What could you do with 400 years of biological history on african americans? Evaluating the potential scientific benefit of systematic studies of dental and skeletal materials on African Americans from the 17th through 20th centuries.

OBJECTIVES: How important is it to be able to reconstruct the lives of a highly diverse, historically recent macroethnic group over the course of 400 years? How many insights into human evolutionary biology and disease susceptibilities could be gained, even with this relatively recent window into the past? In this article, we explore the potential ramifications of a newly constructed dataset of Four Centuries of African American Biological Variation (4Cs). METHODS: This article provides initial lists of digitized variables formatted as SQL tables for the 17th and 18th century samples and for the 19th and 20th century samples. RESULTS: This database is dynamic and new information is added yearly. The database provides novel opportunities for significant insights into the past biological history of this group and three case study applications are detailed for comparative computational systems biology studies of (1) hypertension, (2) the oral microbiome, and (3) mental health disorders. CONCLUSIONS: The 4Cs dataset is ideal for interdisciplinary "next generation" science research and these data represent a unique step toward the accumulation of historically contextualized Big Data on an underrepresented group known to have experienced differential survival over time. Am. J. Hum. Biol. 28:510-513, 2016. © 2016 The Authors American Journal of Human Biology Published byWiley Periodicals, Inc.

Use of a community advisory board to build equitable algorithms for participation in clinical trials: a protocol paper for HoPeNET.

INTRODUCTION: Participation from racial and ethnic minorities in clinical trials has been burdened by issues surrounding mistrust and access to healthcare. There is emerging use of machine learning (ML) in clinical trial recruitment and evaluation. However, for individuals from groups who are recipients of societal biases, utilisation of ML can lead to the creation and use of biased algorithms. To minimise bias, the design of equitable ML tools that advance health equity could be guided by community engagement processes. The Howard University Partnership with the National Institutes of Health for Equitable Clinical Trial Participation for Racial/Ethnic Communities Underrepresented in Research (HoPeNET) seeks to create an ML-based infrastructure from community advisory board (CAB) experiences to enhance participation of African-Americans/Blacks in clinical trials. METHODS AND ANALYSIS: This triphased cross-sectional study (24 months, n=56) will create a CAB of community members and research investigators. The three phases of the study include: (1) identification of perceived barriers/facilitators to clinical trial engagement through qualitative/quantitative methods and systems-based model building participation; (2) operation of CAB meetings and (3) development of a predictive ML tool and outcome evaluation. Identified predictors from the participant-derived systems-based map will be used for the ML tool development. ETHICS AND DISSEMINATION: We anticipate minimum risk for participants. Institutional review board approval and informed consent has been obtained and patient confidentiality ensured.

COVID-19 SeroHub, an online repository of SARS-CoV-2 seroprevalence studies in the United States.

Seroprevalence studies provide useful information about the proportion of the population either vaccinated against SARS-CoV-2, previously infected with the virus, or both. Numerous studies have been conducted in the United States, but differ substantially by dates of enrollment, target population, geographic location, age distribution, and assays used. This can make it challenging to identify and synthesize available seroprevalence data by geographic region or to compare infection-induced versus combined infection- and vaccination-induced seroprevalence. To facilitate public access and understanding, the National Institutes of Health and the Centers for Disease Control and Prevention developed the COVID-19 Seroprevalence Studies Hub (COVID-19 SeroHub, https://covid19serohub.nih.gov/ ), a data repository in which seroprevalence studies are systematically identified, extracted using a standard format, and summarized through an interactive interface. Within COVID-19 SeroHub, users can explore and download data from 178 studies as of September 1, 2022. Tools allow users to filter results and visualize trends over time, geography, population, age, and antigen target. Because COVID-19 remains an ongoing pandemic, we will continue to identify and include future studies.

DNA methylation and exposure to violence among African American young adult males.

Exposure to violence (ETV) has been linked to epigenomics mechanisms such as DNA methylation (DNAm). We used epigenetic profiling of blood collected from 32 African American young adult males who lived in Washington DC to determine if changes in DNAm at CpG sites affiliated with nervous and immune system were associated with exposure to violence. Pathway analysis of differentially methylated regions comparing high and low ETV groups revealed an enrichment of gene sets annotated to nervous system and immune ontologies. Many of these genes are known to interact with each other which suggests DNAm alters gene function in the nervous and immune system in response to ETV. Using data from a unique age group, young African American adult males, we provide evidence that lifetime ETV could impact DNA methylation in genes impacted at Central Nervous System and Immune Function sites. METHOD: Methylation analysis was performed on DNA collected from the blood of participants classified with either high or low lifetime ETV. Illumina®MethylationEPIC Beadchips (~850k CpG sites) were processed on the iScan System to examine whole-genome methylation differences. Differentially methylated CpG-sites between high (n â€‹= â€‹19) and low (n â€‹= â€‹13) groups were identified using linear regression with violence and substance abuse as model covariates. Gene ontology analysis was used to identify enrichment categories from probes annotated to the nearest gene. RESULTS: A total of 595 probes (279 hypermethylated; 316 hypomethylated) annotated to 383 genes were considered differentially methylated in association with ETV. Males with high ETV showed elevated methylation in several signaling pathways but were most impacted at Central Nervous System and Immune Function affiliated sites. Eight candidate genes were identified that play important biological roles in stress response to violence with HDAC4 (10%), NR4A3 (11%), NR4A2 (12%), DSCAML1(12%), and ELAVL3 (13%) exhibiting higher levels in the low ETV group and DLGAP1 (10%), SHANK2 (10%), and NRG1(11%) having increased methylation in the high ETV group. These findings suggest that individuals subjected to high ETV may be at risk for poor health outcomes that have not been reported previously.

