A genome-wide scan for signatures of directional selection in domesticated pigs.

BACKGROUND: Animal domestication involved drastic phenotypic changes driven by strong artificial selection and also resulted in new populations of breeds, established by humans. This study aims to identify genes that show evidence of recent artificial selection during pig domestication. RESULTS: Whole-genome resequencing of 30 individual pigs from domesticated breeds, Landrace and Yorkshire, and 10 Asian wild boars at ~16-fold coverage was performed resulting in over 4.3 million SNPs for 19,990 genes. We constructed a comprehensive genome map of directional selection by detecting selective sweeps using an F ST-based approach that detects directional selection in lineages leading to the domesticated breeds and using a haplotype-based test that detects ongoing selective sweeps within the breeds. We show that candidate genes under selection are significantly enriched for loci implicated in quantitative traits important to pig reproduction and production. The candidate gene with the strongest signals of directional selection belongs to group III of the metabolomics glutamate receptors, known to affect brain functions associated with eating behavior, suggesting that loci under strong selection include loci involved in behaviorial traits in domesticated pigs including tameness. CONCLUSIONS: We show that a significant proportion of selection signatures coincide with loci that were previously inferred to affect phenotypic variation in pigs. We further identify functional enrichment related to behavior, such as signal transduction and neuronal activities, for those targets of selection during domestication in pigs.

Genome-wide candidate regions for selective sweeps revealed through massive parallel sequencing of DNA across ten turkey populations.

BACKGROUND: The domestic turkey (Meleagris gallopavo) is an important agricultural species that is largely used as a meat-type bird. Characterizing genetic variation in populations of domesticated species and associating these variation patterns with the evolution, domestication, and selective breeding is critical for understanding the dynamics of genomic change in these species. Intense selective breeding and population bottlenecks are expected to leave signatures in the genome of domesticated species, such as unusually low nucleotide diversity or the presence of exceptionally extended haplotype homozygosity. These patterns of variation in selected populations are highly useful to not only understand the consequences of selective breeding and population dynamics, but also to provide insights into biological mechanisms that may affect physiological processes important to bring changes in phenotype of interest. RESULTS: We observed 54 genomic regions in heritage and commercial turkey populations on 14 different chromosomes that showed statistically significant (P < 0.05) reduction in genomic variation indicating candidate selective sweeps. Areas with evidence of selective sweeps varied from 1.5 Mb to 13.8 Mb in length. Out of these 54 sweeps, 23 overlapped at least partially between two or more populations. Overlapping sweeps were found on 13 different chromosomes. The remaining 31 sweeps were population-specific and were observed on 12 different chromosomes, with 26 of these regions present only in commercial populations. Genes that are known to affect growth were enriched in the sweep regions. CONCLUSION: The turkey genome showed large sweep regions. The relatively high number of sweep regions in commercial turkey populations compared to heritage varieties and the enrichment of genes important to growth in these regions, suggest that these sweeps are the result of intense selection in these commercial lines, moving specific haplotypes towards fixation.

Distinct traces of mixed ancestry in western commercial pig genomes following gene flow from Chinese indigenous breeds.

Studying gene flow between different livestock breeds will benefit the discovery of genes related to production traits and provide insight into human historical breeding. Chinese pigs have played an indispensable role in the breeding of Western commercial pigs. However, the differences in the timing and volume of the contribution of pigs from different Chinese regions to Western pigs are not yet apparent. In this paper, we combine the whole-genome sequencing data of 592 pigs from different studies and illustrate patterns of gene flow from Chinese pigs into Western commercial pigs. We describe introgression patterns from four distinct Chinese indigenous groups into five Western commercial groups. There were considerable differences in the number and length of the putative introgressed segments from Chinese pig groups that contributed to Western commercial pig breeds. The contribution of pigs from different Chinese geographical locations to a given western commercial breed varied more than that from a specific Chinese pig group to different Western commercial breeds, implying admixture within Europe after introgression. Within different Western commercial lines from the same breed, the introgression patterns from a given Chinese pig group seemed highly conserved, suggesting that introgression of Chinese pigs into Western commercial pig breeds mainly occurred at an early stage of breed formation. Finally, based on analyses of introgression signals, allele frequencies, and selection footprints, we identified a ∼2.65 Mb Chinese-derived haplotype under selection in Duroc pigs (CHR14: 95.68-98.33 Mb). Functional and phenotypic studies demonstrate that this PRKG1 haplotype is related to backfat and loin depth in Duroc pigs. Overall, we demonstrate that the introgression history of domestic pigs is complex and that Western commercial pigs contain distinct traces of mixed ancestry, likely derived from various Chinese pig breeds.

Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries.

BACKGROUND: Variation within individual genomes ranges from single nucleotide polymorphisms (SNPs) to kilobase, and even megabase, sized structural variants (SVs), such as deletions, insertions, inversions, and more complex rearrangements. Although much is known about the extent of SVs in humans and mice, species in which they exert significant effects on phenotypes, very little is known about the extent of SVs in the 2.5-times smaller and less repetitive genome of the chicken. RESULTS: We identified hundreds of shared and divergent SVs in four commercial chicken lines relative to the reference chicken genome. The majority of SVs were found in intronic and intergenic regions, and we also found SVs in the coding regions. To identify the SVs, we combined high-throughput short read paired-end sequencing of genomic reduced representation libraries (RRLs) of pooled samples from 25 individuals and computational mapping of DNA sequences from a reference genome. CONCLUSION: We provide a first glimpse of the high abundance of small structural genomic variations in the chicken. Extrapolating our results, we estimate that there are thousands of rearrangements in the chicken genome, the majority of which are located in non-coding regions. We observed that structural variation contributes to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl. We expect that, because of their high abundance, SVs might explain phenotypic differences and play a role in the evolution of the chicken genome. Finally, our study exemplifies an efficient and cost-effective approach for identifying structural variation in sequenced genomes.

