AGR_Project___African_Genome_Resource__total_RNA_sequencing_for_1000_Genomes_African_populations-sc-4482
AGR_Project___African_Genome_Resource__total_RNA_sequencing_for_1000_Genomes_African_populations
GWAS by developing large scale sequencing resources among African populations for imputation and fine mapping, the AGR will in parallel develop and curate resources to interpret identified associations in African populations, given that a substantial number of genetic associations arise from population specific variation (Gurdasani et al, Nature (under review)). Using 1000 genomes cell lines, we will generate high coverage RNAseq data (Illumina HiSeq) from 6 different populations across Africa; the first large-scale resource to examine eQTLs within Africa. Specifically we intend to sequence transcriptomes from 600 unrelated individuals from six populations from West (Gambian, The Gambia; Mende, Sierra Leone; Esan and Yoruba, Nigeria) and East Africa (Luhya and Masai, Kenya). This resource will help understand the transcriptional landscape in African populations, population differences in the diversity of splicing isoforms, as well as identify novel transcripts and exons across the genome. Such a tool will be invaluable for the interpretation of findings from large-scale GWAS being conducted in Africa, supporting initiatives such as the H3A consortium. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/
As part of the African Genome Resource (AGR) project we aim to create an African transcriptome panel. Using 1000 genomes cell lines, we generated high coverage RNAseq data (Illumina HiSeq) from 6 different populations across Africa; the first large-scale resource to examine eQTLs within Africa. Specifically we sequenced transcriptomes from 600 unrelated individuals from six populations from West (Gambian, The Gambia; Mende, Sierra Leone; Esan and Yoruba, Nigeria) and East Africa (Luhya and Masai, Kenya). Such a resource will help understand the transcriptional landscape in African populations, population differences in the diversity of splicing isoforms, as well as identify novel transcripts and exons across the genome.