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The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

OBJECTIVE To examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort. RESEARCH DESIGN AND METHODS We collected fecal samples from 5,572 Finns (mean age 48.7 years; 54.1% women) in 2002 who were followed up for incident type 2 diabetes until 31 December 2017. The samples were sequenced using shotgun metagenomics. We examined associations between gut microbiome composition and incident diabetes using multivariable-adjusted Cox regression models. We first used the eastern Finland subpopulation to obtain initial findings and validated these in the western Finland subpopulation. RESULTS Altogether, 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected four species and two clusters consistently associated with incident diabetes in the validation models. These four species were Clostridium citroniae (hazard ratio [HR] 1.21; 95% CI 1.04–1.42), C. bolteae (HR 1.20; 95% CI 1.04–1.39), Tyzzerella nexilis (HR 1.17; 95% CI 1.01–1.36), and Ruminococcus gnavus (HR 1.17; 95% CI 1.01–1.36). The positively associated clusters, cluster 1 (HR 1.18; 95% CI 1.02–1.38) and cluster 5 (HR 1.18; 95% CI 1.02–1.36), mostly consisted of these same species. CONCLUSIONS We observed robust species-level taxonomic features predictive of incident type 2 diabetes over long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome can potentially be used to improve disease prediction and uncover novel therapeutic targets for diabetes.

Data analysis relies heavily on computation, and algorithms have grown more demanding in terms of hardware and energy. Monitoring their environmental impacts is and will continue to be an essential part of sustainable research. Here, we provide guidance on how to do so accurately and with limited overheads.

Longitudinal data from electronic health records (EHR) has immense potential to improve clinical diagnoses and personalised medicine, motivating efforts to identify disease subtypes from age-dependent patient comorbidity information. We introduce an age-dependent topic modelling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR data sets. The model learns, and assigns to each individual, topic weights for several disease topics, each of which reflects a set of diseases that tend to co-occur as a function of age. Simulations show that ATM attains high accuracy in distinguishing distinct age-dependent comorbidity profiles. We applied ATM to 282,957 UK Biobank samples, analysing 1,726,144 disease diagnoses spanning 348 diseases with ≥1,000 incidences. We inferred 10 disease topics optimising model fit. We identified 52 diseases with heterogeneous comorbidity profiles (≥500 incidences assigned to each of ≥2 topics), including breast cancer, type 2 diabetes (T2D), hypertension, and hypercholesterolemia; for most of these diseases, topic assignments were highly age-dependent, suggesting differences in disease aetiology for early-onset vs. late-onset disease. We defined subtypes of the 52 heterogeneous diseases based on the topic assignments, and compared genetic risk across subtypes using polygenic risk scores (PRS). We identified 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease, including a subtype of T2D characterised by cardiovascular comorbidities and a subtype of asthma characterised by dermatological comorbidities. We further identified specific SNPs underlying these differences. For example, the T2D-associated SNP rs1063192 in the CDKN2B locus has a higher odds ratio in the top quartile of cardiovascular topic weight (1.19±0.02) than in the bottom quartile (1.08±0.02) (P=4×10-5 for difference). In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

Neuromorphic architectures have been introduced as platforms for energy efficient spiking neural network execution. The massive parallelism offered by these architectures has also triggered interest from non-machine learning application domains. In order to lift the barriers to entry for hardware designers and application developers we present RANC: a Reconfigurable Architecture for Neuromorphic Computing, an open-source highly flexible ecosystem that enables rapid experimentation with neuromorphic architectures in both software via C++ simulation and hardware via FPGA emulation. We present the utility of the RANC ecosystem by showing its ability to recreate behavior of the IBM's TrueNorth and validate with direct comparison to IBM's Compass simulation environment and published literature. RANC allows optimizing architectures based on application insights as well as prototyping future neuromorphic architectures that can support new classes of applications entirely. We demonstrate the highly parameterized and configurable nature of RANC by studying the impact of architectural changes on improving application mapping efficiency with quantitative analysis based on Alveo U250 FPGA. We present post routing resource usage and throughput analysis across implementations of Synthetic Aperture Radar classification and Vector Matrix Multiplication applications, and demonstrate a neuromorphic architecture that scales to emulating 259K distinct neurons and 73.3M distinct synapses., 18 pages, 12 figures, accepted for publication in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. For associated source files see https://github.com/UA-RCL/RANC

The number of publicly available microbiome samples is continually growing. As data set size increases, bottlenecks arise in standard analytical pipelines. Faith's phylogenetic diversity (Faith's PD) is a highly utilized phylogenetic alpha diversity metric that has thus far failed to effectively scale to trees with millions of vertices. Stacked Faith's phylogenetic diversity (SFPhD) enables calculation of this widely adopted diversity metric at a much larger scale by implementing a computationally efficient algorithm. The algorithm reduces the amount of computational resources required, resulting in more accessible software with a reduced carbon footprint, as compared to previous approaches. The new algorithm produces identical results to the previous method. We further demonstrate that the phylogenetic aspect of Faith's PD provides increased power in detecting diversity differences between younger and older populations in the FINRISK study's metagenomic data.

Background and Purpose: Polygenic risk scores (PRSs) can be used to predict ischemic stroke (IS). However, further validation of PRS performance is required in independent populations, particularly older adults in whom the majority of strokes occur. Methods: We predicted risk of incident IS events in a population of 12 792 healthy older individuals enrolled in the ASPREE trial (Aspirin in Reducing Events in the Elderly). The PRS was calculated using 3.6 million genetic variants. Participants had no previous history of cardiovascular events, dementia, or persistent physical disability at enrollment. The primary outcome was IS over 5 years, with stroke subtypes as secondary outcomes. A multivariable model including conventional risk factors was applied and reevaluated after adding PRS. Area under the curve and net reclassification were evaluated. Results: At baseline, mean population age was 75 years. In total, 173 incident IS events occurred over a median follow-up of 4.7 years. When PRS was added to the multivariable model as a continuous variable, it was independently associated with IS (hazard ratio, 1.41 [95% CI, 1.20–1.65] per SD of the PRS; P P =0.004) compared with the lowest. The area under the curve of the conventional model was 66.6% (95% CI, 62.2–71.1) and after inclusion of the PRS, improved to 68.5 ([95% CI, 64.0–73.0] P =0.095). In subgroup analysis, the continuous PRS remained an independent predictor for large vessel and cardioembolic stroke subtypes but not for small vessel stroke. Reclassification was improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.17–0.43). Conclusions: PRS predicts incident IS in a healthy older population but only moderately improves prediction over conventional risk factors. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01038583.

Background: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. Methods: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. Results: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values 105 mL·min –1 ·1.73 m –2 , compared with those with eGFR between 60 and 105 mL·min –1 ·1.73 m –2 . Mendelian randomization analyses for CHD showed an association among participants with eGFR –1 ·1.73 m –2 , with a 14% (95% CI, 3%–27%) higher CHD risk per 5 mL·min –1 ·1.73 m –2 lower genetically predicted eGFR, but not for those with eGFR >105 mL·min –1 ·1.73 m –2 . Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. Conclusions: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.

Whole genome sequencing (WGS) and phenotypic drug susceptibility testing was performed on a collection of 2,542Mycobacterium tuberculosis (Mtb)isolates from tuberculosis (TB) patients recruited in Ho Chi Minh City (HCMC), Vietnam, to investigateMtbdiversity, the prevalence and phylodynamics of drug resistance, andin silicoresistance prediction with sequencing data. Amongst isolates tested phenotypically against first-line drugs, we observed high rates of streptomycin [STR, 37.7% (N=573/1,520)] and isoniazid resistance [INH, 25.7% (N=459/1,786)], and lower rates of resistance to rifampicin [RIF, 4.9% (N=87/1,786)] and ethambutol [EMB, 4.2% (N=75/1,785)]. Resistance to STR and INH was predicted moderately well when applying the TB-Profiler algorithm to WGS data (sensitivities of 0.81 and 0.87 respectively), while resistance to RIF and EMB was predicted relatively poorly (sensitivities of 0.70 and 0.44 respectively). Rates of multidrug-resistance [(MDR, 3.9% (N=69/1,786)], and resistance to a number of second-line drugs [Para-aminosalicylic acid (29.6% N=79/267), Amikacin (15.4% N=41/267) and Moxifloxacin (21.3%), N=57/267], were found to be high within a global context. Comparing rates of drug resistance among lineages, and exploring the dynamics of resistance acquisition through time, suggest the Beijing lineage (lineage 2.2) acquiresde novoresistance mutations at higher rates and suffers no apparent fitness cost acting to impede the transmission of resistance. We infer resistance to INH and STR to have arisen earlier, on average, than resistance to RIF, and to be more widespread across the phylogeny. The high prevalence of ‘background’ INH resistance, combined with high rates of RIF mono-resistance (20.7%, N=18/87) suggests that rapid assays for INH resistance will be valuable in this setting. These tests will allow the detection of INH mono-resistance, and will allow MDR isolates to be distinguished from isolates with RIF mono-resistance.