Including Vulnerable Populations in the Assessment of Data From Vulnerable Populations.

Data science has made great strides in harnessing the power of big data to improve human life across a broad spectrum of disciplines. Unfortunately this informational richesse is not equitably spread across human populations. Vulnerable populations remain both under-studied and under-consulted on the use of data derived from their communities. This lack of inclusion of vulnerable populations as data collectors, data analyzers and data beneficiaries significantly restrains the utility of big data applications that contribute to human well-ness. Here we present three case studies: (1) Describing a novel genomic dataset being developed with clinical and ethnographic insights in African Americans, (2) Demonstrating how a tutorial that enables data scientists from vulnerable populations to better understand criminal justice bias using the COMPAS dataset, and (3) investigating how Indigenous genomic diversity contributes to future biomedical interventions. These cases represent some of the outstanding challenges that big data science presents when addressing vulnerable populations as well as the innovative solutions that expanding science participation brings.

Identification of trace metals and potential anthropogenic influences on the historic New York African Burial Ground population: A pXRF technology approach.

The New York African Burial Ground (NYABG) is the country's oldest and largest burial site of free and enslaved Africans. Re-discovered in 1991, this site provided evidence of the biological and cultural existence of a 17th and 18th Century historic population viewing their skeletal remains. However, the skeletal remains were reburied in October 2003 and are unavailable for further investigation. The analysis of grave soil samples with modern technology allows for the assessment of trace metal presence. Portable X-ray fluorescence (pXRF) spectrometry provides a semi-quantitative and non-destructive method to identify trace metals of this population and in the surrounding environment. Sixty-five NYABG soil samples were analyzed on a handheld Bruker Tracer III- SD XRF with 40 kV of voltage and a 30µA current. Presence of As, Cu, and Zn can potentially decipher the influence of the local 18th Century pottery factories. Elevated levels of Sr validate the assumed heavy vegetative diets of poor and enslaved Africans of the time. Decreased levels of Ca may be due in part to the proximity of the Collect Pond, the existing water table until the early 19th Century, and Manhattan's rising sea level causing an elevated water table washing away the leached Ca from human remains. These data help us reconstruct the lives of these early Americans in what became New York City.

Cancer in an Historic Washington DC African American Population and Its Geospatial Distribution.

Background: Cancer continues to be a major cause of morbidity and mortality in the African American community but insights into the types and incidence of cancer 85 years ago have been virtually non-existent and little is known of its geospatial distribution. Historical information on cancer can shed light on current health disparities, particularly among African Americans. Objective: The aims of this study were to: (1) assess the frequencies of the cancer types present among Cobb Collection individuals; (2) compare these data with current research on cancer in African Americans; and (3) evaluate the pattern of cancer expression, including its geospatial distributions, as a cause of death between 1931 and 1969 in an historic African American subgroup and compare this pattern with the historic and contemporary patterns of cancer etiology and incidence. Methods: Systematic assessments of the existing clinical, demographic, and anatomical records in the Cobb Research Laboratory were made of individuals identified as dying from specific cancers from 1931 to 1969. These were compared with the national profiles of cancer during the historic time an individual died as well as the contemporary patterns of cancer deaths. Analysis of their residential addresses just prior to death were assessed using a commercial geographic information system. Each location was assigned a geospatial location and proximity between each site and clusters of sites were investigated. Results: Seventeen different cancer types were found within 28 individuals of the Cobb Collection between 1931 and 1969. The cancer types with the highest frequencies were carcinoma of stomach, lung, esophagus, larynx and bronchogenic carcinoma. Eighty-four percent of all cancer incidents occurred in males and 76% were among individuals identified as African American. Seventy-one percent of the highest incidence cancers were among African American males. Geospatial clustering was observed most noticeably in the redistribution of carcinoma of the esophagus. Conclusion: Our results provide historical depth to our knowledge of the common cancer causes of morbidity among African Americans of Washington DC from 1931 to 1969. We contrast these findings with national historical data on cancer etiology and ethnic disparities in incidence. Our study suggests that historic data can provide longitudinal depth to our understanding of the persistence of cancer susceptibilities in a vulnerable subgroup.