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Background The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study. Results We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state. Conclusions Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships.

Type 2 diabetes (T2D) is a very common disease in humans. Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry. We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants. Integration of the gene expression data from blood ( n  = 14,115 and 2765) with the GWAS results identifies 33 putative functional genes for T2D, 3 of which were targeted by approved drugs. A further integration of DNA methylation ( n  = 1980) and epigenomic annotation data highlight 3 genes ( CAMK1D , TP53INP1 , and ATP5G1 ) with plausible regulatory mechanisms, whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. Our study uncovers additional loci, proposes putative genetic regulatory mechanisms for T2D, and provides evidence of purifying selection for T2D-associated variants. GWAS have so far identified 129 loci associated with type 2 diabetes (T2D). Here, the authors meta-analyse three large T2D GWA studies which uncovers 42 additional loci, further prioritize 33 functional genes using eQTL and mQTL data and propose regulatory mechanisms for three putative T2D genes.

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis -expression or -DNA methylation (DNAm) quantitative trait loci ( cis -eQTLs or cis -mQTLs) between brain and blood ( r b ). Using publicly available data, we find that genetic effects at the top cis -eQTLs or mQTLs are highly correlated between independent brain and blood samples ( r ^ b = 0.70 for cis -eQTLs and r ^ b = 0.78 for cis -mQTLs). Using meta-analyzed brain cis -eQTL/mQTL data ( n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes ( n  = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis -eQTL/mQTL data with large sample sizes. To comprehend the genetic regulatory mechanisms underlying brain-related traits in humans, Qi et al. estimate the correlation of expression and DNA methylation QTL effects in cis between blood and brain and show that using blood eQTL/mQTL data of large sample size  can increase power in gene discovery for brain-related traits and diseases.

Most candidate drugs currently fail later-stage clinical trials, largely due to poor prediction of efficacy on early target selection 1 . Drug targets with genetic support are more likely to be therapeutically valid 2 , 3 , but the translational use of genome-scale data such as from genome-wide association studies for drug target discovery in complex diseases remains challenging 4 – 6 . Here, we show that integration of functional genomic and immune-related annotations, together with knowledge of network connectivity, maximizes the informativeness of genetics for target validation, defining the target prioritization landscape for 30 immune traits at the gene and pathway level. We demonstrate how our genetics-led drug target prioritization approach (the priority index) successfully identifies current therapeutics, predicts activity in high-throughput cellular screens (including L1000, CRISPR, mutagenesis and patient-derived cell assays), enables prioritization of under-explored targets and allows for determination of target-level trait relationships. The priority index is an open-access, scalable system accelerating early-stage drug target selection for immune-mediated disease. A genetics-led translational approach integrating functional genomic predictors, knowledge of network connectivity and immune ontologies defines the drug target prioritization landscape for 30 immune traits at the gene and pathway level.

To date, the locus with the most robust human genetic association to COVID-19 severity is 3p21.31. Here, we integrate genome-scale CRISPR loss-of-function screens and eQTLs in diverse cell types and tissues to pinpoint genes underlying COVID-19 risk. Our findings identify SLC6A20 and CXCR6 as putative causal genes that modulate COVID-19 risk and highlight the usefulness of this integrative approach to bridge the divide between correlational and causal studies of human biology.

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may elucidate the underlying biology of disease processes. However, the types of tissues currently collected typically assay a single post-mortem timepoint or are limited to investigating cell types found in blood. Noninvasive tissues may improve disease-relevant discovery by enabling more complex longitudinal study designs, by capturing different and potentially more applicable cell types, and by increasing sample sizes due to reduced collection costs and possible higher enrollment from vulnerable populations. Here, we develop methods for sampling noninvasive biospecimens, investigate their performance across commercial and in-house library preparations, characterize their biology, and assess the feasibility of using noninvasive tissues in a multitude of transcriptomic applications. We collected buccal swabs, hair follicles, saliva, and urine cell pellets from 19 individuals over three to four timepoints, for a total of 300 unique biological samples, which we then prepared with replicates across three library preparations, for a final tally of 472 transcriptomes. Of the four tissues we studied, we found hair follicles and urine cell pellets to be most promising due to the consistency of sample quality, the cell types and expression profiles we observed, and their performance in disease-relevant applications. This is the first study to thoroughly delineate biological and technical features of noninvasive samples and demonstrate their use in a wide array of transcriptomic and clinical analyses. We anticipate future use of these biospecimens will facilitate discovery and development of clinical applications.

Inappropriate activation or inadequate regulation of CD4+ and CD8+ T cells may contribute to the initiation and progression of multiple autoimmune and inflammatory diseases. Studies on disease-associated genetic polymorphisms have highlighted the importance of biological context for many regulatory variants, which is particularly relevant in understanding the genetic regulation of the immune system and its cellular phenotypes. Here we show cell type-specific regulation of transcript levels of genes associated with several autoimmune diseases in CD4+ and CD8+ T cells including a trans-acting regulatory locus at chr12q13.2 containing the rs1131017 SNP in the RPS26 gene. Most remarkably, we identify a common missense variant in IL27, associated with type 1 diabetes that results in decreased functional activity of the protein and reduced expression levels of downstream IRF1 and STAT1 in CD4+ T cells only. Altogether, our results indicate that eQTL mapping in purified T cells provides novel functional insights into polymorphisms and pathways associated with autoimmune diseases.

