Inferring positive selection in humans from genomic data

Adaptation can be described as an evolutionary process that leads to an adjustment of the phenotypes of a population to their environment. In the classical view, new mutations can introduce novel phenotypic features into a population that leave footprints in the genome after fixation, such as selective sweeps. Alternatively, existing genetic variants may become beneficial after an environmental change and increase in frequency. Although they may not reach fixation, they may cause a shift of the optimum of a phenotypic trait controlled by multiple loci. With the availability of polymorphism data from various organisms, including humans and chimpanzees, it has become possible to detect molecular evidence of adaptation and to estimate the strength and target of positive selection. In this review, we discuss the two competing models of adaptation and suitable approaches for detecting the footprints of positive selection on the molecular level

Detecting individual ancestry in the human genome

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

Dating the age of admixture via wavelet transform analysis of genome-wide data

We describe a PCA-based genome scan approach to analyze genome-wide admixture structure, and introduce wavelet transform analysis as a method for estimating the time of admixture. We test the wavelet transform method with simulations and apply it to genome-wide SNP data from eight admixed human populations. The wavelet transform method offers better resolution than existing methods for dating admixture, and can be applied to either SNP or sequence data from humans or other species

A genome-wide scan for genes under balancing selection in Drosophila melanogaster

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

GAGA: A New Algorithm for Genomic Inference of Geographic Ancestry Reveals Fine Level Population Substructure in Europeans

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

The HIrisPlex system for simultaneous prediction of hair and eye colour from DNA

AbstractRecently, the field of predicting phenotypes of externally visible characteristics (EVCs) from DNA genotypes with the final aim of concentrating police investigations to find persons completely unknown to investigating authorities, also referred to as Forensic DNA Phenotyping (FDP), has started to become established in forensic biology. We previously developed and forensically validated the IrisPlex system for accurate prediction of blue and brown eye colour from DNA, and recently showed that all major hair colour categories are predictable from carefully selected DNA markers. Here, we introduce the newly developed HIrisPlex system, which is capable of simultaneously predicting both hair and eye colour from DNA. HIrisPlex consists of a single multiplex assay targeting 24 eye and hair colour predictive DNA variants including all 6 IrisPlex SNPs, as well as two prediction models, a newly developed model for hair colour categories and shade, and the previously developed IrisPlex model for eye colour. The HIrisPlex assay was designed to cope with low amounts of template DNA, as well as degraded DNA, and preliminary sensitivity testing revealed full DNA profiles down to 63pg input DNA. The power of the HIrisPlex system to predict hair colour was assessed in 1551 individuals from three different parts of Europe showing different hair colour frequencies. Using a 20% subset of individuals, while 80% were used for model building, the individual-based prediction accuracies employing a prediction-guided approach were 69.5% for blond, 78.5% for brown, 80% for red and 87.5% for black hair colour on average. Results from HIrisPlex analysis on worldwide DNA samples imply that HIrisPlex hair colour prediction is reliable independent of bio-geographic ancestry (similar to previous IrisPlex findings for eye colour). We furthermore demonstrate that it is possible to infer with a prediction accuracy of >86% if a brown-eyed, black-haired individual is of non-European (excluding regions nearby Europe) versus European (including nearby regions) bio-geographic origin solely from the strength of HIrisPlex eye and hair colour probabilities, which can provide extra intelligence for future forensic applications. The HIrisPlex system introduced here, including a single multiplex test assay, an interactive tool and prediction guide, and recommendations for reporting final outcomes, represents the first tool for simultaneously establishing categorical eye and hair colour of a person from DNA. The practical forensic application of the HIrisPlex system is expected to benefit cases where other avenues of investigation, including STR profiling, provide no leads on who the unknown crime scene sample donor or the unknown missing person might be

Efficacy assessment of SNP sets for genome-wide disease association studies

The power of a genome-wide disease association study depends critically upon the properties of the marker set used, particularly the number and physical spacing of markers, and the level of inter-marker association due to linkage disequilibrium. Extending our previously devised theoretical framework for the entropy-based selection of genetic markers, we have developed a local measure of the efficacy of a marker set, relative to including a maximally polymorphic single nucleotide polymorphism (SNP) at the map position of interest. Using this quantitative criterion, we evaluated five currently available SNP sets, namely Affymetrix 100K and 500K, and Illumina 100K, 300K and 550K in the CEU, YRI and JPT + CHB HapMap populations. At 50% relative efficacy, the commercial marker sets cover between 19 and 68% of the human genome, depending upon the population under study. An optimal technology-independent 500K marker set constructed from HapMap for Caucasians, in contrast, would achieve 73% coverage at the same relative efficacy

Global skin colour prediction from DNA

Human skin colour is highly heritable and externally visible with relevance in medical, forensic, and anthropological genetics. Although eye and hair colour can already be predicted with high accuracies from small sets of carefully selected DNA markers, knowledge about the genetic predictability of skin colour is limited. Here, we investigate the skin colour predictive value of 77 single-nucleotide polymorphisms (SNPs) from 37 genetic loci previously associated with human pigmentation using 2025 individuals from 31 global populations. We identified a minimal set of 36 highly informative skin colour predictive SNPs and developed a statistical prediction model capable of skin colour prediction on a global scale. Average cross-validated prediction accuracies expressed as area under the receiver-operating characteristic curve (AUC) ± standard deviation were 0.97 ± 0.02 for Light, 0.83 ± 0.11 for Dark, and 0.96 ± 0.03 for Dark-Black. When using a 5-category, this resulted in 0.74 ± 0.05 for Very Pale, 0.72 ± 0.03 for Pale, 0.73 ± 0.03 for Intermediate, 0.87±0.1 for Dark, and 0.97 ± 0.03 for Dark-Black. A comparative analysis in 194 independent samples from 17 populations demonstrated that our model outperformed a previously proposed 10-SNP-classifier approach with AUCs rising from 0.79 to 0.82 for White, comparable at the intermediate level of 0.63 and 0.62, respectively, and a large increase from 0.64 to 0.92 for Black. Overall, this study demonstrates that the chosen DNA markers and prediction model, particularly the 5-category level; allow skin colour predictions within and between continental regions for the first time, which will serve as a valuable resource for future applications in forensic and anthropologic genetics

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing