The purpose of this study was to determine if the set of genomic regions inferred as accounting for the majority of genetic variation in quantitative traits remain stable over multiple generations of selection. The data set contained phenotypes for five generations of broiler chicken for body weight, breast meat, and leg score. The population consisted of 294,632 animals over five generations and also included genotypes of 41,036 single nucleotide polymorphism (SNP) for 4,866 animals, after quality control. The SNP effects were calculated by a GWAS type analysis using single step genomic BLUP approach for generations 1–3, 2–4, 3–5, and 1–5. Variances were calculated for windows of 20 SNP. The top ten windows for each trait that explained the largest fraction of the genetic variance across generations were examined. Across generations, the top 10 windows explained more than 0.5% but less than 1% of the total variance. Also, the pattern of the windows was not consistent across generations. The windows that explained the greatest variance changed greatly among the combinations of generations, with a few exceptions. In many cases, a window identified as top for one combination, explained less than 0.1% for the other combinations. We conclude that identification of top SNP windows for a population may have little predictive power for genetic selection in the following generations for the traits here evaluated. Past studies of genomics in livestock usually focused either on best estimation of breeding values  or on idGenetic selection using major SNP is successful if they explain a sizeable portion of the genetic variation and if their effects change little over time. Earlier simulation studies showed that linkage disequilibrium (LD) identified in one generation decays very slowly over generations . HoweverIdentification of an individual SNP linked to a QTL is difficult because of the high collinearity of SNPs. SNPs may be in LD with a QTL so windows of consecutive SNPs can capture the effect of a QTL better than a single SNP . Also, SThere is a shortage in studies searching for stability of marker effects across generations in production traits for broiler chicken. Despite this, in a layer population, One common issue on genome association studies is the large number of false positive gene discovery. Information from the chicken QTL database  shows a The purpose of this study was to identify SNP windows that explain major portions of genetic variance and see if those values are preserved during a course of selection for growth in chicken.Table 1. The number of observations, means, and SD for all the traits are shown in Table 2.The data was provided by Cobb-Vantress Inc. . A total of 294,632 phenotypes from a pure line of broiler chicken collected across five consecutive generations  were used in this study. This was the sire line, selected mainly for growth rate, meat yield, feed conversion and livability, and secondarily for reproduction traits. The numerator relationship matrix included 297,017 animals. For the first two generations, animals were selected for genotyping based on body weight and conformation scores; leg defects were very unlikely. The remaining animals (from G3 to G5) were randomly selected for genotyping. The number of animals in each generation are shown in Initially, genotype information from 4,922 animals in a chip with 57,635 SNPs was available . The gen22A), which took into account the effect of non-random genotyping caused by selection  was estimated as :ith SNP marker effect, pi is the observed allele frequency for the second allele of the ith marker in the current population.In which: n adjacent SNPs were used; the variances attributed to them were calculated by summing the variance of the next n SNPs, for each SNP. Next, the combination that contained the highest values for exclusive windows was chosen to avoid double counting. It could happen that some windows had less than n SNPs if they were between two windows explaining more variance or in a window at the end or beginning of a chromosome. However, those smaller windows do not explain significant part of the variance.When windows of The analyses were performed in four scenarios: complete data set; only genotypes and phenotypes from generations G1, G2, and G3; generations G2, G3, and G4; and from generations G3, G4, and G5. Numerator relationship matrix was complete in all scenarios. All ssGWAS computations were performed using the BLUPF90 family programs  modifiedThe choice for ssGWAS was due to its ability to support phenotypes from ungenotyped animals directly, to handle multiple trait models, and to avoid spurious solutions on SNP effects due to sampling. Sampling in Bayesian alphabet family models is strongly dependent on priors and may produce spurious SNP estimates . CompariFigure 1). Experiments with different SNP window sizes exhibited large noise with small sizes and absence of peaks with large sizes. Subsequently, windows of 20 SNP were