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Sun Q, Horimoto ARVR, Chen B, Ockerman F, Mohlke KL, Blue E, Raffield LM, Li Y. Opportunities and challenges of local ancestry in genetic association analyses. Am J Hum Genet 2025; 112:727-740. [PMID: 40185073 DOI: 10.1016/j.ajhg.2025.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2024] [Revised: 03/05/2025] [Accepted: 03/05/2025] [Indexed: 04/07/2025] Open
Abstract
Recently, admixed populations make up an increasing percentage of the US and global populations, and the admixture is not uniform over space or time or across genomes. Therefore, it becomes indispensable to evaluate local ancestry in addition to global ancestry to improve genetic epidemiological studies. Recent advances in representing human genome diversity, coupled with large-scale whole-genome sequencing initiatives and improved tools for local ancestry inference, have enabled studies to demonstrate that incorporating local ancestry information enhances both genetic association analyses and polygenic risk predictions. Along with the opportunities that local ancestry provides, there exist challenges preventing its full usage in genetic analyses. In this review, we first summarize methods for local ancestry inference and illustrate how local ancestry can be utilized in various analyses, including admixture mapping, association testing, and polygenic risk score construction. In addition, we discuss current challenges in research involving local ancestry, both in terms of the inference itself and its role in genetic association studies. We further pinpoint some future study directions and methodology development opportunities to help more effectively incorporate local ancestry in genetic analyses. It is worth the effort to pursue those future directions and address these analytical challenges because the appropriate use of local ancestry estimates could help mitigate inequality in genomic medicine and improve our understanding of health and disease outcomes.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Center for Computational and Genomic Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
| | - Andrea R V R Horimoto
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brian Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Frank Ockerman
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Elizabeth Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA; Brotman Baty Institute, Seattle, WA 98195, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Ndong Sima CAA, Step K, Swart Y, Schurz H, Uren C, Möller M. Methodologies underpinning polygenic risk scores estimation: a comprehensive overview. Hum Genet 2024; 143:1265-1280. [PMID: 39425790 PMCID: PMC11522080 DOI: 10.1007/s00439-024-02710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Accepted: 10/06/2024] [Indexed: 10/21/2024]
Abstract
Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.
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Affiliation(s)
- Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Yolandi Swart
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Haiko Schurz
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - Caitlin Uren
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Cape Town, South Africa
| | - Marlo Möller
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Cape Town, South Africa.
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Zhou J, Li J, Hu Y, Li S. Epidemiological characteristics, diagnosis and treatment effect of rifampicin-resistant pulmonary tuberculosis (RR-PTB) in Guizhou Province. BMC Infect Dis 2024; 24:1058. [PMID: 39333894 PMCID: PMC11429120 DOI: 10.1186/s12879-024-09976-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 09/23/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Rifampicin-resistant pulmonary tuberculosis (RR-PTB) presents a significant threat to global public health security. China bears a substantial burden of RR-PTB cases globally, with Guizhou Province experiencing particularly alarming trends, marked by a continual increase in patient numbers. Understanding the population characteristics and treatment modalities for RR-PTB is crucial for mitigating morbidity and mortality associated with this disease. METHODS We gathered epidemiological, diagnostic, and treatment data of all RR-PTB cases recorded in Guizhou Province from January 1, 2017 to December 31, 2023. Utilizing composition ratios as the analytical metric, we employed Chi-square tests to examine the spatiotemporal distribution patterns of RR-PTB patients and the evolving trends among different patient classifications over the study period. RESULTS In our study, 3396 cases of RR-PTB were analyzed, with an average age of 45 years. The number of RR-PTB patients rose significantly from 176 in 2017 to 960 in 2023, peaking notably among individuals aged 23-28 and 44-54, with a rising proportion in the 51-80 age group (P < 0.001). Since 2021, there has been a notable increase in the proportion of female patients. While individuals of Han ethnic group comprised the largest group, their proportion decreased over time (P < 0.001). Conversely, the Miao ethnicity showed an increasing trend (P < 0.05). The majority of patients were farmers, with their proportion showing an upward trajectory (P < 0.001), while students represented 4.33% of the cases. Geographically, most patients were registered in Guiyang and Zunyi, with a declining trend (P < 0.001), yet household addresses primarily clustered in Bijie, Tongren, and Zunyi. The proportion of floating population patients gradually decreased, alongside an increase in newly treated patients and those without prior anti-tuberculosis therapy. Additionally, there was a notable rise in molecular biological diagnostic drug sensitivity (real-time PCR and melting curve analysis) (P < 0.001). However, the cure rate declined, coupled with an increasing proportion of RR-PTB patients lost to follow-up and untreated (P < 0.05). CONCLUSIONS Enhanced surveillance is crucial for detecting tuberculosis patients aged 23-28 and 44-54 years. The distribution of cases varies among nationalities and occupations, potentially influenced by cultural and environmental factors. Regional patterns in RR-PTB incidence suggest tailored prevention and control strategies are necessary. Despite molecular tests advances, challenges persist with low cure rates and high loss to follow-up. Strengthening long-term management, resource allocation, and social support systems for RR-PTB patients is essential.