BackgroundTo provide quantitative evidence of the use of polygenic risk scores (PRS) for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment.Methods108,685 participants aged 40-69, with measured biomarkers, linked primary care records and genetic data in UK Biobank were used for model derivation and population health modelling. Prioritisation tools using age, PRS for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. Rescaling to account for the healthy cohort effect, we modelled the implications of initiating guideline-recommended statin therapy after prioritising individuals for invitation to a formal CVD risk assessment.Results1,838 CVD events were observed over median follow up of 8.2 years. If primary care records were used to prioritise individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false negative rates then we would capture 65% and 43% events amongst men and women respectively. The numbers of men and women needed to be screened to prevent one CVD event (NNS) are 74 and 140 respectively. In contrast, adding PRS to both prioritisation and formal assessments, and selecting thresholds to capture the same number of events resulted in a NNS of 60 for men and 90 for women.ConclusionThe use of PRS together with primary care records to prioritise individuals at highest risk of a CVD event for a formal CVD risk assessment can more efficiently prioritise those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of formal risk assessments in primary care while still preventing the same number CVD events.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for >45 000 published GWAS across >5000 human traits, and >40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

Summary Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.

Author(s): Koponen, Kari K; Salosensaari, Aaro; Ruuskanen, Matti O; Havulinna, Aki S; Mannisto, Satu; Jousilahti, Pekka; Palmu, Joonatan; Salido, Rodolfo; Sanders, Karenina; Brennan, Caitriona; Humphrey, Gregory C; Sanders, Jon G; Meric, Guillaume; Cheng, Susan; Inouye, Michael; Jain, Mohit; Niiranen, Teemu J; Valsta, Liisa M; Knight, Rob; Salomaa, Veikko V | Abstract: BackgroundDiet has a major influence on the human gut microbiota, which has been linked to health and disease. However, epidemiological studies on associations of a healthy diet with the microbiota utilizing a whole-diet approach are still scant.ObjectivesTo assess associations between healthy food choices and human gut microbiota composition, and to determine the strength of association with functional potential.MethodsThis population-based study sample consisted of 4930 participants (ages 25-74; 53% women) in the FINRISK 2002 study. Intakes of recommended foods were assessed using a food propensity questionnaire, and responses were transformed into healthy food choices (HFC) scores. Microbial diversity (alpha diversity) and compositional differences (beta diversity) and their associations with the HFC score and its components were assessed using linear regression. Multiple permutational multivariate ANOVAs were run from whole-metagenome shallow shotgun-sequenced samples. Associations between specific taxa and HFC were analyzed using linear regression. Functional associations were derived from Kyoto Encyclopedia of Genes and Genomes orthologies with linear regression models.ResultsBoth microbial alpha diversity (β/SD, 0.044; SE, 6.18n×n10-5; Pn=n2.21n×n10-3) and beta diversity (R2, 0.12; Pn≤n1.00n×n10-3) were associated with the HFC score. For alpha diversity, the strongest associations were observed for fiber-rich breads, poultry, fruits, and low-fat cheeses (all positive). For beta diversity, the most prominent associations were observed for vegetables, followed by berries and fruits. Genera with fiber-degrading and SCFA-producing capacities were positively associated with the HFC score. The HFC score was associated positively with functions such as SCFA metabolism and synthesis, and inversely with functions such as fatty acid biosynthesis and the sulfur relay system.ConclusionsOur results from a large, population-based survey confirm and extend findings of other, smaller-scale studies that plant- and fiber-rich dietary choices are associated with a more diverse and compositionally distinct microbiota, and with a greater potential to produce SCFAs.

Introduction: Screening for type 1 diabetes (T1D) genetic risk in early life can prevent life threatening complications such as diabetic ketoacidosis and allow cost-effective recruitment into intervention and immunotherapy trials. Celiac disease (CD) frequently presents as a comorbidity in those with T1D and shares a strong genetic basis with T1D. Single nucleotide polymorphism (SNP) based genetic risk scores (GRS) for both T1D and CD have shown to be very predictive of future disease but are difficult to generate and typically require SNP array genotyping. We aimed to develop a combined GRS screening panel that could be genotyped from a single dried blood spot at birth. Methods: We developed assays for proxy SNPs of common HLA-DQ haplotypes reported in previous GRS and additional loci from the most recent available genome-wide association studies (GWAS) . We used backwards stepwise regression to identify a subset of variants able to be genotyped with DNA eluted from 800 6mm dried blood spots. We developed and validated neural network models to quantify genetic risk of both T1D and CD using T1DGC and UK Celiac case-control SNP array data, validated in UK Biobank. Assays were developed with LGC Genomics and validated on 675 Seattle area samples. Results: The complete panel consisted of 71 validated SNP assays, including 11 backup variants for key loci. We generated neural nework models which demonstrate equivalent or greater predictive power (AUC: T1D=0.914, CD=0.893) to previously published GRS yet require much less expertise to apply. We have developed an algorithm for preparation of raw genotyping data and subsequent generation of GRS requiring little expertise to apply. Conclusion: A 71 SNP blood-spot screening panel is highly effective at screening genetic risk associated with T1D and CD at birth. Using a neural network model the panel enables widely available, easy to generate and inexpensive population screening of genetic risk for T1D and CD. Disclosure S. A. Sharp: None. J. M. Locke: None. Y. Xu: None. D. P. Fraser: None. L. A. Ferrat: None. M. N. Weedon: None. M. Inouye: None. R. A. Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. W. Hagopian: Research Support; Janssen Research & Development, LLC. Funding Diabetes UK (16/0005529) JDRF (3-SRA-2019-827-S-B)

We present the Polygenic Score (PGS) Catalog ( https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

BackgroundThe gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults.Objectives3To investigate the associations between gut microbiome and respiratory disease and to construct predictive models from baseline gut microbiome profiles for incident asthma or chronic obstructive pulmonary disease (COPD).MethodsShallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N=7,115 adults) with linked national administrative health register derived classifications for incident asthma and COPD up to 15 years after baseline. Generalised linear models and Cox regressions were utilised to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index and smoking status.ResultsA total of 695 and 392 significant microbial associations at different taxonomic levels were found with incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome predicted incident asthma and COPD with mean area under the curves of 0.608 and 0.780, respectively. For both incident asthma and COPD, the baseline gut microbiome had C-indices of 0.623 for asthma and 0.817 for COPD, which were more predictive than other conventional risk factors. The integration of gut microbiome and conventional risk factors further improved prediction capacities. Subgroup analyses indicated gut microbiome was significantly associated with incident COPD in both current smokers and non-smokers, as well as in individuals who reported never smoking.ConclusionsThe gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

IntroductionPeptide markers of inflammation have been associated with the development of type 2 diabetes. The role of upstream, lipid-derived mediators of inflammation such as eicosanoids, remains less clear. The aim of this study was to examine whether eicosanoids are associated with incident type 2 diabetes.Research design & methodsIn the FINRISK (Finnish Cardiovascular Risk Study) 2002 study, a population-based sample of Finnish men and women aged 25–74 years, we used directed, non-targeted liquid chromatography-mass spectrometry to identify 545 eicosanoids and related oxylipins in the participants’ plasma samples (n=8292). We used multivariable-adjusted Cox regression to examine associations between eicosanoids and incident type 2 diabetes. The significant independent findings were replicated in the Framingham Heart Study (FHS, n=2886) and DIetary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome (DILGOM) 2007 (n=3905). Together, these three cohorts had 1070 cases of incident type 2 diabetes.ResultsIn the FINRISK 2002 cohort, 76 eicosanoids were associated individually with incident type 2 diabetes. We identified three eicosanoids independently associated with incident type 2 diabetes using stepwise Cox regression with forward selection and a Bonferroni-corrected inclusion threshold. A three-eicosanoid risk score produced an HR of 1.56 (95% CI 1.41 to 1.72) per 1 SD increment for risk of incident diabetes. The HR for comparing the top quartile with the lowest was 2.80 (95% CI 2.53 to 3.07). In the replication analyses, the three-eicosanoid risk score was significant in FHS (HR 1.24 (95% CI 1.10 to 1.39, pConclusionsPlasma eicosanoid profiles predict incident type 2 diabetes and the clearest signals replicate in three independent cohorts. Our findings give new information on the biology underlying type 2 diabetes and suggest opportunities for early identification of people at risk.

BACKGROUND: Genome-wide polygenic scores (GPS) integrate information from many common DNA variants into a single number. Because rates of coronary artery disease (CAD) are substantially higher among South Asians, a GPS to identify high-risk individuals may be particularly useful in this population. OBJECTIVES: This analysis used summary statistics from a prior genome-wide association study to derive a new GPSCAD for South Asians. METHODS: This GPSCAD was validated in 7,244 South Asian UK Biobank participants and tested in 491 individuals from a case-control study in Bangladesh. Next, a static ancestry and GPSCAD reference distribution was built using whole-genome sequencing from 1,522 Indian individuals, and a framework was tested for projecting individuals onto this static ancestry and GPSCAD reference distribution using 1,800 CAD cases and 1,163 control subjects newly recruited in India. RESULTS: The GPSCAD, containing 6,630,150 common DNA variants, had an odds ratio (OR) per SD of 1.58 in South Asian UK Biobank participants and 1.60 in the Bangladeshi study (p

