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448 results for “Genomic selection”
Data from: Genomic signatures of fine‐scale local selection in Atlantic salmon suggest involvement of sexual maturation, energy homeostasis, and immune defence‐related genes
Elucidating the genetic basis of adaptation to the local environment can improve our understanding of how the diversity of life has evolved. In this study we used a dense SNP array to identify candidate loci potentially underlying fine-scale local adaptation within a large Atlantic salmon (Salmo salar) population. By combining outlier, gene–environment association, and haplotype homozygosity analyses, we identified multiple regions of the genome with strong evidence for diversifying selection. Several of these candidate regions had previously been identified in other studies, demonstrating that the same loci could be adaptively important in Atlantic salmon at sub-drainage, regional and continental scales. Notably, we identified signals consistent with local selection around genes associated with variation in sexual maturation, energy homeostasis, and immune defence. These included the large-effect age-at-maturity gene vgll3, the known obesity gene mc4r, and major histocompatibility complex II. Most strikingly, we confirmed a genomic region on Ssa09 that was extremely differentiated among subpopulations, and that is also a candidate for local selection over the global range of Atlantic salmon. This region co-localized with a haplotype strongly associated with spawning ecotype in sockeye salmon (Oncorhynchus nerka), with circumstantial evidence that the same gene (six6) may be the selective target in both cases. The phenotypic effect of this region in Atlantic salmon remains cryptic, although allelic variation is related to upstream catchment area and co-varies with timing of the return spawning migration. Our results further inform management of Atlantic salmon and open multiple avenues for future research.
Data from: Parallel signatures of selection at genomic islands of divergence and the major histocompatibility complex in ecotypes of sockeye salmon across Alaska
Understanding the genetic mechanisms that facilitate adaptive radiation is an important component of evolutionary biology. Here, we genotyped 82 neutral SNPs, seven SNPs in islands of divergence identified in a previous study (island SNPs), and a region of the major histocompatibility complex (MHC) in 32 populations of sockeye salmon to investigate whether conserved genes and genomic regions are involved in adaptive radiation. Populations representing three ecotypes were sampled from seven drainages with differing habitats and colonization histories spanning a range of 2,000 km. We found strong signatures of parallel selection across drainages at the island SNPs and MHC, suggesting that the same loci undergo divergent selection during adaptive radiation. However, patterns of differentiation at most island SNPs and the MHC were not associated with ecotypes, suggesting that these loci are responding differently to a mosaic of selective pressures. Our study provides some of the first evidence that conserved genomic islands may be involved in adaptive divergence of salmon populations. Additionally, our data provide further support for the hypothesis that sockeye salmon inhabiting rivers unconnected to lakes harbor similar genetic diversity across large distances, are likely the ancestral form of the species, and have repeatedly recolonized lake systems as they have become available after glacial recession. Finally, our results highlight the value and importance of validating outlier loci by screening additional populations and regions, a practice that will hopefully become more common in the future.
Data from: A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: testing co-localization among outlier markers, candidate genes, and QTLs for production traits
Comparative genome scans can be used to identify chromosome regions, but not traits, that are putatively under selection. Identification of targeted traits may be more likely in recently domesticated populations under strong artificial selection for increased production. We used a North American Atlantic salmon 6K SNP dataset to locate genome regions of an aquaculture strain (Saint John River) that were highly diverged from that of its putative wild founder population (Tobique River). First, admixed individuals with partial European ancestry were detected using STRUCTURE and removed from the dataset. Outlier loci were then identified as those showing extreme differentiation between the aquaculture population and the founder population. All Arlequin methods identified an overlapping subset of 17 outlier loci, 3 of which were also identified by BayeScan. Many outlier loci were near candidate genes and some were near published quantitative trait loci (QTLs) for growth, appetite, maturity, or disease-resistance. Parallel comparisons using a wild, non-founder population (Stewiacke River) yielded only one overlapping outlier locus as well as a known maturity QTL. We conclude that genome scans comparing a recently domesticated strain with its wild founder population can facilitate identification of candidate genes for traits known to have been under strong artificial selection.
