The extraordinary phenotypic diversity of dog breeds has been sculpted by a unique population history accompanied by selection for novel and desirable traits. Here we perform a comprehensive analysis using multiple test statistics to identify regions under selection in 509 dogs from 46 diverse breeds using a newly developed high-density genotyping array consisting of >170,000 evenly spaced SNPs. We first identify 44 genomic regions exhibiting extreme differentiation across multiple breeds. Genetic variation in these regions correlates with variation in several phenotypic traits that vary between breeds, and we identify novel associations with both morphological and behavioral traits. We next scan the genome for signatures of selective sweeps in single breeds, characterized by long regions of reduced heterozygosity and fixation of extended haplotypes. These scans identify hundreds of regions, including 22 blocks of homozygosity longer than one megabase in certain breeds. Candidate selection loci are strongly enriched for developmental genes. We chose one highly differentiated region, associated with body size and ear morphology, and characterized it using high-throughput sequencing to provide a list of variants that may directly affect these traits. This study provides a catalogue of genomic regions showing extreme reduction in genetic variation or population differentiation in dogs, including many linked to phenotypic variation. The many blocks of reduced haplotype diversity observed across the genome in dog breeds are the result of both selection and genetic drift, but extended blocks of homozygosity on a megabase scale appear to be best explained by selection. Further elucidation of the variants under selection will help to uncover the genetic basis of complex traits and disease. There are hundreds of dog breeds that exhibit massive differences in appearance and behavior sculpted by tightly controlled selective breeding. This large-scale natural experiment has provided an ideal resource that geneticists can use to search for genetic variants that control these differences. With this goal, we developed a high-density array that surveys variable sites at more than 170,000 positions in the dog genome and used it to analyze genetic variation in 46 breeds. We identify 44 chromosomal regions that are extremely variable between breeds and are likely to control many of the traits that vary between them, including curly tails and sociality. Many other regions also bear the signature of strong artificial selection. We characterize one such region, known to associate with body size and ear type, in detail using “next-generation” sequencing technology to identify candidate mutations that may control these traits. Our results suggest that artificial selection has targeted genes involved in development and metabolism and that it may have increased the incidence of disease in dog breeds. Knowledge of these regions will be of great importance for uncovering the genetic basis of variation between dog breeds and for finding mutations that cause disease. There are more than 400 breeds of domestic dog, which exhibit characteristic variation in morphology, physiology and behavior. This astonishing phenotypic diversity has been molded by two main phases of evolution: 1) the initial domestication from wolves more than 15,000 years ago, where dogs became adapted to life in closer proximity to humans and 2) the formation of distinct breeds in the last few hundred years, where humans chose small groups of dogs from the gene pool and strongly selected for novel and desirable traits Its unique population history makes the dog an ideal model organism for mapping the genetic basis of phenotypic traits due to extensive linkage disequilibrium (LD) and a reduction in haplotype diversity due to genetic drift in isolated populations et al.et al.Several loci have now been identified that control variation in morphological traits between dog breeds. In some cases, variation in a trait occurs within a breed, and long blocks of LD can be used to identify the locus responsible using genome wide association studies (GWAS). Using this approach loci involved in traits including size (IGF1) STF between dog breeds are found in loci associated with phenotypic traits such as size, ear morphology and coat color et al.STF in particular breeds. This scan identified many regions likely to be under selection in one or more of the breeds in their dataset. Notably, a highly differentiated interval in Shar-Pei on chromosome 13 contains the HAS2 gene and is likely associated with the wrinkled skin phenotype of this breed Genomic regions with a high degree of genetic differentiation between breeds are also indicative of selection. A large proportion of SNPs with high Although a large number of loci under selection have now been identified, the genetic basis of much of the phenotypic variation in dog breeds and particularly behavioral traits remains unexplained. One drawback of previous studies is the use of SNP arrays with relatively low coverage of the genome. With the development of a new high-density array it is now possible to examine the dog genome at much higher resolution, allowing a comprehensive characterization of regions under selection. Genetic variants under selection in dogs can be loosely divided into two categories: 1) those that control variation in common traits such as size and ear carriage, which segregate across many breeds STF statistics to identify additional SNPs that have high variability in frequency between breeds. These methods identify known loci and indicate new regions that may be involved in common trait variation.Here we implement a variety of approaches to identify both these types of loci. In cases where a common trait has been identified, it is possible to search for genotype-phenotype correlations. We attempt to identify both behavioral and morphological traits that vary between breeds using across-breed GWAS. We also use STF and differences in heterozygosity are likely to be most powerful. STF and heterozygosity between breeds.The action of selection can potentially be identified by examining patterns of variation in individual breeds in order to detect the characteristic signature of selective sweeps. This signature is characterized by the presence of long haplotypes, a skew in allele frequency, reduced heterozygosity, and elevated population differentiation. A large number of statistical methods have been developed to detect sweeps based on these different patterns The presence of long blocks of homozygosity in the dog genome Our first goal was to develop a high-density, high-accuracy mapping array with uniform SNP coverage across the whole genome. Since the SNP map from the canine genome project, although containing >2.8 million SNPs at fairly even coverage, still contained gaps, we first performed targeted resequencing within 1,555 regions that lie within intervals >40 kb containing no known SNPs in unique sequence. We performed Roche NimbleGen array capture to enrich these regions followed by sequencing using the Illumina Genome Analyzer on 4 pools containing multiple samples of a single dog breed  and one pool of wolf samples. In total, we discovered 4,353 additional high-quality SNPs using this method. We selected SNPs from this improved map to form the “CanineHD” array panel. We generated an initial panel of 174,943 SNPs that were included on the array of which 173,622 (99.2%) give reliable data. These loci are distributed with a mean spacing of 13 kb and only 21 gaps larger than 200 kb. Loci with unreliable SNP calls, potentially due to copy number polymorphism, were not included in the analysis. In total, 172,115 are validated for SNP genotyping and 1,547 are used only for probe intensity analyses. This is a significant improvement compared with the largest previously existing array, which has 49,663 well performing SNPs, with a mean spacing of 47 kb and 1,688 gaps larger than 200 kb. Of all the SNPs on the array, 0.9% are novel SNPs discovered by the targeted resequencing experiment. The remaining SNPs have been previously described: 65.1% of them were present in a comparison of the boxer reference genome with a previously sequenced poodle, 21.7% were present in alignments of low coverage sequencing reads from various dog breeds to the boxer reference genome, 25.4% were present within the boxer reference and 1.2% were present in alignments of wolf and/or coyote sequencings with the reference boxer genome. There is therefore a bias in the way that SNPs were ascertained: all of them were identified in a comparison involving the boxer reference assembly. However this has not had a great impact on the number of SNPs polymorphic in different breeds (see below). The array was initially evaluated using 450 samples from 26 breeds termed the “Gentrain” dataset. Within this dataset, average call rates were 99.8% and reproducibility and Mendelian consistency were both >99.9%. A subset of 24 samples generated by whole genome amplification (WGA) of 12 blood and 12 cheek swab samples produced slightly lower call rates . Probe intensities from the array can also be used to analyze copy number polymorphisms, although this is not evaluated here.To perform a broader analysis of canine breed relationships and selective sweeps, we constructed a larger dataset consisting of unrelated samples from the Gentrain dataset, and unrelated control dogs genotyped for disease gene mapping studies from multiple breeds as part of the LUPA consortium. This dataset, which we refer to here as the “full LUPA genotype dataset” consists of 509 dogs from 46 diverse breeds and 15 wolves, genotyped on the CanineHD array. These include 156 dogs from 13 breeds derived from LUPA control dogs and 353 dogs from 33 breeds from the Gentrain dataset , 44 kb from the causative SNP previously identified in RSPO2 genome = 0.004), which is within the IGF1 gene, previously implicated in size variation genome<0.05) were observed at 7 SNPs within an interval between 44.23 - 44.44 Mb. In addition, we observed an association within a previously defined region on chr10 . The SNP at chr10:11,169,956 is about 500kb upstream of HMGA2, which has been established to be associated with body size variation in other species Using this method we were able to replicate several known associations. We first performed a GWAS comparing 4 breeds with furnishings  most strongly associated to size shows a steady decline according to the size of the breed. However, the differences in allele frequency at the SNP chr10:11,169,956 are more striking, as one allele appears at very low frequencies in all breeds apart from a number of very small breeds , where it is at or close to fixation . Hence, genome<0.05; genome<0.001), which lies between the HGMA2 and MSRB3 genes. This region has been associated with ear type and body size in previous studies genome<0.05; Dog breeds show extreme variation in ear morphology ranging from pricked ears to low hanging dropped ears. We performed a GWAS using 12 breeds with pricked ears and 15 breeds with dropped ears. Within an interval between 10.27 - 11.79 Mb, 23 SNPs had genome-wide significant associations (Pgenome<0.001) and 10,804,969 (Pgenome = 0.006), in the same region associated with both drop ear and size. Variation within this region is therefore associated with at least two morphological and one behavioral trait, which may be correlated. The region contains several genes including WIF1, HMGA2, GNS, and MSRB3 (see We performed GWAS to search for variants that affect breed differences in behavior. We first performed a GWAS by comparing 18 bold and 19 non-bold breeds using phenotypic definitions from ref. SRB3 see . HoweverSRB3 see .genome<0.05) in the interval 106.03–106.61 Mb on the X chromosome . We then generated a list of highly differentiated regions, by merging all SNPs in this list within 500kb of each other into single regions, resulting in 44 regions containing between 1 and 94 SNPs with elevated STF. Regions with two or more SNPs are presented in pq, where p is the number of breeds where allele A is fixed or close to fixation (frequency >0.95) and q is the number of breeds where allele B is fixed or close to fixation (frequency >0.95). In total 53,944 out of 154,034 variable SNPs have a pq value > 0, indicating that they are fixed for different alleles in at least 2 breeds. The regions of high STF correspond strongly to loci where trait associations have been reported. In particular, 8 of the 9 regions comprised of more than 3 high-STF SNPs overlap known trait-associated regions, and it is likely that most or all of the remaining regions with high STF show a correlation with an as yet undefined trait. Three of these regions were not previously reported in a study based on a less dense array A total of 240 SNPs have a STF across 50 kb. Other genes of note identified are LCORL, known to associate with human height Three regions longer than 1Mb are identified by this measure, likely signifying regions under strong selection in many breeds. These consist of a 2.6 Mb region on chromosome X that associates with body size, skull shape and sociability, a 2.0 Mb region on chromosome 10 that associates with drop ear, size and boldness and a 2.1 Mb region on chromosome X associated with limb and tail length see also . Other lSTF values at single SNPs across all breeds compared to regions under selection in many breeds. In order to identify such rare sweeps, we scanned patterns of variation in the reduced LUPA genotype dataset to identify extended regions where haplotypes had become fixed in one or more breeds, leading to a local reduction in genetic variation and increase in population differentiation. We analyzed 150 kb sliding windows, overlapping by 25 kb in each breed compared with other breeds using two statistics. The