scholarly journals The Impact of Ethnicity and Genetic Ancestry on Disease Prevalence and Risk in Colombia

2021 ◽  
Vol 12 ◽  
Author(s):  
Aroon T. Chande ◽  
Shashwat Deepali Nagar ◽  
Lavanya Rishishwar ◽  
Leonardo Mariño-Ramírez ◽  
Miguel A. Medina-Rivas ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Disease Risk ◽  
Genomic Medicine ◽  
Disease Prevalence ◽  
Genetic Ancestry ◽  
Genomic Research ◽  
European Ancestry ◽  
Full Potential ◽  
Strongly Correlated ◽  
The Impact

Currently, the vast majority of genomic research cohorts are made up of participants with European ancestry. Genomic medicine will only reach its full potential when genomic studies become more broadly representative of global populations. We are working to support the establishment of genomic medicine in developing countries in Latin America via studies of ethnically and ancestrally diverse Colombian populations. The goal of this study was to analyze the effect of ethnicity and genetic ancestry on observed disease prevalence and predicted disease risk in Colombia. Population distributions of Colombia’s three major ethnic groups – Mestizo, Afro-Colombian, and Indigenous – were compared to disease prevalence and socioeconomic indicators. Indigenous and Mestizo ethnicity show the highest correlations with disease prevalence, whereas the effect of Afro-Colombian ethnicity is substantially lower. Mestizo ethnicity is mostly negatively correlated with six high-impact health conditions and positively correlated with seven of eight common cancers; Indigenous ethnicity shows the opposite effect. Malaria prevalence in particular is strongly correlated with ethnicity. Disease prevalence co-varies across geographic regions, consistent with the regional distribution of ethnic groups. Ethnicity is also correlated with regional variation in human development, partially explaining the observed differences in disease prevalence. Patterns of genetic ancestry and admixture for a cohort of 624 individuals from Medellín were compared to disease risk inferred via polygenic risk scores (PRS). African genetic ancestry is most strongly correlated with predicted disease risk, whereas European and Native American ancestry show weaker effects. African ancestry is mostly positively correlated with disease risk, and European ancestry is mostly negatively correlated. The relationships between ethnicity and disease prevalence do not show an overall correspondence with the relationships between ancestry and disease risk. We discuss possible reasons for the divergent health effects of ethnicity and ancestry as well as the implication of our results for the development of precision medicine in Colombia.

ABCA7, a Genetic Risk Factor Associated with Alzheimer’s Disease Risk in African Americans

2022 ◽  
pp. 1-15
Author(s):  
Kaitlyn E. Stepler ◽  
Taneisha R. Gillyard ◽  
Calla B. Reed ◽  
Tyra M. Avery ◽  
Jamaine S. Davis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
African American ◽  
Amyloid Beta ◽  
Disease Risk ◽  
African Ancestry ◽  
European Ancestry ◽  
Black Adults ◽  
American Black ◽  
The Impact

African American/Black adults are twice as likely to have Alzheimer’s disease (AD) compared to non-Hispanic White adults. Genetics partially contributes to this disparity in AD risk, among other factors, as there are several genetic variants associated with AD that are more prevalent in individuals of African or European ancestry. The phospholipid-transporting ATPase ABCA7 (ABCA7) gene has stronger associations with AD risk in individuals with African ancestry than in individuals with European ancestry. In fact, ABCA7 has been shown to have a stronger effect size than the apolipoprotein E (APOE) ɛ4 allele in African American/Black adults. ABCA7 is a transmembrane protein involved in lipid homeostasis and phagocytosis. ABCA7 dysfunction is associated with increased amyloid-beta production, reduced amyloid-beta clearance, impaired microglial response to inflammation, and endoplasmic reticulum stress. This review explores the impact of ABCA7 mutations that increase AD risk in African American/Black adults on ABCA7 structure and function and their contributions to AD pathogenesis. The combination of biochemical/biophysical and ‘omics-based studies of these variants needed to elucidate their downstream impact and molecular contributions to AD pathogenesis is highlighted.

scholarly journals Impact of Genetic Ancestry on Outcomes in ECOG-ACRIN-5103

2017 ◽  
pp. 1-9 ◽  
Author(s):  
Bryan P. Schneider ◽  
Fei Shen ◽  
Guanglong Jiang ◽  
Anne O’Neill ◽  
Milan Radovich ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
The United States ◽  
Genetic Ancestry ◽  
Phase Iii ◽  
European Ancestry ◽  
Induced Hypertension ◽  
The Impact ◽  
Dose Reductions

