Phasing Diploid Genome Assembly Graphs with Single-Cell Strand Sequencing.

Haplotype information is crucial for biomedical and population genetics research. However, current strategies to produce de-novo haplotype-resolved assemblies often require either difficult-to-acquire parental data or an intermediate haplotype-collapsed assembly. Here, we present Graphasing, a workflow which synthesizes the global phase signal of Strand-seq with assembly graph topology to produce chromosome-scale de-novo haplotypes for diploid genomes. Graphasing readily integrates with any assembly workflow that both outputs an assembly graph and has a haplotype assembly mode. Graphasing performs comparably to trio-phasing in contiguity, phasing accuracy, and assembly quality, outperforms Hi-C in phasing accuracy, and generates human assemblies with over 18 chromosome-spanning haplotypes.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data.

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

Seroprevalence of antibodies to SARS-CoV-2 in healthcare workers: a cross-sectional study.

OBJECTIVE: We sought to determine the extent of SARS-CoV-2 seroprevalence and the factors associated with seroprevalence across a diverse cohort of healthcare workers. DESIGN: Observational cohort study of healthcare workers, including SARS-CoV-2 serology testing and participant questionnaires. SETTINGS: A multisite healthcare delivery system located in Los Angeles County. PARTICIPANTS: A diverse and unselected population of adults (n=6062) employed in a multisite healthcare delivery system located in Los Angeles County, including individuals with direct patient contact and others with non-patient-oriented work functions. MAIN OUTCOMES: Using Bayesian and multivariate analyses, we estimated seroprevalence and factors associated with seropositivity and antibody levels, including pre-existing demographic and clinical characteristics; potential COVID-19 illness-related exposures; and symptoms consistent with COVID-19 infection. RESULTS: We observed a seroprevalence rate of 4.1%, with anosmia as the most prominently associated self-reported symptom (OR 11.04, p<0.001) in addition to fever (OR 2.02, p=0.002) and myalgias (OR 1.65, p=0.035). After adjusting for potential confounders, seroprevalence was also associated with Hispanic ethnicity (OR 1.98, p=0.001) and African-American race (OR 2.02, p=0.027) as well as contact with a COVID-19-diagnosed individual in the household (OR 5.73, p<0.001) or clinical work setting (OR 1.76, p=0.002). Importantly, African-American race and Hispanic ethnicity were associated with antibody positivity even after adjusting for personal COVID-19 diagnosis status, suggesting the contribution of unmeasured structural or societal factors. CONCLUSION AND RELEVANCE: The demographic factors associated with SARS-CoV-2 seroprevalence among our healthcare workers underscore the importance of exposure sources beyond the workplace. The size and diversity of our study population, combined with robust survey and modelling techniques, provide a vibrant picture of the demographic factors, exposures and symptoms that can identify individuals with susceptibility as well as potential to mount an immune response to COVID-19.

An Early-Onset Subgroup of Type 2 Diabetes: A Multigenerational, Prospective Analysis in the Framingham Heart Study.

OBJECTIVE: To assess the relation of type 2 diabetes occurring earlier (age <55 years) versus later in life to the risk of cardiovascular death and to diabetes in offspring. RESEARCH DESIGN AND METHODS: In the Framingham Heart Study, a community-based prospective cohort study, glycemic status was ascertained at serial examinations over six decades among 5,571 first- and second-generation participants with mortality data and 2,123 second-generation participants who initially did not have diabetes with data on parental diabetes status. We assessed cause of death in a case (cardiovascular death)-control (noncardiovascular death) design and incident diabetes in offspring in relation to parental early-onset diabetes. RESULTS: Among the participants in two generations (N = 5,571), there were 1,822 cardiovascular deaths (including 961 coronary deaths). The odds of cardiovascular versus noncardiovascular death increased with decreasing age of diabetes onset (P < 0.001 trend). Compared with never developing diabetes, early-onset diabetes conferred a 1.81-fold odds (95% CI 1.10-2.97, P = 0.02) of cardiovascular death and 1.75-fold odds (0.96-3.21, P = 0.07) of coronary death, whereas later-onset diabetes was not associated with greater risk for either (P = 0.09 for cardiovascular death; P = 0.51 for coronary death). In second-generation participants, having a parent with early-onset diabetes increased diabetes risk by 3.24-fold (1.73-6.07), whereas having one or both parents with late-onset diabetes increased diabetes risk by 2.19-fold (1.50-3.19). CONCLUSIONS: Our findings provide evidence for a diabetes subgroup with an early onset, a stronger association with cardiovascular death, and higher transgenerational transmission.

Statistical Workflow for Feature Selection in Human Metabolomics Data.

High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations.

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS).

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

Directed Non-targeted Mass Spectrometry and Chemical Networking for Discovery of Eicosanoids and Related Oxylipins.

Eicosanoids and related oxylipins are critical, small bioactive mediators of human physiology and inflammation. While ∼1,100 distinct species have been predicted to exist, to date, less than 150 of these molecules have been measured in humans, limiting our understanding of their role in human biology. Using a directed non-targeted mass spectrometry approach in conjunction with chemical networking of spectral fragmentation patterns, we find over 500 discrete chemical signals highly consistent with known and putative eicosanoids and related oxylipins in human plasma including 46 putative molecules not previously described. In plasma samples from 1,500 individuals, we find members of this expanded oxylipin library hold close association with markers of inflammation, as well as clinical characteristics linked with inflammation, including advancing age and obesity. These experimental and computational approaches enable discovery of new chemical entities and will shed important insight into the role of bioactive molecules in human health and disease.

Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis.

BACKGROUND: Low carbohydrate diets, which restrict carbohydrate in favour of increased protein or fat intake, or both, are a popular weight-loss strategy. However, the long-term effect of carbohydrate restriction on mortality is controversial and could depend on whether dietary carbohydrate is replaced by plant-based or animal-based fat and protein. We aimed to investigate the association between carbohydrate intake and mortality. METHODS: We studied 15 428 adults aged 45-64 years, in four US communities, who completed a dietary questionnaire at enrolment in the Atherosclerosis Risk in Communities (ARIC) study (between 1987 and 1989), and who did not report extreme caloric intake (<600 kcal or >4200 kcal per day for men and <500 kcal or >3600 kcal per day for women). The primary outcome was all-cause mortality. We investigated the association between the percentage of energy from carbohydrate intake and all-cause mortality, accounting for possible non-linear relationships in this cohort. We further examined this association, combining ARIC data with data for carbohydrate intake reported from seven multinational prospective studies in a meta-analysis. Finally, we assessed whether the substitution of animal or plant sources of fat and protein for carbohydrate affected mortality. FINDINGS: During a median follow-up of 25 years there were 6283 deaths in the ARIC cohort, and there were 40 181 deaths across all cohort studies. In the ARIC cohort, after multivariable adjustment, there was a U-shaped association between the percentage of energy consumed from carbohydrate (mean 48·9%, SD 9·4) and mortality: a percentage of 50-55% energy from carbohydrate was associated with the lowest risk of mortality. In the meta-analysis of all cohorts (432 179 participants), both low carbohydrate consumption (<40%) and high carbohydrate consumption (>70%) conferred greater mortality risk than did moderate intake, which was consistent with a U-shaped association (pooled hazard ratio 1·20, 95% CI 1·09-1·32 for low carbohydrate consumption; 1·23, 1·11-1·36 for high carbohydrate consumption). However, results varied by the source of macronutrients: mortality increased when carbohydrates were exchanged for animal-derived fat or protein (1·18, 1·08-1·29) and mortality decreased when the substitutions were plant-based (0·82, 0·78-0·87). INTERPRETATION: Both high and low percentages of carbohydrate diets were associated with increased mortality, with minimal risk observed at 50-55% carbohydrate intake. Low carbohydrate dietary patterns favouring animal-derived protein and fat sources, from sources such as lamb, beef, pork, and chicken, were associated with higher mortality, whereas those that favoured plant-derived protein and fat intake, from sources such as vegetables, nuts, peanut butter, and whole-grain breads, were associated with lower mortality, suggesting that the source of food notably modifies the association between carbohydrate intake and mortality. FUNDING: National Institutes of Health.