chosen as a reasonable size.Preliminary results showed small individual SNP variances for all three traits, with just a few SNPs explaining more than 0.5% of the variance of the trait ; also, the 10 largest points were marked with a red vertical line. It is possible to see that all those traits are mainly affected by many regions with small effects, with few regions that explain more variance. These regions tended to change across the generations, but some of them retain a consistent value among the top 10 regions in all the scenarios, even though, the variance explained by those windows did not contribute significantly to the genetic variability of the trait.The variance explained by each SNP window is shown in Figure 2). Although these top three regions have been described before, the percentage of variance explained was small; only one region was above 2.5% and all the others were below 1.6%. The total variance explained by the top 10 windows summed up to 7.63%.For body weight, there were three regions that persisted among the top 10 in all the scenarios . The window with larger effect for this trait explained 1.14% of the total variance, in the subset containing generations 3–5. The other windows explained at most 1%. The total variance explained by the top 10 windows was 6.26%.For breast meat, two regions were consistent among the scenarios , the variance explained by this windows was 1.12% in the subset containing generations 3–5. All the other windows explained less than 1% of the genetic variance for this trait. The total variance explained by the sum of the top 10 windows was 6.01%.For leg score, the value of just one region was constant across the analysis in chromosome 7  is equal to 2NeL/log(4NeL), where eN is the effective population size and L is the length of chromosome in Morgans. Assuming eN = 50 (lower range showed in L= 39, q = 435. Subsequently there are >100 SNP per one chromosome segment, if we apply the formula to this dataset. This causes collinearity and possibly a high variance inflation factor for the estimators, amplified by changes to the effective population size during the selection. While 435 segments suggest that 435 SNP could explain nearly all variation, this is not so as the boundary between segments is fluid.Large changes in the variance explained by SNP windows could be indirectly due to small effective population size and subsequent low number of independent chromosome segments. According to The small values for SNP effect and percentage of variance explained that were obtained in this study can be related to the findings on The short-term decay in accuracy depends more on the decrease of genomic relationships captured by markers rather than on LD . TherefoExcept for a few regions, the variation explained by the top SNP windows changes over generations. Therefore, even if SNP windows with large variance are detected in a particular data set, their usefulness for genomic selection over many generations is limited. The variance explained by an individual window is not enough to lead selection decisions based on the top regions for the studied traits.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer’s rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved.Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs.The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits. In tropical regions, Zebu cattle are the main breed used for beef production since they are better adapted to the climate and more resistant to parasites than taurine breeds. One factor that increases production costs is the late onset of reproduction in Zebu females. According to Brummati et al. , for beeIn Brazil, beef cattle breeding programs have put emphasis mainly on traits such as growth and weight gain and little attention has been paid to reproductive traits, in spite of the considerable influence that these traits have on the productivity and reproductive efficiency of herds –4. AccorHeifer rebreeding (HR) refers to the calving success of cows that have already calved once. The success rate of rebreeding after first calving is a major issue in beef cattle farming. Several studies have reported significant reductions in calving rates between the first and second services –8. MercaSince age at first calving (AFC) is easily measured and is a good indicator of female sexual precocity, this trait has been included as a selection objective in breeding programs . HoweverFemale reproductive traits are measured only on females and late in life, and generally have low heritability. As a consequence, a large number of individuals need to be analyzed for sufficiently accurate genetic evaluations. The advantage of genomic selection to estimate such reproductive traits is that, unlike traditional selection based on pedigree information and phenotypes only, it allows to select animals accurately without their own phenotypic measurements or those of their relatives and thus, genetic gains are increased compared to that obtained