Search

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.

Regulatory DNA has the potential to be adaptive, and large numbers of expression quantitative trait loci (eQTLs) have recently been identified in humans. For the first time, a comprehensive study of adaptive eQTLs is possible. Many eQTLs have large allele frequency differences between populations, and these differences can be due to natural selection. Here, we combined population branch statistics with tissue-specific eQTL data to identify positively selected loci in human populations. Adaptive eQTLs tend to affect fewer tissues than non-adaptive eQTLs. Because the tissue breadth of an eQTL can be viewed as a measure of pleiotropy, these results suggest that pleiotropy can inhibit adaptation. The proportion of eQTLs that are adaptive varies by tissue, and we find that eQTLs that regulate expression in testis, thyroid, blood, or sun-exposed skin are enriched for adaptive outliers. By contrast, eQTLs that regulate expression in the cerebrum or female-specific tissues have a relative lack of adaptive outliers. These results reveal tissues that have been the targets of adaptation during the last 100,000 years of human evolution. The strongest adaptive signal in many regions of the human genome is an eQTL, including an eQTL associated with the Duffy blood group and malaria resistance. Scans of selection also reveal that many adaptive eQTLs are closely linked to disease-associated loci. Taken together, our results indicate that adaptive eQTLs have played an important role in human evolution.