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Background Runs of Homozygosity (ROH) are genomic regions where identical haplotypes are inherited from each parent. Since their first detection due to technological advances in the late 1990s, ROHs have been shedding light on human population history and deciphering the genetic basis of monogenic and complex traits and diseases. ROH studies have predominantly exploited SNP array data, but are gradually moving to whole genome sequence (WGS) data as it becomes available. WGS data, covering more genetic variability, can add value to ROH studies, but require additional considerations during analysis. Results Using SNP array and low coverage WGS data from 1885 individuals from 20 world populations, our aims were to compare ROH from the two datasets and to establish software conditions to get comparable results, thus providing guidelines for combining disparate datasets in joint ROH analyses. By allowing heterozygous SNPs per window, using the PLINK homozygosity function and non-parametric analysis, we were able to obtain non-significant differences in number ROH, mean ROH size and total sum of ROH between data sets using the different technologies for almost all populations. Conclusions By allowing 3 heterozygous SNPs per ROH when dealing with WGS low coverage data, it is possible to establish meaningful comparisons between data using SNP array and WGS low coverage technologies.

The deep evolutionary history of African populations, since the emergence of modern humans more than 300,000 years ago, has resulted in high genetic diversity and considerable population structure. Selected genetic variants have increased in frequency due to environmental adaptation, but recent exposures to novel pathogens and changes in lifestyle render some of them with properties leading to present health liabilities. The unique discoverability potential from African genomic studies promises invaluable contributions to understanding the genomic and molecular basis of health and disease. Globally, African populations are understudied, and precision medicine approaches are largely based on data from European and Asian-ancestry populations, which limits the transferability of findings to the continent of Africa. Africa needs innovative precision medicine solutions based on African data that use knowledge and implementation strategies aligned to its climatic, cultural, economic and genomic diversity.Africa is a continent with deep evolutionary history, which has implications for the genetic underpinnings of disease. In this Review, the authors discuss how genetic features of African populations provide both challenges and opportunities for understanding disease genetics in Africa. They describe how this genetic knowledge — combined with initiatives including capacity-building, data sharing and increased representation of African genomes in genetic variation databases — can be leveraged towards achieving precision medicine approaches in African healthcare.

Long runs of homozygosity (ROH) arise when identical haplotypes are inherited from each parent and thus a long tract of genotypes is homozygous. Cousin marriage or inbreeding gives rise to such autozygosity; however, genome-wide data reveal that ROH are universally common in human genomes even among outbred individuals. The number and length of ROH reflect individual demographic history, while the homozygosity burden can be used to investigate the genetic architecture of complex disease. We discuss how to identify ROH in genome-wide microarray and sequence data, their distribution in human populations and their application to the understanding of inbreeding depression and disease risk.

Her legacy project was to establish the Dikgale Health and Demographic Surveillance System (DHDSS) and Research Centre in 1995. The DHDSS was a founder member of the International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) established in 1998. Professor Osman Sankoh, who stepped down as Executive Director of INDEPTH in 2018, said that Marianne never missed an Annual General Meeting or International Scientific Meeting, whether in Africa, Asia or elsewhere. Professor Steve Tollman, Scientific Director of INDEPTH, described her as 'a grande dame of INDEPTH and a testament to what determination can achieve; and that, if anything, age is not a barrier but a blessing'. Through her work she gave the communities from the villages around the university a voice, and created awareness about the rising levels of non-communicable and infectious diseases and their devastating toll on health and wellbeing. The DHDSS collected longitudinal population data on vital events, health and socio-economic factors to monitor lifestyle changes, to study prevalent diseases and their causes, with a view to applying these insights to health programmes. In 2018, the target surveyed population was expanded to reach 100 000 individuals, enabled through an ambitious national programme, funded by the Department of Science and Innovation, with the DHDSS becoming a nodal centre of the South African Population Research Infrastructure Network, and being renamed DIMAMO. Marianne was the Emeritus Director of the DIMAMO Population Health Research Centre that was formally inaugurated on 10 December 2018. The grand opening of DIMAMO was in a marquee tent in the garden on a stiflingly hot day in Limpopo, and it was a great pleasure for me to spend 2 days with Marianne at that time. The opening event was packed with university and governmental partners, funders, collaborators, students and her family, all of whom came to celebrate this great achievement, spearheaded by such a remarkable woman. She energetically stepped up unassisted onto the podium and in her low-key manner explained the genesis and challenges that had led to that moment. The next day Marianne took me on a long drive to the villages of the region and we talked about the people and their complex lives, and the communities and their hardships. It was clear that she knew them well, had spent much time within the villages and truly cared for the people. She showed me the schools, the clinics and the community areas, and on discovering some beautiful flowers on a cluster of succulent plants, we stopped for a while to have a closer look. Marianne's research and her collaborative work highlighted the rise in non-communicable diseases such as hypertension and diabetes, which are serious public health problems in the country. She lamented the fact that patients had little knowledge of the risks and that compliance with medication remained low. Referring to DIMAMO, she told audiences that the centre's work would boost South Africa's research into inequality, poverty and population health, including non-communicable diseases, and that the work was expected to inform interventions to significantly improve the health and socio-economic well-being of the whole population. The DIMAMO HDSS will continue to collect data that will document the changing exposure to risk factors to various non-communicable diseases in rural South Africa, hopefully saving lives through better knowledge and targeted interventions. Described by her granddaughter as a 'rebellious trailblazer', and by her students, colleagues and friends as extraordinary, humble, knowledgeable, dignified, dependable, wise and compassionate, thoughtful and generous of mind and heart, the world is a better place for Marianne's life, well lived.