with traditional evaluation methods .Usually, genome-wide association studies (GWAS) of traits of economic interest using high-density single nucleotide polymorphism (SNP) panels aim at identifying regions that can explain the inheritance of the traits . GWAS reGWAS were first performed using the least square method, by applying Bonferroni correction to make inferences about the significance of individual SNPs. According to Peters , the maiThe objective of this study was to perform a GWAS using a Bayesian approach to identify candidate genes that influence reproductive traits. Candidate genes are the target for future fine-mapping studies to search for causal mutations. Causal mutations that explain more than 1 % of the phenotypic variance are useful for inclusion in commercial low-density SNP arrays because since they are causal and are not in linkage disequilibrium (LD) with nearby SNPs, they are more cost-effective and informative, contribute information for the estimation of breeding values and it is not necessary to revalidate their SNP effects at each generation.Bos indicus reaches puberty later in life than Bos taurus. In order to detect sexually precocious heifers, Agropecuária Jacarezinho Ltda. performs two breeding seasons. During the early breeding season, which occurs between February and April and lasts approximately 60 days, heifers are exposed to bulls at an early age (14 to 16 months). All heifers are exposed to bulls, irrespective of weight and body condition. Artificial insemination, controlled breeding or multiple-sire breeding, with a bull:cow ratio of 1:30, is used. Pregnancy is confirmed about 60 days after the end of the anticipated breeding season. Heifers that did not conceive during their first breeding season are exposed again to bulls at 2 years of age. The criteria for culling of females are: failure to conceive before 2 years of age, inability to conceive in any subsequent year, and low progeny performance. A small percentage of females are culled due to health problems. Close to the expected calving date, females are moved to calving paddocks. After calving, calves are divided according to sex and age and are transferred to another pasture together with their mothers where they remain until weaning. The calves are weaned at 7 months and grouped together until yearling age (18 months).We used data from Nellore females that belonged to the company Agropecuária Jacarezinho Ltda. Heifers were born either in Valparaiso-SP or in Cotegipe-BA, Brazil. Heifer rebreeding (HR) is a binary trait and was defined by attributing a value of 2 (success) or 1 (failure) to females that calved or not, respectively, given that they had calved once. Age at first calving, measured in days, was obtained as the difference between the date of first calving and the date of birth of the heifer.The contemporary group (CG) for HR was defined by farm, year and season of birth of the cow, and calf sex. Contemporary groups without variability in HR, i.e., groups in which all animals showed the same response category (1 or 2) were eliminated. For AFC, the CG was formed by farm, year and season of birth, and management group at birth, weaning and yearling. Phenotypic data outside the intervals given by the mean of the CG ± 3 standard deviations were also excluded. Fifteen CG were formed for HR with an average of 92 animals in each group, and 17 CG were formed for AFC with an average of 89 animals in each group. Age at first calving varied between 748 and 1253 days, with a mean of 1049 ± 141.3 days. The percentage of success in HR was equal to 72,42 % ± 19,21.−5 and 26 341 SNPs that were highly correlated (r2 > 0.995) with other SNPs from the same window that contained 100 consecutive SNPs. Samples with a call rate less than 0.90 were also excluded from the analysis. The QC process was repeated until no further SNP or sample was excluded which resulted in a final dataset of 1853 heifer and 305 348 SNPs.Data from 2056 females born between 2007 and 2009, which were genotyped with the Illumina Bovine HD assay , were used. The quality control (QC) of genotypes was performed iteratively according to the following criteria: we excluded 22 851 SNPs in non-autosomal regions, 32 856 SNPs with a gene call score less than 0.70, 18 982 SNPs with a call rate less than 0.98, 362 148 SNPs with a minor allele frequency (MAF) less than 0.02, 9466 SNPs with a p-value for the Hardy-Weinberg equilibrium test less than 10a priori, with νg degrees of freedom and a scale parameter Sg2. Thus, the effect of an SNP with probability (1-π) is a univariate Student’s t  distribution. In this study, νg was set to 4.2 and Sg2 was calculated based on additive genetic variance according to Habier et al. [SNP effects were estimated using the BAYESCπ method  in whichr et al. .http://snp.tolouse.inra.fr/~alegarra). A total of 300 000 MCMC iterations were used, with a burn-in period of 30 000 cycles and the results were saved every 30 cycles. Convergence was assessed