We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p<5x10-8), including 237 attaining a more stringent trans-ancestry threshold (p<5x10-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

Skull bone mineral density (SK-BMD) provides a suitable trait for the discovery of genes important to bone biology in general, and particularly for identifying components unique to intramembranous ossification, which cannot be captured at other skeletal sites. We assessed genetic determinants of SK-BMD in 43,800 individuals, identifying 59 genome-wide significant loci (4 novel), explaining 12.5% of its variance. Pathway and enrichment analyses of the association signals resulted in clustering within gene-sets involved in regulating the development of the skeleton; overexpressed in the musculoskeletal system; and enriched in enhancer and transcribed regions in osteoblasts. From the four novel loci (mapping to ZIC1, PRKAR1A, ATP6V1C1, GLRX3), two (ZIC1 and PRKAR1A) have previously been related to craniofacial developmental defects. Functional validation of skull development in zebrafish revealed abnormal cranial bone initiation that culminated in ectopic sutures and reduced BMD in mutated zic1 and atp6v1c1 fish and asymmetric bone growth and elevated BMD in mutated prkar1a fish. We confirmed a role of ZIC1 loss-of-function in suture patterning and discovered ATP6V1C1 gene associated with suture development. In light of the evidence presented suggesting that SK-BMD is genetically related to craniofacial abnormalities, our study opens new avenues to the understanding of the pathophysiology of craniofacial defects and towards the effective pharmacological treatment of bone diseases.

words Text 4160 words Journal Subject Terms:Genetic, Association Studies; ECG; Epidemiology; Race and Ethnicity All rights reserved. No reuse allowed without permission.was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/363457 doi: bioRxiv preprint first posted online Jul. 6, 2018; 4 ABSTRACT Background. The electrocardiographically quantified QRS duration measures

Motivation: Genome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C).These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.Results: The SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations * Corresponding author Email address: joel.bader@jhu.edu (Joel S. Bader) Preprint submitted to Nature Communications February 28, 2018peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/273540 doi: bioRxiv preprint first posted online Feb. 28, 2018; at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.

Prediction of mortality from COVID-19 infection might help triage patients to hospitalization and intensive care. To estimate the risk of inpatient mortality, we analyzed the data of 13,190 adult patients in the New York City Health + Hospitals system admitted for COVID-19 infection from March 1 to June 30, 2020. They had a mean age 58 years, 40% were Latinx, 29% Black, 9% White and 22% of other races/ethnicities and 2,875 died. We used Machine learning (Gradient Boosted Decision Trees; XGBoost) to select predictors of inpatient mortality from demographics, vital signs and lab tests results from initial encounters. XGBoost identified O2 saturation, systolic and diastolic blood pressure, pulse rate, respiratory rate, age, and BUN with an Area Under the Receiver Operating Characteristics Curve = 94%. We applied CART to find cut-points in these variables, logistic regression to calculate odds-ratios for those categories, and assigned points to the categories to develop a score. A score = 0 indicates a 0.8% (95% confidence interval, 0.5 – 1.0%) risk of dying and ≥ 12 points indicates a 98% (97-99%) risk, and other scores have intermediate risks. We translated the models into an online calculator for the probability of mortality with 95% confidence intervals (as pictured):Abstract Figuredanielevanslab.shinyapps.io/COVID_mortality/