In his memoire, My Life in Science (BioMed Central Limited; 2001), Sydney Brenner described his greatest skill as ‘getting things started’. Throughout his life, he inspired breathtaking research projects and ambitious scientific institutes that are thriving today across several continents.

Background Cardiovascular diseases (CVD) are a major health concern in Africa. Improved identification and treatment of high-risk individuals can reduce adverse health outcomes. Current CVD risk calculators are largely unvalidated in African populations and overlook genetic factors. Polygenic scores (PGS) can enhance risk prediction by measuring genetic susceptibility to CVD, but their effectiveness in genetically diverse populations is limited by a European-ancestry bias. To address this, we developed models integrating genetic data and conventional risk factors to assess the risk of developing cardiometabolic outcomes in African populations. Methods We used summary statistics from a genome-wide association meta-analysis ( n  = 14,126) in African populations to derive novel genome-wide PGS for 14 cardiometabolic traits in an independent African target sample (Africa Wits-INDEPTH Partnership for Genomic Research (AWI-Gen), n  = 10,603). Regression analyses assessed relationships between each PGS and corresponding cardiometabolic trait, and seven CVD outcomes (CVD, heart attack, stroke, diabetes mellitus, dyslipidaemia, hypertension, and obesity). The predictive utility of the genetic data was evaluated using elastic net models containing multiple PGS (MultiPGS) and reference-projected principal components of ancestry (PPCs). An integrated risk prediction model incorporating genetic and conventional risk factors was developed. Nested cross-validation was used when deriving elastic net models to enhance generalisability. Results Our African-specific PGS displayed significant but variable within- and cross- trait prediction (max. R 2  = 6.8%, p  = 1.86 × 10 −173 ). Significantly associated PGS with dyslipidaemia included the PGS for total cholesterol (logOR = 0.210, SE = 0.022, p  = 2.18 × 10 −21 ) and low-density lipoprotein (logOR =  − 0.141, SE = 0.022, p  = 1.30 × 10 −20 ); with hypertension, the systolic blood pressure PGS (logOR = 0.150, SE = 0.045, p = 8.34 × 10 −4 ); and multiple PGS associated with obesity: body mass index (max. logOR = 0.131, SE = 0.031, p  = 2.22 × 10 −5 ), hip circumference (logOR = 0.122, SE = 0.029, p  = 2.28 × 10 −5 ), waist circumference (logOR = 0.013, SE = 0.098, p  = 8.13 × 10 −4 ) and weight (logOR = 0.103, SE = 0.029, p  = 4.89 × 10 −5 ). Elastic net models incorporating MultiPGS and PPCs significantly improved prediction over MultiPGS alone. Models including genetic data and conventional risk factors were more predictive than conventional risk models alone (dyslipidaemia: R 2  increase = 2.6%, p  = 4.45 × 10 −12 ; hypertension: R 2  increase = 2.6%, p  = 2.37 × 10 −13 ; obesity: R 2  increase = 5.5%, 1.33 × 10 −34 ). Conclusions In African populations, CVD and associated cardiometabolic trait prediction models can be improved by incorporating ancestry-aligned PGS and accounting for ancestry. Combining PGS with conventional risk factors further enhances prediction over traditional models based on conventional factors. Incorporating data from target populations can improve the generalisability of international predictive models for CVD and associated traits in African populations.

