New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Prevalence and descriptive epidemiology of Turner syndrome in the United States, 2000-2017: A report from the National Birth Defects Prevention Network

The lack of United States population-based data on Turner syndrome limits assessments of prevalence and associated characteristics for this sex chromosome abnormality. Therefore, we collated 2000-2017 data from seven birth defects surveillance programs within the National Birth Defects Prevention Network. We estimated the prevalence of karyotype-confirmed Turner syndrome diagnosed within the first year of life. We also calculated the proportion of cases with commonly ascertained birth defects, assessed associations with maternal and infant characteristics using prevalence ratios (PR) with 95% confidence intervals (CI), and estimated survival probability. The prevalence of Turner syndrome of any pregnancy outcome was 3.2 per 10,000 female live births (95% CI = 3.0-3.3, program range: 1.0-10.4), and 1.9 for live birth and stillbirth (≥20 weeks gestation) cases (95% CI = 1.8-2.1, program range: 0.2-3.9). Prevalence was lowest among cases born to non-Hispanic Black women compared to non-Hispanic White women (PR = 0.5, 95% CI = 0.4-0.6). Coarctation of the aorta was the most common defect (11.6% of cases), and across the cohort, individuals without hypoplastic left heart had a five-year survival probability of 94.6%. The findings from this population-based study may inform surveillance practices, prenatal counseling, and diagnosis. We also identified racial and ethnic disparities in prevalence, an observation that warrants further investigation

A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species

We thank the countless individuals who collected and/or processed the nearly 85,000 images used in this study and those who assisted, particularly those who sorted these images from the millions that did not end up in the catalogues. Additionally, we thank the other Kaggle competitors who helped develop the ideas, models and data used here, particularly those who released their datasets to the public. The graduate assistantship for Philip T. Patton was funded by the NOAA Fisheries QUEST Fellowship. This paper represents HIMB and SOEST contribution numbers 1932 and 11679, respectively. The technical support and advanced computing resources from University of Hawaii Information Technology Services—Cyberinfrastructure, funded in part by the National Science Foundation CC* awards # 2201428 and # 2232862 are gratefully acknowledged. Every photo–identification image was collected under permits according to relevant national guidelines, regulation and legislation.Peer reviewedPublisher PD

The IRF5–TNPO3 association with systemic lupus erythematosus has two components that other autoimmune disorders variably share

Exploiting genotyping, DNA sequencing, imputation and trans-ancestral mapping, we used Bayesian and frequentist approaches to model the IRF5-TNPO3 locus association, now implicated in two immunotherapies and seven autoimmune diseases. Specifically, in systemic lupus erythematosus (SLE), we resolved separate associations in the IRF5 promoter (all ancestries) and with an extended European haplotype. We captured 3230 IRF5-TNPO3 high-quality, common variants across 5 ethnicities in 8395 SLE cases and 7367 controls. The genetic effect from the IRF5 promoter can be explained by any one of four variants in 5.7 kb (P-valuemeta = 6 × 10(-49); OR = 1.38-1.97). The second genetic effect spanned an 85.5-kb, 24-variant haplotype that included the genes IRF5 and TNPO3 (P-valuesEU = 10(-27)-10(-32), OR = 1.7-1.81). Many variants at the IRF5 locus with previously assigned biological function are not members of either final credible set of potential causal variants identified herein. In addition to the known biologically functional variants, we demonstrated that the risk allele of rs4728142, a variant in the promoter among the lowest frequentist probability and highest Bayesian posterior probability, was correlated with IRF5 expression and differentially binds the transcription factor ZBTB3. Our analytical strategy provides a novel framework for future studies aimed at dissecting etiological genetic effects. Finally, both SLE elements of the statistical model appear to operate in Sjögren's syndrome and systemic sclerosis whereas only the IRF5-TNPO3 gene-spanning haplotype is associated with primary biliary cirrhosis, demonstrating the nuance of similarity and difference in autoimmune disease risk mechanisms at IRF5-TNPO3

Virology—the path forward

In the United States (US), biosafety and biosecurity oversight of research on viruses is being reappraised. Safety in virology research is paramount and oversight frameworks should be reviewed periodically. Changes should be made with care, however, to avoid impeding science that is essential for rapidly reducing and responding to pandemic threats as well as addressing more common challenges caused by infectious diseases. Decades of research uniquely positioned the US to be able to respond to the COVID-19 crisis with astounding speed, delivering life-saving vaccines within a year of identifying the virus. We should embolden and empower this strength, which is a vital part of protecting the health, economy, and security of US citizens. Herein, we offer our perspectives on priorities for revised rules governing virology research in the US

