BackgroundThe current large spectrum of sheep phenotypic diversity results from the combined product of sheep selection for different production traits such as wool, milk and meat, and its natural adaptation to new environments. In this study, we scanned the genome of 25 Sasi Ardi and 75 Latxa sheep from the Western Pyrenees for three types of regions under selection: (1) regions underlying local adaptation of Sasi Ardi semi-feral sheep, (2) regions related to a long traditional dairy selection pressure in Latxa sheep, and (3) regions experiencing the specific effect of the modern genetic improvement program established for the Latxa breed during the last three decades.ResultsThirty-two selected candidate regions including 147 annotated genes were detected by using three statistical parameters: pooled heterozygosity H, Tajima’s D, and Wright’s fixation index Fst. For Sasi Ardi sheep, chromosomes Ovis aries (OAR)4, 6, and 22 showed the strongest signals and harbored several candidate genes related to energy metabolism and morphology (BBS9, ELOVL3 and LDB1), immunity (NFKB2), and reproduction (H2AFZ). The major genomic difference between Sasi Ardi and Latxa sheep was on OAR6, which is known to affect milk production, with highly selected regions around the ABCG2, SPP1, LAP3, NCAPG, LCORL, and MEPE genes in Latxa sheep. The effect of the modern genetic improvement program on Latxa sheep was also evident on OAR15, on which several olfactory genes are located. We also detected several genes involved in reproduction such as ESR1 and ZNF366 that were affected by this selection program.ConclusionsNatural and artificial selection have shaped the genome of both Sasi Ardi and Latxa sheep. Our results suggest that Sasi Ardi traits related to energy metabolism, morphological, reproductive, and immunological features have been under positive selection to adapt this semi-feral sheep to its particular environment. The highly selected Latxa sheep for dairy production showed clear signatures of selection in genomic regions related to milk production. Furthermore, our data indicate that the selection criteria applied in the modern genetic improvement program affect immunity and reproduction traits.Electronic supplementary materialThe online version of this article (10.1186/s12711-018-0378-x) contains supplementary material, which is available to authorized users.

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.

Background: Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (Apis mellifera). Methods: We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Results: Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. Conclusions: If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.

Tench (Tinca tinca L.) has great economic potential due to its high rate of fecundity and long-life span. Population genetic studies based on allozymes, microsatellites, PCR-RFLP and sequence analysis of genes and DNA fragments have revealed the presence of Eastern and Western phylogroups. However, the lack of genomic resources for this species has complicated the development of genetic markers. In this study, the tench transcriptome and genome were sequenced by high-throughput sequencing. A total of 60,414 putative SNPs were identified in the tench transcriptome using a computational pipeline. A set of 96 SNPs was selected for validation and a total of 92 SNPs was validated, resulting in the highest conversion and validation rate for a non-model species obtained to date (95.83%). The validated SNPs were used to genotype 140 individuals belonging to two tench breeds (Tabor and Hungarian), showing low (FST = 0.0450) but significant (<0.0001) genetic differentiation between the two tench breeds. This implies that set of validated SNPs array can be used to distinguish the tench breeds and that it might be useful for studying a range of associations between DNA sequence and traits of importance. These genomic resources created for the tench will provide insight into population genetics, conservation fish stock management, and aquaculture.

This article documents the public availability of (i) microbiomes in diet and gut of larvae from the dipteran Dilophus febrilis using massive parallel sequencing, (ii) SNP and SSR discovery and characterization in the transcriptome of the Atlantic mackerel (Scomber scombrus, L) and (iii) assembled transcriptome for an endangered, endemic Iberian cyprinid fish (Squalius pyrenaicus).