Purpose Racial disparity in breast cancer outcomes exists between African American and white women in the United States. We have evaluated the impact of genetically determined ancestry on disparity in efficacy and therapy-induced toxicity for patients with breast cancer in the context of a randomized, phase III adjuvant trial. Methods This study compared outcomes between 386 patients of African ancestry (AA) and 2,473 patients of European ancestry (EA) in a randomized, phase III breast cancer trial, ECOG-ACRIN-5103. The primary efficacy end point, invasive disease–free survival (DFS), and clinically significant toxicities were compared, including anthracycline-induced congestive heart failure, taxane-induced peripheral neuropathy (TIPN), and bevacizumab-induced hypertension. Results Overall, AAs had significantly inferior DFS ( P = .002; hazard ratio, 1.5) compared with EAs. This was significant in the estrogen receptor–positive subgroup ( P = .03), with a similar, nonsignificant trend for those who had triple-negative breast cancer ( P = .12). AAs also had significantly more grades 3 to 4 TIPN (odds ratio [OR], 2.9; P = 2.4 × 10−11) and grades 3 to 4 bevacizumab-induced hypertension (OR, 1.6; P = .02), with a trend for more congestive heart failure (OR, 1.8; P = .08). AAs had significantly more dose reductions in paclitaxel ( P = 6.6 × 10−6). In AAs, dose reductions in paclitaxel had a significant negative impact on DFS ( P = .03), whereas in EAs, dose reductions did not have an impact on outcome ( P = .35). Conclusion AAs had inferior DFS, with more clinically important toxicities, in ECOG-ACRIN-5103. The altered risk-to-benefit ratio for adjuvant breast cancer chemotherapy should lead to additional research with the focus on the impact of genetic ancestry on both efficacy and toxicity. Strategies to minimize dose reductions in paclitaxel, especially as the result of TIPN, are warranted for this population.

scholarly journals Accurate genetic profiling of anthropometric traits using a big data approach

2015 ◽  
Author(s):  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
John A. Woolliams ◽  
Albert Tenesa
Keyword(s):  
Disease Management ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Genomic Medicine ◽  
Full Potential ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Starting Point ◽  
The Individual

Genome-wide association studies (GWAS) promised to translate their findings into clinically beneficial improvements of patient management by tailoring disease management to the individual through the prediction of disease risk. However, the ability to translate genetic findings from GWAS into predictive tools that are of clinical utility and which may inform clinical practice has, so far, been encouraging but limited. Here we propose to use a more powerful statistical approach that enables the prediction of multiple medically relevant phenotypes without the costs associated with developing a genetic test for each of them. As a proof of principle, we used a common panel of 319,038 SNPs to train the prediction models in 114,264 unrelated White-British for height and four obesity related traits (body mass index, basal metabolic rate, body fat percentage, and waist-to-hip ratio). We obtained prediction accuracies that ranged between 46% and 75% of the maximum achievable given their explained heritable component. This represents an improvement of up to 75% over the phenotypic variance explained by the predictors developed through large collaborations, which used more than twice as many training samples. Across-population predictions in White non-British individuals were similar to those of White-British whilst those in Asian and Black individuals were informative but less accurate. The genotyping of circa 500,000 UK Biobank participants will yield predictions ranging between 66% and 83% of the maximum. We anticipate that our models and a common panel of genetic markers, which can be used across multiple traits and diseases, will be the starting point to tailor disease management to the individual. Ultimately, we will be able to capitalise on whole-genome sequence and environmental risk factors to realise the full potential of genomic medicine.

Impact of African ancestry on the relationship between BMI and survival in early stage breast cancer: Retrospective analysis from E5103.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1010-1010
Author(s):  
Tarah Jean Ballinger ◽  
Guanglong Jiang ◽  
Fei Shen ◽  
Kathy Miller ◽  
Bryan P. Schneider
Keyword(s):  
Breast Cancer ◽  
Racial Disparities ◽  
Early Stage ◽  
African Ancestry ◽  
Genetic Ancestry ◽  
Early Stage Breast Cancer ◽  
European Ancestry ◽  
A Genome ◽  
The Impact ◽  
The Relationship