by visual inspection of trace plots of the posterior density of genetic and residual variances.The analyses were performed using the GS3 software developed by Legarra et al.  consisting of the genotypes of s SNPs for each animal n; g is a random vector of SNP effects; and e is a vector of residual effects. A systematic effect of CG was assumed for each trait. For HR, the linear effect of the rest period (number of postpartum days until the beginning of the second breeding season) was included as a covariate. An inverted chi-square distribution with 4.2° of freedom was assumed for the a priori distribution of residual variance.The dependent variables used in the analysis were the phenotypes observed for HR and AFC. All females with available genotypes and phenotypes were used. The following model was applied:p is the posterior probability of an SNP to be assigned a non-zero effect and π is the a priori probability of an SNP to be included in the analysis. The following scale adapted by Kass and Raftery [if BF are between 3 and 20, they provide suggestive evidence;if BF are between 20 and 150, they provide strong evidence;and if BF are greater than 150, they provide very strong evidence.It is not possible to obtain p-values for the SNP effects using Bayesian approaches. An alternative to p-values is the Bayes factor , which w Raftery  and appl Raftery  and Vida Raftery  was usedWhen BF are used, there is no need for Bonferroni correction because all the SNPs are introduced simultaneously in the analysis, and their estimates are already penalized by their prior information. In this study, SNPs with a BF greater than 150 were considered significant.http://www.ncbi.nlm.nih.gov/projects/mapview/map_search.cgi?taxid=9913&build=6) was used to determine the location of the significant SNPs on the genome. Genes that contained significant SNPs were listed. For SNPs that were not located within genes, the closest gene (either on the 5’ or 3’ end) was recorded with the distance between gene and SNP.The Map Viewer tool of the bovine genome  RNAs. A pseudogene is a nucleotide sequence that is similar to a normal gene, but is not expressed. Miscellaneous nc RNAs are small noncoding RNAs sequences that do not carry information for producing proteins but can have various important functions in the cell.Bos taurus breeds, a distance of less than 30 kb was required for genomic prediction/association. However, in our study, although some genes were more than 30 kb away from a significant SNP, they were retained because they were the closest annotated genes.Genes that contained significant SNPs were listed (distance = 0); if the SNPs were not located within genes, the closest gene (either on the 5’ or 3’ end) was identified with the distance between SNP and gene. Espigolan et al.  reportedLOC781274, NEUROD6, GPR98, GALC, MIR124A-2, MAPK8IP3, RBFOX1, and ODZ3); in the formation and physiology of the female reproductive system ; in lipid metabolism (ELOVOL5) and in bone growth (DDR2 and COL24A1); genes that act as olfactory receptors (LOC514434) and genes involved in basal metabolism . The last group of genes plays a role in different metabolic pathways, including cell-cell signaling, protein synthesis and transport, oxygen transport, cell proliferation and survival, transcription and metabolism of nucleotides and histones, membrane transport, and the formation of the cell membrane and its receptors, among others.The identified genes can be grouped according to metabolic function, including groups of genes that are involved in the formation and physiology of the central nervous system , which is larger than the region that we identified here. However, the SNPs that we found significant for AFC and HR are within the region reported in [Our results on the chromosome location of the identified SNPs are similar to those of previous investigations on the association between SNPs and reproductive traits. Sahana et al.  detectedorted in .Schulman et al.  identifiPausch et al.  describeHawken et al.  reportedBos taurus and Bos indicus, since Bos indicus females reach puberty later than Bos taurus females. Evidence from Hawken et al. [Bos taurus [Although several studies have reported significant SNPs associated with fertility traits in cattle on BTA14 , 29–31, n et al. , who repTaken together, the 42 SNPs significant for HR and the 19 SNPs significant for AFC explained 11.35 % (Table The use of Bayes factors to determine the significance of SNPs allowed us to identify two sets of significant SNPs, i.e., 42 for HR and 19 for AFC that explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information about HR and AFC that will contribute to elucidate which genes affect these traits. Our results led us to suggest a list of candidate genes for reproductive traits in beef cattle.


Fragaria virginiana and F. chiloensis (FVC 11) was generated and evaluated for a set of phenotypic traits. A total of 106 individuals in the FVC 11 were genotyped for 29,251 single nucleotide polymorphisms (SNPs) utilizing a commercially available, genome-wide scanning platform (Affymetrix Axiom IStraw90TW). The marker trait associations were deduced using TASSEL software. The FVC 11 population segregating for daughters per mother, inflorescence number, inflorescence height, crown production, flower number, fruit size, yield, internal color, soluble solids, fruit firmness, and plant vigor. Coefficients of variations ranged from 10% for fruit firmness to 68% for daughters per mother, indicating an underlying quantitative inheritance for each trait. A total of 2,474 SNPs were found to be polymorphic in FVC 11 and strong marker trait associations were observed for vigor, daughters per mother, yield and fruit weight. These data indicate that FVC 11 can be used as a reference population for quantitative trait loci detection and subsequent MAB across different breeding programs and geographical locations.Much of the cost associated with marker discovery for marker assisted breeding (MAB) can be eliminated if a diverse, segregating population is generated, genotyped, and made available to the global breeding community. Herein, we present an example of a hybrid, wild-derived family of the octoploid strawberry that can be used by other breeding programs to economically find and tag useful genes for MAB. A pseudo test cross population between two wild species of  The only costs incurred by the recipient breeding program would be those associated with the phenotyping and computer analysis and use of only the relevant markers. The high costs associated with the generation of a dense linkage map of a segregating population could be avoided, saving the recipient breeding program hundreds of thousands of dollars in development costs. Costs could be further reduced by utilizing publicly available association mapping software such as ‘Tassel’ (The expense of developing a dense linkage map of single sequence repeats (SSRs)  and/or s‘Tassel’ .In this paper, we describe a hybrid population of the octoploid strawberry that should be a rich source of novel genes for the global breeding community. We have genotyped this population for 29,251 SNPs that were previously mapped in another segregating population . The objFragaria ×ananassa Duchesne ex Rozier, initially arose from a hybridization between F. chiloensis (L.) Miller subsp. chiloensis forma chiloensis and F. virginiana Miller subsp. virginiana in Europe 250 years ago , F. virginiana ssp. glauca from Montana , F. chiloensis ssp. chiloensis forma patagonica from Chile , and F. chiloensis ssp. pacifica from California . This population likely contains as much diversity as is possible in a single octoploid strawberry family as it is composed of four different subspecies from four distant ecological regions spanning two continents.We have generated a hybrid population of the octoploid strawberry consisting of four subspecies. The primary cultivated strawberry, ears ago . Wild-coears ago ,b. Elited Oregon . The mosTW), according to manufactures instructions . This platform was developed as part of the international RosBREED project, focused on enabling marker-assisted breeding through identification and validation of QTLs for traits of importance to breeders  were included on the array. After removing monomorphic, segregation wise distorted and ambiguous markers by applying necessary data filters in excel, we identified polymorphisms in the FVC 11 family in a subset of 6,594 SNPs that had already been placed in a dense genetic linkage map of the full sib-family ‘Holiday’ × ‘Korona’  of each genotype were planted in the field in Benton Harbor, MI, in a randomized complete block design. Plants were set in rows at 1.2 m × 1.2 m spacing and all runners were trained by cross-cultivation into a 0.5 m wide square.-2) was measured on five ripe fruit per plot (when available) using the compression test of BioWorks’ FirmTech 2 . Two ripe fruits from each replication were cut in half and percent internal color was estimated visually based on how deep the color penetrated the flesh. Soluble solids were taken by squeezing one drop of juice onto the handheld refractometer from the two fruits for two separate readings. These data were previously reported by A total of three random inflorescences were selected per mother and daughter plant and their heights were measured from crown to tip and their flower numbers were counted. The number of crowns was also counted on each mother plant and the three daughter plants as well as the total number of daughters produced by each mother plant . Overall plant vigor was estimated on a 1–7 (least to most vigorous) scale based on plot fill and individual plant vigor. The first five ripe fruit were harvested to determine an average fruit weight, and after another five fruits had ripened all ripe and unripe fruits were picked to determine yield. Fruit firmness . Coefficients of Variations (CVs) ranged from 10% for fruit firmness to 68% for yield. When progeny means were compared with those of the parental means, many traits exhibited transgressive segregation, most notably yield, and fruit weight. Of the two parents, the F. virginiana had the highest value for vigor, crown production, inflorescence number, inflorescence height, flower numbers, yield, and depth of fruit color, while the F. chiloensis had the highest values for fruit weight and firmness.As was previously reported by p values and -log10 (p) values of ≥3.0. We discovered a SNP on LG 6 (AX-89796183) that was associated with both plant vigor and the production of daughter plants. SNPs in two regions of LG 2 were associated with fruit weight . A SNP on LG 5 (AX-89849271) was associated with yield per plant, while two other SNPs on LG 1 (AX-898760359) and LG 6 (AX-89898803) were significantly associated with yield per plot (Table 2).The FVC 11 segregating population proved to be highly polymorphic. Out of the 6,594 SNPs that had been placed on the genetic linkage map of ‘Holiday’ × ‘Korona’ by F. virginiana parent Frederick 9 is resistant to powdery mildew and leaf scorch, and very highly resistant to the northern root knot nematode. The other original F. virginiana parent LH-50-4 is unusually cold-hardy and is also very highly resistant to the northern root knot nematode. The F. chiloensis parent Scotts Creek has high salt and drought tolerance, and low nutrient needs. The other F. chiloensis parent MAR 1A also has high salt and drought tolerance, and low nutrient requirements. The environmental adaptations of the parents ranged from the Mediterranean climates where Scotts Creek and MAR 1A grew, to the high elevation Rocky Mountain habitat of LH 50-4 and the mid-south Canadian, continental climate of Frederick 9.Based on the results described above, it is clear that the FVC-11 population is highly polymorphic for numerous horticulturally important traits and could provide useful genetic variability to other strawberry breeding programs. It is likely that the population also segregates for a number of other important horticultural traits that we did not evaluate. The original We have demonstrated how an existing, soon to be published linkage map can be used to find QTL cheaply, using publically available software   and a coJH – Designed study and was primary source of funding, wrote manuscript. SS – Collected leaf samples for SNP analysis, conducted QTL analysis, generated tables for manuscript NB – Supervised DNA extraction and development of SNPs. TS – Collected phenotypic data. LC – Organized SNP data for analysis. CF – Generated FVC family that was analyzed. EW – Provided unpublished linkage map for QTL analysis. CW – Was secondary source of funding, played integral role in planning, and interpretation of data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The advent of next-generation DNA sequencing (NGS) technologies has led to the development of rapid genome-wide Single Nucleotide Polymorphism (SNP) detection applications in various plant species. Recent improvements in sequencing throughput combined with an overall decrease in costs per gigabase of sequence is allowing NGS to be applied to not only the evaluation of small subsets of parental inbred lines, but also the mapping and characterization of traits of interest in much larger populations. Such an approach, where sequences are used simultaneously to detect and score SNPs, therefore bypassing the entire marker assay development stage, is known as genotyping-by-sequencing (GBS). This review will summarize the current state of GBS in plants and the promises it holds as a genome-wide genotyping application. The analysis of genomic variation is an essential part of plant genetics and crop improvement programs. DNA polymorphisms can be directly related to phenotype differences, be genetically linked to its causative factor, or indicate relationships between individuals in populations . Over thThe field of agricultural genomics is in the midst of a technological revolution caused by the relatively sudden emergence of “next-generation” DNA sequencing technologies, driven in part by the completion of the human genome and the desire to apply the benefits of genomics to a better understanding of diseases and a more personalized view of medicine. By greatly reducing limitations in generating sequence information, these technological advances have facilitated the characterization of genes and genomes, and started to provide a more comprehensive view of diversity and gene function in plants. The increased ability to sequence in a cost-effective manner large numbers of individuals within the same species has altered the concept of variant discovery and genotyping in mapping studies, especially in plant species with complex genomes or limited public resources available. A new concept, namely genotyping-by-sequencing (GBS), has emerged, where the detection of sequence differences (namely SNPs) in a large segregating or mutant population is combined with scoring, thus allowing a rapid and direct study of its diversity targeted towards the mapping of a trait or a mutation of interest. This review will summarize the current state of genotyping and next-generation DNA sequencing technologies then provide some examples of studies where next-generation DNA sequencing has been used in plant species for genotyping applications.The first plant DNA markers were based on restriction fragment length polymorphisms (RFLPs) . Early hFor the past 15 years, automation and miniaturization in SNP-based marker technologies has increased marker density and reduced genotyping costs and time by orders of magnitude in relation to earlier approaches. Some of the most commonly used systems are based on fluorescent detection of SNP-specific hybridization probes on PCR products such as Taqman, Molecular Beacons and Invader ,14,15,16The emergence of massively parallel array systems has further enabled parallel scoring of up to hundreds of thousands of markers in plants . These uIn plant genetics, not all marker-related applications require massively parallel, genome-wide genotyping. Plant phylogenetic and diversity studies have successfully exploited relatively low marker densities or regional markers to determine relationship in plants at the interspecific and intraspecific levels ,20. The Beta vulgaris ssp. maritima)  was used to improve the order and orientation of genome sequence scaffolds of diploid ancestors B. rapa (which contributed to the B. napus A genome) and B. oleracea (which contributed to the B. napus C genome), creating pseudomolecules representative of the polyploid B. napus chromosomes. The pseudomolecules were then used to infer gene order for a set of reference unigenes assembled de novo from a previous B. napus mRNA-Seq dataset [B. napus mRNA-Seq data generated from an 84-line diversity panel were subsequently aligned to the reference unigenes, leading to the detection of 101,644 SNPs within 11,743 unigenes, out of which 62,980 were kept for further analysis, following the removal of SNPs with minor allele frequencies of less than 5%. These data were used in conjunction with the putative gene order on the B. napus pseudomolecules to study the genetic basis of two traits of interest (erucic acid content of seed oil and seed glucosinolate content). Diversity analysis on 53 of the B. napus accessions showed strong associations over previously identified QTLs for both traits. In addition, mRNA-Seq data also can be used to profile transcript abundance , and profiling data for the A and C genome copies of each unigene were used to detect unigenes (in one or both genomes) showing significant association between transcript abundance and glucosinolate content of seeds. Positioning SNP and GEM markers on the pseudomolecules identified two QTL regions containing orthologs of a transcription factor, known to control aliphatic glucosinolate biosynthesis in A. thaliana, whose loss by deletion causes a reduced seed glucosinolate phenotype in selected B. napus accessions.In another study, Harper et al.  develope dataset . B. napuet al. [Arabidopsis thaliana parents and 58 RILs on the Roche 454 and Illumina platforms. Prior to sequencing, the genomic DNA from each individual first was digested with the restriction endonucleases EcoRI and BfaI, and the resulting DNA fragments were ligated to specific barcoded adapters, PCR amplified, pooled and size-selected. A total of 6,159 SNPs and 701 SNPs were discovered from the Roche 454 and Illumina data sets, respectively. 1,712 Roche 454 SNPs (selected after applying a 20% threshold for maximum missing data) were used for linkage mapping analysis. After removing a subset of SNPs showing either significant segregation distortion or linkage disequilibrium (LD) with other SNPs, pairwise linkage analysis grouped the remaining 1,555 SNPs into five distinct linkage groups . The linkage order of the SNPs on the genetic map also was shown to be highly related to the order of the SNPs on the physical map.In a separate study, Maughan et al.  re-sequeet al. [2 plants generated by crossing a recessive ethane methyl sulfonate (EMS)-induced Col-0 mutant characterized by slow growth and light green leaves, with a wild type Ler (Landsberg erecta) line. Interval analysis of the relative parental allele frequencies using the newly developed software package SHOREmap [et al. [2 cell wall-related mutants generated by crossing individuals corresponding to three separate EMS-induced Col-0 lines with a wild type Ler mapping line. At least 230,000 SNPS were discovered for each mutant pool by aligning to the A. thaliana Col-0 reference genome. Regions of the genome lacking SNPs were discovered, corresponding to non-recombinant haplotypic blocks linked to the recessive mutations. A modification of the Illumina “chastity” statistic, which is normally used by the basecalling software to measure cross-talk between dyes during the sequencing process  was then used to measure the proportion of reads that