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Background: In the history of population genetics balancing selection has been considered as an important evolutionary force, yet until today little is known about its abundance and its effect on patterns of genetic diversity. Several well-known examples of balancing selection have been reported from humans, mice, plants, and parasites. However, only very few systematic studies have been carried out to detect genes under balancing selection. We performed a genome scan in Drosophila melanogaster to find signatures of balancing selection in a derived (European) and an ancestral (African) population. We screened a total of 34 genomes searching for regions of high genetic diversity and an excess of SNPs with intermediate frequency. Results: In total, we found 183 candidate genes: 141 in the European population and 45 in the African one, with only three genes shared between both populations. Most differences between both populations were observed on the X chromosome, though this might be partly due to false positives. Functionally, we find an overrepresentation of genes involved in neuronal development and circadian rhythm. Furthermore, some of the top genes we identified are involved in innate immunity. Conclusion: Our results revealed evidence of genes under balancing selection in European and African populations. More candidate genes have been found in the European population. They are involved in several different functions. [ABSTRACT FROM AUTHOR]

We studied Drosophila melanogaster populations from Europe (the Netherlands and France) and Africa (Rwanda and Zambia) to uncover genetic evidence of adaptation to cold. We present here four lines of evidence for genes involved in cold adaptation from four perspectives: (i) the frequency of SNPs at genes previously known to be associated with chill-coma recovery time ( CCRT), startle reflex ( SR) and resistance to starvation stress ( RSS) vary along environmental gradients and therefore among populations; (ii) SNPs of genes that correlate significantly with latitude and altitude in African and European populations overlap with SNPs that correlate with a latitudinal cline from North America; (iii) at the genomewide level, the top candidate genes are enriched in gene ontology ( GO) terms that are related to cold tolerance; (iv) GO enriched terms from North American clinal genes overlap significantly with those from Africa and Europe. Each SNP was tested in 10 independent runs of Bayenv2, using the median Bayes factors to ascertain candidate genes. None of the candidate genes were found close to the breakpoints of cosmopolitan inversions, and only four candidate genes were linked to QTLs related to CCRT. To overcome the limitation that we used only four populations to test correlations with environmental gradients, we performed simulations to estimate the power of our approach for detecting selection. Based on our results, we propose a novel network of genes that is involved in cold adaptation. [ABSTRACT FROM AUTHOR]

Detecting and quantifying the population substructure present in a sample of individuals are of main interest in the fields of genetic epidemiology, population genetics, and forensics among others. To date, several algorithms have been proposed for estimating the amount of genetic ancestry within an individual. In the present review, we introduce the most widely used methods in population genetics for detecting individual genetic ancestry. We further show, by means of simulations, the performance of popular algorithms for detecting individual ancestry in various controlled demographic scenarios. Finally, we provide some hints on how to interpret the results from these algorithms. [ABSTRACT FROM AUTHOR]

Attempts to detect genetic population substructure in humans are troubled by the fact that the vast majority of the total amount of observed genetic variation is present within populations rather than between populations. Here we introduce a new algorithm for transforming a genetic distance matrix that reduces the within-population variation considerably. Extensive computer simulations revealed that the transformed matrix captured the genetic population differentiation better than the original one which was based on the T1 statistic. In an empirical genomic data set comprising 2,457 individuals from 23 different European subpopulations, the proportion of individuals that were determined as a genetic neighbour to another individual from the same sampling location increased from 25% with the original matrix to 52% with the transformed matrix. Similarly, the percentage of genetic variation explained between populations by means of Analysis of Molecular Variance (AMOVA) increased from 1.62% to 7.98%. Furthermore, the first two dimensions of a classical multidimensional scaling (MDS) using the transformed matrix explained 15% of the variance, compared to 0.7% obtained with the original matrix. Application of MDS with Mclust, SPA with Mclust, and GemTools algorithms to the same dataset also showed that the transformed matrix gave a better association of the genetic clusters with the sampling locations, and particularly so when it was used in the AMOVA framework with a genetic algorithm. Overall, the new matrix transformation introduced here substantially reduces the within population genetic differentiation, and can be broadly applied to methods such as AMOVA to enhance their sensitivity to reveal population substructure. We herewith provide a publically available (http://www.erasmusmc.nl/fmb/resources/GAGA) model-free method for improved genetic population substructure detection that can be applied to human as well as any other species data in future studies relevant to evolutionary biology, behavioural ecology, medicine, and forensics. [ABSTRACT FROM AUTHOR]