Data from: Composite measures of selection can improve the signal-to-noise ratio in genome scans
The growing wealth of genomic data is yielding new insights into the genetic basis of adaptation, but it also presents the challenge of extracting the relevant signal from multi-dimensional datasets. Different statistical approaches vary in their power to detect selection depending on the demographic history, type of selection, genetic architecture and experimental design. Here, we develop and evaluate new approaches for combining results from multiple tests, including multivariate distance measures and methods for combining P-values. We evaluate these methods on (i) simulated landscape genetic data analysed for differentiation outliers and genetic-environment associations and (ii) empirical genomic data analysed for selective sweeps within dog breeds for loci known to be selected for during domestication. We also introduce and evaluate how robust statistical algorithms can be used for parameter estimation in statistical genomics. On the simulated data, many of the composite measures performed well and had decreased variation in outcomes across many sampling designs. On the empirical dataset, methods based on combining P-values generally performed better with clearer signals of selection, higher significance of the signal, and in closer proximity to the known selected locus. Although robust algorithms could identify neutral loci in our simulations, they did not universally improve power to detect selection. Overall, a composite statistic that measured a robust multivariate distance from rank-based P-values performed the best. We found that composite measures of selection could improve the signal of selection in many cases, but they were not a panacea and their power is limited by the power of the univariate statistics they summarize. Since genome scans are widely used, improving inference for prioritizing candidate genes may be beneficial to medicine, agriculture, and breeding. Our results also have application to outlier detection in high-dimensional datasets and to combining results in meta-analyses in many disciplines. The compound measures we evaluate are implemented in the r package minotaur.
Data from: Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories
Genomic selection is expected to enhance the genetic improvement of forest tree species by providing more accurate estimates of breeding values through marker-based relationship matrices compared with pedigree-based methodologies. When adequately robust genomic prediction models are available, an additional increase in genetic gains can be made possible with the shortening of the breeding cycle through elimination of the progeny testing phase and early selection of parental candidates. The potential of genomic selection was investigated in an advanced Eucalyptus nitens breeding population focused on improvement for solid wood production. A high-density SNP chip (EUChip60K) was used to genotype 691 individuals in the breeding population, which represented two seed orchards with different selection histories. Phenotypic records for growth and form traits at age six, and for wood quality traits at age seven were available to build genomic prediction models using GBLUP which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated that breeding value accuracy would be improved and substantial increases in genetic gains towards solid wood production would be achieved. Cross-validation within and across two different seed orchards indicated that genomic predictions would likely benefit in terms of higher predictive accuracy from increasing the size of the training data sets through higher relatedness and better utilization of LD
Data from: Genomic architecture of habitat-related divergence and signature of directional selection in the body shapes of Gnathopogon fishes
Evolution of ecomorphologically relevant traits such as body shapes is important to colonize and persist in a novel environment. Habitat-related adaptive divergence of these traits is therefore common among animals. We studied the genomic architecture of habitat-related divergence in the body shape of Gnathopogon fishes, a novel example of lake–stream ecomorphological divergence, and tested for the action of directional selection on body shape differentiation. Compared to stream-dwelling Gnathopogon elongatus, the sister species Gnathopogon caerulescens, exclusively inhabiting a large ancient lake, had an elongated body, increased proportion of the caudal region and small head, which would be advantageous in the limnetic environment. Using an F2 interspecific cross between the two Gnathopogon species (195 individuals), quantitative trait locus (QTL) analysis with geometric morphometric quantification of body shape and restriction-site associated DNA sequencing-derived markers (1622 loci) identified 26 significant QTLs associated with the interspecific differences of body shape-related traits. These QTLs had small to moderate effects, supporting polygenic inheritance of the body shape-related traits. Each QTL was mostly located on different genomic regions, while colocalized QTLs were detected for some ecomorphologically relevant traits that are proxy of body and caudal peduncle depths, suggesting different degree of modularity among traits. The directions of the body shape QTLs were mostly consistent with the interspecific difference, and QTL sign test suggested a genetic signature of directional selection in the body shape divergence. Thus, we successfully elucidated the genomic architecture underlying the adaptive changes of the quantitative and complex morphological trait in a novel system.