Few studies have compared the utility of serum levels of lipid fractions in cardiovascular disease (CVD) risk assessment in sub-Saharan Africa (SSA). The current study interrogated this question among men and women aged 40–60 years in rural northern Ghana. This was a cross-sectional study in which data was collected on socio-demography, behaviour, health history, anthropometry and lipid levels. Adjusted multivariable logistic regression models were used to assess the association of various lipid metrics with CVD. All tests were considered statistically significant at P<0.05. Data were available for 1839 participants. The prevalence of self-reported CVD was 1.6% (n = 29). Non-HDL-C (median (interquartile range): 2.4 (1.9–3.0) vs 2.0 (1.6–2.5) mmol/L; P = 0.009), LDL-C/HDL-C (1.8 (1.4–2.4) vs 1.5 (1.1–2.6); P = 0.019) and TC/HDL-C (3.3 (2.9–3.9) vs 2.9 (2.4–3.5); P = 0.003) were all significantly higher in participants with self-reported CVD compared to those without. However, after adjusting for socioeconomic status (SES) and meals from vendors in a logistic regression model, only non-HDL-C (odds ratio [95% CIs]): (1.58 [1.05, 2.39]), P = 0.029 and LDL-C/HDL-C levels (odds ratio [95% CIs]): (1.26 [1.00, 1.59]), P = 0.045 remained significantly associated with self-reported CVD. While our findings suggest non-HDL-C and LDL-C/HDL-C measures may be appropriate biomarkers for assessing CVD risk in this population, further studies using established clinical endpoints are required to validate these findings in sub-Saharan Africans.

The study of runs of homozygosity (ROH) can shed light on population demographic history and cultural practices. We present a fine-scale ROH analysis of 1679 individuals from 28 sub-Saharan African (SSA) populations along with 1384 individuals from 17 worldwide populations. Using high-density SNP coverage, we could accurately identify ROH > 300 kb using PLINK software. The genomic distribution of ROH was analysed through the identification of ROH islands and regions of heterozygosity (RHZ). The analyses showed a heterogeneous distribution of autozygosity across SSA, revealing complex demographic histories. They highlight differences between African groups and can differentiate the impact of consanguineous practices (e.g. among the Somali) from endogamy (e.g. among several Khoe and San groups). Homozygosity cold and hotspots were shown to harbour multiple protein coding genes. Studying ROH therefore not only sheds light on population history, but can also be used to study genetic variation related to adaptation and potentially to the health of extant populations.

The objective was to explore changes in gene expression in Wnt pathway genes in skin samples of black South Africans with diffuse cutaneous systemic sclerosis (dcSSc). Affected (forearm) and unaffected (upper back) skin samples of eight Black South Africans with active early dcSSc were compared to skin samples from seven ethnically matched control subjects. The Wnt Pathway Plus RT2 Profiler qPCR Array was used to determine gene expression and analyzed for differential expression between cases and controls. Selective validation was done using single-gene TaqMan assays. Several genes were similarly upregulated in both affected and unaffected skin of the dcSSc patients compared to controls. These included the Wnt ligands WNT7A and WNT10A, the frizzled receptors FZD8 and FZD9, intracellular signaling proteins AXIN1 and AXIN2, and the pathway target genes FGF4 and MMP7. Principal component analysis revealed patients clustering into two groups, which co-segregated with clinical features of interstitial lung disease and/or inflammatory myopathy, or the absence of an inflammation phenotype. These two groups showed paradoxical gene expression of the genes TCF7, SOX17, and FRZB in affected and unaffected skin. This study provides further evidence of dysregulation of gene expression at various levels of the Wnt signaling pathway in dcSSc. Moreover, principal component analysis showed two distinct patient clusters of gene expression, which co-segregated with the presence or absence of clinical inflammatory features, and may reflect different pathological pathways in dcSSc.

Few studies have compared the utility of serum levels of lipid fractions in cardiovascular disease (CVD) risk assessment in sub-Saharan Africa (SSA). The current study interrogated this question among men and women aged 40–60 years in rural northern Ghana. This was a cross-sectional study in which data was collected on socio-demography, behaviour, health history, anthropometry and lipid levels. Adjusted multivariable logistic regression models were used to assess the association of various lipid metrics with CVD. All tests were considered statistically significant at P<0.05. Data were available for 1839 participants. The prevalence of self-reported CVD was 1.6% (n = 29). Non-HDL-C (median (interquartile range): 2.4 (1.9–3.0) vs 2.0 (1.6–2.5) mmol/L; P = 0.009), LDL-C/HDL-C (1.8 (1.4–2.4) vs 1.5 (1.1–2.6); P = 0.019) and TC/HDL-C (3.3 (2.9–3.9) vs 2.9 (2.4–3.5); P = 0.003) were all significantly higher in participants with self-reported CVD compared to those without. However, after adjusting for socioeconomic status (SES) and meals from vendors in a logistic regression model, only non-HDL-C (odds ratio [95% CIs]): (1.58 [1.05, 2.39]), P = 0.029 and LDL-C/HDL-C levels (odds ratio [95% CIs]): (1.26 [1.00, 1.59]), P = 0.045 remained significantly associated with self-reported CVD. While our findings suggest non-HDL-C and LDL-C/HDL-C measures may be appropriate biomarkers for assessing CVD risk in this population, further studies using established clinical endpoints are required to validate these findings in sub-Saharan Africans.