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Affiliation(s)
- Jian Zhou
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, No.9 Beijing Road, Yunyan District, Guiyang city, 550025, Guizhou Province, China
- Guizhou Center for Disease Control and Prevention, No.73, Bageyan Road, Yunyan District, Guiyang city, 550004, Guizhou Province, China
| | - Jinlan Li
- Guizhou Center for Disease Control and Prevention, No.73, Bageyan Road, Yunyan District, Guiyang city, 550004, Guizhou Province, China.
| | - Yong Hu
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, No.9 Beijing Road, Yunyan District, Guiyang city, 550025, Guizhou Province, China.
| | - Shijun Li
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, No.9 Beijing Road, Yunyan District, Guiyang city, 550025, Guizhou Province, China.
- Guizhou Center for Disease Control and Prevention, No.73, Bageyan Road, Yunyan District, Guiyang city, 550004, Guizhou Province, China.
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Palittapongarnpim P, Tantivitayakul P, Aiewsakun P, Mahasirimongkol S, Jaemsai B. Genomic Interactions Between Mycobacterium tuberculosis and Humans. Annu Rev Genomics Hum Genet 2024; 25:183-209. [PMID: 38640230 DOI: 10.1146/annurev-genom-021623-101844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
Mycobacterium tuberculosis is considered by many to be the deadliest microbe, with the estimated annual cases numbering more than 10 million. The bacteria, including Mycobacterium africanum, are classified into nine major lineages and hundreds of sublineages, each with different geographical distributions and levels of virulence. The phylogeographic patterns can be a result of recent and early human migrations as well as coevolution between the bacteria and various human populations, which may explain why many studies on human genetic factors contributing to tuberculosis have not been replicable in different areas. Moreover, several studies have revealed the significance of interactions between human genetic variations and bacterial genotypes in determining the development of tuberculosis, suggesting coadaptation. The increased availability of whole-genome sequence data from both humans and bacteria has enabled a better understanding of these interactions, which can inform the development of vaccines and other control measures.
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Affiliation(s)
- Prasit Palittapongarnpim
- Pornchai Matangkasombut Center for Microbial Genomics, Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand; , ,
| | - Pornpen Tantivitayakul
- Department of Oral Microbiology, Faculty of Dentistry, Mahidol University, Bangkok, Thailand;
| | - Pakorn Aiewsakun
- Pornchai Matangkasombut Center for Microbial Genomics, Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand; , ,
| | - Surakameth Mahasirimongkol
- Medical Life Sciences Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
- Information and Communication Technology Center, Office of Permanent Secretary, Ministry of Public Health, Nonthaburi, Thailand;
| | - Bharkbhoom Jaemsai
- Pornchai Matangkasombut Center for Microbial Genomics, Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, Thailand; , ,
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Croock D, Swart Y, Schurz H, Petersen DC, Möller M, Uren C. Data Harmonization Guidelines to Combine Multi-platform Genomic Data from Admixed Populations and Boost Power in Genome-Wide Association Studies. Curr Protoc 2024; 4:e1055. [PMID: 38837690 DOI: 10.1002/cpz1.1055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole-genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non-existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi-platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case-control samples from six separate in-house datasets and conducted a genome-wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Processing separate datasets comprising array genotype data Alternate Protocol 1: Processing separate datasets comprising array genotype and whole-genome sequencing data Alternate Protocol 2: Performing imputation using a local reference panel Basic Protocol 2: Merging separate datasets Basic Protocol 3: Ancestry inference using ADMIXTURE and RFMix Basic Protocol 4: Batch effect correction using pseudo-case-control comparisons.