Data from: Genome-wide association mapping of phenotypic traits subject to a range of intensities of natural selection in Timema cristinae
The genetic architecture of adaptive traits can reflect the evolutionary history of populations and also shape divergence among populations. Despite this central role in evolution, relatively little is known regarding the genetic architecture of adaptive traits in nature, particularly for traits subject to known selection intensities. Here we quantitatively describe the genetic architecture of traits that are subject to known intensities of differential selection between host plant species in Timema cristinae stick insects. Specifically, we used phenotypic measurements of 10 traits and 211,004 single-nucleotide polymorphisms (SNPs) to conduct multilocus genome-wide association mapping. We identified a modest number of SNPs that were associated with traits and sometimes explained a large proportion of trait variation. These SNPs varied in their strength of association with traits, and both major and minor effect loci were discovered. However, we found no relationship between variation in levels of divergence among traits in nature and variation in parameters describing the genetic architecture of those same traits. Our results provide a first step toward identifying loci underlying adaptation in T. cristinae. Future studies will examine the genomic location, population differentiation, and response to selection of the trait-associated SNPs described here.
Data from: Genome size variation affects song attractiveness in grasshoppers: evidence for sexual selection against large genomes
Genome size is largely uncorrelated to organismal complexity and adaptive scenarios. Genetic drift as well as intragenomic conflict have been put forward to explain this observation. We here study the impact of genome size on sexual attractiveness in the bow-winged grasshopper Chorthippus biguttulus. Grasshoppers show particularly large variation in genome size due to the high prevalence of supernumerary chromosomes that are considered (mildly) selfish, as evidenced by non-Mendelian inheritance and fitness costs if present in high numbers. We ranked male grasshoppers by song characteristics that are known to affect female preferences in this species and scored genome sizes of attractive and unattractive individuals from the extremes of this distribution. We find that attractive singers have significantly smaller genomes, demonstrating that genome size is reflected in male courtship songs and that females prefer songs of males with small genomes. Such a genome size dependent mate preference effectively selects against selfish genetic elements that tend to increase genome size. The data therefore provide a novel example of how sexual selection can reinforce natural selection and can act as an agent in an intragenomic arms race. Furthermore, our findings indicate an underappreciated route of how choosy females could gain indirect benefits.
Data from: Genomic signatures of geographic isolation and natural selection in coral reef fishes
The drivers of speciation remain among the most controversial topics in evolutionary biology. Initially, Darwin emphasized natural selection as a primary mechanism of speciation, but the architects of the modern synthesis largely abandoned that view in favour of divergence by geographic isolation. The balance between selection and isolation is still at the forefront of the evolutionary debate, especially for the world's tropical oceans where biodiversity is high, but isolating barriers are few. Here, we identify the drivers of speciation in Pacific reef fishes of the genus Acanthurus by comparative genome scans of two peripheral populations that split from a large Central-West Pacific lineage at roughly the same time. Mitochondrial sequences indicate that populations in the Hawaiian Archipelago and the Marquesas Islands became isolated approximately 0.5 Ma. The Hawaiian lineage is morphologically indistinguishable from the widespread Pacific form, but the Marquesan form is recognized as a distinct species that occupies an unusual tropical ecosystem characterized by upwelling, turbidity, temperature fluctuations, algal blooms and little coral cover. An analysis of 3737 SNPs reveals a strong signal of selection at the Marquesas, with 59 loci under disruptive selection including an opsin Rh2 locus. While both the Hawaiian and Marquesan populations indicate signals of drift, the former shows a weak signal of selection that is comparable with populations in the Central-West Pacific. This contrast between closely related lineages reveals one population diverging due primarily to geographic isolation and genetic drift, and the other achieving taxonomic species status under the influence of selection.