first statistic, iS, is calculated by summing regional deviations in levels of relative heterozygosity across the genome between two breeds compared to the genomic average and summing across all pairwise comparisons. Relative heterozygosity is defined as the number of SNPs segregating in a genomic window in one breed divided by the number of SNPs segregating in that window in two breeds under comparison. Hence, regions with low iS in a breed contain few segregating SNPs compared to other breeds. The second statistic, id, was implemented by Akey et al. STF values normalized for a given breed relative to the genome-wide average, summed across all pairwise combinations involving the given breed. Regions of high id in a particular breed exhibit a large difference in allele frequencies compared with other breeds.Rare selective sweeps corresponding to regions of the genome under selection in only one or a small number of breeds in our dataset cannot be detected by across-breed GWAS due to lack of power. They also have a weak effect on iS or id values in an extreme 1% tail of their respective distributions (the bottom 1% for iS and top 1% for id). Overlapping windows were then collapsed into larger regions (see id and iS on the simulated data (see above). For both statistics, the average length of regions identified was similar in real versus simulated datasets. However, there was a strong excess of regions >250 kb in the real compared with simulated datasets, which likely reflects regions influenced by selection. In order to distinguish regions generated by genetic drift compared with those generated by selective sweeps we first estimated a marginal p-value for each block, equal to the proportion of simulated blocks with longer lengths in the same breed. We then adjusted these p-values using a 5% False Discovery Rate , which could potentially represent single selective sweeps. The longest region we identified is 3.1 Mb long (chr22: 5.3–8.4 Mb) in beagles. This region overlaps 3 other putative sweeps within the top 20 in other breeds  whereas no other regions in the top 20 are overlapping. As this and other regions with strongest evidence for sweeps are long and contain many genes, it is not possible to identify the locus under selection in a single sweep. However, it is interesting to note that they contain genes associated with disease in humans and dogs including epilepsy (KCNQ5), cancer , and autoimmune disease (IL6). A long sweep on chromosome 30 in Golden retrievers spans the RYR1 gene, involved in the skeletal muscle calcium release channel and implicated in canine malignant hyperthermia by linkage analysis iS that overlapped between different breeds. We then identified the maximal region where the same haplotype was fixed in all breeds identified. For many significant long regions we were able to identify shorter regions of IBS. The regions shared by 3 or more breeds are shown in In cases where multiple breeds are affected by selection acting on the same variant, it may be possible to narrow an interval containing the causative mutation by identifying a core region of identity by state (IBS) between all breeds where haplotypes are shared, most likely reflecting common ancestry. We searched our dataset for regions with significant drops in The inferred selective sweep shared by the most breeds in this analysis was a 485 kb haplotype on chromosome 22 (5.4–5.9 Mb) shared by 8 breeds . This region contains 2 genes: FNDC3A, fibronectin type III domain containing 3A iS and id statistics in the 10 longest regions identified by the iS test is presented in id and iS tests reveals that the increases in id often occur within a more restricted region of a large block of fixed haplotype from the iS tests, indicating that they represent regions where an otherwise rare ancestral sub-haplotype has been fixed in a certain breed. It therefore appears that many regions detected by id and iS tests are complementary. Among the top 20 longest putative sweeps identified by the id statistic , which is also identified by the iS test. One gene of note in this region is VPS13B, which may have an important role in development and is associated with Cohen syndrome, which has an effect on development of many parts of the body iS on chromosome 26 is also identified in two of the top 20 longest id regions.There are many extremely differentiated regions although none of them passed the 5% FDR correction for length (see tatistic  are 3 oviS and id tests. For the regions constructed from the top 1% of id (6404 regions) and iS (7618 regions) statistics, mean XP-EHH was -0.94 and -1.13 respectively across all breeds compared with a genome average of zero. This difference is consistent across all 30 breeds and is highly significant . This confirms that regions identified by the iS and id tests are associated with unusually long haplotypes at or near fixation in the breeds under selection compared with other breeds.We performed an additional validation of our results using a third statistic, XP-EHH, which identifies regions where a long haplotype has reached fixation, or is close to fixation in one breed compared with other breeds STF for enrichment in gene ontology (GO) categories. The six most significantly overrepresented GO categories were all involved in development. 11 of the 22 genes were found in the “developmental processes category” (P = 0.00036) and tissue, system, organ, anatomical structure and multicellular organismal development were all significantly overrepresented (P<0.0007). These highly differentiated SNPs therefore highlight a number of regions involved in development that are likely to have been modified by artificial selection and contribute to the high diversity of dog breeds.We analyzed genes closest to all singleton SNPs with high id and iS distributions that pass the marginal p-value <0.05 for each breed. We only considered regions containing a single gene, in order to enrich the analysis for true targets of selection, although this list is still expected to contain false positives. There were 119 id regions and 272 iS regions containing one gene only (29 genes shared). We performed GO analysis using human genes with 1∶1 human-dog orthologous relationship. As longer genes are over-represented within long genomic segments containing only one gene, we compared these candidate selection genes to a background dataset with similar length  is involved in 25 enriched GO categories, including central nervous system and regulation of nucleotide biosynthetic process. This gene is suggested to have an essential role in photoperiod control of reproduction in vertebrates, in organ development and in metabolic regulation and has been recently been implicated as an important domestication gene in chicken A large number of genes detected by the id regions are ‘cell communication’ and ‘signal transduction’, which are represented by 16 and 15 genes, respectively. A region on chromosome 3 with strong statistical support contains the gene for insulin-like growth factor receptor1 (IGF1R), also detected by iS statistics. This is a strong candidate gene in relation to selection for growth, a phenotype that has been strongly selected in dog. Another example is ANGPT1, which plays roles in vascular development and angiogenesis and contributes to blood vessel maturation and stability. This gene has been identified in a set of positively-selected genes in human Tibetan populations for which selection may have occurred to allow for more efficient oxygen utilization The two larger biological processes over-represented by STF analysis is 9.8 – 11.8 Mb on chromosome 10. Variation in this region was also found to correlate with multiple traits: drop ear, size and boldness. As boldness shows a strong correlation with the other traits, we focused on analyzing the contribution of variants in this region to drop ear and size. We first analyzed the variation in allele frequencies of the SNPs most associated with size and drop ear across breeds scored for these traits. Size was measured as the breed average in kg, and drop ear was scored on a scale of 1-5  using Roche NimbleGen hybrid capture and sequencing using an Illumina Genome Analyzer. We choose 3 breeds with the dropped ear phenotype  and 3 with the pricked ear phenotype . Two of the pricked ear breeds are small, with breed average <6 kg: Chinese Crested and Schipperke. We sequenced each breed independently, using a pool of 5 dogs from each breed. On average 8 million reads per pool were produced, of which 48% mapped to the 3 Mb region on chr10. In total, 61% of this region was mapped by at least one read. In the 68% of the region defined as non-repetitive, reads mapped to 98% of bases, at an average coverage depth of 114x. By comparison with the reference sequence, we identified fixed differences and polymorphic sites within each breed. Differences in the pattern of polymorphism between dropped and pricked eared dogs are clearly apparent, and drop eared breeds exhibit a lower level of variation on average compared with prick eared dogs, which is mainly restricted to a ∼2 Mb region between 9.5 Mb and 11.5 Mb .We next identified SNPs in this region that were completely fixed for different alleles in dropped and pricked eared breeds. These SNPs are distributed unevenly across the region, and a peak in the number of such fixed SNPs occurs around 11.3-11.5 Mb. In total 287 SNPs or small indels were completely fixed for different alleles in the drop ear compared with pricked ear breeds. Twenty-five of these SNPs reside in regions that show evidence for sequence conservation and are therefore candidates for being the causative mutation . Of the Here we present a comprehensive catalogue of genomic regions that are candidates for being affected by artificial selection in dogs using the densest panel of SNPs to date. We focus on two main types of variant: 1) common variants that affect variation in a trait in many breeds and 2) rare variants that have undergone selective sweeps in one or a few breeds. For the first category, we identify loci where variation correlates with morphological traits such as body size and tail curl, and behavioral traits such as sociability and boldness. We also identify several loci with evidence for a high degree of population differentiation between breeds, for which the connection with phenotypic traits in dogs is not known, but that are known to associate with traits such as pigmentation and body size. To identify loci in the second category, we searched for regions with reduced heterozygosity and high population differentiation, characteristic of selective sweeps. This analysis identified loci known to be associated with breed-defining characteristics such as chondrodysplasia, skin wrinkling, and furnishings. In addition, we identify several extended regions with reduced heterozygosity > 1 Mb consistent with recent selective sweeps in one or more breeds, including striking examples such as a region containing the FNDC3A and CYSLTR2 genes, and a region containing the MSTN (myostatin) gene that both bear the signal of selection in multiple breeds.The candidate selection loci we identified are strongly enriched for genes involved in developmental and metabolic processes. In general, the GO terms we find to be significantly enriched are different from analyses of selection in natural populations, in which genes commonly targeted by positive selection include those involved in immunity and defense, olfaction and responses to external stimuli Artificial selection on dog breeds coincided with breed creation bottlenecks leading to genetically distinct breeds fixed for novel traits iS and id that pass the 5% FDR cut off are strong candidates for selective sweeps, and contain a number of regions already associated with phenotypic traits.We performed coalescent modeling using realistic estimates of recombination and demographic parameters in order to compare the length distributions of genomic segments identified by our analyses with those expected under neutrality. These simulations are by necessity an approximation of the actual evolutionary and demographic forces that shaped patterns of genetic variation in dog breeds. In particular, we do not model selection, which may reduce effective population size. Secondly, we assume a simplified demographic model, involving a single domestication bottleneck, and simultaneous breed creation. The true history of dog evolution is likely to be more complex than this, with some breeds showing closer relatedness than others. Nevertheless, long segments identified by the STF are expected under neutrality but longer segments of reduced heterozygosity, particularly those longer than 1 Mb, are not expected to occur due to drift alone and hence are more likely to reflect selection. In general we expect segments of reduced heterozygosity to contain causative variants under selection, however, in some cases we observe large blocks of reduced heterozygosity that appear to be broken up into adjacent regions separated by more variable regions. This pattern may reflect heterogeneity in ancestral haplotypes, which makes it difficult to pinpoint the focus of selection. Smaller blocks of elevated id often occur within extended regions of reduced heterozygosity. These probably reflect the fixation of variants that are otherwise rare in the dog population due to hitchhiking on the selected haplotype. However, most variants that are fixed by hitchhiking during a selective sweep are likely to be already common in the population, and therefore will not have a big effect on the id value of a region. This leads to stochasticity in the id statistic, which may explain the fact that even the longest id segments still do not pass a 5% FDR. When even denser surveys of SNP variation (e.g. from whole genome sequencing) are available, a more promising approach could be to identify selective sweeps using reductions in heterozygosity, and identify potential causative variants within these sweeps by their elevated STF . However, less extreme selective episodes (soft sweeps), such as incomplete selective sweeps or selection on standing variation may also be common The huge phenotypic diversity present in dogs raises the question as to whether levels of functional genetic variation in the ancestral dog population were elevated, adding to the raw material that artificial selection could act on. Relatively higher levels of replacement amino acid changes are found in dogs compared with wolves, possibly indicating a relaxation of selective constraint It is likely that artificial selection in dogs  has led to the proliferation of mutations with large effects. This has contributed to the success of the dog as a model for genetic dissection of phenotypic traits. Such variants are likely to be maladaptive in the wild, and may also increase susceptibility to disease. Hence examining regions under selection in breeds may aid in identification of genetic risk factors affecting susceptibility to disease. Studying the extreme variation in forms produced by artificial selection also gives us a window into studying the effects of selection in natural populations, as first realized by Darwin Blood samples were taken from dogs by trained veterinarians according to relevant national and international guidelines.We scanned the existing list of 2.8 million high quality SNPs and identified 1,555 regions >40 kb (gaps) with no known SNPs in non-repetitive DNA (588 of these are on chromosome X). Gaps >100 kb were divided into a series of shorter ones resulting in a set of 2,375 genomic segments with no known SNPs of average length 50kb. We designed a Roche NimbleGen sequence capture array containing probes matching on average 2.1 kb within each segment, giving a total of 5 Mb. This array was used to enrich pools of DNA from Belgian Shepherds, Irish Wolfhounds, West Highland White Terrier, Shar-Pei and wolves. The samples were then sequenced using an Illumina Genome Analyzer and aligned to the CanFam2 dog reference sequence using MAQ. We identified 4,353 novel SNPs (973 on chromosome X). After updating the canine SNP map with these variants the number of gaps >40 kb was reduced to 714 (392 on chrX).We selected SNPs from initial list of 2.8 million augmented by the resequencing to be included in the Illumina CanineHD array. We selected SNPs by scanning the genome using non-overlapping windows of length 11,500 bp . Every SNP in each window was scored and ranked according to a number of different criteria in order to maximize quality, coverage of the genome and a number of other factors according to a scoring criteria . The maihttp://dogs.genouest.org/SWEEP.dir/Supplemental.html.Genotyping was performed by Illumina Inc., USA (Gentrain dataset) and Centre National de Genotypage, France (LUPA dataset). All data is available at: http://pngu.mgh.harvard.edu/~purcell/plink/), using a breed permutation procedure to determine genome-wide significance implemented using a perl script. Each sample within a breed was first assigned a phenotype corresponding to the breed-specific value of a trait. Traits were either coded as dichotomous or quantitative depending on how they were measured (see below). An association study was performed for each trait followed by a permutation procedure, where the phenotypes of each breed were randomized, always assigning an identical phenotype value to each sample within the same breed. For each GWAS, 1000 permutations were performed, and the real significance values at each SNP were compared to the maximum permuted values across all SNPs in order to calculate genomewide significance.For each trait we performed a GWAS with plink  compared with all other breeds. Drop ear association: drop ear breeds  compared with pricked ear breeds . Curly tail association: straight tail breed  compared with curly tail breeds . Boldness association: bold breeds  compared with non-bold breeds .The rs numbers corresponding to SNPs mentioned in the text are listed in We phased the genotypes in the reduced dataset containing 471 dogs from breeds with 10 or more samples using fastPHASE iS statistic is a measure of the proportion of SNPs that are variable in a region in a particular breed relative to all other breeds. We first divided the genome into 150 kb sliding windows, overlapping by 25 kb. Each window contained on average 10 SNPs; windows with less than 5 SNPs were not retained in the analysis. The same sliding window coordinates were used for the iS and id analyses. Given a pair of breeds i and j and a given genomic window, we define relative heterozygosity as:ih is the number of polymorphic SNPs in breed i and jh is the number of polymorphic SNPs in breed j in a given genomic window.The iS for a given genomic window in breed i is then calculated as:ij]Esd and sd[STijF] represent respectively, the expected value and the standard deviation of STF between breed i and j computed from all SNPs. For each breed, id values were calculated for the 150 kb windows used for the iS analysis. We retained, for each breed, windows with an average id within the top 1% of all id values.Using the same dataset, we calculated iS and id statistics. Overlapping windows were then combined to create a set of larger regions for each statistic. We applied this method to both the real and simulated data (see below) after which we compared the distribution of lengths. We then computed a marginal p-value for each region as the proportion of regions defined from the simulated dataset of the same breed that were longer. Finally we corrected the p-values using the Benjamini-Hochberg FDR method For each breed, we retained the top 1% of windows in each breed based on both iS and id statistics as well as id values for all SNPs from the CanineHD array are available at the following URL:The UCSC graphical display of regions identified by the http://dogs.genouest.org/SWEEP.dir/Supplemental.htmliS sweeps (at the 5% FDR) and were completely homozygous in each breed. Once these fixed regions were defined they were then grouped into clusters of overlapping physical locations between breeds. Where possible, we then identified the region of maximal overlap between all homozygous regions in all of the breeds in a cluster that had been fixed for identical haplotypes.The aim of this analysis was to identify putative regions of Identity By Descent (IBD) within haplotypes inferred to be involved in selective sweeps in multiple breeds in order to narrow down the boundaries of putative sweep regions. This is based on the assumption that the selected variant was present on an ancestral haplotype prior to breed creation and is shared by multiple breeds. We first identified core regions that overlapped In order to calculate XP-EHH for SNPs in our dataset, we first removed SNPs with a minor allele frequency < 5% in the entire dataset. We calculated the EHH statistic between all SNP pairs across all breeds in the whole dataset. We retained SNP pairs with EHH between 0.03 and 0.05 for the XP-EHH analysis. We calculated normalized log XP-EHH scores between these SNP pairs from iHS scores as described by We performed whole genome simulations under a realistic demographic model, using variable regional recombination rates as inferred from the original data. The simulation process consisted of three main steps: (1) recombination rate inference, (2) breed bottleneck modeling and (3) main simulations.http://www.stats.ox.ac.uk/~mcvean/LDhat/instructions.html,which assumes a population mutation rate of 0.01. The PAR was analyzed separately from rest of the X-chromosome. To assess the general performance of interval on dog data, we averaged the regional rate estimates obtained here across 5 Mb windows to make it compatible with a previously published, coarse, linkage map dog Autosomes  =  7752) by comparing the autosomal part of the population genetic map generated here, with that of the linkage map dog X = 9134).We used interval in the LDhat package wolf) of 22600 dog and that breed formation took place 100 generations ago −8wolf - 0.03Newolf (this corresponds to an increment in bottle neck size of 0.0005Newolf for every new simulation). We repeated these simulations for each of the sample sizes represented in the original data (ranging from 10 to 52 haplotypes), in total rendering 767 simulated datasets. All simulations were run using regional recombination rates as inferred in the real data. We also corrected simulated data for ascertainment bias in the original data by providing MaCS with allele frequency distributions from the original data. Next, we estimated LD decay, measured as r2, for markers separated up to a maximum of 500 kb, in the original, as well as all simulated datasets. We subsequently used least squares to fit LD decay curves of real and simulated datasets. The best fitting simulation provided an estimation of bottleneck size for each breed individually.We built on previous dog demographic modeling efforts in setting up a simple simulation scheme to estimate the strength of bottlenecks at breed formation for each breed in our dataset. Our model thus assumed an effective population size of the ancient wolf population . The PAR and X-specific parts of the X-chromosome were simulated separately. For the X-specific simulations we adjusted all population sizes according to the difference between NeWe selected human orthologs with a 1∶1 human-dog orthologous relationship to perform GO analyses. Biomart version 0.8 (Ensembl v.62) was used to collect orthologous human protein-coding genes. WebGestalt http://bio-bwa.sourceforge.net/) followed by SNP calling using samtools (http://samtools.sourceforge.net/). Mapping and SNP calling was done independently for each breed and custom scripts were used to identify SNPs with certain patterns of segregation. SNPs in conserved elements were identified relative to those defined by the dog genome analysis based on human-dog-mouse-rat alignments, and on identification of phastcons elements within mammals based on alignments of 44 vertebrates, converted from human to dog coordinates by LiftOver (http://genome.ucsc.edu/).We selected a 3 Mb region on chromosome 10 (9.5-12-5 Mb) for resequencing in 6 breeds. We first prepared pools of DNA containing 5 samples from each of the breeds . We next performed sequence capture using a Roche NimbleGen array containing probes designed to hybridize to this region. This was followed by sequencing using the Illumina Genome Analyzer. Reads were mapped to the dog genome reference sequence using bwa  autosomes and B) X chromosome. For autosomes, there is an average of 9 SNPs per 100 kb window on the HD array and only 5% of windows do not contain SNPs, whereas the majority (>25%) of 100 kb windows do not contain SNPs on the Affymetrix array. For the X chromosome, >75% of windows do not contain SNPs on the Affymetrix array, whereas <5% of windows do not contain SNPs on the HD array.(PDF)Click here for additional data file.Figure S22 for markers separated by at most 400kb and averaged across bins of 10kb. Simulation were run in MaCS −8 per site per generation e) as well as (2) a sample size to match that of the real dataset (10-52 haplotypes). Furthermore, recombination rates were allowed to vary locally as inferred in real data using LDhat Decay of linkage disequilibrium in real versus simulated data. Decay of linkage disequilibrium (LD decay) across the autosomes of all breeds included in this study (solid lines) compared with that of simulated datasets (circles). Wolf is included as comparison. LD decay was calculated as r(PDF)Click here for additional data file.Figure S3Allele frequency of 3 SNPs strongly associated with body size in the dataset across breeds plotted against body size. Data on body size is presented in (PDF)Click here for additional data file.Figure S4Signal of association with curly tail of chr1: 95-98 Mb. The y-axis shows the raw p-value and genes in the region are shown below the graph.(PDF)Click here for additional data file.Figure S5Signal of association between drop ear, boldness and size in the region chr10:9.5-12.5 Mb. The y-axis show the raw p-value for each association, and genes in the region are display beneath the graph.(PDF)Click here for additional data file.Figure S6Signal of association with sociability on chrX: 102-110 Mb. The y-axis shows the raw p-value and genes in the region are shown below the graph.(PDF)Click here for additional data file.Figure S7iS that pass the 5% FDR cut-off. Each region is color coded according to the breed in which the significant reduction is observed. The breed codes are shown in Map of extended segments in the dog genome of significantly reduced (PDF)Click here for additional data file.Figure S8iS and id statistics in top 10 longest iS regions. Variation in these statistics is shown independently in genomic segments encompassing each region.Examples of (PDF)Click here for additional data file.Table S1Total samples in dataset.(DOCX)Click here for additional data file.Table S2Breed bottleneck sizes used in simulations.(DOCX)Click here for additional data file.Table S3Phenotypes used in across-breed GWAs.(DOCX)Click here for additional data file.Table S4STF. SNPs with minor allele frequency >0.15 and STF>0.55. Nearby SNPs <500kb are clustered into regions.Single-SNP (DOCX)Click here for additional data file.Table S5(XLSX)Click here for additional data file.Table S6(XLS)Click here for additional data file.Table S7(XLS)Click here for additional data file.Table S8(DOCX)Click here for additional data file.Table S9(DOCX)Click here for additional data file.Table S10(DOCX)Click here for additional data file.Table S11(XLSX)Click here for additional data file.


Residual feed intake (RFI), a measure of feed efficiency, is the difference between observed feed intake and the expected feed requirement predicted from growth and maintenance. Pigs with low RFI have reduced feed costs without compromising their growth. Identification of genes or genetic markers associated with RFI will be useful for marker-assisted selection at an early age of animals with improved feed efficiency.GLP1R, CDKAL, SGMS1) with RFI and ADFI, of regions with energy homeostasis  and muscle growth related genes  with ADG, and of fat metabolism genes  with BF. Specifically, a very highly significantly associated QTL for LMA on SSC7 with skeletal myogenesis genes  was identified for subsequent fine mapping.Whole genome association studies (WGAS) for RFI, average daily feed intake (ADFI), average daily gain (ADG), back fat (BF) and loin muscle area (LMA) were performed on 1,400 pigs from the divergently selected ISU-RFI lines, using the Illumina PorcineSNP60 BeadChip. Various statistical methods were applied to find SNPs and genomic regions associated with the traits, including a Bayesian approach using GenSel software, and frequentist approaches such as allele frequency differences between lines, single SNP and haplotype analyses using PLINK software. Single SNP and haplotype analyses showed no significant associations (except for LMA) after genomic control and FDR. Bayesian analyses found at least 2 associations for each trait at a false positive probability of 0.5. At generation 8, the RFI selection lines mainly differed in allele frequencies for SNPs near (<0.05 Mb) genes that regulate insulin release and leptin functions. The Bayesian approach identified associations of genomic regions containing insulin release genes (e.g., Important genomic regions associated with RFI related traits were identified for future validation studies prior to their incorporation in marker-assisted selection programs. Feed is the biggest variable cost in most livestock production systems, including pig farms. Profitability of pork production depends on feed efficiency, which can be measured by the feed to gain ratio. One estimate showed that reducing the feed to gain ratio from 2.75 to 2.45 could save swine producers in the US $500 million dollars annually Whole genome association studies (WGAS) using high-density SNP genotypes are efficient tools to identify genes or genomic regions that explain variation in livestock traits. WGAS studies can be based on different statistical methods, including frequentist and Bayesian approaches Animal care guidelines were followed according to the Institutional Animal Care and Use Committee (IACUC) at ISU (IACUC permit number 11-1-4996-S). Data on a total of 1433 Yorkshire pigs were included in this study. Starting from split litters from purebred Yorkshires in generation 0, these animals belonged to generations 0 to 8 of two selection lines for RFI th-rib BF and LMA were evaluated with an Aloka ultrasound machine . Among 1433 animals genotyped, 1417 had ADFI data, 1418 had ADG data, 1412 had BF data, and 1410 animals had LMA and RFI phenotypes.Daily feed intake was collected on each animal starting around 90 days of age (on-test) using electronic Feed Intake Recording Equipment  feeders donated by PIC  and Newsham Choice Genetics . Pigs were put on-test in 2 to 3 groups based on age. Sixteen pigs of a similar age and weight were penned together in one of 12 pens, which contained either a FIRE feeder or similar feeder. Prior to generation 7, only 6 pens contained a FIRE feeder with the other 6 pens containing a FIRE-like feeder. Pigs were switched every two weeks between a pen with a FIRE feeder and one with a FIRE-like feeder. Starting with generation 7, all 12 pens contained FIRE feeders. Body weights were collected every 2 weeks during the testing period. Upon reaching approximately 115 kg, 10The ADG phenotype was calculated by regressing weights on days on test with ADG being equal to the slope of the regression line. Feed intake data were edited using methods developed by DNA was isolated from tail tissue using the Qiagen  DNeasy blood & tissue kit. Genotyping was completed with the Illumina  PorcineSNP60 BeadChip by GeneSeek, Inc.  with approved standard techniques outlined by the manufacturer. A total of 50,953 SNPs that met quality control criteria  were used.Population stratification was analyzed by identity-by-state (IBS) and multi dimensional scaling (MDS) clustering methods available in the PLINK software. Diagnostic tools to understand the stratification included Q-Q plots based on the associated P values for individual SNPs and haplotypes from the PLINK software.http://www.chem.agilent.com/cag/bsp/sig/downloads/pdf/mtc.pdf, Accessed 2012 October 20th).The allele frequency differences method used animals from generation 8 of the low (n = 78) and high (n = 79) RFI lines, as allele frequencies are expected to have been changing over generations as the divergent selection continued. Allele frequencies for each line were calculated for each SNP, and then used to compute differences between lines. Allele frequency differences were categorized into five groups based on minor allele frequencies (MAF)  of the SNP in generation zero. This step was to control for the effect of genetic drift, which is expected to be greater for SNP with low MAF. Later, the mean and standard deviations (SD) of allele frequencies were calculated for the SNP in each group, and were used to obtain Z scores for allele frequency differences in each group. The P-values corresponding to each Z score were log transformed and plotted using R software e is the vector of random residuals assumed to be normally distributed. The fixed effects used in this model were sex and pen by group. Fixed covariates were age when animals were put on-test, with a different slope for each combination of selection line and parity. As explained in detail in our earlier papers Two approaches were followed for association analyses. Primarily, a Bayesian approach based on genomic selection called Bayes B The pig genome includes 2,815 non-overlapping 1 Mb windows based on the 50,953 genotyped markers. Therefore, the expected percent of genetic variance accounted for by one window was 100%/2815, which was nearly 0.04%. Hence, 1 Mb windows that explained at least 0.2% of genetic variance, which is 5 times greater than expected (0.04%*5 = 0.2%), were considered to contain putative QTL. The markers from unassigned contigs and those completely unmapped to any contig or the genome were considered as unmapped markers and were not included in the results. The SNP in QTL regions were considered for further haplotype analyses to determine their association with the studied traits.Sus scrofa genome build 10.2 . Protein sequences of those genes, which were not annotated with a specific name in these regions in Sus scrofa genome build 10.2, were used in NCBI-BLAST to identify their names based on homologous sequences from other species with an E value <1e09. The gene functions were examined by using a literature search. Previously reported QTL in these regions were obtained from the GBrowse option in Animal QTLdb  on the basis of Sus scrofa genome build 10.2.Positional candidate genes were searched in the putative QTL regions and neighboring upstream and downstream 1 Mb regions based on Single SNP association analyses were performed using PLINK software For each trait, SNP from 1 Mb genomic window regions that explained at least 0.2% of genetic variance in the Bayes B approach were selected for construction of linkage disequilibrium (LD) blocks. The LD blocks were constructed using the method of confidence intervals and default parameters of the Haploview v4.1 software Raw data will be shared upon request.Population stratification with IBS clustering showed that all animals belonged to one cluster . HoweverKCNJ15 gene  is located within 0.05 Mb upstream of SNP ASGA0060074 (T/C) on SSC 13, which had the most significant difference in allele frequencies between the lines  (KCNJ15 gene encodes one of the potassium inward rectifying channels and is a type-2 diabetes risk gene and its up-regulation causes down regulation of glucose induced insulin release in mice and humans ELOVL2 gene (elongation of very long chain fatty acids 2) is located within 0.01 Mb upstream from SNP ASGA0030976 on SSC7 and is involved in enhancing the synthesis of triglycerides and deposition of lipid droplets in adipocytes TFAP2A (transcription factor AP-2 (activating enhancer binding protein 2) - alpha), a master regulator of other transcription factors in mouse liver GPX2 (glutathione peroxidase 2), an isozyme responsible for the majority of the glutathione dependent hydrogen peroxide reducing activity in the gastrointestinal tract , are located within 0.4 Mb downstream of SNP ASGA0030976 and within 0.2 Mb downstream of SNP ALGA0043495, respectively, on SSC7. This indicates that the RFI selection lines showed significant differences in allele frequencies near genes involved in metabolism in the liver and the gastrointestinal tract. Further functional studies with different tissues from RFI selection lines need to be conducted to confirm the role of these genes in the RFI phenotype.Some SNP on SSC13 (ASGA0060074) and SSC7  showed very significant  differences in allele frequencies between the high and low RFI lines at generation 8 , the lasele “T”)  and 4. TGNG4 (guanine nucleotide binding protein 4) gene in this region is a trimeric G protein with alpha, beta and gamma subunits. In its active state, the Gbetagamma subunit of GNG4 can activate potassium inward rectifying channels  in conjunction with M1 (Muscarinic 1 acetylcholine) receptors. The importance of potassium inward rectifying channels was emphasized in the allele frequency differences between high and low RFI lines in generation 8 for KCNJ15 and again here with results involving GNG4 for RFI. The second most significant 1 Mb SNP window (ALGA0040519-ASGA0032851), explaining 0.59% of the genetic variance for