Trajectories of Blood Pressure Elevation Preceding Hypertension Onset: An Analysis of the Framingham Heart Study Original Cohort.

Importance: Given that hypertension remains a leading risk factor for chronic disease globally, there are substantial ongoing efforts to define the optimal range of blood pressure (BP). Objective: To identify a common threshold level above which BP rise tends to accelerate in progression toward hypertension. Design, Setting, and Participants: This longitudinal, community-based epidemiological cohort study of adults enrolled in Framingham, Massachusetts, included 1252 participants (mean [SD] age, 35.3 [2.7] years) from the Framingham Original Cohort, of whom 790 (63.1%) were women. Each participant contributed up to 28 serial examinations of standardized resting BP measurements between 1948 and 2005. Exposures: Age and systolic BP. Main Outcomes and Measures: Via a segmented mixed model, we identified significant change points in the association between advancing age and increasing systolic BP among individuals categorized by their age at hypertension onset. Results: Individuals maintained a relatively stable resting systolic BP level prior to hypertension onset. Systolic BP level began to rise at a more rapid rate after reaching a level of 123.2 mm Hg (95% CI, 122.7-130.1 mm Hg) in people with onset at 40 to 49 years; 122.0 mm Hg (95% CI, 120.3-123.9 mm Hg) in those with onset between 50 and 59 years, 124.9 mm Hg (95% CI, 120.2-127.9 mm Hg) in those with onset between 60 and 69 years, and 120.5 mm Hg (95% CI, 118.0-123.2 mm Hg) in those with onset between 70 and 79 years (P = .29 for between-group heterogeneity). Conclusions and Relevance: We observed that individuals in the community generally maintained a systolic BP of less than 120 to 125 mm Hg, above which systolic BP increased at a relatively rapid rate toward overt hypertension. This trend was consistent whether the hypertension manifested earlier or later in life. Thus, a resting systolic BP that chronically exceeds the range of approximately 120 to 125 mm Hg may represent an important threshold of underlying vascular remodeling and signal incipient hypertension irrespective of age. Further investigations are needed to unravel the sequence of hemodynamic and vascular changes occurring prior to hypertension onset.

Machine Learning Approaches in Cardiovascular Imaging.

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging.

Heritability and risks associated with early onset hypertension: multigenerational, prospective analysis in the Framingham Heart Study.

Objective To determine the role of early onset versus late onset hypertension as a risk factor for hypertension in offspring and cardiovascular death.Design Multigenerational, prospective cohort study.Setting Framingham Heart Study.Participants Two generations of community dwelling participants with blood pressure measurements performed at serial examinations spanning six decades: 3614 first generation participants with mortality data and 1635 initially non-hypertensive second generation participants with data available on parental blood pressure.Main outcome measures The main outcome measures were relation of parental early onset hypertension (age <55 years) with incidence of hypertension in offspring, using regression analyses, and relation of age at hypertension onset with cause specific mortality using a case (cardiovascular death) versus control (non-cardiovascular death) design.Results In second generation participants, having one or both parents with late onset hypertension did not increase the risk of hypertension compared with having parents with no hypertension; by contrast, the hazard ratios of hypertension were 2.0 (95% confidence interval 1.2 to 3.5) and 3.5 (1.9 to 6.1) in participants with one and both parents with early onset hypertension, respectively. In first generation decedents, 1151 cardiovascular deaths occurred (including 630 coronary deaths). The odds of cardiovascular death increased linearly with decreasing age of hypertension onset (P<0.001 for trend). Compared with non-hypertensive participants, hypertension onset at age <45 years conferred an odds ratios of 2.2 (1.8 to 2.7) for cardiovascular death and 2.3 (1.8 to 2.9) for coronary death, whereas hypertension onset at age ≥65 years conferred a lower magnitude odds ratios of 1.5 (1.2 to 1.9) for cardiovascular death and 1.4 (0.98 to 1.9) for coronary death (P≤0.002 for differences in odds ratios between hypertension onset at age <45 and age ≥65).Conclusions Early onset and not late onset hypertension in parents was strongly associated with hypertension in offspring. In turn, early onset compared with late onset hypertension was associated with greater odds of cardiovascular, and particularly coronary, death. These findings suggest it may be important to distinguish between early onset and late onset hypertension as a familial trait when assessing an individual's risk for hypertension, and as a specific type of blood pressure trait when estimating risk for cardiovascular outcomes in adults with established hypertension.