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Affiliation(s)
- Dayna Croock
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Yolandi Swart
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Haiko Schurz
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Desiree C Petersen
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
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Step K, Ndong Sima CAA, Mata I, Bardien S. Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction. Front Neurosci 2024; 18:1380860. [PMID: 38859922 PMCID: PMC11163124 DOI: 10.3389/fnins.2024.1380860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Affiliation(s)
- Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
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7
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Pfennig A, Petersen LN, Kachambwa P, Lachance J. Evolutionary Genetics and Admixture in African Populations. Genome Biol Evol 2023; 15:evad054. [PMID: 36987563 PMCID: PMC10118306 DOI: 10.1093/gbe/evad054] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 03/15/2023] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
As the ancestral homeland of our species, Africa contains elevated levels of genetic diversity and substantial population structure. Importantly, African genomes are heterogeneous: They contain mixtures of multiple ancestries, each of which have experienced different evolutionary histories. In this review, we view population genetics through the lens of admixture, highlighting how multiple demographic events have shaped African genomes. Each of these historical vignettes paints a recurring picture of population divergence followed by secondary contact. First, we give a brief overview of genetic variation in Africa and examine deep population structure within Africa, including the evidence of ancient introgression from archaic "ghost" populations. Second, we describe the genetic legacies of admixture events that have occurred during the past 10,000 years. This includes gene flow between different click-speaking Khoe-San populations, the stepwise spread of pastoralism from eastern to southern Africa, multiple migrations of Bantu speakers across the continent, as well as admixture from the Middle East and Europe into the Sahel region and North Africa. Furthermore, the genomic signatures of more recent admixture can be found in the Cape Peninsula and throughout the African diaspora. Third, we highlight how natural selection has shaped patterns of genetic variation across the continent, noting that gene flow provides a potent source of adaptive variation and that selective pressures vary across Africa. Finally, we explore the biomedical implications of population structure in Africa on health and disease and call for more ethically conducted studies of genetic variation in Africa.
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Affiliation(s)
- Aaron Pfennig
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
| | | | | | - Joseph Lachance
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia
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Phelan J, Gomez-Gonzalez PJ, Andreu N, Omae Y, Toyo-Oka L, Yanai H, Miyahara R, Nedsuwan S, de Sessions PF, Campino S, Sallah N, Parkhill J, Smittipat N, Palittapongarnpim P, Mushiroda T, Kubo M, Tokunaga K, Mahasirimongkol S, Hibberd ML, Clark TG. Genome-wide host-pathogen analyses reveal genetic interaction points in tuberculosis disease. Nat Commun 2023; 14:549. [PMID: 36725857 PMCID: PMC9892022 DOI: 10.1038/s41467-023-36282-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/24/2023] [Indexed: 02/03/2023] Open
Abstract
The genetics underlying tuberculosis (TB) pathophysiology are poorly understood. Human genome-wide association studies have failed so far to reveal reproducible susceptibility loci, attributed in part to the influence of the underlying Mycobacterium tuberculosis (Mtb) bacterial genotype on the outcome of the infection. Several studies have found associations of human genetic polymorphisms with Mtb phylo-lineages, but studies analysing genome-genome interactions are needed. By implementing a phylogenetic tree-based Mtb-to-human analysis for 714 TB patients from Thailand, we identify eight putative genetic interaction points (P < 5 × 10-8) including human loci DAP and RIMS3, both linked to the IFNγ cytokine and host immune system, as well as FSTL5, previously associated with susceptibility to TB. Many of the corresponding Mtb markers are lineage specific. The genome-to-genome analysis reveals a complex interactome picture, supports host-pathogen adaptation and co-evolution in TB, and has potential applications to large-scale studies across many TB endemic populations matched for host-pathogen genomic diversity.
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Affiliation(s)
- Jody Phelan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Nuria Andreu
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Yosuke Omae
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Licht Toyo-Oka
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hideki Yanai
- Fukujuji Hospital and Research Institute of Tuberculosis, Japan Anti-Tuberculosis Association, Kiyose, Japan
| | - Reiko Miyahara
- Genome Medical Science Project, National Center for Global Health and Medicine, Tokyo, Japan
| | | | | | - Susana Campino
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Neneh Sallah
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Cambridge, UK
| | - Nat Smittipat
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand
| | - Prasit Palittapongarnpim
- National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathumthani, Thailand
| | | | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Katsushi Tokunaga
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Surakameth Mahasirimongkol
- Medical Genetics Center, Medical Life Sciences Institute, Department of Medical Sciences, Ministry of Public Health, Nonthaburi, Thailand
| | - Martin L Hibberd
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
| | - Taane G Clark
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, United Kingdom.
