·94–0·95] and specificity of 0·82 [0·81–0·83]) and 0·93 (0·92–0·93; sensitivity of 0·90 [0·89–0·90] and specificity of 0·88 [0·87–0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91–0·91; sensitivity of 0·84 [0·83–0·84] and specificity of 0·83 [0·82–0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0–1·0], 0·2% prevalence; decile 6: 11 [3·9–31], 3·1% prevalence; and decile 10: 56 [20–158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines. Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. National Institutes of Health.

Background Genome-wide association studies (GWAS) have identified common variants associated with metabolic dysfunction-associated steatotic liver disease (MASLD). However, rare coding variant studies have been limited by phenotyping challenges and small sample sizes. We test associations of rare and ultra-rare coding variants with proton density fat fraction (PDFF) and MASLD case–control status in 736,010 participants of diverse ancestries from the UK Biobank, All of Us, and BioMe and performed a trans-ancestral meta-analysis. We then developed models to accurately predict PDFF and MASLD status in the UK Biobank and tested associations with these predicted phenotypes to increase statistical power. Results The trans-ancestral meta-analysis with PDFF and MASLD case–control status identifies two single variants and two gene-level associations in APOB , CDH5 , MYCBP2 , and XAB2 . Association testing with predicted phenotypes, which replicates more known genetic variants from GWAS than true phenotypes, identifies 16 single variants and 11 gene-level associations implicating 23 additional genes. Two variants were polymorphic only among African ancestry participants and several associations showed significant heterogeneity in ancestry and sex-stratified analyses. In total, we identified 27 genes, of which 3 are monogenic causes of steatosis ( APOB , G6PC1 , PPARG ), 4 were previously associated with MASLD ( APOB , APOC3 , INSR , PPARG ), and 23 had supporting clinical, experimental, and/or genetic evidence. Conclusions Our results suggest that trans-ancestral association analyses can identify ancestry-specific rare and ultra-rare coding variants in MASLD pathogenesis. Furthermore, we demonstrate the utility of machine learning in genetic investigations of difficult-to-phenotype diseases in trans-ancestral biobanks.

Many drug failures in clinical trials are due to inadequate safety profiles. We developed an in-silico side effect genetic priority score (SE-GPS) that leverages human genetic evidence to inform side effect risk for a given drug target. We construct the SE-GPS in the Open Target dataset using post-marketing side effect data, externally test it in OnSIDES using side effects reported from drug labels and then generate a SE-GPS for 19,422 protein coding genes and 502 phecodes, of which 1.7% had a SE-GPS > 0. To consider drug mechanism, we incorporated the direction of genetic effect into a directional version of the score called the SE-GPS-DOE. We observe that restricting to at least two lines of genetic evidence conferred a 2.3- and 2.5-fold increased risk in side effects in Open Targets and OnSIDES respectively, with increased enrichments in severe drugs. We make all predictions publicly available in a web portal. Here the authors develop a genetic priority score to predict side effects by integrating multiple lines of genetic evidence. By applying this score, they provide evidence of known side effects and suggest ones with no clinical trial evidence.

Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants was recently made possible by the development of high-density arrays that permit the genotyping of hundreds of thousands of polymorphisms. We tested 392,935 single-nucleotide polymorphisms in a French case–control cohort. Markers with the most significant difference in genotype frequencies between cases of type 2 diabetes and controls were fast-tracked for testing in a second cohort. This identified four loci containing variants that confer type 2 diabetes risk, in addition to confirming the known association with the TCF7L2 gene. These loci include a non-synonymous polymorphism in the zinc transporter SLC30A8, which is expressed exclusively in insulin-producing β-cells, and two linkage disequilibrium blocks that contain genes potentially involved in β-cell development or function ( IDE–KIF11–HHEX and EXT2–ALX4 ). These associations explain a substantial portion of disease risk and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits. Diabetes in the genes Overeating and physical inactivity are major causes of type 2 diabetes mellitus, but they affect only genetically susceptible individuals and the genetic basis of the disease is notoriously complex. Recent research has suggested that specific genes may be associated with the risk of developing the disease, however. Now a genome-wide search using high-density genotyping arrays has identified four previously unknown genes as diabetes risk factors, and confirmed a known association with the TCF7L2 gene. Together these five genes may contribute a sizeable fraction of the disease risk in type 2 diabetes, and analysis of their function should clarify the pathogenesis of diabetes and point to new drug targets. In addition, individuals shown to have these mutations could minimize their risk by adjusting diet. A survey of the entire human genome has found that five genetic loci contribute a large fraction of disease risk in type 2 diabetes.

Identifying genetic drivers of chronic diseases is necessary for drug discovery. Here, we develop a machine learning-assisted genetic priority score, which we call ML-GPS, that incorporates genetic associations with predicted disease phenotypes to enhance target discovery. First, we construct gradient boosting models to predict 112 chronic disease phecodes in the UK Biobank and analyze associations of predicted and observed phenotypes with common, rare, and ultra-rare variants to model the allelic series. We integrate these associations with existing evidence using gradient boosting with continuous feature encoding to construct ML-GPS, training it to predict drug indications in Open Targets and externally testing it in SIDER. We then generate ML-GPS predictions for 2,362,636 gene-phecode pairs. We find that the use of predicted phenotypes, which identify substantially more genetic associations than observed phenotypes across the allele frequency spectrum, significantly improves the performance of ML-GPS. ML-GPS increases coverage of drug targets, with the top 1% of all scores providing support for 15,077 gene-phecode pairs that previously had no support. ML-GPS can also identify well-known target-disease relationships, promising targets without indicated drugs, and targets for several drugs in clinical trials, including LRRK2 inhibitors for Parkinson’s disease and olpasiran for cardiovascular disease. Here, the authors introduce ML-GPS, a machine learning framework that prioritizes drug targets for 112 chronic diseases and integrates genetic associations with predicted phenotypes.

Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing. Early diagnosis can significantly improve treatment options and prevent severe organ damage in individuals with autoimmune diseases. Here, the authors develop a machine learning model that uses electronic health records to identify patients with clinical suspicion of autoimmune diseases.

Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH, and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems. Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p<0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR (MVMR) controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels. Our results identified seven disease traits reaching Bonferroni-corrected significance in both the discovery (p<1.92 × 10 ) and replication analyses (p<2.26 × 10 ), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p=2.47 × 10 ). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p<0.05), identifying plasma TG levels as a novel potential risk factor for nine non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p=1.17 × 10 ). Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies. RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).

Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries. A phenome-wide genomic analysis on the UK Biobank cohort links >9,000 co-localized genes and >1,400 human phenotypes across a broad selection of human tissues, representing a useful resource for the investigation of relationships between gene expression, tissues, and human diseases.

Dietary interventions involving caloric restriction represent a powerful strategy to prevent or delay age-related deteriorations and diseases. Their beneficial effects have been observed in several tissues and species. This microarray study investigated the effects of aging, long-term moderate caloric restriction (LTMCR) and long-term dietary soy on the regulation of gene expression in the anterior pituitary and hypothalamus of 20-month-old Sprague-Dawley rats. In both tissues, aging regulated genes mainly involved in cell defense and repair mechanisms related to apoptosis, DNA repair, cellular stress, inflammatory and immune response. In the aging pituitary, the highest upregulated gene was the regenerating islet-derived 3β (5.77-fold), coding for a secretory protein involved in acute stress and inflammation. A protective effect of LTMCR on age-related change of gene expression was observed for 35 pituitary genes. In addition, beneficial effects of LTMCR in the pituitary were observed on new regulated genes mainly involved in cell death and cell stress response. In the hypothalamus, the effects of LTMCR on age-related changes were modest. Finally, changing the quality of dietary protein (20% casein for soy) had a low impact on the regulation of mRNA levels in both tissues. Genes associated with the somatotroph function were also differentially expressed in the aging pituitary. Interestingly, LTMCR prevented the effect of aging on insulin-like growth factor-binding protein-3 gene. Altogether, this study proposes novel pituitary and hypothalamic molecular targets and signaling pathways to help in understanding the mechanisms involved in aging processes and LTMCR.