Dataset Information

Integrating evolutionary and functional approaches to infer adaptation at specific loci.

ABSTRACT: Inferences about adaptation at specific loci are often exclusively based on the static analysis of DNA sequence variation. Ideally,population-genetic evidence for positive selection serves as a stepping-off point for experimental studies to elucidate the functional significance of the putatively adaptive variation. We argue that inferences about adaptation at specific loci are best achieved by integrating the indirect, retrospective insights provided by population-genetic analyses with the more direct, mechanistic insights provided by functional experiments. Integrative studies of adaptive genetic variation may sometimes be motivated by experimental insights into molecular function, which then provide the impetus to perform population genetic tests to evaluate whether the functional variation is of adaptive significance. In other cases, studies may be initiated by genome scans of DNA variation to identify candidate loci for recent adaptation. Results of such analyses can then motivate experimental efforts to test whether the identified candidate loci do in fact contribute to functional variation in some fitness-related phenotype. Functional studies can provide corroborative evidence for positive selection at particular loci, and can potentially reveal specific molecular mechanisms of adaptation.

SUBMITTER: Storz JF

PROVIDER: S-EPMC2981648 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrating evolutionary and functional approaches to infer adaptation at specific loci.

Storz Jay F JF Wheat Christopher W CW

Evolution; international journal of organic evolution 20100901 9

Inferences about adaptation at specific loci are often exclusively based on the static analysis of DNA sequence variation. Ideally,population-genetic evidence for positive selection serves as a stepping-off point for experimental studies to elucidate the functional significance of the putatively adaptive variation. We argue that inferences about adaptation at specific loci are best achieved by integrating the indirect, retrospective insights provided by population-genetic analyses with the more ...[more]

PMID: 20500215

Similar Datasets

Project description:BackgroundThe chamois, distributed over most of the medium to high altitude mountain ranges of southern Eurasia, provides an excellent model for exploring the effects of historical and evolutionary events on diversification. Populations have been grouped into two species, Rupicapra pyrenaica from southwestern Europe and R. rupicapra from eastern Europe. However, a previous study of cytochrome b revealed that the two proposed species were non-monophyletic. The reconstruction of phylogenetic relationships between animal species often depends on the markers studied. To further elucidate the evolutionary history of chamois, we extended earlier studies by analysing DNA sequences of four mitochondrial regions (ND1, 12S, tRNApro and Control Region) and microsatellites (20 loci) to include all subspecies and cover its entire distribution range.ResultsWe found discordant microsatellite (musat) and mitochondrial (mt) DNA phylogenies. Mitochondrial phylogenies form three clades, West, Central and East (mtW, mtC and mtE), at variance with taxonomic classification. Our divergence age estimates indicate an initial separation into branches mtW-mtC and mtE 1.7 million years ago (mya), in the late Pliocene-early Pleistocene, quickly followed by the split of clades mtW and mtC. Clade mtW contains haplotypes from the Iberian peninsula and the western Alps, Clade mtC includes haplotypes from the Apennines and the Massif of Chartreuse and Clade mtE comprises populations to the east of the Alps. Divergence among populations within these three major clades is recent (< 0.5 mya). New microsatellite multilocus genotypes added to previously published data revealed differences between every pair of subspecies, forming three well defined groups (musatW, musatC and musatE) also with a strong geographic signature. Grouping does not correspond with the mitochondrial lineages but is closer to morphology and taxonomic classification. Recent drastic reductions in population size can be noted for the subspecies ornata as an extremely low diversity.ConclusionsThe phylogeographic patterns for mtDNA and microsatellites suggest an evolutionary history with limited range contractions and expansions during the Quaternary period and reflect a major effect of the Alpine barrier on west-east differentiation. The contrasting phylogenies for mtDNA and microsatellites indicate events of hybridization among highly divergent lineages in the central area of distribution. Our study points to the importance of reticulate evolution, with periods of isolation and reduction of population size followed by expansions and hybridizations, in the diversification at the level of close species or subspecies.

Project description:BackgroundFunctional gene networks (FGNs) capture functional relationships among genes that vary across tissues and cell types. Construction of cell-type-specific FGNs enables the understanding of cell-type-specific functional gene relationships and insights into genetic mechanisms of human diseases in disease-relevant cell types. However, most existing FGNs were developed without consideration of specific cell types within tissues.ResultsIn this study, we created a multimodal deep learning model (MDLCN) to predict cell-type-specific FGNs in the human brain by integrating single-nuclei gene expression data with global protein interaction networks. We systematically evaluated the prediction performance of the MDLCN and showed its superior performance compared to two baseline models (boosting tree and convolutional neural network). Based on the predicted cell-type-specific FGNs, we observed that cell-type marker genes had a higher level of hubness than non-marker genes in their corresponding cell type. Furthermore, we showed that risk genes underlying autism and Alzheimer's disease were more strongly connected in disease-relevant cell types, supporting the cellular context of predicted cell-type-specific FGNs.ConclusionsOur study proposes a powerful deep learning approach (MDLCN) to predict FGNs underlying a diverse set of cell types in human brain. The MDLCN model enhances prediction accuracy of cell-type-specific FGNs compared to single modality convolutional neural network (CNN) and boosting tree models, as shown by higher areas under both receiver operating characteristic (ROC) and precision-recall curves for different levels of independent test datasets. The predicted FGNs also show evidence for the cellular context and distinct topological features (i.e. higher hubness and topological score) of cell-type marker genes. Moreover, we observed stronger modularity among disease-associated risk genes in FGNs of disease-relevant cell types. For example, the strength of connectivity among autism risk genes was stronger in neurons, but risk genes underlying Alzheimer's disease were more connected in microglia.

Dataset Information

Integrating evolutionary and functional approaches to infer adaptation at specific loci.

Publications

Integrating evolutionary and functional approaches to infer adaptation at specific loci.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets