Search

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors, however recent work suggests prevalence may differ between sub-groups. Here we examined a large cohort of diverse adults in the BioMebiobank in New York City (NYC). We observed the prevalence of PAD at 1.7% in EAs vs 8.5% and 9.4% in AAs and HLs, respectively; and among HL sub-groups, at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs (OR=3.15 (95% CI 2.33-4.25),P-14). To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry (LA) and PAD in Dominican BioMeparticipants (N=1,940) separately for European (EUR), African (AFR) and Native American (NAT) continental ancestry tracts. We identified a NAT ancestry tract at chromosome 2q35 that was significantly associated with PAD (OR=2.05 (95% CI 1.51-2.78),P-6) with 22.5% vs 12.5% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA)LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. In summary, we showed how leveraging health systems data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a novel risk locus in a Dominican population.

BackgroundMagnetic resonance imaging (MRI) enables direct measurements of muscle volume and quality, allowing for an in-depth understanding of their associations with anthropometric traits, and health conditions. However, it is unclear which muscle measurements: total muscle volume, regional measurements or measurements of muscle quality, such as intermuscular adipose tissue (IMAT) or proton density fat fraction (PDFF), are most informative to detect changes and associations with relevant health outcomes such as sarcopenia and frailty.MethodsWe developed a pipeline to automatically segment and extract image-derived phenotypes (IDPs) including total and regional muscle volumes and measures of muscle quality, and applied it to the neck-to-knee Dixon images in 44,520 UK Biobank participants. We also segmented paraspinal muscle from 2D quantitative MRI to quantify muscle PDFF and iron concentration. We performed linear regression to assess associations with anthropometric and lifestyle factors. We further applied logistic regression to investigate the association between these IDPs and sarcopenia and frailty.ResultsAll muscle measurements were negatively associated with age, waist-to-hip ratio, and Townsend deprivation index, with p-values below the Bonferroni-corrected threshold (p<5.7 × 10−5) while they were positively associated with body mass index, alcohol intake, hand grip strength, physical activity and were significantly higher in men (p<5.7 × 10−5). Additionally, IMAT, (corrected for muscle volume) and paraspinal muscle PDFF were significantly higher in female compared with male participants (p<5.7 × 10−5). In sarcopenia, muscle volume IDPs were most informative, particularly total muscle showing odds ratios (OR) of 0.361 and 95% confidence intervals (CI)= 0.332-0.392,p<5.7 × 10−5, while for frailty, muscle quality was most informative, particularly mid-thigh IMAT/Muscle ratio (OR = 1.328, 95% CI = 1.304-1.353,p<5.7 × 10−5).ConclusionsOur fully automated method enables the quantification of muscle volumes and quality suitable for large population-based studies. While the choice of muscle measurements is important particularly when investigating associations with health conditions, most provide consistent differences relating to the detection of age, sex, body composition and lifestyle changes on muscularity.

Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.

BackgroundMorphometric image analysis enables the quantification of differences in the shape and size of organs between individuals.MethodsHere we have applied morphometric methods to the study of the liver by constructing surface meshes from liver segmentations from abdominal MRI images in 33,434 participants in the UK Biobank. Based on these three-dimensional mesh vertices, we evaluated local shape variations and modelled their association with anthropometric, phenotypic and clinical conditions, including liver disease and type-2 diabetes.ResultsWe found that age, body mass index, hepatic fat and iron content, as well as, health traits were significantly associated with regional liver shape and size. Interaction models in groups with specific clinical conditions showed that the presence of type-2 diabetes accelerates age-related changes in the liver, while presence of liver fat further increased shape variations in both type-2 diabetes and liver disease.ConclusionsThe results suggest that this novel approach may greatly benefit studies aiming at better categorisation of pathologies associated with acute and chronic clinical conditions.

Features extracted from cardiac MRI (CMR) are correlated with cardiovascular disease outcomes such as aneurysm, and have a substantial heritable component. To determine whether disease-relevant measurements are feasible in non-cardiac specific MRI, and to explore their associations with disease outcomes, and genetic and environmental risk factors. We segmented the heart, aorta, and vena cava from abdominal MRI scans using deep learning, and generated six image-derived phenotypes (IDP): heart volume, four aortic and one vena cava cross-sectional areas (CSA), from 44,541 UK Biobank participants. We performed genome- and phenome-wide association studies, and constructed a polygenic risk score for each phenotype. We demonstrated concordance between our IDPs and related IDPs from CMR, the current gold standard. We replicated previous findings related to sex differences and age-related changes in heart and vessel dimensions. We identified a significant association between infrarenal descending aorta CSA and incident abdominal aortic aneurysm, and between heart volume and several cardiovascular disorders. In a GWAS, we identified 72 associations at 59 loci (15 novel). We derived a polygenic risk score for each trait and demonstrated an association with TAA diagnosis, pointing to a potential screening method for individuals at high-risk of this condition. We demonstrated substantial genetic correlation with cardiovascular traits including aneurysms, varicose veins, dysrhythmia, and cardiac failure. Finally, heritability enrichment analysis implicated vascular tissue in the heritability of these traits. Our work highlights the value of non-specific MRI for exploring cardiovascular disease risk in cohort studies.

The main drivers of COVID-19 disease severity and the impact of COVID-19 on long-term health after recovery are yet to be fully understood. Medical imaging studies investigating COVID-19 to date have mostly been limited to small datasets and post-hoc analyses of severe cases. The UK Biobank recruited recovered SARS-CoV-2 positive individuals (n=967) and matched controls (n=913) who were extensively imaged prior to the pandemic and underwent follow-up scanning. In this study, we investigated longitudinal changes in body composition, as well as the associations of pre-pandemic image-derived phenotypes with COVID-19 severity. Our longitudinal analysis, in a population of mostly mild cases, associated a decrease in lung volume with SARS-CoV-2 positivity. We also observed that increased visceral adipose tissue and liver fat, and reduced muscle volume, prior to COVID-19, were associated with COVID-19 disease severity. Finally, we trained a machine classifier with demographic, anthropometric and imaging traits, and showed that visceral fat, liver fat and muscle volume have prognostic value for COVID-19 disease severity beyond the standard demographic and anthropometric measurements. This combination of image-derived phenotypes from abdominal MRI scans and ensemble learning to predict risk may have future clinical utility in identifying populations at-risk for a severe COVID-19 outcome.

Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3,088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom.

The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.

OBJECTIVE Fat content and volume of liver and pancreas are associated with risk of diabetes in observational studies; whether these associations are causal is unknown. We conducted a Mendelian randomization (MR) study to examine causality of such associations. RESEARCH DESIGN AND METHODS We used genetic variants associated (P < 5 × 10−8) with the exposures (liver and pancreas volume and fat content) using MRI scans of UK Biobank participants (n = 32,859). We obtained summary-level data for risk of type 1 (9,358 cases) and type 2 (55,005 cases) diabetes from the largest available genome-wide association studies. We performed inverse-variance weighted MR as main analysis and several sensitivity analyses to assess pleiotropy and to exclude variants with potential pleiotropic effects. RESULTS Observationally, liver fat and volume were associated with type 2 diabetes (odds ratio per 1 SD higher exposure 2.16 [2.02, 2.31] and 2.11 [1.96, 2.27], respectively). Pancreatic fat was associated with type 2 diabetes (1.42 [1.34, 1.51]) but not type 1 diabetes, and pancreas volume was negatively associated with type 1 diabetes (0.42 [0.36, 0.48]) and type 2 diabetes (0.73 [0.68, 0.78]). MR analysis provided evidence only for a causal role of liver fat and pancreas volume in risk of type 2 diabetes (1.27 [1.08, 1.49] or 27% increased risk and 0.76 [0.62, 0.94] or 24% decreased risk per 1SD, respectively) and no causal associations with type 1 diabetes. CONCLUSIONS Our findings assist in understanding the causal role of ectopic fat in the liver and pancreas and of organ volume in the pathophysiology of type 1 and type 2 diabetes.

To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with “adverse” metabolic effects (UFA) and the other with, paradoxically, “favorable” metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.