RFI with a PPA of 0.48, is on SSC7 at 39 Mb. This window also includes two potassium channels , one of which  is highly expressed in pancreas in humans GLP1R (glucagon-like peptide 1 receptor) in this window on SSC7 increases synthesis and release of insulin from the pancreas by activating the adenylyl cyclase pathway CDKAL1 (cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1), which is involved in the first phase insulin release through provision of ATP and potassium-ATP channel responsiveness WGAS were carried out with a larger population comprising both the lines and all genotyped individuals from generation 0 to generation 8. The Bayes B approach in the GenSel software revealed that the genomic heritability or proportion of phenotypic variance captured by genome wide markers was 0.52 for RFI, which is a little higher than the genomic heritabilities obtained for ADFI, ADG, BF and LMA and indicates that RFI is moderate to highly heritable . For theGLP1R gene  and haplotypes (P<0.05 after GC) in the 1 Mb SNP windows on SSC14 that explained 0.4% and 0.32% of genetic variance in the Bayes B approach, with a PPA of 0.28. However, the SNPs and haplotypes in the 1 Mb SNP window that contains the GNG4 gene on SSC14 did not show significant associations in the PLINK analyses  of individual SNP in the 1 Mb SNP window (ALGA0040519-ASGA0032851) at 39 Mb on SSC7 that contains the P1R gene . Similaranalyses . No SNP SGMS1 (spingomyelin synthase 1) on SSC14 and CBLN4 (cerebellin 4) on SSC 17, are important for brain functions  and PTEN (tumor-suppressor phosphatase and tensin homologue) are located within 1 Mb upstream and downstream, respectively, to the genomic window at 107 Mb on SSC 14. In beef cattle, an intronic SNP (rs29013727) in the PRKG1 gene was significantly (P<0.05) associated with dry matter intake (DMI) by whole genome single SNP association analyses PTEN gene enhanced insulin sensitivity by modulating the PI3K-AKT pathway BMP7 (bone morphogenic protein 7) and MC3R (melanocortin-3 receptor) genes are located within 1 Mb downstream of the genomic window (ALGA0096110-M1GA0022378) on SSC17 at 63 Mb. The BMP7 protein reduces food intake and increases energy expenditure through its leptin-independent mechanism such as central mTOR-p70S6 kinase pathway In the Bayes B approach, several 1 Mb SNP windows, including at 107 Mb (ASGA0065520-ALGA0080315) on SSC 14 and at 63 Mb (ALGA0096110-M1GA0022378) on SSC17, explained more than 0.8% of the genetic variance in ADFI, with a PPA greater than 0.5 . The gens RefSeq  and for KCNK1  gene. Interestingly, a 1 Mb SNP window region (ALGA0006599 - INRA0004954) close (<0.1 Mb upstream) to MC4R (melanocortin 4 receptor) at 177 MbMb on SSC1) explained 0.77% of the genetic variance, with a PPA of 0.30. The current Illumina PorcineSNP60 BeadChip does not have a SNP in the MC4R gene. Therefore, this nearby window might not capture the full effect of MC4R on ADFI, considering that MC4R is well known to be associated with feed intake in pigs GNG4 on SSC14, which was also associated with RFI, explained 0.68% of the genetic variance for ADFI and had a PPA of 0.38 (PAQR5 gene (progestin and adipoQ receptor family member V) and it explained 0.35% of genetic variance in the Bayesian analysis, with a PPA of 0.265. Although the PLINK and HAA analyses showed no significant associations after accounting for multiple testing, this modest association of PAQR5 containing genomic window with ADFI can be cautiously considered to be important due to the physiological function of PAQR5. This non-genomic progesterone membrane receptor facilitates the actions of progesterone on glucose homeostasis through the secretion of incretin (hormones enhancing insulin secretion) from enteroendocrine cells The third most significant 1 Mb SNP window (MARC0044077-ALGA0077929) on SSC14 at 61 Mb, explained 0.79% of the genetic variance, with a PPA of 0.469, and contains another potassium channel  of 0.38 . All the of 0.38 . MoreoveMC4R gene is near (<0.1 Mb downstream) this genomic window, as described above for ADFI. In pigs, SNPs in MC4R have not only been shown to be associated with ADFI but also with ADG in many studies OLFM4 (olfactomedin 4) gene is within 1 Mb upstream to the genomic window at 28 Mb on SSC11. The OLFM4 gene encodes a secreted glycoprotein that helps cell adhesion through lectins and cadherins on the cell surface, and some observations have linked it to innate immunity and gut microflora OLFM4 gene with childhood obesity in a genome-wide meta-analysis supported its possible role in the relationship between gut microbiome and obesity risk TGFBI (transforming growth factor beta induced protein ig-h3) is an extracellular matrix protein, which has a role in myofibril bundling and muscle fiber growth PGM1 (phosphoglucomutase 1) is one of the genes in the window (DIAS0000949-DRGA0006954) at 137 Mb on SSC6. Differential expression of PGM1 is associated with the glycolytic potential of muscle and its energy dependence on either fat or glucose, which is a determinant of lean meat production GPR81 (G-protein coupled receptor 81), whose expression is restricted to only adipose tissues, is located on SSC14 (INRA0043392-ALGA0076686) at 31 Mb. GPR81 facilitates the anti-lipolytic activity of lactate, a product of glycolysis during exercise and oxygen deficit MC4R (SSC1), OLFM4 (SSC11), PGM1 (SSC6) and GPR81 (SSC14)) and muscle growth ) might be associated with ADG in the RFI population.The most significant 1 Mb window (ALGA0006599-INRA0004954) in the Bayesian approach was on SSC1 at 177 Mb , which ehttp://www.genecards.org/cgi-bin/carddisp.pl?gene=ACOXL, Accessed on 2012 June 10th ). Additionally, the GPAT2 gene , an isoform that catalyzes the first step in triglyceride synthesis AEBP1 gene (adipocyte enhancer binding protein 1) is located in the 1 Mb window (ALGA0119800-ALGA0123577) at 55 Mb on SSC18 and is a transcriptional repressor that regulates the expression of fatty acid binding protein 4 (FABP4) by binding to a regulatory element called adipocyte enhancer 1 (AE1) in the proximal promoter FABP4 gene . Higher expression of FABP4 in adipose tissue has been found to be associated with leanness in humans The Bayesian approach identified 1 Mb windows on SSC7 (ALGA0044374-DRGA0008090) at 112 Mb, on SSC3 (MARC0085867-ALGA0018683) at 47 Mb, and on SSC18 (ALGA0119800-ALGA0123577) at 55 Mb to explain higher genetic variance (>0.8% with PPA>0.50) than other 1 Mb windows . WindowsKLHL31 (kelch like 31) gene explained 6.82% of the genetic variance for LMA, with a PPA of 0.93  at 31 Mb on SSC7 that contains the  of 0.93  based on of 0.93  and 9. H of 0.93 . The KLHIn general, the results from allele frequency difference method showed very significant differences between the high and low RFI lines at certain SNPs   and 4. TGLP1R, CDKAL, SGMS1) partly explained variation in RFI and ADFI. Other energy homeostasis genes  and muscle growth genes , were found to be associated with ADG. Genomic regions containing fat metabolism genes  and a gene for skeletal myogenesis (KLHL31) were associated with BF and LMA, respectively, in this population. Overall, the current study provided a list of genomic regions and candidate genes associated with RFI and its related traits for future validation studies in other populations prior to incorporation in marker assisted selection programs. Specifically, the study provided a very highly significantly associated QTL for LMA for fine mapping.The proportion of the phenotypic variance captured by markers across the genome was relatively high for RFI (0.52) compared to its related traits ADFI (0.47), ADG (0.34), and BF (0.49). Allele frequency differences at generation 8 identified that RFI selection lines likely differ in genes related to insulin and leptin regulation and in genes involved in metabolism in the liver and gastrointestinal tract. Although the posterior probabilities of association were modest, genomic selection based Bayesian methods were more powerful to detect associations by WGAS than frequentist in this data set. The WGAS revealed that genes involved in insulin release Click here for additional data file.Figure S2Q-Q plots based on unadjusted P-values and corrected P-values using genomic control for the haplotype association analyses performed by the PLINK software. The X and Yaxes represent expected and observed P-values, respectively. Population stratification represented by deviations of most of the unadjusted empirical P-values from expection was corrected by genomic control for all traits. The deviation of the two SNPs associated with LMA from the expectations after genomic control indicates that they are not likely to be false positives. RFI: Residual feed intake; ADFI: Average daily feed intake; ADG: Average daily gain; BF: Back fat; LMA: Loin muscle area.(TIFF)Click here for additional data file.Table S1Posterior means of variance components explained by genome wide markers for RFI and its related traits using RFI selection lines by a Bayesian approach.(DOCX)Click here for additional data file.Table S2Detailed information about candidate QTL regions associated with the residual feed intake (RFI) by 1 Mb SNP window, single SNP and haplotype analyses.(DOCX)Click here for additional data file.Table S3Detailed information about candidate QTL regions associated with the average daily feed intake (ADFI) by 1 Mb SNP window, single SNP and haplotype analyses.(DOCX)Click here for additional data file.Table S4Detailed information about candidate QTL regions associated with the average daily gain (ADG) by 1 Mb SNP window, single SNP and haplotype analyses.(DOCX)Click here for additional data file.Table S5Detailed information about candidate QTL regions associated with the back fat (BF) by 1 Mb SNP window, single SNP and haplotype analyses.(DOCX)Click here for additional data file.Table S6Detailed information about candidate QTL regions associated with the loin muscle area (LMA) by 1 Mb SNP window, single SNP and haplotype analyses.(DOCX)Click here for additional data file.


