Research
Kidney disease is a growing public health concern worldwide, with millions of individuals at risk due to a combination of genetic, environmental and socioeconomic factors. Despite this, genetic research on Kidney disease in African populations remains limited and estimates are likely underreported, with estimates suggesting the true burden is three to five times higher than current figures indicate (Fabian et al., 2022).
Unique Risk Factors and Genetic Insight
Traditional CKD risk factors like smoking, hypertension, and diabetes don't fully explain the disease's high prevalence in Africa. In fact, 45-66% of CKD cases in sub-Saharan Africa occur without these pre-disposing risk factors (Stanifer, 2014; Muiru, 2020). This points to local risk factors such as infections (e.g., HIV, COVID-19) and inflammation.
Moreover, genetic predisposition plays a critical role, with heritability estimates ranging from 30-75% (Cañadas-Garre et al., 2019). Key discoveries, such as the APOL1 genetic variants associated with CKD and protection against trypanosomiasis, underscore the need for more genomic studies on Africans, who currently make up just 1.1% of global GWAS data (Fatumo et al., 2022).
This provides a powerful opportunity to expand the previous studies in Africa, which up until now have included just over 3,000 participants (Fatumo et al., 2021).
Our Research Framework
To uncover the genetics behind kidney disease, data harmonisation is critical. That's why through KidneyGenAfrica we are creating a specialised working group, to ensure we can accurately compare and analyse data on kidney disease. Our partners will all be contributing genetic data, and by analysing genetic data from 40,000 individuals, we will have power to detect even the smallest genetic variations affecting kidney disease. This provides a powerful opportunity to expand the previous studies in Africa, which up until now have included just over 3,000 participants (Fatumo et al., 2021).
This group will focus on standardising key biomarkers like creatinine, cystatin C, proteinuria, and albuminuria, as well as streamlining questionnaires and other biomarkers. By harmonising this data, we can conduct powerful multi-cohort genetic analyses, reaching levels of precision critical for understanding new insights into kidney disease.
Publications
- KidneyGenAfrica: A multi-cohort Genome-wide association study and polygenic prediction of kidney function in 110,000 continental and diasporan Africans - Learn More
Datasets
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