Risk for hypertension crosses generations in the community: a multi-generational cohort study.

AIMS: Parental hypertension is known to predict high blood pressure (BP) in children. However, the extent to which risk for hypertension is conferred across multiple generations, notwithstanding the impact of environmental factors, is unclear. Our objective was therefore to evaluate the degree to which risk for hypertension extends across multiple generations of individuals in the community. METHODS AND RESULTS: We studied three generations of Framingham Heart Study participants with standardized blood pressure measurements performed at serial examinations spanning 5 decades (1948 through 2005): First Generation (n = 1809), Second Generation (n = 2631), and Third Generation (n = 3608, mean age 39 years, 53% women). To capture a more precise estimate of conferrable risk, we defined early-onset hypertension (age <55 years) as the primary exposure. In multinomial logistic regression models adjusting for standard risk factors as well as physical activity and daily intake of dietary sodium, risk for hypertension in the Third Generation was conferred simultaneously by presence of early-onset hypertension in parents [OR 2.10 (95% CI, 1.66-2.67), P < 0.001] as well as in grandparents [OR 1.33 (95% CI, 1.12-1.58), P < 0.01]. CONCLUSION: Early-onset hypertension in grandparents raises the risk for hypertension in grandchildren, even after adjusting for early-onset hypertension in parents and lifestyle factors. These results suggest that a substantial familial predisposition for hypertension exists, and this predisposition is not identical when assessed from one generation to the next. Additional studies are needed to elucidate the mechanisms underlying transgenerational risk for hypertension and its clinical implications.

Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ∼3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.

Reconfigurable self-assembly through chiral control of interfacial tension.

From determining the optical properties of simple molecular crystals to establishing the preferred handedness in highly complex vertebrates, molecular chirality profoundly influences the structural, mechanical and optical properties of both synthetic and biological matter on macroscopic length scales. In soft materials such as amphiphilic lipids and liquid crystals, the competition between local chiral interactions and global constraints imposed by the geometry of the self-assembled structures leads to frustration and the assembly of unique materials. An example of particular interest is smectic liquid crystals, where the two-dimensional layered geometry cannot support twist and chirality is consequently expelled to the edges in a manner analogous to the expulsion of a magnetic field from superconductors. Here we demonstrate a consequence of this geometric frustration that leads to a new design principle for the assembly of chiral molecules. Using a model system of colloidal membranes, we show that molecular chirality can control the interfacial tension, an important property of multi-component mixtures. This suggests an analogy between chiral twist, which is expelled to the edges of two-dimensional membranes, and amphiphilic surfactants, which are expelled to oil-water interfaces. As with surfactants, chiral control of interfacial tension drives the formation of many polymorphic assemblages such as twisted ribbons with linear and circular topologies, starfish membranes, and double and triple helices. Tuning molecular chirality in situ allows dynamical control of line tension, which powers polymorphic transitions between various chiral structures. These findings outline a general strategy for the assembly of reconfigurable chiral materials that can easily be moved, stretched, attached to one another and transformed between multiple conformational states, thus allowing precise assembly and nanosculpting of highly dynamical and designable materials with complex topologies.