Vasovagal tonus index (VVTI) as an indirect assessment of remission status in canine multicentric lymphoma undergoing multi-drug chemotherapy

Vasovagal tonus index (VVTI) is an indirect measure of heart rate variability and may serve as a marker of disease severity. Higher heart rate variability has predicted lower tumour burden and improved survival in humans with various tumour types. The purpose of this pilot study was to evaluate VVTI as a biomarker of remission status in canine lymphoma. The primary hypothesis was that VVTI would be increased in dogs in remission compared to dogs out of remission. Twenty-seven dogs were prospectively enrolled if they had a diagnosis of intermediate to high-grade lymphoma and underwent multidrug chemotherapy. Serial electrocardiogram data were collected under standard conditions and relationships between VVTI, remission status and other clinical variables were evaluated. VVTI from dogs in remission (partial or complete) did not differ from dogs with fulminant lymphoma (naive or at time of relapse). Dogs in partial remission had higher VVTI than dogs in complete remission (p&nbsp;=&nbsp;0.021). Higher baseline VVTI was associated with higher subsequent scores (p&nbsp;&lt;&nbsp;0.001). VVTI also correlated with anxiety level (p&nbsp;=&nbsp;0.03). Based on this pilot study, VVTI did not hold any obvious promise as a useful clinical biomarker of remission status. Further investigation may better elucidate the clinical and prognostic utility of VVTI in dogs with lymphoma

Advancing South American Water and Climate Science through Multidecadal Convection-Permitting Modeling

South America’s hydroclimate sustains vibrant communities and natural ecosystems of extraordinary biodiversity including the Andes Cordillera, and the Orinoco, La Plata, and Amazon basins. Global warming and land-use change are endangering ecosystem health, exacerbating hydrometeorological extremes, and threatening water and food security for millions of people on the continent (Castellanos et al. 2022). Reductions in rainfall and streamflow have been observed in southern Amazonia, the Cerrado region, northeast Brazil, and Chile (Muñoz et al. 2020; Garreaud et al. 2020; Espinoza et al. 2019; Fu et al. 2013). The increased aridity has affected agricultural yield, water supply for reservoirs, hydropower generation and impacted tens of millions of people in the large metropolitan areas of Sao Paulo, Rio de Janeiro, and Santiago de Chile (Nobre et al. 2016). Andean glaciers, an important source of water, have lost 30% of their area in the tropics and up to 60% in the southern Andes—the highest glacier mass loss rates in the world (Braun et al. 2019; Dussaillant et al. 2019; Reinthaler et al. 2019; Masiokas et al. 2020; Fox-Kemper et al. 2021). Conversely, southeastern South America is facing increasing annual rainfall and intensification of heavy precipitation since the early twentieth century (Doyle et al. 2012; Barros et al. 2015; Pabón-Caicedo et al. 2020; Arias et al. 2021; Gutiérrez et al. 2021; Morales-Yokobori 2021; Seneviratne et al. 2021). Extreme precipitation is projected to intensify throughout the continent (Arias et al. 2021; Seneviratne et al. 2021). This poses significant risk to people and infrastructure along the Andes and other mountainous areas, particularly for lower-income communities living in informal housing (Poveda et al. 2020; Ozturk et al. 2022). The overarching goals of the SAAG community are twofold: improved physical understanding and application-relevant research. Two multidecadal convection-permitting simulations are at the heart of SAAG. The historical simulation will allow us to validate the model and better understand detailed hydroclimate features over the continent, while the future climate simulation will show the projected changes of these features in a warmer climate. Furthermore, SAAG scientists are working directly with local communities, so the information can be used for improved decision making. The specific goals and science questions are as follows; goal 1 Physical understanding: Advance insights and improve prediction of key hydroclimate processes in the region including projected changes in a changing climate and Goal 2, Provide information that can be used by local communities and stakeholders for better informed decision-making in a changing climate

Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) &lt; 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p &lt; 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

\ua9 2015 The Authors. This is an open access article under the CC BY-NC-ND license. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk