Dataset Information

Genomewide identification of genes under directional selection: gene transcription Q(ST) scan in diverging Atlantic salmon subpopulations.

ABSTRACT: Evolutionary genomics has benefited from methods that allow identifying evolutionarily important genomic regions on a genomewide scale, including genome scans and QTL mapping. Recently, genomewide scanning by means of microarrays has permitted assessing gene transcription differences among species or populations. However, the identification of differentially transcribed genes does not in itself suffice to measure the role of selection in driving evolutionary changes in gene transcription. Here, we propose and apply a "transcriptome scan" approach to investigating the role of selection in shaping differential profiles of gene transcription among populations. We compared the genomewide transcription levels between two Atlantic salmon subpopulations that have been diverging for only six generations. Following assessment of normality and unimodality on a gene-per-gene basis, the additive genetic basis of gene transcription was estimated using the animal model. Gene transcription h(2) estimates were significant for 1044 (16%) of all detected cDNA clones. In an approach analogous to that of genome scans, we used the distribution of the Q(ST) values estimated from intra- and intersubpopulation additive genetic components of the transcription profiles to identify 16 outlier genes (average Q(ST) estimate = 0.11) whose transcription levels are likely to have evolved under the influence of directional selection within six generations only. Overall, this study contributes both empirically and methodologically to the quantitative genetic exploration of gene transcription data.

SUBMITTER: Roberge C

PROVIDER: S-EPMC2034609 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

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Genomewide identification of genes under directional selection: gene transcription Q(ST) scan in diverging Atlantic salmon subpopulations.

Roberge C C Guderley H H Bernatchez L L

Genetics 20070824 2

Evolutionary genomics has benefited from methods that allow identifying evolutionarily important genomic regions on a genomewide scale, including genome scans and QTL mapping. Recently, genomewide scanning by means of microarrays has permitted assessing gene transcription differences among species or populations. However, the identification of differentially transcribed genes does not in itself suffice to measure the role of selection in driving evolutionary changes in gene transcription. Here, ...[more]

PMID: 17720934

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Project description:Mechanisms of host-parasite co-adaptation have long been of interest in evolutionary biology; however, determining the genetic basis of parasite resistance has been challenging. Current advances in genome technologies provide new opportunities for obtaining a genome-scale view of the action of parasite-driven natural selection in wild populations and thus facilitate the search for specific genomic regions underlying inter-population differences in pathogen response. European populations of Atlantic salmon (Salmo salar L.) exhibit natural variance in susceptibility levels to the ectoparasite Gyrodactylus salaris Malmberg 1957, ranging from resistance to extreme susceptibility, and are therefore a good model for studying the evolution of virulence and resistance. However, distinguishing the molecular signatures of genetic drift and environment-associated selection in small populations such as land-locked Atlantic salmon populations presents a challenge, specifically in the search for pathogen-driven selection. We used a novel genome-scan analysis approach that enabled us to i) identify signals of selection in salmon populations affected by varying levels of genetic drift and ii) separate potentially selected loci into the categories of pathogen (G. salaris)-driven selection and selection acting upon other environmental characteristics. A total of 4631 single nucleotide polymorphisms (SNPs) were screened in Atlantic salmon from 12 different northern European populations. We identified three genomic regions potentially affected by parasite-driven selection, as well as three regions presumably affected by salinity-driven directional selection. Functional annotation of candidate SNPs is consistent with the role of the detected genomic regions in immune defence and, implicitly, in osmoregulation. These results provide new insights into the genetic basis of pathogen susceptibility in Atlantic salmon and will enable future searches for the specific genes involved.

Dataset Information

Genomewide identification of genes under directional selection: gene transcription Q(ST) scan in diverging Atlantic salmon subpopulations.

Publications

Genomewide identification of genes under directional selection: gene transcription Q(ST) scan in diverging Atlantic salmon subpopulations.

Similar Datasets

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