1010 Background: Both Black race and obesity are associated with worse survival in early stage breast cancer. Obesity disproportionately affects Black women; however, the degree this contributes to racial disparities in breast cancer remains unclear. Prior work evaluated heterogeneous populations or used self- reported race, rather than genetic ancestry. African ancestry is associated with higher BMI and worse survival in breast cancer; however, the intersection between genetic ancestry and obesity on survival outcomes remains unknown. Methods: We analyzed data from the adjuvant trial E5103. Patients with high risk, HER2 negative breast cancer received doxorubicin/cyclophosphamide x 4, followed by weekly paclitaxel x 12, with or without bevacizumab. Genetic ancestry was determined on the 2,854 patients with available germline DNA, BMI, and outcome data using principal components from a genome-wide array. The primary objective assessed impact of BMI on DFS and OS by ancestry. Multivariate Cox proportional hazard models evaluated correlation between continuous or binary BMI and survival in African (AA) and European (EA) Americans. Results: 13.4% of patients were genetically classified as AA and 86.6% as EA. Higher continuous BMI was significantly associated with worse DFS and OS only in AAs (DFS: HR = 1.25 95% CI 1.07-1.46, p = 0.004; OS: HR = 1.38 95% CI 1.10-1.73, p = 0.005); not in EAs (DFS HR = 0.97 95% CI 0.90-1.05, p = 0.50; OS HR = 1.03 95% CI 0.93-1.14, p = 0.52). By disease subtype, BMI was associated with worse outcomes only in AAs with ER+, and not TNBC. By categorical BMI, WHO class III obesity (³ 40) significantly associated with worse DFS and OS only in AAs (DFS HR = 1.98, p = 0.010; OS HR = 2.07, p = 0.064), not in EAs (DFS HR = 0.97, p = 0.86; OS HR = 1.28, p = 0.30). Proportion of African ancestry (proAA) was associated with higher BMI and worse outcomes in the total population; however, within AAs there was no significant interaction between proAA and BMI on DFS (HR = 0.36, p = 0.06) or OS (HR = 0.38, p = 0.24). In AAs, BMI remained associated with DFS (HR = 2.78, p = 0.019), suggesting higher BMI is associated with worse DFS regardless of proAA. Coefficients for the interaction term indicate that as proAA increases the impact of BMI on outcome is lessened. Conclusions: Higher BMI is significantly associated with worse breast cancer outcomes in women of African ancestry in E5103, but not in those of European ancestry. Categorically, this association was significant only for severe obesity, indicating the relationship may depend on the degree of obesity. As proAA increased in AAs, the impact of BMI on outcome was lessened, suggesting other host factors may contribute more to obesity’s influence on outcome than genetics. Determination of the optimal populations for weight loss interventions will advance precision medicine efforts to impact racial disparities and outcomes in early stage breast cancer.

Just DO(HaD) It! Testing the clinical potential of the DOHaD hypothesis to prevent mental disorders using experimental study designs

2016 ◽  
Vol 7 (6) ◽  
pp. 565-573 ◽  
Author(s):  
R. J. Van Lieshout ◽  
J. E. Krzeczkowski
Keyword(s):  
Mental Disorders ◽  
Disease Risk ◽  
Maternal Diet ◽  
Full Potential ◽  
Scientific Validity ◽  
Major Step ◽  
Exercise Interventions ◽  
Study Designs ◽  
Clinical Potential ◽  
The Impact

Optimal early cognitive and emotional development are vital to reaching one’s full potential and represent our best chance to improve the mental health of the population. The developmental origins of health and disease (DOHaD) hypothesis posits that adverse perinatal exposures can alter physiology and increase disease risk. As physiological plasticity decreases with age, interventions applied during gestation may hold the most promise for reducing the impact of mental disorders across the lifespan. However, this vast clinical potential remains largely unrealized as the majority of human DOHaD research is observational in nature. The application of more rigorous experimental designs [e.g. Randomized Controlled Trials (RCTs)] not only represents a major step toward unlocking this potential, but are required to fully test the scientific validity of the DOHaD hypothesis as it pertains to mental illness. Here, we argue that the optimization of maternal diet and exercise during pregnancy represents our best chance to improve offspring neurodevelopment and reduce the burden of mental disorders. Follow-up studies of the offspring of pregnant women enrolled in new and existing RCTs of maternal gestational nutrition+exercise interventions are required to determine if acting during pregnancy can prevent and/or meaningfully reduce the prevalence and severity of mental disorders in the population.