P ≤ α, belonging to a given SNP set The typical objective of Genome-wide association (GWA) studies is to identify single-nucleotide polymorphisms (SNPs) and corresponding genes with the strongest evidence of association (the 'most-significant SNPs/genes' approach). Borrowing ideas from micro-array data analysis, we propose a new method, named RS-SNP, for detecting sets of genes enriched in SNPs moderately associated to the phenotype. RS-SNP assesses whether the number of significant SNPs, with p-value We applied RS-SNP to the Crohn's disease (CD) data set collected by the Wellcome Trust Case Control Consortium (WTCCC) and compared the results with GENGEN, an approach recently proposed in literature. The enrichment analysis using RS-SNP and the set of pathways contained in the MSigDB C2 CP pathway collection highlighted 86 pathways rich in SNPs weakly associated to CD. Of these, 47 were also indicated to be significant by GENGEN. Similar results were obtained using the MSigDB C5 pathway collection. Many of the pathways found to be enriched by RS-SNP have a well-known connection to CD and often with inflammatory diseases.The proposed method is a valuable alternative to other techniques for enrichment analysis of SNP sets. It is well founded from a theoretical and statistical perspective. Moreover, the experimental comparison with GENGEN highlights that it is more robust with respect to false positive findings. The objective of genome-wide association studies (GWAS) is to identify genetic variants, a subset of single nucleotide polymorphisms (SNPs), associated with the onset and progression of complex disease phenotypes at a genome-wide scale . AlthougRecently a new trend is emerging in genetics and computational biology in which groups of genes are analyzed simultaneously for association with a phenotype or disease -5. The gThe same principle has been recently applied in GWAS for assessing association of sets of SNPs and phenotypes -12, and P ≤ α,) belonging to a given gene (SNP) set is statistically significant. The null model we specify assumes that the genotype and the phenotype are independent and the number of significant SNPs does not depend on the identity of the SNP set, but only on the size of the gene (SNP) set. We use non-parametric permutation procedures [In this paper we describe a new methodology that assesses the association of gene sets to a trait by including simultaneously strong association signals as well as SNPs moderately associated to the phenotype. The approach belongs to the general class of Random Set methods ,15 desigocedures  to test ocedures  has beenocedures , indicatBefore introducing a detailed description of the method used to perform SNP set analysis, it is important to clarify how a SNP set can be defined.i Vin a GWA study, with i = 1,..., n, is associated to a gene jG, where j indexes the total M genes, if the gene contains the SNP or is the closest gene to the SNP. In cases where a SNP is located within shared regions of two overlapping genes, it is mapped into both genes. SNPs that are a fixed number of kilo-bases (kb) away from any gene are not considered. In [The first step in defining a SNP set is mapping SNPs to genes. SNPs may fall within coding regions of genes, non-coding regions of genes, or in the inter-genic regions between genes. Each SNP ered. In  SNPs area priori biological knowledge, for example genes which are co-expressed in a particular cellular mechanism or function [The second step is mapping genes to pathways. The pathways are pre-defined lists of genes based on a function -19. We ufunction  as a souRandom Set (RS) scoring methods were primary introduced by Efron and Tibshirani  to studyThe main idea pointed out by RS methods is that any method for assessing gene sets should compare a given gene set score not only to scores from permutations of the sample labels, but also taking into account scores from sets formed by random selections of genes.In fact, any approach to gene set analysis begins with the computation of some enrichment score irani in  argue thIn order to better clarify RS positions let us consider a simplified statement of the gene set problem, proposed by Efron and Tibshirani but adapted to the SNP data framework.X indicate an n × ℓ matrix of genotypic observations, where n is the total number of SNPs and ℓ is the total number of samples, with the first ℓ1 columns of X representing healthy control samples and the remaining ℓ2 are case samples, ℓ1 + ℓ2 = ℓ. A statistic iD, i = 1,..., n is computed for each marker. Consider a single gene set m genes and the hypothesis that m D-values have large magnitude (positive or negative), with large to be defined. The basic idea underlying enrichment, as nicely stated by Subramanian [m D-values in Let ramanian , is thatPermutation Model. Let XS be the m × ℓ submatrix of X corresponding to ℓ columns of m-vectors (i.i.d.). The null density of ES, • Randomization Model. The null hypothesis m SNPs from the full set of n SNPs. In this case the null density of ES, say m rows of the data matrix X are drawn at random, giving randomized values • The randomization of the markers and the permutation of the labels can be combined into a method that is called "Restandardization". Restandardization can be thought as a method for correcting the permutation values of ES to take into account the overall null distribution of ES in the randomization model. The restandardized enrichment score (RES) used is defined as:μ†, σ†) are the mean and standard deviation of ES† and  are the corresponding quantities based on label permutations. Two nested permutation procedures are needed in this case which is computationally intensive. Fortunately, the RS method has an appealing feature: for certain choices of the summary statistic where  on genotype entries to compute association. The multiplicative risk model uses a χ2 test or Fisher's exact test on allelic entries to compute association. The additive risk model uses a Cochran-Armitage test for trend [The first step in the method is computing a correlation or association statistic c models : generalor trend  to associ Vto genes is discussed above. The relevant components of the method include:After computing the single SNP associations, RS-SNP computes the enrichment of these associations in a predefined gene set n = the number of genotyped SNPs;• d = the number of SNPs with p-value P less than or equal to a given threshold α;• m = the number of SNPs in • y = the number of SNPs belonging to P ≤ α.• y of SNPs associated to the phenotype and belonging to y requires the distribution of y under two null hypotheses, as previously stated [y greater than the observed ones when genotype and phenotype are independent random variables. In addition, a second cause of randomness for y comes from d of n SNPs have p-value P ≤ α and that y of them fall in a gene set m, how many SNPs fall in a set composed of m SNPs drawn randomly from the n SNPs available? To take into account this source of randomness, the probability of observing values of y greater than the ones observed in the actual experimental conditions has to be assessed under the hypothesis  with mean y under the two null hypotheses simultaneously, the following procedure is carried out.RS-SNP assesses whether the number y stated . The firπ = 1, ..., Π:(1) Permute the labels of the samples Π times. For each permutation (i) Compute the number of significant SNPs (ii) Compute the number of significant SNPs belonging to (iii) Compute the mean (iv) From the above (2) Compute the p-valueI is the indicator function.where Since several gene sets are considered in the analysis, the false-discovery rate (FDR) and the family-wise error rate (FWER) are computed as proposed by Wang et al.  in orderFDR, i.e. the fraction of expected false-positive findings, is calculated as:T is the total number of gene sets. The FWER is evaluated as the fraction of all permutations whose highest standardized enrichment score in all gene sets is higher than the RES(where The data set provided by WTCCC is composed of 2005 Crohn's Disease (CD) patients and 3004 healthy controls (HC). The control individuals came from two sources: 1504 individuals from the 1958 British Birth Cohort (58 C) and 1500 individuals selected from blood donors recruited as part of the WTCCC project (UK Blood Services (UKBS) controls). All 5009 samples were genotyped with the GeneChip 500 K Mapping Array set (Affymetrix chip), which comprises 500,568 SNPs. The quality control analysis was carried out following the details specified by WTCCC [P < 5.7 × 10-7 in the combined set of 2938 controls;• SNPs with Hardy-Weinberg exact p-value P < 5.7 × 10-7 for either a one or two-degree of freedom test of association between the two control groups;• SNPs with p-value MAF < 1%.• SNPs with a n = 414, 483 SNPs in autosomal chromosomes passed the quality control filters. More detailed information on WTCCC data set are in [In total, t are in .http://www.broad.mit.edu/gsea/msigdb/index.jsp:Two different collections of gene sets were used, that can be downloaded from the MSigDB website • MSigDB C2 CP collection, composed of pathways collected from various sources such as online databases, biomedical literature in PubMed, and knowledge of domain experts. In particular, the canonical pathways (CP) collection consists of 639 gene sets;http://www.geneontology.org/GO.evidence.shtml. Only associations with the following evidence codes are included in MSigDB gene sets: IDA IPI, IMP IGI, IEP ISS, TAS. Moreover, GO gene sets for very broad categories, such as Biological Process, have been omitted from MSigDB. GO gene sets with fewer than 10 genes have also been omitted. Gene sets with the same members have been resolved based on the GO tree structure: if a parent term has only one child term and their gene sets have the same members, the child gene set is omitted; if the gene sets of sibling terms have the same members, the sibling gene sets are omitted.• MSigDB C5 collection, composed of 1454 gene sets derived from Gene Ontology (GO). This collection is composed of 825 GO biological processes, 233 GO cellular components and 396 GO molecular functions. We have considered only those GO terms associated with a specific reference that describes the work or analysis upon which the association between a specific GO term and gene product is based. Each annotation includes an evidence code to indicate how the annotation to a particular term is supported The mapping of SNPs to genes has been carried out by using the Affymetrix annotation files Mapping250 K Nsp Annotations and Mapping250 K Sty Annotations, CSV format, version 26. In this study, SNPs were assigned to a given gene if they are within 5 kb from it.α = 0.01 was used and d = 6803 signals with p-value P ≤ α were found.The association of each SNP to CD was assessed by using the Cochran-Armitage trend test with 1 degree of freedom . A signiStatistical significance and adjustment for multiple hypothesis testing were determined by a permutation-based procedure with Π = 10,000 random permutations of the phenotypic status of the subjects. The FDR and FWER were also computed.P ≤ 0.05) enriched in SNPs weakly associated to the trait. The enrichment analysis, performed by GENGEN on C2 CP collection, highlighted 115 pathways  enriched in SNPs weakly associated to the trait. Intersecting the two lists of gene sets found to be significant by RS-SNP and GENGEN resulted in 47 pathways.The enrichment analysis highlighted 86 pathways  enriched in SNPs weakly associated to the trait. The enrichment analysis performed by GENGEN on MSigDB C5 collection highlighted 256 pathways  enriched in SNPs weakly associated to the trait. Intersecting the lists of gene sets resulted in 89 pathways.The enrichment analysis performed by RS-SNP on the MSigDB C5 collection highlighted 196 pathways  structure is preserved by the proposed method and does not alter the statistical significance of the identified pathways. This is due to the fact that the method uses random permutations of the phenotypic status of the subjects in the sample to assess the significance of the enrichment score. The column permutation procedure does not modify the genotypic profile of the subjects because it limits itself to assign randomly phenotypic states to subjects. The row permutation procedure adopted by the method has the objective of normalizing the enrichment score. This is realized comparing the actual number of markers associated to the phenotype in the gene set with the one obtained by chance. So, the LD structure of a given gene set remains the same under both null and alternative hypothesis. Finally note that the row permutations are only implicitly realized in our approach. This is due to the fact that the number of markers belonging to the gene set and associated to the trait has a hypergeometric distribution. For this reason the computational complexity of RS-SNP is proportional only to the number of column permutations required, that is equal to the inverse of the minimum observable P-value.pathways. Some of the highlighted pathways were also found to be associated to CD and other inflammatory diseases (rheumatoid arthritis and type I diabetes) by another pathway based method [From a biological point of view, significant associations were highlighted by RS-SNP analysis between CD and key inflammatory d method : HSA0463d method . We alsod method  and Wangd method . These fP ≤ 0.01) to CD and RS-SNP assigned a p-value P = 0.82 to this pathway. This type of result is recapitulated with several pathways in the analysis.A comparative study of RS-SNP and GENGEN suggests that gene set methods that use both types of null hypotheses may reduce false positives, GENGEN does not randomize with respect to gene set size. It is worth noting that GENGEN found a greater number of significant pathways, but several pathways of these pathways may be false positives. For example, the HSA04810 REGULATION OF ACTIN CYTOSKELETON pathway was found significant by GENGEN. This is a very large pathway composed of 166 genes and 2650 SNPs. Only 32 SNPs are weakly associated (p-value A new method for detecting association of SNP sets to a trait has been proposed. The approach, named RS-SNP, assesses whether the number of SNPs associated to the phenotype and belonging to a given SNP set is statistically significant. Strong signals as well as SNPs weakly associated to the trait are taken into account simultaneously for assessing association of a given SNP set. The proposed method, well founded from a theoretical perspective, is a valuable alternative to other techniques for enrichment analysis of SNP sets. When applied to the CD data set collected by the WTCCC, the method highlighted many relevant pathways which play a key role in CD as well as in other inflammatory diseases.The RS-SNP approach has been implemented in Matlab in the compute_rs.m file (see additional file To compute the association of each single SNP with the trait, the compute_association.m program is also enclosed in the RS-SNP package. It allows to perform sample and marker quality controls and then to test the association by choosing the more suitable genetic model.All the authors conceived the study. AD'A, SM and NA designed the algorithms and conduced the experiments and, together with OP, AL and VA, evaluated and compared the experimental results. All the authors contributed to the drafting of the article.Experimental results of RS-SNP and GENGEN on MSigDB C2 collection. Tables reporting the experimental results obtained by the proposed method, RS-SNP, and by GENGEN on the MSigDB C2 pathway collection.Click here for fileExperimental results of RS-SNP and GENGEN on MSigDB C5 collection. Tables reporting the experimental results obtained by the proposed method, RS-SNP, and by GENGEN on the MSigDB C5 pathway collection.Click here for fileRS-SNP package. The proposed RS-SNP software is contained in this compressed file, together with: • the help documentation, • example files with the SNP-gene mapping and gene-pathway mapping; • example of input files.Click here for file


Brassica napus L., AACC, 2n=38). The main goal of this project was to combine sequence capture with next generation sequencing (NGS) to discover single nucleotide polymorphisms (SNPs) in specific areas of the B. napus genome historically associated  to traits of agronomical and nutritional importance. A 2.1 million feature sequence capture platform was designed to interrogate DNA sequence variation across 47 specific genomic regions, representing 51.2 Mb of the Brassica A and C genomes, in ten diverse rapeseed genotypes. All ten genotypes were sequenced using the 454 Life Sciences chemistry and to assess the effect of increased sequence depth, two genotypes were also sequenced using Illumina HiSeq chemistry. As a result, 589,367 potentially useful SNPs were identified. Analysis of sequence coverage indicated a four-fold increased representation of target regions, with 57% of the filtered SNPs falling within these regions. Sixty percent of discovered SNPs corresponded to transitions while 40% were transversions. Interestingly, fifty eight percent of the SNPs were found in genic regions while 42% were found in intergenic regions. Further, a high percentage of genic SNPs was found in exons . Two different genotyping assays were used to validate the discovered SNPs. Validation rates ranged from 61.5% to 84% of tested SNPs, underpinning the effectiveness of this SNP discovery approach. Most importantly, the discovered SNPs were associated with agronomically important regions of the B. napus genome generating a novel data resource for research and breeding this crop species.Targeted genomic selection methodologies, or sequence capture, allow for DNA enrichment and large-scale resequencing and characterization of natural genetic variation in species with complex genomes, such as rapeseed canola ( Brassica napus L. var oleifera Delile  known as rape, oilseed rape or rapeseed belongs to one of the three cultivated allotetraploid Brassica crops that form U’s triangle  with the B. rapa reference sequence [B. napus genome; (ii) using a more representative genomic reference, (iii) detecting more than 500 thousand SNPs among 10 contrasting genotypes, including genic  and intergenic  SNPs with an average SNP assay validation rate of over 80%. The development of high densities of robust molecular markers (e.g. SNPs) will be very powerful towards predicting the best individuals in plant breeding schemes through the use of genomic selection [B. napus crops (spring and winter types) will help to elucidate the genetic background of complex traits and broaden gene pools as a basis for more successful breeding.A previous study on P assays . Signifisequence . Our worelection . IlluminFigure S1Minimally filtered SNP types and their classification according to genomic regions. Transition and transversion SNP types were classified to show the proportion of SNPs annotated to each of three genomic regions . In the minimally filtered SNP set, only positions with more than one variant call were removed.(TIFF)(TIF)Click here for additional data file.Figure S2Minimally filtered SNP counts characterization summary. Total SNP counts were classified by genomic location (Intergenic vs. Genic) and further separated into transitions and transversions. Genic SNPs are also described in terms of their location within the gene (CDS vs. intron). In the minimally filtered SNP set, only positions with more than one variant call were removed. CDS: coding sequence. (TIFF)(TIF)Click here for additional data file.Figure S3B. napus.Linkage mapping of polymorphic SNP markers discovered using sequence capture in  Mapped SNP markers are illustrated in red, each horizontal bar represents a molecular marker. Linkage analysis and map position was conducted separately for each population using JoinMap® v4.0. Linked loci were grouped using a LOD threshold of 5-8 and a maximum recombination fraction of 0.4. Grouped SNP marker loci were arranged into a scoring matrix using MSExcel. Distances were assigned in centiMorgans (cM) using the Kosambi mapping function. Scoring matrixes are available upon request.(TIFF)(TIF)Click here for additional data file.Table S1Genotype descriptions, fragment size estimation of captured DNA and final sample concentrations.(DOC).(DOCX)Click here for additional data file.Table S2Brassica A Genome Sequence Capture Coordinates.(XLS).(XLSX)Click here for additional data file.Table S3Genotypes tested in KasPar Assay.(XLS).(XLSX)Click here for additional data file.Table S4List of 100 SNP and oligonucleotides used in KasPar validation assays.(XLS).(XLSX)Click here for additional data file.


