Meeting Abstract
P1.143 Wednesday, Jan. 4 Genomics of adaptive radiation: Gene duplication in African cichlid lineages BEZAULT, Etienne*; MACHADO, Heather; HUNTER, Jeff; JOYCE, Domino; LUNT, David; RENN, Suzy CP; Reed College; Stanford ; Reed College; University of Hull; University of Hull; Reed College renns@reed.edu
Among African cichlids, the repeated and independent origin of adaptive radiations, in combination with closely related lineages that have not undergone dramatic radiation, offers an excellent model in which to study the genetic basis of adaptation and diversification. Structural variation has recently been shown to be a major source of evolutionary novelty. We investigate the relationship between gene duplication and the potential for adaptive radiation using Array-based Comparative Genomic Hybridization (aCGH). We quantify gene duplications among three divergent species for each of two independent radiations (Lake Malawi and Lake Victoria) relative to their closely related non-radiating riverine species. We find an increased number of gene duplications among the radiating lineages compared to the non-radiating relatives. While the majority of these gene duplications are specific to the different radiations, we also identify repeated instances of duplicated genes across the lake-radiations. These candidate duplicates represent Gene Ontology categories that are discussed in terms of potentially adaptive phenotypes. Our results support the hypothesized association between gene duplication and adaptive radiation. With the recent completion of genome sequence for 4 African Cichlids, we can address the genomic architecture of duplication events. Currently, genome wide sequence comparison for duplicated genes against the genome draft assemblies (CGC & Broad Institute, unpublished) does not recover these recent paralogs. Therefore, to quantitatively investigate the relationship between dynamics of gene-duplication, we are developing novel high throughput technologies by designing a multi-species whole genome CGH-array platform. This approach can be applied on a population level as well on broader phylogenetic context.