scholarly journals Genomic research perspectives in Kazakhstan

2014 ◽  
Vol 2 ◽  
Author(s):  
Ainur Akilzhanova
Keyword(s):  
Personalized Medicine ◽  
Population Genomics ◽  
Cancer Genomics ◽  
Clinical Symptoms ◽  
Disease Risk ◽  
Genomic Medicine ◽  
Life Sciences ◽  
Genomic Research ◽  
Laboratory Staff ◽  
Research Perspectives

Introduction: Technological advancements rapidly propel the field of genome research. Advances in genetics and genomics such as the sequence of the human genome, the human haplotype map, open access databases, cheaper genotyping and chemical genomics, have transformed basic and translational biomedical research. Several projects in the field of genomic and personalized medicine have been conducted at the Center for Life Sciences in Nazarbayev University. The prioritized areas of research include: genomics of multifactorial diseases, cancer genomics, bioinformatics, genetics of infectious diseases and population genomics. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. Results: To further develop genomic and biomedical projects at Center for Life Sciences, the development of bioinformatics research and infrastructure and the establishment of new collaborations in the field are essential.Widespread use of genetic tools will allow the identification of diseases before the onset of clinical symptoms, the individualization of drug treatment, and could induce individual behavioral changes on the basis of calculated disease risk. However, many challenges remain for the successful translation of genomic knowledge and technologies into health advances, such as medicines and diagnostics.It is important to integrate research and education in the fields of genomics, personalized medicine, and bioinformatics, which will be possible with opening of the new Medical Faculty at Nazarbayev University. People in practice and training need to be educated about the key concepts of genomics and engaged so they can effectively apply their knowledge in a matter that will bring the era of genomic medicine to patient care. This requires the development of well-equipped laboratories, bioinformatics, as well as qualified trained physicians and laboratory staff.

scholarly journals Evaluation of phenotyping errors on polygenic risk score predictions

2019 ◽  
Author(s):  
Ruowang Li ◽  
Jiayi Tong ◽  
Rui Duan ◽  
Yong Chen ◽  
Jason H. Moore
Keyword(s):  
Risk Prediction ◽  
Risk Score ◽  
Disease Risk ◽  
Treatment Strategies ◽  
Genomic Research ◽  
Polygenic Risk Score ◽  
Contributing Factors ◽  
Polygenic Risk ◽  
Promising Tool ◽  
The Impact

Accurate disease risk prediction is essential in healthcare to provide personalized disease prevention and treatment strategies not only to the patients, but also to the general population. In addition to demographic and environmental factors, advancements in genomic research have revealed that genetics play an important role in determining the susceptibility of diseases. However, for most complex diseases, individual genetic variants are only weakly to moderately associated with the diseases. Thus, they are not clinically informative in determining disease risks. Nevertheless, recent findings suggest that the combined effects from multiple disease-associated variants, or polygenic risk score (PRS), can stratify disease risk similar to that of rare monogenic mutations. The development of polygenic risk score provides a promising tool to evaluate the genetic contribution of disease risk; however, the quality of the risk prediction depends on many contributing factors including the precision of the target phenotypes. In this study, we evaluated the impact of phenotyping errors on the accuracies of PRS risk prediction. We utilized electronic Medical Records and Genomics Network (eMERGE) data to simulate various types of disease phenotypes. For each phenotype, we quantified the impact of phenotyping errors generated from the differential and non-differential mechanism by comparing the prediction accuracies of PRS on the independent testing data. In addition, our results showed that the rate of accuracy degradation depended on both the phenotype and the mechanism of phenotyping error.

Clinical Pharmacokinetic Studies in Pregnant Women and the Relevance of Pharmacometric Tools

2019 ◽  
Vol 25 (5) ◽  
pp. 483-495 ◽  
Author(s):  
André Dallmann ◽  
Paola Mian ◽  
Johannes Van den Anker ◽  
Karel Allegaert
Keyword(s):  
Pregnant Women ◽  
Clinical Pharmacokinetic ◽  
Individual Variability ◽  
Current Status ◽  
Pharmacokinetic Studies ◽  
Full Potential ◽  
Pbpk Models ◽  
Pregnancy And Postpartum ◽  
Population Pk ◽  
The Impact

