Genomewide analysis of admixture and adaptation in the Africanized honeybee.

Genetic exchange by hybridization or admixture can make an important contribution to evolution, and introgression of favourable alleles can facilitate adaptation to new environments. A small number of honeybees (Apis mellifera) with African ancestry were introduced to Brazil ~60 years ago, which dispersed and hybridized with existing managed populations of European origin, quickly spreading across much of the Americas in an example of a massive biological invasion. Here, we analyse whole-genome sequences of 32 Africanized honeybees sampled from throughout Brazil to study the effect of this process on genome diversity. By comparison with ancestral populations from Europe and Africa, we infer that these samples have 84% African ancestry, with the remainder from western European populations. However, this proportion varies across the genome and we identify signals of positive selection in regions with high European ancestry proportions. These observations are largely driven by one large gene-rich 1.4-Mbp segment on chromosome 11 where European haplotypes are present at a significantly elevated frequency and likely confer an adaptive advantage in the Africanized honeybee population. This region has previously been implicated in reproductive traits and foraging behaviour in worker bees. Finally, by analysing the distribution of ancestry tract lengths in the context of the known time of the admixture event, we are able to infer an average generation time of 2.0 years. Our analysis highlights the processes by which populations of mixed genetic ancestry form and adapt to new environments.

Genetics of Interactive Behavior in Silver Foxes (Vulpes vulpes).

Individuals involved in a social interaction exhibit different behavioral traits that, in combination, form the individual's behavioral responses. Selectively bred strains of silver foxes (Vulpes vulpes) demonstrate markedly different behaviors in their response to humans. To identify the genetic basis of these behavioral differences we constructed a large F2 population including 537 individuals by cross-breeding tame and aggressive fox strains. 98 fox behavioral traits were recorded during social interaction with a human experimenter in a standard four-step test. Patterns of fox behaviors during the test were evaluated using principal component (PC) analysis. Genetic mapping identified eight unique significant and suggestive QTL. Mapping results for the PC phenotypes from different test steps showed little overlap suggesting that different QTL are involved in regulation of behaviors exhibited in different behavioral contexts. Many individual behavioral traits mapped to the same genomic regions as PC phenotypes. This provides additional information about specific behaviors regulated by these loci. Further, three pairs of epistatic loci were also identified for PC phenotypes suggesting more complex genetic architecture of the behavioral differences between the two strains than what has previously been observed.

A century after Fisher: time for a new paradigm in quantitative genetics.

Quantitative genetics traces its roots back through more than a century of theory, largely formed in the absence of directly observable genotype data, and has remained essentially unchanged for decades. By contrast, molecular genetics arose from direct observations and is currently undergoing rapid changes, making the amount of available data ever greater. Thus, the two disciplines are disparate both in their origins and their current states, yet they address the same fundamental question: how does the genotype affect the phenotype? The rapidly accumulating genomic data necessitate sophisticated analysis, but many of the current tools are adaptations of methods designed during the early days of quantitative genetics. We argue here that the present analysis paradigm in quantitative genetics is at its limits in regards to unraveling complex traits and it is necessary to re-evaluate the direction that genetic research is taking for the field to realize its full potential.

Sex ratio dependent dispersal when sex ratios vary between patches.

Female biased sex ratios reduce competition between brothers when mating takes place within local patches. Male dispersal prior to mating is another strategy that reduces competition between brothers. One may thus expect these two traits to co-evolve and this is partially met in that sex ratios becomes less female biased as dispersal increases. However, the evolutionary stable degree of dispersal is unaffected by the sex ratio. The analytical models developed to reach these conclusions ignored variance in sex ratios, since this increases the structural complexity of models. For similar reasons finite clutch sizes are also routinely ignored. To overcome these shortfalls, we developed individual based simulations that allowed us to incorporate realistic clutch sizes and binomial variance in sex ratios between patches. We show that under variable sex ratios, males evolve to more readily disperse away from patches with higher sex ratios than lower sex ratios. We show that, while the dispersal rate is insensitive to the sex ratio when sex ratios are precise, it is affected by the number of males with dispersal decreasing as the number of males decreases.

Characterization of genetic diversity and gene mapping in two Swedish local chicken breeds.

The aim of this paper is to study genetic diversity in the two Swedish local chicken breeds Bohuslän-Dals svarthöna and Hedemorahöna. The now living birds of both of these breeds (about 500 for Bohuslän-Dals svarthöna and 2600 for Hedemorahöna) originate from small relicts of earlier larger populations. An additional aim was to make an attempt to map loci associated with a trait that are segregating in both these breeds. The 60k SNP chip was used to genotype 12 Bohuslän-Dals svarthöna and 22 Hedemorahöna. The mean inbreeding coefficient was considerably larger in the samples from Hedemorahöna than in the samples from Bohuslän-Dals svarthöna. Also the proportion of homozygous SNPs in individuals was larger in Hedemorahöna. In contrast, on the breed level, the number of segregating SNPs were much larger in Hedemorahöna than in Bohuslän-Dals svarthöna. A multidimensional scaling plot shows that the two breeds form clusters well-separated from each other. Both these breeds segregate for the dermal hyperpigmentation phenotype. In Bohuslän-Dals svarthöna most animals have dark skin, but some individuals with lighter skin exists (most easily detected by their red comb). An earlier study of the Fm locus showed that this breed has the same complex rearrangement involving the EDN3 gene as Silkie chicken and two other studied Asian breeds. In the breed Hedemorahöna, most individuals have normal skin pigmentation (and red comb), but there are some birds with darker skin and dark comb. In this study the involvement of the EDN3 gene is confirmed also in Hedemorahöna. In addition we identify a region on chromosome 21 that is significantly associated with the trait.