Fusarium head blight resistance. Allele frequency differentiation between the experimental population and the base population identified three primary genomic regions putatively subject to selection for resistance. All three genomic regions have been previously identified by quantitative trait locus (QTL) and association mapping. Based on the degree of identity-by-state relative to donor parents, putative donors of resistance alleles were also identified. The successful application of comparative population genetic approaches in this barley breeding experiment suggests that the approach could be applied to other breeding populations that have undergone defined breeding and selection histories, with the potential to provide valuable information for genetic improvement.Introgression of novel genetic variation into breeding populations is frequently required to facilitate response to new abiotic or biotic pressure. This is particularly true for the introduction of host pathogen resistance in plant breeding. However, the number and genomic location of loci contributed by donor parents are often unknown, complicating efforts to recover desired agronomic phenotypes. We examined allele frequency differentiation in an experimental barley breeding population subject to introgression and subsequent selection for  Drosophila populations selected for longevity . The prevalence and spread of this pathogen increased in the Midwestern United States in the early 1990s , plant height, stem breakage, lodging, and plump kernels. For the Reopened population, F4:5 lines were planted in single-row plots at two disease nurseries with two replicates and evaluated for FHB disease severity. For lines selected with low levels of disease, harvested grain from each plot was analyzed for DON produced by the pathogen. Further selection was imposed for low DON concentration in the grain. In both the Closed and Reopened populations, ∼10% selection was imposed. The selected lines were advanced to preliminary yield trials in the third summer after the cross. More detailed information on the experimental population can be found in The typical development of breeding lines begins with a cross between two parents in the fall followed by self-pollination of the F4:6 seedling from each breeding line and from a bulk of five or more seedlings for each of the exotic donor lines using the CTAB and chloroform method (http://triticeaetoolbox.org/), an updated version of The Hordeum Toolbox  based on the contextual sequence used for the Illumina SNP assay design (2r (correlation coefficient)   for all ficient) , and theficient)  was usedkage ape , includikage ape  within eTable S1). On each linkage group, we jointly considered all IBS segments based on each donor line, thus there were many overlapping IBS segments along the linkage group for each donor line. The number of IBS segments at each SNP was determined by summing over the number of lines in the Reopened panel that included an IBS segment for each SNP.Segments of IBS were identified using GERMLINE (File S1). An initial set of simulations was focused on differentiation between the Ancestral population (donor lines) and the MN breeding population, which forms the basis of the Closed panel . The Ancestral and Closed panels were each represented by 120 chromosomes. The Ancestral panel includes the 11 donor parents genotyped in this study supplemented with 109 lines chosen at random from parents for the barley nested association mapping (NAM) population to balance the panel size to the same as the Closed and Reopened panels and to better-represent the donor population. The NAM parents are a randomly chosen sample of USDA National Small Grains Collection and therefore represent the diverse panel of cultivated barley lines serving as a source for the donor population.To determine if patterns of allele frequency differentiation between the Closed and Reopened patterns could occur in the absence of selection, we performed coalescent simulation implemented in the program ms  . To simulate ascertainment bias, we used a custom Python script that conditions on a discovery panel of fixed size and a minimum minor allele frequency for samples in the discovery panel. The first n chromosomes in the simulation were designated as the discovery panel, and sites that had a minor allele frequency below a user-defined threshold in the discovery panel were removed from the simulation dataset. If migration matrices were specified, then the script assumed that the first population listed was the population in which discovery was performed. Thus, SFS in simulation reflects what is observed in the empirical data.The folded site frequency spectrum (SFS) for the genotyped SNPs is skewed toward common variants  calculated using tools from the libsequence library . Time T1 was set to 8000 generations before present, when barley began to be disseminated from Western Asia . The Reopened panel started ∼15 generations ago (T3). The relative size of the Closed panel is a proportion of the donor population U. We refined this uniform interval for the relative size based on initial simulations.The mutation parameter θ =  library  for each2) and the relative size of the Closed panel, we performed one million simulations and compared the simulated and observed values of pairwise diversity (PS and PO) in the Closed panel. Using rejection sampling, we retained simulations if |PS − PO|/PO < ε. After preliminary survey, we chose the acceptance rate of ε = 0.2 and confirmed that any choice of ε did not affect the result (not shown). The end of the bottleneck (T2) and the relative size were determined by averaging these simulations.To determine the likely time of the end of the bottleneck . The migration rate is based on 0m4N. We retained simulations if |PS − PO|/PO < ε. The most likely migration rate was determined by averaging these simulations. Using the most likely migration rate from the donor lines to the Reopened panel, we simulated the complete population history and calculated FST between the simulated Closed and Reopened panels using 100,000 simulations.To determine the expected distribution for Quality control resulted in a data set with 990 SNPs in 237 lines. This included the elimination of 546 SNPs, 465 of which were monomorphic in both panels. We also eliminated two samples in the Closed panel and one sample in the Reopened panel because of SNP genotype quality. The average observed heterozygosity across the complete data set was 0.43%. There were 54 SNPs private to the Closed panel compared with 482 SNPs private to the Reopened panel . There wFST averaged 0.057 between the Closed and Reopened panels. Three genomic regions with FST values exceeding the 97.5th percentile (FST ≥ 0.315) were identified on linkage groups 2H, 4H, and 6H  were above the 97.5th percentile threshold . The three primary regions on 2H, 4H, and 6H with the largest difference in MAF between the Closed and Reopened panels corresponded to the three high FST blocks on these linkage groups. In the Closed panel, all SNPs in the high FST block on 4H were monomorphic, but they were polymorphic in the Reopened panel .Comparison of minor allele frequency (MAF) demonstrated that, genome-wide, there were far fewer SNPs segregating in the Closed than the Reopened panels  tended to occur at higher frequencies in the Reopened panel than in the Closed panel. In the high  11 SNPs . The reged panel .Figure S1). There were two primary haplotypes for these 34 SNPs (data not shown).One feature of the MAF that could not be explained by donor introgression is a region involving 34 SNPs at 64–81 cM on linkage group 3H with ∼0.3 MAF in the Closed panel  .A discovery panel of eight chromosomes with a minimum minor allele count of three reflected the design parameters of the barley OPAs (Table S2). In our simulations, there was convergence in posterior density of relative size of the Closed panel, which was ∼1% of the Ancestral population . There was a wide interval for the most likely timing of the end of the bottleneck, which varied across the range of prior values. When we plotted the density of these two parameters together, the most likely timing of the end of the bottleneck corresponded to the highest likelihood of the relative size, which was at 0.0015, ∼900 generations ago . The relative size of the bottleneck and duration of the bottleneck were confounded as suggested by a previous study . In the Closed panel, the median of simulated pairwise diversity was 0.007 and pairwise diversity was zero in 35% of simulations. The pairwise diversity in both the Ancestral panel and the Closed panel provided a close fit to the observed data . In these simulations, in which demography alone impacted allele frequency , the 97.5th percentile of FST was 0.09 between the simulated Closed and Reopened panel compared with FST of 0.315 in the empirical data . The highest degree of IBS between the donor lines and their progeny in the Reopened panel on 2H and 6H overlapped the high FST regions . However, the highest IBS region on 4H did not overlap with the high FST region, but rather it occurred ∼40 cM away . The degree of IBS declines dramatically at both ends of the chromosome, where SNP number limits the potential to identify long segments that were IBS. Donor line Zhedar1 contributed most to the Reopened panel in the high FST region on 2H . We summed the number of IBS segments at each SNP across the genome and across the three major high FST regions. We found little correlation between the timing of introgression of donor lines and the number of IBS segments genome-wide (r2 = 0.20) as well as in the high FST regions (2r = 0.10).Individual donors contributed to an average of 12 progeny in the Reopened panel. Zhedar1 contributed to the largest number of progeny, 49, whereas Comp351 and BT463 contributed to only a single individual  among all pairs of SNPs was higher in the Closed panel (0.051) than in the Reopened panel (0.028). LD between adjacent SNPs was also higher in the Closed panel (0.653) compared with the Reopened panel (0.490) .Average genome-wide LD (r2 of adjacent SNPs (0.58). There were 28 blocks in the Closed panel covering a total of 65.13 cM and 45 blocks in the Reopened panel covering a total of 80.63 cM. The average block size in the Closed panel was 2.33 cM, which was greater than that in the Reopened panel (1.79 cM). In the Closed panel, 15.9% of SNPs were in LD blocks, whereas 21.8% of SNPs were in blocks in the Reopened panel.Blocks of LD were defined as sets of at least three adjacent SNPs that showed greater LD than the median FST blocks on linkage groups 2H and 4H were also in high LD  with each other in the Reopened panel (2r = 0.477 vs. 0.613) and the SNPs were monomorphic in the linkage group 4H block in the Closed panel (FST region on 6H was similar in the Closed and Reopened panels (2r = 0.438) .All the SNPs in the two high ed panel . The LD ed panel . The LD FST genomic regions in our comparison (Table S3). The high FST regions on 2H, 4H, and 6H overlapped with the relative genetic map positions of markers associated with DON concentration and FHB resistance in previous QTL mapping studies (We identified markers from previous studies that occur in genetic map locations adjacent to high http://bioinf.hutton.ac.uk/iselect/app/) that fall within annotated genes in the high FST blocks on 2H, 4H, and 6H are listed in Table S4. The genomic regions identified are ∼5 cM (4H) and ∼10 cM (2H and 6H) and include a minimum of 40 (on 4H) or 100 genes (on 2H and 6H).In addition to SNPs surveyed here (BOPA1), three additional sets of SNPs have been mapped in barley genetic mapping populations  shows clusters of high FST SNPs on the same linkage group having similar MAF, which suggests these SNPs co-occur, potentially because of selection favoring a relatively small number of haplotypes. Interestingly, all SNPs in the high FST block on 2H are polymorphic within the Closed panel, but SNPs in the high FST block on 4H are monomorphic in the Closed panel  population. Although there are QTL associated with heading date and plant height that are coincident with FHB or DON in the high FST regions . The IBS analysis identified a narrower region, because the number of IBS segments at SNP 11_21459 on 2H was much higher than that at both adjacent SNPs. However, the excess IBS region in the high FST region on 4H did not have the highest number of IBS segment on this linkage group .Along with assaying allele frequency changes, we used IBS analysis to identify regions that were putatively subject to selection. The high IBS regions on 2H and 6H were within or adjacent to the high ge group , which wFST regions were not among the most recently used donors  differed dramatically between these two panels . The introduction of allelic diversity to the Reopened panel resulted in a larger number of polymorphic SNPs and a reduction in both average genome-wide and average adjacent SNP LD within this population. Although average LD was lower in the Reopened panel, the LD was higher in the two high FST regions on linkage groups 2H and 4H in the Reopened panel, as can occur in genomic regions subject to recent strong selection (The distribution and pattern of LD (election  (Figure election . TherefoIntroducing genetic diversity and shifting selection pressure changes the distribution and pattern of LD among markers and, therefore, between markers and QTL. This suggests that it will be important to use panels of germplasm that are contemporary and relevant to current breeding goals for association analysis and generating prediction models for genomic selection. As genetic distance between two breeding populations increases, the correlation between closely linked markers in the two populations decreases. Based on this, We have used population genetic approaches to identify genomic regions putatively subject to selection subsequent to introgression in a barley breeding experiment. The progenitor-derivative relationship between the two populations in our study is among the simplest possible scenarios for detecting the effects of recent strong selection . Other ca priori identification of phenotypes to be measured and can benefit dramatically from increased SNP density (There are several advantages to the comparative approach as applied here. The first is the relative speed and minimal expense associated with identification of putatively trait-associated loci. This experiment was conducted within the confines of a breeding program, obviating the need for multiple QTL mapping populations to identify sources of resistance. The program developed two high-yielding malting cultivars from material represented in this population. Rasmusson is representative of the Closed population ( density . We noteThe fact that we identified signals of selection for FHB resistance that are substantiated by previous mapping efforts in barley suggests that this approach may also be effective when applied to crop species without previous information from QTL mapping. Application of inexpensive genotyping to any breeding population that has a defined history of breeding and selection should provide valuable insight into the genetic architecture of the traits under selection and guidance for marker-based breeding efforts.