Data from: Genomic landscape of early ecological speciation initiated by selection on nuptial colour
Ecological speciation is the evolution of reproductive isolation as a consequence of direct divergent natural selection or ecologically mediated divergent sexual selection. While the genomic signature of the former has been extensively studied in recent years, only few examples exist for genomic differentiation where environment-dependent sexual selection has played an important role. Here, we describe a very young (~90 years old) population of threespine sticklebacks exhibiting phenotypic and genomic differentiation between two habitats within the same pond. We show that differentiation among habitats is limited to male throat colour and nest type, traits known to be subject to sexual selection. Divergence in these traits mirrors divergence in much older benthic and limnetic stickleback species pairs from North American west coast lakes, which also occur in sympatry but are strongly reproductively isolated from each other. We demonstrate that in our population, differences in throat colour and breeding have been stable over a decade, but in contrast to North American benthic and limnetic stickleback species, these mating trait differences are not accompanied by divergence in morphology related to feeding, predator defence or swimming performance. Using genomewide SNP data, we find multiple genomic islands with moderate differentiation spread across several chromosomes, whereas the rest of the genome is undifferentiated. The islands contain potential candidate genes involved in visual perception of colour. Our results suggest that phenotypic and multichromosome genomic divergence of these morphs was driven by environment-dependent sexual selection, demonstrating incipient speciation after only a few decades of divergence in sympatry.
Data from: Selection and genomic differentiation during ecological speciation: isolating the contributions of host association via a comparative genome scan of Neochlamisus bebbianae leaf beetles
This study uses a comparative genome scan to evaluate the contributions of host plant related divergent selection to genetic differentiation and ecological speciation in maple- and willow-associated populations of Neochlamisus bebbianae leaf beetles. For each of 15 pairwise population comparisons, we identified "outlier loci" whose strong differentiation putatively reflects divergent selection. Of 447 AFLP loci, 15% were outliers across multiple population comparisons, and low linkage disequilibrium indicated that these outliers derived from multiple regions of the genome. Outliers were further classified as "host-specific" if repeatedly observed in "different-host" population comparisons but never in "same-host" comparisons. Outliers exhibiting the opposite pattern were analogously classified as "host-independent." Host-specific outliers represented 5% of all loci and were more frequent than host-independent outliers, thus revealing a large role for host-adaptation in population genomic differentiation. Evidence that host-related selection can promote divergence despite gene flow was provided by population trees. These were structured by host-association when datasets included host-specific outliers, but not when based on neutral loci, which united sympatric populations. Lastly, three host-specific outliers were highly differentiated in all nine different-host comparisons. Because host-adaptation promotes reproductive isolation in these beetles, these loci provide promising candidate gene regions for future molecular studies of ecological speciation.
Data from: Genomic regions with a history of divergent selection affect fitness of hybrids between two butterfly species
Speciation is the process by which reproductively isolated lineages arise, and is one of the fundamental means by which the diversity of life increases. Whereas numerous studies have documented an association between ecological divergence and reproductive isolation, relatively little is known about the role of natural selection in genome divergence during the process of speciation. Here we use genome-wide DNA sequences and Bayesian models to test the hypothesis that loci under divergent selection between two butterfly species (Lycaeides idas and L. melissa) also affect fitness in an admixed population. Locus-specific measures of genetic differentiation between L. idas and L. melissa and genomic introgression in hybrids varied across the genome. The most differentiated genetic regions were characterized by elevated L. idas ancestry in the admixed population, which occurs in L. idas-like habitat, consistent with the hypothesis that local adaptation contributes to speciation. Moreover, locus-specific measures of genetic differentiation (a metric of divergent selection) were positively associated with extreme genomic introgression (a metric of hybrid fitness). Interestingly, concordance of differentiation and introgression was only partial. We discuss multiple, complementary explanations for this partial concordance.
Data from: Intra-population genomics in a model mutualist: population structure and candidate symbiosis genes under selection in Medicago truncatula
Bottom-up evolutionary approaches, including geographically-explicit population genomic analyses, have the power to reveal the mechanistic basis of adaptation. Here we conduct a population genomic analysis in the model legume, Medicago truncatula, in order to characterize population genetic structure and identify symbiosis-related genes showing evidence of spatially-variable selection. Using RAD-seq, we generated over 26,000 SNPs from 191 accessions from within three regions of the native range in Europe. Results from STRUCTURE analysis identify 5 distinct genetic clusters with divisions that separate east and west regions in the Mediterranean basin. Much of the genetic variation is maintained within sampling sites, and there is evidence for isolation by distance. Extensive linkage disequilibrium was identified, particularly within populations. We conducted genetic outlier analysis with FST-based genome scans and a bayesian modeling approach (PCAdapt). There were 70 core outlier loci shared between these distinct methods with one clear candidate symbiosis related gene, DMI1. This work sets that stage for functional experiments to determine the important phenotypes that selection has acted upon and complementary efforts in rhizobium populations.
Data from: Accuracy of genomic selection models in a large population of open-pollinated families in white spruce
Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.
Data from: Genomic signatures of parasite-driven natural selection in north European Atlantic salmon (Salmo salar)
Understanding the genomic basis of host-parasite adaptation is important for predicting the long-term viability of species and developing successful management practices. However, in wild populations, identifying specific signatures of parasite-driven selection often presents a challenge, as it is difficult to unravel the molecular signatures of selection driven by different, but correlated, environmental factors. Furthermore, separating parasite-mediated selection from similar signatures due to genetic drift and population history can also be difficult. Populations of Atlantic salmon (Salmo salar L.) from northern Europe have pronounced differences in their reactions to the parasitic flatworm Gyrodactylus salaris Malmberg 1957 and are therefore a good model to search for specific genomic regions underlying inter-population differences in pathogen response. We used a dense Atlantic salmon SNP array, along with extensive sampling of 43 salmon populations representing the two G. salaris response extremes (extreme susceptibility vs resistant), to screen the salmon genome for signatures of directional selection while attempting to separate the parasite effect from other factors. After combining the results from two independent genome scan analyses, 57 candidate genes potentially under positive selection were identified, out of which 50 were functionally annotated. This candidate gene set was shown to be functionally enriched for lymph node development, focal adhesion genes and anti-viral response, which suggests that the regulation of both innate and acquired immunity might be an important mechanism for salmon response to G. salaris. Overall, our results offer insights into the apparently complex genetic basis of pathogen susceptibility in salmon and highlight methodological challenges for separating the effects of various environmental factors
Data from: Differential effect of selection against LINE retrotransposons among vertebrates inferred from whole-genome data and demographic modeling
Variation in LINE composition is one of the major determinants for the substantial size and structural differences among vertebrate genomes. In particular, the larger genomes of mammals are characterized by hundreds of thousands of copies from a single LINE clade, L1, whereas nonmammalian vertebrates possess a much greater diversity of LINEs, yet with orders of magnitude less in copy number. It has been proposed that such variation in copy number among vertebrates is due to differential effect of LINE insertions on host fitness. To investigate LINE selection, we deployed a framework of demographic modeling, coalescent simulations, and probabilistic inference against population-level whole-genome data sets for four model species: one population each of threespine stickleback, green anole, and house mouse, as well as three human populations. Specifically, we inferred a null demographic background utilizing SNP data, which was then exploited to simulate a putative null distribution of summary statistics that was compared with LINE data. Subsequently,we applied the inferred null demographic model with an additional exponential size change parameter, coupled with model selection, to test for neutrality as well as estimate the strength of either negative or positive selection. We found a robust signal for purifying selection in anole and mouse, but a lack of clear evidence for selection in stickleback and human. Overall, we demonstrated LINE insertion dynamics that are not in accordance to a mammalian versus nonmammalian dichotomy, and instead the degree of existing LINE activity together with host-specific demographic history may be the main determinants of LINE abundance.