BACKGROUND: Microbial factors are likely to be involved in the recurrence of Crohn’s disease (CD) after bowel resection. We investigated the luminal microbiota before and longitudinally after surgery, in relation to disease recurrence, using 16S metagenomic techniques. METHODS: In the prospective Post-Operative Crohn’s Endoscopic Recurrence (POCER) study, fecal samples were obtained before surgery and 6, 12, and 18 months after surgery from 130 CD patients. Endoscopy was undertaken to detect disease recurrence, defined as Rutgeerts score ≥i2, at 6 months in two-thirds of patients and all patients at 18 months after surgery. The V2 region of the 16S rRNA gene was sequenced using Illumina MiSeq. Cluster analysis was performed at family level, assessing microbiome community differences between patients with and without recurrence. RESULTS: Six microbial cluster groups were identified. The cluster associated with maintenance of remission was enriched for the Lachnospiraceae family [adjusted OR 0.47 (0.27–0.82), P = .007]. The OTU diversity of Lachnospiraceae within this cluster was significantly greater than in all other clusters. The cluster enriched for Enterobacteriaceae was associated with an increased risk of disease recurrence [adjusted OR 6.35 (1.24–32.44), P = .026]. OTU diversity of Enterobacteriaceae within this cluster was significantly greater than in other clusters. CONCLUSIONS: Luminal bacterial communities are associated with protection from, and the occurrence of, Crohn’s disease recurrence after surgery. Recurrence may relate to a higher abundance of facultatively anaerobic pathobionts from the Enterobacteriaceae family. The ecologic change of depleted Lachnospiraceae, a genus of butyrate-producing bacteria, may permit expansion of Enterobacteriaceae through luminal environmental perturbation.

Funder: Wellcome Trust, Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this work, we estimate the carbon footprint of bioinformatics (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org, last accessed 2022). We assessed 1) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics, and molecular simulations, as well as 2) computation strategies, such as parallelization, CPU (central processing unit) versus GPU (graphics processing unit), cloud versus local computing infrastructure, and geography. In particular, we found that biobank-scale GWAS emitted substantial kgCO2e and simple software upgrades could make it greener, for example, upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Moreover, switching from the average data center to a more efficient one can reduce carbon footprint by approximately 34%. Memory over-allocation can also be a substantial contributor to an algorithm's greenhouse gas emissions. The use of faster processors or greater parallelization reduces running time but can lead to greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimize kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.

Protein-protein interactions (PPIs) are essential to understanding biological pathways as well as their roles in development and disease. Computational tools have been successful at predicting PPIsin silico, but the lack of consistent and reliable frameworks for this task has led to network models that are difficult to compare and discrepancies between algorithms that remain unexplained. To better understand the underlying inference mechanisms that underpin these models, we designed B4PPI, an open-source framework for benchmarking that accounts for a range of biological and statistical pitfalls while facilitating reproducibility. We use B4PPI to shed light on the impact of network topology and how different algorithms deal with highly connected proteins. By studying functional genomics-based and sequence-based models (two of the most popular approaches) on human PPIs, we show their complementarity as the former performs best on lone proteins while the latter specialises in interactions involving hubs. We also show that algorithm design has little impact on performance with functional genomic data. We replicate our results between both human andS. cerevisiaedata and demonstrate that models using functional genomics are better suited to PPI prediction across species. With rapidly increasing amounts of sequence and functional genomics data, our study provides a principled foundation for future construction, comparison and application of PPI networks.

Co-evolution between humans and the microbial communities colonizing them has resulted in an intimate assembly of thousands of microbial species mutualistically living on and in their body and impacting multiple aspects of host physiology and health. Several studies examining whether human genetic variation can affect gut microbiota suggest a complex combination of environmental and host factors. Here, we leverage a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial shotgun metagenomes, dietary information and health records up to 16 years post-sampling, to characterize human genetic variations associated with microbial abundances, and predict possible causal links with various diseases using Mendelian randomization (MR). Genome-wide association study (GWAS) identified 583 independent SNP-taxon associations at genome-wide significance (p-8), which included notable strong associations withLCT(p=5.02×10-35),ABO(p=1.1×10-12), andMED13L(p=1.84×10-12). A combination of genetics and dietary habits was shown to strongly shape the abundances of certain key bacterial members of the gut microbiota, and explain their genetic association. Genetic effects from theLCTlocus onBifidobacteriumand three other associated taxa significantly differed according to dairy intake. Variation in mucin-degradingFaecalicatena lactarisabundances were associated withABO, highlighting a preferential utilization of secreted A/B/AB-antigens as energy source in the gut, irrespectively of fibre intake.Enterococcus faecalislevels showed a robust association with a variant inMED13L, with putative links to colorectal cancer. Finally, we identified putative causal relationships between gut microbes and complex diseases using MR, with a predicted effect ofMorganellaon major depressive disorder that was consistent with observational incident disease analysis. Overall, we present striking examples of the intricate relationship between humans and their gut microbial communities, and highlight important health implications.

Background: The use of a polygenic risk score (PRS) to improve risk prediction of coronary heart disease (CHD) events has been demonstrated to have clinical utility in the general adult population. However, the prognostic value of a PRS for CHD has not been examined specifically in older populations of individuals aged ≥70 years, who comprise a distinct high-risk subgroup. The objective of this study was to evaluate the predictive value of a PRS for incident CHD events in a prospective cohort of older individuals without a history of cardiovascular events. Methods: We used data from 12 792 genotyped, healthy older individuals enrolled into the ASPREE trial (Aspirin in Reducing Events in the Elderly), a randomized double-blind placebo-controlled clinical trial investigating the effect of daily 100 mg aspirin on disability-free survival. Participants had no previous history of diagnosed atherothrombotic cardiovascular events, dementia, or persistent physical disability at enrollment. We calculated a PRS (meta-genomic risk score) consisting of 1.7 million genetic variants. The primary outcome was a composite of incident myocardial infarction or CHD death over 5 years. Results: At baseline, the median population age was 73.9 years, and 54.9% were female. In total, 254 incident CHD events occurred. When the PRS was added to conventional risk factors, it was independently associated with CHD (hazard ratio, 1.24 [95% CI, 1.08–1.42], P =0.002). The area under the curve of the conventional model was 70.53 (95% CI, 67.00–74.06), and after inclusion of the PRS increased to 71.78 (95% CI, 68.32–75.24, P =0.019), demonstrating improved prediction. Reclassification was also improved, as the continuous net reclassification index after adding PRS to the conventional model was 0.25 (95% CI, 0.15–0.28). Conclusion: A PRS for CHD performs well in older people and improves prediction over conventional cardiovascular risk factors. Our study provides evidence that genomic risk prediction for CHD has clinical utility in individuals aged 70 years and older. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01038583

OBJECTIVETo examine the previously unknown long-term association between gut microbiome composition and incident type 2 diabetes in a representative population cohort.RESEARCH DESIGN AND METHODSWe collected fecal samples of 5 572 Finns (mean age 48.7 years, 54.1% women) in 2002 who were followed up for incident type 2 diabetes until Dec 31st, 2017. The samples were sequenced using shotgun metagenomics. We examined associations between gut microbiome compositions and incident diabetes using multivariable-adjusted Cox regression models. We first used the Eastern Finland sub-population to obtain initial findings and validated these in the Western Finland sub-population.RESULTSAltogether 432 cases of incident diabetes occurred over the median follow-up of 15.8 years. We detected 4 species and 2 clusters consistently associated with incident diabetes in the validation models. These 4 species were Clostridium citroniae (HR, 1.21; 95% CI, 1.04-1.42), C. bolteae (HR, 1.20; 95% CI, 1.04-1.39), Tyzzerella nexilis (HR, 1.17; 95% CI, 1.01-1.36), and Ruminococcus gnavus (HR = 1.17; 95% CI, 1.01-1.36). The positively associated clusters, cluster 1 (HR, 1.18; 95% CI, 1.02-1.38) and cluster 5 (HR, 1.18; 95% CI, 1.02-1.36), mostly consisted of these same species.CONCLUSIONSWe observed robust species-level taxonomic features predictive of incident type 2 diabetes over a long-term follow-up. These findings build on and extend previous mainly cross-sectional evidence and further support links between dietary habits, metabolic diseases, and type 2 diabetes that are modulated by the gut microbiome. The gut microbiome could potentially be used to improve risk prediction and to uncover novel therapeutic targets for diabetes.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1–3. Polygenic scores (PGS) aggregate these into a metric representing an individual’s genetic predisposition to disease. PGS have shown promise for early risk prediction4–7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus., Ritchie et al. use polygenic scores to identify plasma proteins with causal roles in cardiometabolic disease.

For Alzheimer’s disease–a leading cause of dementia and global morbidity–improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer’s disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer’s disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer’s disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer’s disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer’s disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy for 249 circulating metabolites, lipids, and lipoprotein sub-fractions has recently become available in UK Biobank for approximately 121,657 participants. Here, we describe procedures for quality control and removal of technical variation for this biomarker data. We show that technical and biological effects with linear effects on individual biomarkers can combine in a non-linear fashion on (the 61) composite biomarkers and (81) biomarker ratios, and thus composite biomarkers and ratios should be re-derived after removal of unwanted variation to avoid introducing such cryptic effects. We make available an R package, ukbnmr, for extracting the metabolic biomarker data from UK Biobank and removing the unwanted technical variation. We also make available code for re-deriving the 61 composite biomarkers and 81 ratios, and for further derivation of 76 additional biomarker ratios of potential biological significance. Finally, we demonstrate that our removal of technical variation leads to increased signal for genetic and epidemiological studies of the NMR metabolic biomarkers in UK Biobank.

There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.

Funder: Wellcome Trust, BackgroundAdvancements in cancer therapeutics have resulted in increases in cancer-related survival; however, there is a growing clinical dilemma. The current balancing of survival benefits and future cardiotoxic harms of oncotherapies has resulted in an increased burden of cardiovascular disease in breast cancer survivors. Risk stratification may help address this clinical dilemma. This study is the first to assess the association between a coronary artery disease-specific polygenic risk score and incident coronary artery events in female breast cancer survivors.MethodsWe utilized the Studies in Epidemiology and Research in Cancer Heredity prospective cohort involving 12,413 women with breast cancer with genotype information and without a baseline history of cardiovascular disease. Cause-specific hazard ratios for association of the polygenic risk score and incident coronary artery disease (CAD) were obtained using left-truncated Cox regression adjusting for age, genotype array, conventional risk factors such as smoking and body mass index, as well as other sociodemographic, lifestyle, and medical variables.ResultsOver a median follow-up of 10.3 years (IQR: 16.8) years, 750 incident fatal or non-fatal coronary artery events were recorded. A 1 standard deviation higher polygenic risk score was associated with an adjusted hazard ratio of 1.33 (95% CI 1.20, 1.47) for incident CAD.ConclusionsThis study provides evidence that a coronary artery disease-specific polygenic risk score can risk-stratify breast cancer survivors independently of other established cardiovascular risk factors.

We integrated lipidomics and genomics to unravel the genetic architecture of lipid metabolism and identify genetic variants associated with lipid species that are putatively in the mechanistic pathway to coronary artery disease (CAD). We quantified 596 lipid species in serum from 4,492 phenotyped individuals from the Busselton Health Study. In our discovery GWAS we identified 667 independent loci associations with these lipid species (479 novel), followed by meta-analysis and validation in two independent cohorts. Lipid endophenotypes (134) identified for CAD were associated with variation at 186 genomic loci. Associations between independent lipid-loci with coronary atherosclerosis were assessed in ∼456,000 individuals from the UK Biobank. Of the 53 lipid-loci that showed evidence of association (P−3), 43 loci were associated with at least one of the 134 lipid endophenotypes. The findings of this study illustrate the value of integrative biology to investigate the genetics and lipid metabolism in the aetiology of atherosclerosis and CAD, with implications for other complex diseases.

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

AimPeptide markers of inflammation have been associated with the development of type 2 diabetes. The role of upstream, lipid-derived mediators of inflammation such as eicosanoids, remains less clear. The aim was to examine whether eicosanoids are associated with incident type 2 diabetes.MethodsIn the FINRISK 2002 study, a population-based sample of Finnish men and women aged 25-74 years, we used directed, non-targeted liquid chromatography – mass spectrometry to identify 545 eicosanoids and related oxylipins in the participants’ plasma samples (n=8,292). We used multivariable-adjusted Cox regression to examine associations between eicosanoids and incident type 2 diabetes. The findings were replicated in the Framingham Heart Study (FHS, n=2,886) and DILGOM 2007 (n=3,905). Together, these three cohorts had 1070 cases of incident type 2 diabetes.Results76 eicosanoids were associated individually with incident type 2 diabetes. We identified three eicosanoids independently associated with incident type 2 diabetes using stepwise Cox regression with forward selection and a Bonferroni-corrected inclusion threshold. A three-eicosanoid risk score produced a hazard ratio (HR) of 1.56 (95% confidence interval 1.41-1.72) per one standard deviation (SD) increment for risk of incident diabetes. The HR for comparing the top quartile to the lowest was 2.80 (2.53-3.07). Meta-analysis of the three cohorts yielded a pooled HR per SD of 1.31 (1.05-1.56).ConclusionPlasma eicosanoid profiles predict incident type 2 diabetes and the clearest signals replicate in three independent cohorts. Our findings give new information on the biology underlying type 2 diabetes and suggest opportunities for early identification of people at risk.

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, contribute to genetic disease and comorbidities much more than non-allosteric proteins, and there is an association between being allosteric and involvement in disease; 2) they are enriched in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system; 3) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 4) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and less due to their dynamical properties.

BackgroundThe 10-year Atherosclerotic Cardiovascular Disease (ASCVD) risk score is the standard approach to predict risk of incident cardiovascular events and recently, addition of CAD polygenic scores (PGSCAD) have been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined.ObjectivesThis study performed an in-depth evaluation of age and sex effects in genetic CAD risk prediction.MethodsThe population-based Norwegian HUNT2 cohort of 51,036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372,410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards and Harrell’s concordance index, sensitivity, and specificity were compared.ResultsInclusion of age and sex interactions of PGSCAD to the prediction models increased C-index and sensitivity likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. The two-step approach identified a total of 82.6% of incident CAD cases (74.1% by ASCVD risk score and an additional 8.5% by the PGSCAD interaction model).ConclusionThese findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age and sex-interactions terms with polygenic scores to optimize detection of individuals at high-risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.CONDENSED ABSTRACTThis study used two large population-based longitudinal datasets to evaluate genetic prediction of CAD including age and sex interactions. The model fit and sensitivity of the prediction models increased when including age and sex interaction of PGSCAD to the prediction models likely countering the observed survival bias in the baseline. The sensitivity for females was lower than for males in all models including genetic information. Our results highlight the importance and complexity of genetic risk and suggest including age and sex interactions with polygenic scores to identify more high-risk individuals for preventive interventions.

BackgroundIndividuals with South Asian ancestry have higher risk of heart disease than other groups in Western countries; however, most genetic research has focused on European-ancestry (EUR) individuals. It is unknown whether reported genetic loci and polygenic scores (PGSs) for cardiometabolic traits are transferable to South Asians, and whether PGSs have utility in clinical settings.MethodsUsing data from 22,000 British Pakistani and Bangladeshi individuals with linked electronic health records from the Genes & Health cohort (G&H), we conducted genome-wide association studies (GWAS) and characterised the genetic architecture of coronary artery disease (CAD), body mass index (BMI), lipid biomarkers and blood pressure. We applied a new technique to assess the extent to which loci from GWAS in EUR samples were transferable. We tested how well existing findings from EUR studies performed in genetic risk prediction and Mendelian randomisation in G&H.ResultsTrans-ancestry genetic correlations between G&H and EUR samples for the tested traits were not significantly lower than 1, except for BMI (rg=0.85, p=0.02). We found evidence for transferability for the vast majority of loci from EUR discovery studies that were sufficiently powered to replicate in G&H. PGSs showed variable transferability in G&H, with the relative accuracy compared to EUR (ratio of incremental r2/AUC) ≥0.95 for HDL-C, triglycerides, and blood pressure, but lower for BMI (0.78) and CAD (0.42). We observed significant improvement in categorical net reclassification in G&H (NRI=3.9%; 95% CI 0.9–7.0) when adding a previously developed CAD PGS to clinical risk factors (QRISK3). We used transferable loci as genetic instruments in trans-ancestry Mendelian randomisation and found evidence of an increased CAD risk for higher LDL-C and BMI, and for lower HDL-C in G&H, consistent with our findings for EUR samples.ConclusionsThe genetic loci for CAD and its risk factors are largely transferable from EUR studies to British Pakistanis and Bangladeshis, whereas the transferability of PGSs varies greatly between traits. Our analyses suggest clinical utility for addition of PGS to existing clinical risk prediction tools for this population.Clinical PerspectiveWhat is new?This is the first study to explore the transferability of GWAS findings and PGSs for CAD and related cardiometabolic traits in British Pakistani and Bangladeshi individuals from a cohort with real-world electronic clinical data.We propose a new approach to assessing transferability of GWAS loci between populations, which can serve as a new methodological standard in this developing field.We find evidence of overall high transferability of GWAS loci in British Pakistanis and Bangladeshis. BMI, lipids and blood pressure show the highest transferability of loci, and CAD the lowest.The transferability of PGSs varied between traits, being high for HDL-C, triglycerides and blood pressure but more modest for CAD, BMI and LDL-C.Our results suggest that, for some traits, the use of transferable GWAS loci improves the robustness of Mendelian randomisation estimates in non-Europeans.What are the clinical implications?The polygenic score for CAD derived from genetic studies of European individuals improves reclassification on top of clinical risk factors in British Pakistanis and Bangladeshis. The improvement was driven by identification of more cases in younger individuals (25–54 years old), and of controls in older individuals (55–84 years old).Incorporation of the polygenic score for CAD into risk prediction models is likely to prevent cardiovascular events and deaths in this population.

Climate change is profoundly affecting nearly all aspects of life on earth, including human societies, economies, and health. Various human activities are responsible for significant greenhouse gas (GHG) emissions, including data centers and other sources of large‐scale computation. Although many important scientific milestones are achieved thanks to the development of high‐performance computing, the resultant environmental impact is underappreciated. In this work, a methodological framework to estimate the carbon footprint of any computational task in a standardized and reliable way is presented and metrics to contextualize GHG emissions are defined. A freely available online tool, Green Algorithms (www.green‐algorithms.org) is developed, which enables a user to estimate and report the carbon footprint of their computation. The tool easily integrates with computational processes as it requires minimal information and does not interfere with existing code, while also accounting for a broad range of hardware configurations. Finally, the GHG emissions of algorithms used for particle physics simulations, weather forecasts, and natural language processing are quantified. Taken together, this study develops a simple generalizable framework and freely available tool to quantify the carbon footprint of nearly any computation. Combined with recommendations to minimize unnecessary CO2 emissions, the authors hope to raise awareness and facilitate greener computation., The Green Algorithms framework estimates the carbon footprint of computation in a simple and reliable way. It is shown that many research activities have substantial footprints and the freely available online app (www.green‐algorithms.org) empowers researchers to assess the impact of their own work, alongside a list of simple ways to reduce it.