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Figure S1. Cross-validation error in ADMIXTURE analyses. The best fit to data occurs at K = 3.

Successfully called markers on the MADCaP Array. This tab-delimited file includes genomic positions, inclusion criteria, and overlap with other arrays.

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. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array.Significance:This study presents 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.

Motivation Post-genome-wide association studies (pGWAS) analysis is designed to decipher the functional consequences of significant single-nucleotide polymorphisms (SNPs) in the era of GWAS. This can be translated into research insights and clinical benefits such as the effectiveness of strategies for disease screening, treatment and prevention. However, the setup of pGWAS (pGWAS) tools can be quite complicated, and it mostly requires big data. The challenge however is, scientists are required to have sufficient experience with several of these technically complex and complicated tools in order to complete the pGWAS analysis. Results We present SysBiolPGWAS, a pGWAS web application that provides a comprehensive functionality for biologists and non-bioinformaticians to conduct several pGWAS analyses to overcome the above challenges. It provides unique functionalities for analysis involving multi-omics datasets and visualization using various bioinformatics tools. SysBiolPGWAS provides access to individual pGWAS tools and a novel custom pGWAS pipeline that integrates several individual pGWAS tools and data. The SysBiolPGWAS app was developed to be a one-stop shop for pGWAS analysis. It targets researchers in the area of the human genome and performs its analysis mainly in the autosomal chromosomes. Availability and implementation SysBiolPGWAS web app was developed using JavaScript/TypeScript web frameworks and is available at: https://spgwas.waslitbre.org/. All codes are available in this GitHub repository https://github.com/covenant-university-bioinformatics.

Polygenic risk score (PRS) analysis is a powerful method been used to estimate an individual's genetic risk towards targeted traits. PRS analysis could be used to obtain evidence of a genetic effect beyond Genome-Wide Association Studies (GWAS) results i.e. when there are no significant markers. PRS analysis has been widely applied to investigate the genetic basis of several traits including rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies showed that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we reviewed the conventional PRS methods and their application to sub-saharan Africa communities. We concluded that the limiting factor of applying PRS analysis to sub-saharan populations is the lack of sufficient GWAS data. Also, we recommended developing African-specific PRS tools

Purpose Cancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA. Methods We established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array. Results We used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories. Conclusion We provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

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. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array. Significance: This study presents 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.

Prostate cancer is a highly heritable disease that disproportionally affects African and African-American men. With this in mind, the MADCaP (Men of African Descent Carcinoma of the Prostate) Network has developed a custom genotyping platform. This array is optimized for detection of novel genetic associations in sub-Saharan African populations. The MADCaP Array contains approximately 1.6 million markers, many of which overlap the H3Africa Consortium Array and the OncoArray. It includes an imputation backbone that successfully tags 94% of common (MAF > 0.05) genetic variants in African populations (r2 threshold = 0.8). To aid in fine-mapping, the MADCaP Array has a high density of markers in genomic regions surrounding known cancer associations, including 8q24. Markers on the MADCaP Array also include over 27,000 prostate eQTLs. Using the MADCaP Array, we conducted a pilot study of 800 individuals with individual-level phenotype information. These samples include equal numbers of prostate cancer cases and controls, and they were collected from study sites in Ghana, Nigeria, Senegal, and South Africa. Here, we assess the extent to which polygenic risk scores are able to predict prostate cancer risks in African populations. We also identify non-African GWAS signals that replicate well in African populations. Additional analyses in this study include testing whether specific genetic ancestries are over-represented in cases, and quantifying the extent to which runs of homozygosity are found in the genomes of cases and controls. Citation Format: Joseph Lachance, Maxine Harlemon, Paidamoyo Kachambwa, Olabode Ajayi, Michelle Kim, Marcia Adams, Elizabeth Pugh, Lindsay Peterson, Timothy Rebbeck. Development of a custom genotyping platform and genetic prediction of prostate cancer risks in sub-Saharan Africa [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2410.