Data from: Signatures of selection for bonamiosis resistance in European flat oyster (Ostrea edulis): new genomic tools for breeding programs and management of natural resources
The European flat oyster (Ostrea edulis) is a highly appreciated mollusk with an important aquaculture production throughout the 20th century, in addition to playing an important role on coastal ecosystems. Overexploitation of natural beds, habitat degradation, introduction of non-native species and epidemic outbreaks have severely affected this important resource, particularly, the protozoan parasite Bonamia ostreae, which is the main concern affecting its production and conservation. In order to identify genomic regions and markers potentially associated with bonamiosis resistance, six oyster beds distributed throughout the European Atlantic coast were sampled. Three of them have been exposed to this parasite since the early 1980's and showed some degree of innate resistance (long-term affected group, LTA), while the other three were free of B. ostreae at least until sampling date (naïve group, NV). A total of 14,065 SNPs were analyzed, including 37 markers from candidate genes and 14,028 from a medium density SNP array. Gene diversity was similar between LTA and NV groups suggesting no genetic erosion due to long term exposure to the parasite, and three population clusters were detected using the whole dataset. Tests for divergent selection between NV and LTA groups detected the presence of a very consistent set of 22 markers, located within a putative single genomic region, which suggests the presence of a major quantitative trait locus associated with B. ostreae resistance. Moreover, 324 outlier loci associated with factors other than bonamiosis were identified allowing fully discrimination of all the oyster beds. A practical tool which included the 84 highest discriminative markers for tracing O. edulis populations was developed and tested with empirical data. Results reported herein could assist the production of stocks with improved resistance to bonamiosis, and facilitate the management of oyster beds for recovery production and ecosystem services provided by this species.
Data from: Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data
To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult.
Data from: Genomic selection of juvenile height across a single generational gap in Douglas-fir
Here we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. 1,321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for 1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, 2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, 3) F1 mature height (HT35) predicting HTJ EBVs in F2, and 4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60 respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.
Data from: Signatures of selection in the Iberian honey bee (Apis mellifera iberiensis) revealed by a genome scan analysis of single nucleotide polymorphisms
Understanding the genetic mechanisms of adaptive population divergence is one of the most fundamental endeavours in evolutionary biology and is becoming increasingly important as it will allow predictions about how organisms will respond to global environmental crisis. This is particularly important for the honey bee, a species of unquestionable ecological and economical importance that has been exposed to increasing human-mediated selection pressures. Here, we conducted a single nucleotide polymorphism (SNP)-based genome scan in honey bees collected across an environmental gradient in Iberia and used four FST-based outlier tests to identify genomic regions exhibiting signatures of selection. Additionally, we analysed associations between genetic and environmental data for the identification of factors that might be correlated or act as selective pressures. With these approaches, 4.4% (17 of 383) of outlier loci were cross-validated by four FST-based methods, and 8.9% (34 of 383) were cross-validated by at least three methods. Of the 34 outliers, 15 were found to be strongly associated with one or more environmental variables. Further support for selection, provided by functional genomic information, was particularly compelling for SNP outliers mapped to different genes putatively involved in the same function such as vision, xenobiotic detoxification and innate immune response. This study enabled a more rigorous consideration of selection as the underlying cause of diversity patterns in Iberian honey bees, representing an important first step towards the identification of polymorphisms implicated in local adaptation and possibly in response to recent human-mediated environmental changes.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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