Degrees of separation as a statistical tool for evaluating candidate genes.

Selection of candidate genes is an important step in the exploration of complex genetic architecture. The number of gene networks available is increasing and these can provide information to help with candidate gene selection. It is currently common to use the degree of connectedness in gene networks as validation in Genome Wide Association (GWA) and Quantitative Trait Locus (QTL) mapping studies. However, it can cause misleading results if not validated properly. Here we present a method and tool for validating the gene pairs from GWA studies given the context of the network they co-occur in. It ensures that proposed interactions and gene associations are not statistical artefacts inherent to the specific gene network architecture. The CandidateBacon package provides an easy and efficient method to calculate the average degree of separation (DoS) between pairs of genes to currently available gene networks. We show how these empirical estimates of average connectedness are used to validate candidate gene pairs. Validation of interacting genes by comparing their connectedness with the average connectedness in the gene network will provide support for said interactions by utilising the growing amount of gene network information available.

Higher order interactions: detection of epistasis using machine learning and evolutionary computation.

Higher order interactions are known to affect many different phenotypic traits. The advent of large-scale genotyping has, however, shown that finding interactions is not a trivial task. Classical genome-wide association studies (GWAS) are a useful starting point for unraveling the genetic architecture of a phenotypic trait. However, to move beyond the additive model we need new analysis tools specifically developed to deal with high-dimensional genotypic data. Here we show that evolutionary algorithms are a useful tool in high-dimensional analyses designed to identify gene-gene interactions in current large-scale genotypic data.

Variance heterogeneity in Saccharomyces cerevisiae expression data: trans-regulation and epistasis.

Here, we describe the results from the first variance heterogeneity Genome Wide Association Study (VGWAS) on yeast expression data. Using this forward genetics approach, we show that the genetic regulation of gene-expression in the budding yeast, Saccharomyces cerevisiae, includes mechanisms that can lead to variance heterogeneity in the expression between genotypes. Additionally, we performed a mean effect association study (GWAS). Comparing the mean and variance heterogeneity analyses, we find that the mean expression level is under genetic regulation from a larger absolute number of loci but that a higher proportion of the variance controlling loci were trans-regulated. Both mean and variance regulating loci cluster in regulatory hotspots that affect a large number of phenotypes; a single variance-controlling locus, mapping close to DIA2, was found to be involved in more than 10% of the significant associations. It has been suggested in the literature that variance-heterogeneity between the genotypes might be due to genetic interactions. We therefore screened the multi-locus genotype-phenotype maps for several traits where multiple associations were found, for indications of epistasis. Several examples of two and three locus genetic interactions were found to involve variance-controlling loci, with reports from the literature corroborating the functional connections between the loci. By using a new analytical approach to re-analyze a powerful existing dataset, we are thus able to both provide novel insights to the genetic mechanisms involved in the regulation of gene-expression in budding yeast and experimentally validate epistasis as an important mechanism underlying genetic variance-heterogeneity between genotypes.

MAPfastR: quantitative trait loci mapping in outbred line crosses.

MAPfastR is a software package developed to analyze quantitative trait loci data from inbred and outbred line-crosses. The package includes a number of modules for fast and accurate quantitative trait loci analyses. It has been developed in the R language for fast and comprehensive analyses of large datasets. MAPfastR is freely available at: http://www.computationalgenetics.se/?page_id=7.

Selection on variance-controlling genes: adaptability or stability.

Simulations on a model system where a variance-controlling master locus scales the effects of a set of effector loci show that selection affects the variance-controlling locus more strongly than the effector loci, and that the direction of selection is dependent on the frequency of environmental changes.

qtl.outbred: Interfacing outbred line cross data with the R/qtl mapping software.

BACKGROUND: qtl.outbred is an extendible interface in the statistical environment, R, for combining quantitative trait loci (QTL) mapping tools. It is built as an umbrella package that enables outbred genotype probabilities to be calculated and/or imported into the software package R/qtl. FINDINGS: Using qtl.outbred, the genotype probabilities from outbred line cross data can be calculated by interfacing with a new and efficient algorithm developed for analyzing arbitrarily large datasets (included in the package) or imported from other sources such as the web-based tool, GridQTL. CONCLUSION: qtl.outbred will improve the speed for calculating probabilities and the ability to analyse large future datasets. This package enables the user to analyse outbred line cross data accurately, but with similar effort than inbred line cross data.