Background: In clinical pharmacokinetic (PK) studies, pregnant women are significantly underrepresented because of ethical and legal reasons which lead to a paucity of information on potential PK changes in this population. As a consequence, pharmacometric tools became instrumental to explore and quantify the impact of PK changes during pregnancy. Methods: We explore and discuss the typical characteristics of population PK and physiologically based pharmacokinetic (PBPK) models with a specific focus on pregnancy and postpartum. Results: Population PK models enable the analysis of dense, sparse or unbalanced data to explore covariates in order to (partly) explain inter-individual variability (including pregnancy) and to individualize dosing. For population PK models, we subsequently used an illustrative approach with ketorolac data to highlight the relevance of enantiomer specific modeling for racemic drugs during pregnancy, while data on antibiotic prophylaxis (cefazolin) during surgery illustrate the specific characteristics of the fetal compartments in the presence of timeconcentration profiles. For PBPK models, an overview on the current status of reports and papers during pregnancy is followed by a PBPK cefuroxime model to illustrate the added benefit of PBPK in evaluating dosing regimens in pregnant women. Conclusions: Pharmacometric tools became very instrumental to improve perinatal pharmacology. However, to reach their full potential, multidisciplinary collaboration and structured efforts are needed to generate more information from already available datasets, to share data and models, and to stimulate cross talk between clinicians and pharmacometricians to generate specific observations (pathophysiology during pregnancy, breastfeeding) needed to further develop the field.

scholarly journals H3ABioNet genomic medicine and microbiome data portals hackathon proceedings

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Faisal M Fadlelmola ◽  
Kais Ghedira ◽  
Yosr Hamdi ◽  
Mariem Hanachi ◽  
Fouzia Radouani ◽  
...  
Keyword(s):  
Ethnic Groups ◽  
Genomic Medicine ◽  
Lessons Learned ◽  
Team Work ◽  
Significant Progress ◽  
Team Members ◽  
Pan African ◽  
Scientific Expertise ◽  
Microbiome Data ◽  
Made In

Abstract African genomic medicine and microbiome datasets are usually not well characterized in terms of their origin, making it difficult to find and extract data for specific African ethnic groups or even countries. The Pan-African H3Africa Bioinformatics Network (H3ABioNet) recognized the need for developing data portals for African genomic medicine and African microbiomes to address this and ran a hackathon to initiate their development. The two portals were designed and significant progress was made in their development during the hackathon. All the participants worked in a very synergistic and collaborative atmosphere in order to achieve the hackathon's goals. The participants were divided into content and technical teams and worked over a period of 6 days. In response to one of the survey questions of what the participants liked the most during the hackathon, 55% of the hackathon participants highlighted the familial and friendly atmosphere, the team work and the diversity of team members and their expertise. This paper describes the preparations for the portals hackathon and the interaction between the participants and reflects upon the lessons learned about its impact on successfully developing the two data portals as well as building scientific expertise of younger African researchers. Database URL: The code for developing the two portals was made publicly available in GitHub repositories: [https://github.com/codemeleon/Database; https://github.com/codemeleon/AfricanMicrobiomePortal].

scholarly journals ‘CTRL’: an online, Dynamic Consent and participant engagement platform working towards solving the complexities of consent in genomic research

2021 ◽  
Author(s):  
Matilda A. Haas ◽  
Harriet Teare ◽  
Megan Prictor ◽  
Gabi Ceregra ◽  
Miranda E. Vidgen ◽  
...  
Keyword(s):  
Large Scale ◽  
Genomic Medicine ◽  
International Standards ◽  
Security And Privacy ◽  
Genomic Research ◽  
Return Of Results ◽  
Future Research ◽  
Design And Development ◽  
Participant Engagement ◽  
Dynamic Consent

AbstractThe complexities of the informed consent process for participating in research in genomic medicine are well-documented. Inspired by the potential for Dynamic Consent to increase participant choice and autonomy in decision-making, as well as the opportunities for ongoing participant engagement it affords, we wanted to trial Dynamic Consent and to do so developed our own web-based application (web app) called CTRL (control). This paper documents the design and development of CTRL, for use in the Australian Genomics study: a health services research project building evidence to inform the integration of genomic medicine into mainstream healthcare. Australian Genomics brought together a multi-disciplinary team to develop CTRL. The design and development process considered user experience; security and privacy; the application of international standards in data sharing; IT, operational and ethical issues. The CTRL tool is now being offered to participants in the study, who can use CTRL to keep personal and contact details up to date; make consent choices (including indicate preferences for return of results and future research use of biological samples, genomic and health data); follow their progress through the study; complete surveys, contact the researchers and access study news and information. While there are remaining challenges to implementing Dynamic Consent in genomic research, this study demonstrates the feasibility of building such a tool, and its ongoing use will provide evidence about the value of Dynamic Consent in large-scale genomic research programs.

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