Genetic variances, heritabilities and maternal effects on body weight, breast meat yield, meat quality traits and the shape of the growth curve in turkey birds.

BACKGROUND: Turkey is an important agricultural species and is largely used as a meat bird. In 2004, turkey represented 6.5% of the world poultry meat production. The world-wide turkey population has rapidly grown due to increased commercial farming. Due to the high demand for turkey meat from both consumers and industry global turkey stocks increased from 100 million in 1970 to over 276 million in 2004. This rapidly increasing importance of turkeys was a reason to design this study for the estimation of genetic parameters that control body weight, body composition, meat quality traits and parameters that shape the growth curve in turkey birds. RESULTS: The average heritability estimate for body weight traits was 0.38, except for early weights that were strongly affected by maternal effects. This study showed that body weight traits, upper asymptote (a growth curve trait), percent breast meat and redness of meat had high heritability whereas heritabilities of breast length, breast width, percent drip loss, ultimate pH, lightness and yellowness of meat were medium to low. We found high positive genetic and phenotypic correlations between body weight, upper asymptote, most breast meat yield traits and percent drip loss but percent drip loss was found strongly negatively correlated with ultimate pH. Percent breast meat, however, showed genetic correlations close to zero with body weight traits and upper asymptote. CONCLUSION: The results of this analysis and the growth curve from the studied population of turkey birds suggest that the turkey birds could be selected for breeding between 60 and 80 days of age in order to improve overall production and the production of desirable cuts of meat. The continuous selection of birds within this age range could promote high growth rates but specific attention to meat quality would be needed to avoid a negative impact on the quality of meat.

Mining for single nucleotide polymorphisms in pig genome sequence data.

BACKGROUND: Single nucleotide polymorphisms (SNPs) are ideal genetic markers due to their high abundance and the highly automated way in which SNPs are detected and SNP assays are performed. The number of SNPs identified in the pig thus far is still limited. RESULTS: A total of 4.8 million whole genome shotgun sequences obtained from the NCBI trace-repository with center name "SDJVP", and project name "Sino-Danish Pig Genome Project" were analysed for the presence of SNPs. Available BAC and BAC-end sequences and their naming and mapping information, all obtained from SangerInstitute FTP site, served as a rough assembly of a reference genome. In 1.2 Gb of pig genome sequence, we identified 98,151 SNPs in which one of the sequences in the alignment represented the polymorphism and 6,374 SNPs in which two sequences represent an identical polymorphism. To benchmark the SNP identification method, 163 SNPs, in which the polymorphism was represented twice in the sequence alignment, were selected and tested on a panel of three purebred boar lines and wild boar. Of these 163 in silico identified SNPs, 134 were shown to be polymorphic in our animal panel. CONCLUSION: This SNP identification method, which mines for SNPs in publicly available porcine shotgun sequences repositories, provides thousands of high quality SNPs. Benchmarking in an animal panel showed that more than 80% of the predicted SNPs represented true genetic variation.

Comparison of three microarray probe annotation pipelines: differences in strategies and their effect on downstream analysis.

BACKGROUND: Reliable annotation linking oligonucleotide probes to target genes is essential for functional biological analysis of microarray experiments. We used the IMAD, OligoRAP and sigReannot pipelines to update the annotation for the ARK-Genomics Chicken 20 K array as part of a joined EADGENE/SABRE workshop. In this manuscript we compare their annotation strategies and results. Furthermore, we analyse the effect of differences in updated annotation on functional analysis for an experiment involving Eimeria infected chickens and finally we propose guidelines for optimal annotation strategies. RESULTS: IMAD, OligoRAP and sigReannot update both annotation and estimated target specificity. The 3 pipelines can assign oligos to target specificity categories although with varying degrees of resolution. Target specificity is judged based on the amount and type of oligo versus target-gene alignments (hits), which are determined by filter thresholds that users can adjust based on their experimental conditions. Linking oligos to annotation on the other hand is based on rigid rules, which differ between pipelines.For 52.7% of the oligos from a subset selected for in depth comparison all pipelines linked to one or more Ensembl genes with consensus on 44.0%. In 31.0% of the cases none of the pipelines could assign an Ensembl gene to an oligo and for the remaining 16.3% the coverage differed between pipelines. Differences in updated annotation were mainly due to different thresholds for hybridisation potential filtering of oligo versus target-gene alignments and different policies for expanding annotation using indirect links. The differences in updated annotation packages had a significant effect on GO term enrichment analysis with consensus on only 67.2% of the enriched terms. CONCLUSION: In addition to flexible thresholds to determine target specificity, annotation tools should provide metadata describing the relationships between oligos and the annotation assigned to them. These relationships can then be used to judge the varying degrees of reliability allowing users to fine-tune the balance between reliability and coverage. This is important as it can have a significant effect on functional microarray analysis as exemplified by the lack of consensus on almost one third of the terms found with GO term enrichment analysis based on updated IMAD, OligoRAP or sigReannot annotation.

Methods for interpreting lists of affected genes obtained in a DNA microarray experiment.

BACKGROUND: The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. RESULTS: Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached. CONCLUSION: It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.