Biology subjects

Baichoo, S.

Publications and source records attributed to Baichoo, S..

3 recordsLinked to original sources

bioRxiv2026

A Common Pathogenic Founder Variant in Rwandan Breast Cancer Cases

Germline data from African populations remain sparse, limiting characterization of population-specific BRCA1/2 pathogenic variants. In a study of 175 Rwandan women with breast cancer, 7 unrelated carriers (4% of cases; 22% of pathogenic variant carriers) harbored the same BRCA1 frameshift variant, c.4065_4068del (p.Asn1355Lysfs*10), which is extremely rare in gnomAD yet recurrent in European, Asian, and Middle Eastern cohorts. Whole-exome sequencing and haplotype analysis of all 7 carriers revealed a shared ancestral block of approximately 581 kb surrounding the variant, and extended haplotype homozygosity and network analyses confirmed a common founder origin. Coalescent-based age estimation placed the founder event approximately 4,000--10,000 years ago. Comparison with 1000 Genomes Project data showed the founder haplotype is absent or exceedingly rare outside African and South Asian populations. These findings strongly suggest the c.4065_4068del variant as a pre-historical BRCA1 founder variant in Rwanda, with implications for targeted genetic testing, cascade screening, and cancer prevention in the region.

genetics↗

bioRxiv2022

H3AGWAS : A portable workflow for Genome Wide Association Studies

BackgroundGenome-wide association studies (GWAS) are a powerful method to detect associations between variants and phenotypes. A GWAS requires several complex computations with large data sets, and many steps may need to be repeated with varying parameters. Manual running of these analyses can be tedious, error-prone and hard to reproduce. ResultsThe H3AGWAS workflow from the Pan-African Bioinformatics Network for H3Africa is a powerful, scalable and portable workflow implementing pre-association analysis, implementation of various association testing methods and postassociation analysis of results. ConclusionsThe workflow is scalable -- laptop to cluster to cloud (e.g., SLURM, AWS Batch, Azure). All required software is containerised and can run under Docker on Singularity.

bioinformatics↗

bioRxiv2019

A custom genotyping array reveals population-level heterogeneity for the genetic risks of prostate cancer and other cancers in Africa

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. We find that samples from Ghana and Nigeria cluster together, while samples from Senegal and South Africa yield distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores were also generated for each genome in the MADCaP pilot dataset, and we found that predicted risks of CaP are lower in Senegal and higher in Nigeria.\n\nSignificanceWe have developed an Africa-specific genotyping array which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

genomics↗