Image-derived phenotypes of abdominal organs from magnetic resonance imaging reveal variations in volume and shape and may be used to model changes in a normal versus pathological organ and improve diagnosis. Computational atlases of anatomical organs provide many advantages in quantifying and modeling differences in shape and size of organs for population imaging studies. Here we made use of liver segmentations derived from Dixon MRI for 2,730 UK Biobank participants to create 3D liver meshes. We computed the signed distances between a reference and subject-specific meshes to define the surface-to-surface (S2S) phenotype. We employed mass univariate regression analysis to compare the S2S values from the liver meshes to image-derived phenotypes specific to the liver, such as proton density fat fraction and iron concentration while adjusting for age, sex, ethnicity, body mass index and waist-to-hip ratio. Vertex-based associations in the 3D liver mesh were extracted and threshold-free cluster enhancement was applied to improve the sensitivity and stability of the statistical parametric maps. Our findings show that the 3D liver meshes are a robust method for modeling the association between anatomical, anthropometric, and phenotypic variations across the liver. This approach may be readily applied to different clinical conditions as well as extended to other abdominal organs in a larger population.

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.

Cardiometabolic diseases are an increasing global health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of the drivers and mechanisms underlying these complex diseases remains incomplete. A better understanding is required to develop more effective therapeutic interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using 38,683 abdominal MRI scans in the UK Biobank, we used deep learning to systematically quantify parameters from individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots), and demonstrate that image derived phenotypes (volume, fat and iron content) reflect organ health and disease. We show that these traits have a substantial heritable component (8%-44%), and identify 93 independent genome-wide significant associations, including 3 associations with liver fat and one with liver iron that have not previously been reported, and 73 in traits that have not previously been studied. Overall our work demonstrates the utility of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues of the abdomen, and to generate new insights into the genetic architecture of complex traits.

BackgroundCalcification of the abdominal artery is an important contributor to cardiovascular disease in diabetic and chronic kidney disease (CKD) populations. However, prevalence of the pathology, risk factors, and long term disease outcomes in a general population have not been systematically analyzed.MethodWe developed machine learning models to quantify levels of abdominal aortic calcification (AAC) in 29,957 whole body dual-energy X-ray absorptiometry (DEXA) scans from the UK Biobank cohort. Using regression techniques we associated severity of calcification across a wide range of physiological parameters, clinical biomarkers, and environmental risk factors (406 in total). We performed a common variant genetic association study spanning 9,572,557 single-nucleotide polymorphisms to identify genetic loci relevant to AAC. We evaluated the prognostic value of AAC across 151 disease classes using Cox proportional hazard models. We further examined an epidemiological model of calcification on cardiovascular morbidity with and without LDL interactions.FindingsWe find evidence for AAC in >10.4% of the cohort despite low prevalence of diabetes (2.5%) and CKD (0.5%). Increased level of AAC is a strong prognostic indicator of cardiovascular outcomes for stenosis of precerebral arteries (HR~1.5), Myocardial Infarction (HR~1.5), & Ischemic Heart Disease (HR~1.33). We find that AAC is genetically correlated with cardiovascular-related traits and that the genetic signals are enriched in vascular and adipose tissue. We report three loci associated with AAC, with the strongest association occuring at theTWIST1/HDAC9locus (beta=0.078, p-value=1.4e-11) in a region also associated with coronary artery disease. Surprisingly, we find that elevated but still within clinically normal levels of serum phosphate and glycated hemoglobin are linked to increased vascular calcification. Furthermore, we show AAC arises in the absence of hypercholesterolemia. By our estimate, AAC is an LDL-independent risk factor for cardiovascular outcomes, with risk similar to elevated LDL.DataThis research has been conducted using the UK Biobank Resource.

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens specific to sub-populations. Here we propose a framework for repurposing data from Electronic Health Records (EHRs) in concert with genomic data to explore enrichment of disease within sub-populations. Using data from a diverse biobank in New York City, we genetically identified 17 sub-populations, and noted the presence of genetic founder effects in 7. By then linking community membership to the EHR, we were able to identify over 600 health outcomes that were statistically enriched within a specific population, with many representing known associations, and many others being novel. This work reinforces the utility of linking genomic data to EHRs, and provides a framework towards fine-scale monitoring of population health.

BACKGROUND & AIMS: The Ile138Met variant (rs738409) in the PNPLA3 gene has the largest effect on non-alcoholic fatty liver disease (NAFLD), increasing the risk of progression to severe forms of liver disease. It remains unknown if the variant plays a role in age of NAFLD onset. We aimed to determine if rs738409 impacts on the age of NAFLD diagnosis. METHODS: We applied a novel natural language processing (NLP) algorithm to a longitudinal electronic health records (EHR) dataset of >27,000 individuals with genetic data from a multi-ethnic biobank, defining NAFLD cases (n = 1,703) and confirming controls (n = 8,119). We conducted i) a survival analysis to determine if age at diagnosis differed by rs738409 genotype, ii) a receiver operating characteristics analysis to assess the utility of the rs738409 genotype in discriminating NAFLD cases from controls, and iii) a phenome-wide association study (PheWAS) between rs738409 and 10,095 EHR-derived disease diagnoses. RESULTS: The PNPLA3 G risk allele was associated with: i) earlier age of NAFLD diagnosis, with the strongest effect in Hispanics (hazard ratio 1.33; 95% CI 1.15–1.53; p

Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations. Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups. This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: 1) acquisition of REA data via clinical laboratory requisition forms, and 2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites as determined by variants in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about variants at clinically relevant sites in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with “pathogenic” and “likely pathogenic” expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed and adopted across clinical genomics.

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

Achieving confidence in the causality of a disease locus is a complex task that often requires supporting data from both statistical genetics and clinical genomics. Here we describe a combined approach to identify and characterize a genetic disorder that leverages distantly related patients in a health system and population-scale mapping. We utilize genomic data to uncover components of distant pedigrees, in the absence of recorded pedigree information, in the multi-ethnic BioMe biobank in New York City. By linking to medical records, we discover a locus associated with both elevated genetic relatedness and extreme short stature. We link the gene, COL27A1, with a little-known genetic disease, previously thought to be rare and recessive. We demonstrate that disease manifests in both heterozygotes and homozygotes, indicating a common collagen disorder impacting up to 2% of individuals of Puerto Rican ancestry, leading to a better understanding of the continuum of complex and Mendelian disease., eLife digest Diseases often run in families. These disease are frequently linked to changes in DNA that are passed down through generations. Close family members may share these disease-causing mutations; so may distant relatives who inherited the same mutation from a common ancestor long ago. Geneticists use a method called linkage mapping to trace a disease found in multiple members of a family over generations to genetic changes in a shared ancestor. This allows scientists to pinpoint the exact place in the genome the disease-causing mutation occurred. Using computer algorithms, scientists can apply the same technique to identify mutations that distant relatives inherited from a common ancestor. Belbin et al. used this computational technique to identify a mutation that may cause unusually short stature or bone and joint problems in up to 2% of people of Puerto Rican descent. In the experiments, the genomes of about 32,000 New Yorkers who have volunteered to participate in the BioMe Biobank and their health records were used to search for genetic changes linked to extremely short stature. The search revealed that people who inherited two copies of this mutation from their parents were likely to be extremely short or to have bone and joint problems. People who inherited one copy had an increased likelihood of joint or bone problems. This mutation affects a gene responsible for making a form of protein called collagen that is important for bone growth. The analysis suggests the mutation first arose in a Native American ancestor living in Puerto Rico around the time that European colonization began. The mutation had previously been linked to a disorder called Steel syndrome that was thought to be rare. Belbin et al. showed this condition is actually fairly common in people whose ancestors recently came from Puerto Rico, but may often go undiagnosed by their physicians. The experiments emphasize the importance of including diverse populations in genetic studies, as studies of people of predominantly European descent would likely have missed the link between this disease and mutation.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.

Genetic studies of complex traits have mainly identified associations with non-coding variants. To further determine the contribution of regulatory variation, we combined whole genome and transcriptome data for 624 individuals from Sardinia in order to identify common and rare variants that influence gene expression and splicing. We identified 21,183 expression quantitative trait loci (eQTLs) and 6,768 splicing quantitative trait loci (sQTLs), including 619 novel QTLs. We identified high-frequency QTLs and evidence of selection near genes involved in malarial resistance and increased multiple sclerosis risk, reflecting the epidemiological history of Sardinia. Using family relationships, we identified 809 segregating expression outliers (median z-score of 2.97), averaging 13.3 genes per individual. Outlier genes were enriched for proximal rare variants, providing a new approach to study large-effect regulatory variants and their relevance to traits. Our results provide insight into the effects of regulatory variants and their relationship to population history and individual genetic risk.

In multicellular organisms, genetic programs guide cells to adopt cell fates as tissues are formed during development, maintained in adults, and repaired after injury. Here we explore how a small molecule in the environment can switch a genetic program from one fate to another. Wild-type Caenorhabditis elegans XX adult hermaphrodites make oocytes continuously, but certain mutant XX adults make sperm instead in an otherwise hermaphrodite soma. Thus, puf-8; lip-1 XX adults make only sperm, but they can be switched from sperm to oocyte production by treatment with a small-molecule MEK inhibitor. To ask whether this chemical reprogramming is common, we tested six XX sperm-only mutants, but found only one other capable of cell fate switching, fbf-1; lip-1. Therefore, reprogramming competence relies on genotype, with only certain mutants capable of responding to the MEK inhibitor with a cell fate change. To gain insight into the molecular basis of competence for chemical reprogramming, we compared polyadenylated transcriptomes of competent and noncompetent XX sperm-only mutants in the absence of the MEK inhibitor and hence in the absence of cell fate reprogramming. Despite their cellular production of sperm, competent mutants were enriched for oogenic messenger RNAs relative to mutants lacking competence for chemical reprogramming. In addition, competent mutants expressed the oocyte-specific protein RME-2, whereas those lacking competence did not. Therefore, mutants competent for reprogramming possess an intersexual molecular profile at both RNA and protein levels. We suggest that this intersexual molecular signature is diagnostic of an intermediate network state that poises the germline tissue for changing its cellular fate in response to environmental cues.

The strictly conserved arginine residue proximal to the active site tyrosine of type IA topoisomerases is required for the relaxation of supercoiled DNA and was hypothesized to be required for positioning of the scissile phosphate for DNA cleavage to take place. Mutants of recombinant Yersinia pestis topoisomerase I with hydrophobic substitutions at this position were found in genetic screening to exhibit a dominant lethal phenotype, resulting in drastic loss in Escherichia coli viability when overexpressed. In depth biochemical analysis of E. coli topoisomerase I with the corresponding Arg-321 mutation showed that DNA cleavage can still take place in the absence of this arginine function if Mg(2+) is present to enhance the interaction of the enzyme with the scissile phosphate. However, DNA rejoining is inhibited in the absence of this conserved arginine, resulting in accumulation of the cleaved covalent intermediate and loss of relaxation activity. These new experimental results demonstrate that catalysis of DNA rejoining by type IA topoisomerases has a more stringent requirement than DNA cleavage. In addition to the divalent metal ions, the side chain of this arginine residue is required for the precise positioning of the phosphotyrosine linkage for nucleophilic attack by the 3'-OH end to result in DNA rejoining. Small molecules that can interfere or distort the enzyme-DNA interactions required for DNA rejoining by bacterial type IA topoisomerases could be developed into novel antibacterial drugs.

The TOPRIM domain found in many nucleotidyl transferases contains a DxD motif involved in magnesium ion coordination for catalysis. Medium- to high-copy-number plasmid clones of Yersinia pestis topoisomerase I (YpTOP) with Asp-to-Asn substitution at the first aspartate residue (D117N) of this motif could not be generated in Escherichia coli without second-site mutation even when expression was under the control of the tightly regulated BAD promoter and suppressed by 2% glucose in the medium. Arabinose induction of a single-copy YpTOP-D117N mutant gene integrated into the chromosome resulted in approximately 10(5)-fold of cell killing in 2.5 h. Attempt to induce expression of the corresponding E. coli topoisomerase I mutant (EcTOP-D111N) encoded on a high-copy-number plasmid resulted in either loss of viability or reversion of the clone to wild type. High-copy-number plasmid clones of YpTOP-D119N and EcTOP-D113N with the Asn substitution at the second Asp of the TOPRIM motif could be stably maintained, but overexpression also decreased cell viability significantly. The Asp-to-Asn substitutions at these TOPRIM residues can selectively decrease Mg(2+) binding affinity with minimal disruption of the active-site geometry, leading to trapping of the covalent complex with cleaved DNA and causing bacterial cell death. The extreme sensitivity of the first TOPRIM position suggested that this might be a useful site for binding of small molecules that could act as topoisomerase poisons.