Background Understanding interindividual variability in medication dosing is central to precision medicine. Despite significant pharmacogenomic (PGx) insights into key biological pathways influencing drug response, the polygenic contribution to dose variability and the potential of electronic health records for maintenance dose estimation remain largely unexplored. Methods We leveraged longitudinal drug purchase data linked to the Estonian Biobank (N = 212,000) to derive individual-level daily doses per purchase as well as median and maximum doses as consolidated metrics across purchases for cardiovascular and psychiatric drugs: statins, warfarin, metoprolol, antidepressants, and antipsychotics. Associations with polygenic scores (PGSs) for 16 traits were assessed using linear mixed models and multivariable regression with a forward stepwise approach. Genome-wide association studies (GWAS) were followed by gene set enrichment analyses for known PGx genes. Results Sample sizes ranged from 684 (antipsychotics) to 20,642 (statins), with median doses reflecting typical maintenance doses. Trait-specific PGSs were significant for the daily dose of statins (coronary heart disease PGS, β = 0.02, P = 5.9 × 10 –10 ) and metoprolol (systolic blood pressure PGS, β = 0.03, P = 7.5 × 10 –13 ). The PGS for body mass index was linked to daily doses of statins (β = 0.02, P = 6.4 × 10 –7 ), metoprolol (β = 0.03, P = 1.4 × 10 –14 ), and warfarin (β = 0.03, P = 0.001), whereas the PGS for educational attainment showed opposing associations with statins (β = − 0.01, P = 5.9 × 10 –4 ) and antidepressants (β = 0.01, P = 0.002). Median and maximum doses yielded similar, though generally weaker, associations. GWAS confirmed signals for metoprolol ( CYP2D6 , P = 1.1 × 10 –20 ) and warfarin ( CYP2C9 , P = 8.9 × 10 –60 ; VKORC1 , P = 4.2 × 10 –148 ), as well as enrichment of PGx signals for individual statins (P = 0.02 for simvastatin, P = 0.03 for atorvastatin). Associations remained significant after adjusting for disease-specific PGSs, suggesting independent contributions of PGx loci. Conclusions These findings illustrate the feasibility and value of leveraging real-world electronic health records to derive pharmacologically meaningful medication dosing phenotypes. Both polygenic and pharmacogenomic signals contribute to dose variability, underscoring their potential utility in personalized prescribing strategies.

Large biobanks have set a new standard for research and innovation in human genomics and implementation of personalized medicine. The Estonian Biobank was founded a quarter of a century ago, and its biological specimens, clinical, health, omics, and lifestyle data have been included in over 800 publications to date. What makes the biobank unique internationally is its translational focus, with active efforts to conduct clinical studies based on genetic findings, and to explore the effects of return of results on participants. In this review, we provide an overview of the Estonian Biobank, highlight its strengths for studying the effects of genetic variation and quantitative phenotypes on health-related traits, development of methods and frameworks for bringing genomics into the clinic, and its role as a driving force for implementing personalized medicine on a national level and beyond. Large-scale biobanks have become a font of data that have led to discoveries across many fields of research. Here, the authors provide an overview of the Estonian Biobank, highlighting its value for research and towards personalized medicine.

BackgroundTreatment and preventative advances for chronic obstructive pulmonary disease (COPD) have been slow due, in part, to limited subphenotypes. We tested if unsupervised machine learning on CT images would discover CT emphysema subtypes with distinct characteristics, prognoses and genetic associations.MethodsNew CT emphysema subtypes were identified by unsupervised machine learning on only the texture and location of emphysematous regions on CT scans from 2853 participants in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS), a COPD case–control study, followed by data reduction. Subtypes were compared with symptoms and physiology among 2949 participants in the population-based Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study and with prognosis among 6658 MESA participants. Associations with genome-wide single-nucleotide-polymorphisms were examined.ResultsThe algorithm discovered six reproducible (interlearner intraclass correlation coefficient, 0.91–1.00) CT emphysema subtypes. The most common subtype in SPIROMICS, the combined bronchitis-apical subtype, was associated with chronic bronchitis, accelerated lung function decline, hospitalisations, deaths, incident airflow limitation and a gene variant near DRD1, which is implicated in mucin hypersecretion (p=1.1 ×10−8). The second, the diffuse subtype was associated with lower weight, respiratory hospitalisations and deaths, and incident airflow limitation. The third was associated with age only. The fourth and fifth visually resembled combined pulmonary fibrosis emphysema and had distinct symptoms, physiology, prognosis and genetic associations. The sixth visually resembled vanishing lung syndrome.ConclusionLarge-scale unsupervised machine learning on CT scans defined six reproducible, familiar CT emphysema subtypes that suggest paths to specific diagnosis and personalised therapies in COPD and pre-COPD.