BACKGROUND AND QUESTION
The Lachance Lab works with novel Prostate Cancer (PCa) Biological Datasets of men of African Ancestry (AA). These data sets have been largely made available by Men of African Descent and Carcinoma of Prostate (MADCaP) consortium. Our aim is to understand and further study the high prevalence of PCa in men of African Ancestry. Despite men of African Ancestry (AA) having the highest mortality rate from PCa, we have little knowledge about the variants associated with it. Preliminary research conducted in the lab has discovered novel GWAS (Genome-Wide Association Studies) hits. Additionally, considerable heterogeneity was discovered in the genetic architecture of PCa within West, East and South Africa.
To gain a deeper insight into the heterogeneity observed with our Genome Wide Association Studies (GWAS) results, Linkage Disequilibrium (LD) is crucial as it allows the identification of genetic markers that tag the causal variants [1]. LD is the difference between frequency of a particular combination of alleles at two loci that is observed and the expected frequency for random association. In a population where there is no mutation, selection of specific gene combinations will result in LD. However, genetic recombination breaks this LD [2].
Genetic recombination is an important process that gives rise to novel allele combinations enabling evolution in species [3]. Recombination rates are highly variable across species and populations [4]. The recombination rate is represented as the ratio of genetic distance and physical distance that forms a recombination map. Genetic recombination maps are essential for the evolutionary analysis [5] of populations, one important application being the estimation of allele ages. Presently, there are only a limited number of recombination maps available for the sub-Saharan African population for West Africa [6] and the Khoe San population of South Africa [5]. Therefore, we need accurate genetic recombination maps to study the heterogeneity in the African populations.
Previously, I found various tools that are publicly available which could be used to generate accurate and reliable genetic recombination maps. For initial trial, I used HapMap [7] and Thousand Genomes Project [6] data as input for these tools. I aim to find the best tools, most-suited to create recombination maps with MADCaP Datasets.