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Smith MH, Myrick JW, Oyageshio O, Uren C, Saayman J, Boolay S, van der Westhuizen L, Werely C, Möller M, Henn BM, Reynolds AW. Epidemiological correlates of overweight and obesity in the Northern Cape Province, South Africa. PeerJ 2023; 11:e14723. [PMID: 36788809 PMCID: PMC9922494 DOI: 10.7717/peerj.14723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/19/2022] [Indexed: 02/11/2023] Open
Abstract
Background In the past several decades, obesity has become a major public health issue worldwide, associated with increased rates of chronic disease and death. Like many developing nations, South Africa is experiencing rapid increases in BMI, and as a result, evidence-based preventive strategies are needed to reduce the increasing burden of overweight and obesity. This study aimed to determine the prevalence and predictors of overweight and obesity among a multi-ethnic cohort from the rural Northern Cape of South Africa. Methods These data were collected as part of a tuberculosis (TB) case-control study, with 395 healthy control participants included in the final analysis. Overweight and obesity were defined according to WHO classification. Multivariate linear models of BMI were generated using sex, age, education level, smoking, alcohol consumption, and diabetes as predictor variables. We also used multivariable logistic regression analysis to assess the relationship of these factors with overweight and obesity. Results The average BMI in our study cohort was 25.2. The prevalence of overweight was 18.0% and the prevalence of obesity was 25.0%. We find that female sex, being older, having more years of formal education, having diabetes, and being in a rural area are all positively associated with BMI in our dataset. Women (OR = 5.6, 95% CI [3.3-9.8]), rural individuals (OR = 3.3, 95% CI [1.9-6.0]), older individuals (OR = 1.02, 95% CI [1-1.04]), and those with more years of education (OR = 1.2, 95% CI [1.09-1.32]) were all more likely to be overweight or obese. Alternatively, being a smoker is negatively associated with BMI and decreases one's odds of being overweight or obese (OR = 0.28, 95% CI [0.16-0.46]). Conclusions We observed a high prevalence of overweight and obesity in this study. The odds of being overweight and obese were higher in women, those living in rural areas, and those with more education, and increases with age. Community-based interventions to control obesity in this region should pay special attention to these groups.
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Affiliation(s)
| | - Justin W Myrick
- Department of Anthropology and UC Davis Genome Center, University of California, Davis, Davis, United States
| | - Oshiomah Oyageshio
- Center for Population Biology, University of California, Davis, Davis, United States
| | - Caitlin Uren
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, University of Stellenbosch, Cape Town, South Africa
| | - Jamie Saayman
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
| | - Sihaam Boolay
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
| | - Lena van der Westhuizen
- Department of Anthropology and UC Davis Genome Center, University of California, Davis, Davis, United States
| | - Cedric Werely
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
| | - Marlo Möller
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa.,Centre for Bioinformatics and Computational Biology, University of Stellenbosch, Cape Town, South Africa
| | - Brenna M Henn
- Department of Anthropology and UC Davis Genome Center, University of California, Davis, Davis, United States
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10
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Ndong Sima CAA, Smith D, Petersen DC, Schurz H, Uren C, Möller M. The immunogenetics of tuberculosis (TB) susceptibility. Immunogenetics 2022; 75:215-230. [DOI: 10.1007/s00251-022-01290-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
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11
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Exploring the relationship between the gut microbiome and mental health outcomes in a posttraumatic stress disorder cohort relative to trauma-exposed controls. Eur Neuropsychopharmacol 2022; 56:24-38. [PMID: 34923209 DOI: 10.1016/j.euroneuro.2021.11.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/15/2021] [Accepted: 11/22/2021] [Indexed: 12/17/2022]
Abstract
Posttraumatic stress disorder (PTSD) imposes a significant burden on patients and communities. Although the microbiome-gut-brain axis has been proposed as a mediator or moderator of PTSD risk and persistence of symptoms, clinical data directly delineating the gut microbiome's relationship to PTSD are sparse. This study investigated associations between the gut microbiome and mental health outcomes in participants with PTSD (n = 79) and trauma-exposed controls (TECs) (n = 58). Diagnoses of PTSD, major depressive disorder (MDD), and childhood trauma were made using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5), MINI International Neuropsychiatric Interview (MINI), and Childhood Trauma Questionnaire (CTQ), respectively. Microbial communities from stool samples were profiled using 16S ribosomal RNA gene V4 amplicon sequencing and tested for associations with PTSD-related variables of interest. Random forest models identified a consortium of four genera, i.e., a combination of Mitsuokella, Odoribacter, Catenibacterium, and Olsenella, previously associated with periodontal disease, that could distinguish PTSD status with 66.4% accuracy. The relative abundance of this consortium was higher in the PTSD group and correlated positively with CAPS-5 and CTQ scores. MDD diagnosis was also associated with increased relative abundance of the Bacteroidetes phylum. Current use of psychotropics significantly impacted community composition and the relative abundances of several taxa. Early life trauma may prime the microbiome for changes in composition that facilitate a pro-inflammatory cascade and increase the risk of development of PTSD. Future studies should rigorously stratify participants into healthy controls, TECs, and PTSD (stratified by psychotropic drug use) to explore the role of the oral-gut-microbiome-brain axis in trauma-related disorders.
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