Among bacterial topoisomerase I enzymes, a conserved methionine residue is found at the active site next to the nucleophilic tyrosine. Substitution of this methionine residue with arginine in recombinant Yersinia pestis topoisomerase I (YTOP) was the only substitution at this position found to induce the SOS response in Escherichia coli. Overexpression of the M326R mutant YTOP resulted in approximately 4 log loss of viability. Biochemical analysis of purified Y. pestis and E. coli mutant topoisomerase I showed that the Met to Arg substitution affected the DNA religation step of the catalytic cycle. The introduction of an additional positive charge into the active site region of the mutant E. coli topoisomerase I activity shifted the pH for optimal activity and decreased the Mg(2+) binding affinity. This study demonstrated that a substitution outside the TOPRIM motif, which binds Mg(2+)directly, can nonetheless inhibit Mg(2+) binding and DNA religation by the enzyme, increasing the accumulation of covalent cleavage complex, with bactericidal consequence. Small molecules that can inhibit Mg(2+) dependent religation by bacterial topoisomerase I specifically could be developed into useful new antibacterial compounds. This approach would be similar to the inhibition of divalent ion dependent strand transfer by HIV integrase in antiviral therapy.

The TOPRIM DXDXXG residues of type IA and II topoisomerases are involved in Mg(II) binding and the cleavage-rejoining of DNA. Mutation of the strictly conserved glycine to serine in Yersinia pestis and Escherichia coli topoisomerase I results in bacterial cell killing due to inhibition of DNA religation after DNA cleavage. In this study, all other substitutions at the TOPRIM glycine of Y. pestis topoisomerase I were examined. While the Gly to Ala substitution allowed both DNA cleavage and religation, other mutations abolished DNA cleavage. DNA cleavage activity retained by the Gly to Ser mutant could be significantly enhanced by a second mutation of the methionine residue adjacent to the active site tyrosine. Induction of mutant topoisomerase with both the TOPRIM glycine and active site region methionine mutations resulted in up to 40-fold higher cell killing rate when compared with the single TOPRIM Gly to Ser mutant. Bacterial type IA topoisomerases are potential targets for discovery of novel antibiotics. These results suggest that compounds that interact simultaneously with the TOPRIM motif and the molecular surface around the active site tyrosine could be highly efficient topoisomerase poisons through both enhancement of DNA cleavage and inhibition of DNA rejoining.

My laboratory focuses on the in vivo regulation of stem cell self-renewal and differentiation in the germline tissue of the small nematode C. elegans. In this system, Notch signaling from a single-celled mesenchymal niche promotes maintenance of germline stem cells (GSCs)(1). My talk will focus on two broadly important questions. First, what key downstream effectors of Notch signaling are transcriptionally activated in stem cells to maintain their totipotent state? Second, how does Notch-dependent transcriptional activation maintain a pool of stem cells? We have known for some time that an RNA regulatory network acts downstream of Notch signaling to drives the choice between self-renewal and differentiation. We recently identified two Notch-dependent direct target genes, lst-1 and sygl-1, that fully account for the role of GLP-1/Notch signaling in GSC control (2). My talk will report recent efforts to understand lst-1 and sygl-1, to identify additional direct Notch targets in GSCs and to visualize Notch signaling within the stem cell pool. References: (1) Austin and Kimble (1987) Cell 51:589-599 (2) Kershner et al. (2014) PNAS,111(10):3739-3744. Citation Format: Erika B. Sorensen, ChangHwan Lee Lee, Elena Sorokin, Amy C. Groth, Judith Kimble. C. elegans GLP-1/Notch signaling: Direct targets and visualization in stem cells. [abstract]. In: Proceedings of the AACR Special Conference: Developmental Biology and Cancer; Nov 30-Dec 3, 2015; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(4_Suppl):Abstract nr IA11.