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Chambers, G. K.

Publications and source records attributed to Chambers, G. K..

2 recordsLinked to original sources

Integrating genome-wide neutral and putatively adaptive variation resolves population structure and differentiation in a recently diverged albatross complex

Understanding patterns of evolution and divergence in populations is important for defining conservation units and informing taxonomy. However, when populations have low genetic diversity, or are closely related, genetic differentiation can be difficult to detect, particularly when relying on small numbers of genetic markers. Whole-genome sequencing allows for genome-wide identification of both neutral and outlier variation, improving the resolution of subtle population structure and providing insight into evolutionary processes. Here, we investigated genomic differentiation in a recently diverged and highly threatened albatross complex, the Antipodean (D. antipodensis antipodensis) and Gibson's albatross (D. a. gibsoni), using genome-wide neutral and outlier datasets. Whole-genome resequencing of 86 individuals sampled across Antipodes Island (D. a. antipodensis) and the Auckland Islands (D. a. gibsoni) identified 381,176 neutrally evolving and 57 independently segregating outlier (putatively adaptive) SNPs. Analyses of both datasets revealed significant genetic differentiation between the Antipodean and Gibson's albatross, no evidence of contemporary gene flow and evidence of selective sweeps suggesting local adaptation. Within-population structure was also identified for the Gibson's albatross, with genetic differentiation among sample sites from different islands (Adams Island and Disappointment Island). Patterns of heterogenous differentiation across the genome suggest the taxa are on different evolutionary trajectories. Together with existing morphological and behavioural evidence, these genomic results support reassessment of their conservation and taxonomic status. More broadly, this study demonstrates the value of combining neutral and putatively adaptive genomic variation to resolve subtle population structure in recently diverged taxa.

evolutionary biology↗

Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

Genome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.\n\nA bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.\n\nWhile previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

bioinformatics↗