are completely homozygous for bases that differ from the reference genome, thus further narrowing the search window for a putative causative SNP of interest within these blocks. Finally, density interval analysis measuring the frequency of SNPs with discordant “chastity” values returned several non-synonymous SNPs for all three mutants, including three likely candidates located in putative genes of interest with roles ranging from actin cytoskeleton organization to sugar transport. Finally, Trick et al. [In addition to resequencing segregating populations, GBS also has been used to sequence pools of mutants in bulked segregant analysis studies. In Arabidopsis, Schneeberger et al.  sequenceSHOREmap  revealed [et al.  used an k et al.  used a rHigh-throughput variant discovery has been made possible in multiple species by the recent advent of next-generation DNA sequencing technologies. Continuous increase in sequencing throughput and the accompanying decrease in consumable cost per Gbp has allowed researchers to switch focus from resequencing small panels of parental individuals for the sole purpose of discovering variants to resequencing much larger pools of individuals within a population, where the sequenced differences are used directly as genotypic markers. This genotyping-by-sequencing (GBS) approach has several advantages, including the facts that no preliminary sequence information is required and that all newly discovered markers originate from the population being genotyped. On the other hand, due to several biological and technical factors, such as PCR amplification bias during the library construction step, not all sequenced regions of interest are evenly covered in all individuals within a population, reaffirming the need for imputing missing data using pedigree or parental information when available.Because DNA fragments are more readily prepared using a genome-wide approach , the advent of GBS is expected to have a more profound impact on mapping strategies benefiting from a dense genome-wide distribution of markers. Such strategies include Genome-Wide Association Studies (GWAS), Bulked Segregant Analysis (BSA) and Genomic Selection (GS). Successive improvements of the sequencing chemistries and basecalling software are allowing NGS technologies to deliver higher sequencing throughputs per run, which in turn enables deeper multiplexing for a fixed average sequencing depth per sample. Although the cost of sample preparation and bioinformatics analysis are not decreasing as rapidly as the cost of sequencing, such a trend is already enabling GBS to be a cost-competitive alternative to other whole-genome genotyping platforms. It is expected that, as the amount and quality of sequencing information generated per run keeps increasing, thus allowing even higher plexing and lower costs per samples, plant breeders soon may be able to sequence even larger populations, allowing genomic selection or the determination of a population structure without prior knowledge of the diversity present in the species .


Genotyping-by-sequencing (GBS), and related methods, are based on high-throughput short-read sequencing of genomic complexity reductions followed by discovery of single nucleotide polymorphisms (SNPs) within sequence tags. This provides a powerful and economical approach to whole-genome genotyping, facilitating applications in genomics, diversity analysis, and molecular breeding. However, due to the complexity of analyzing large data sets, applications of GBS may require substantial time, expertise, and computational resources. Haplotag, the novel GBS software described here, is freely available, and operates with minimal user-investment on widely available computer platforms. Haplotag is unique in fulfilling the following set of criteria: (1) operates without a reference genome; (2) can be used in a polyploid species; (3) provides a discovery mode, and a production mode; (4) discovers polymorphisms based on a model of tag-level haplotypes within sequenced tags; (5) reports SNPs as well as haplotype-based genotypes; and (6) provides an intuitive visual “passport” for each inferred locus. Haplotag is optimized for use in a self-pollinating plant species. Both of these programming packages are open source, available on multiple platforms, and actively supported by developer communities. Most algorithms within Haplotag were written to operate in parallel when executed on a computer with multiple processors. The code was compiled for the Windows 64-bit environment , and tested with Windows XP, Windows 7, Windows 8, and Windows 10, as well as server 2008. Haplotag was tested on many different computers, but the evaluations reported below were executed on a computer running Windows server 2008 with two Intel  Xeon X5670 processors running at 2.93 GHz. Each processor had six cores, and each core was divided into 12 threads . The test machine contained 96 GB RAM, but all reported analyses were confirmed to run within 24 GB RAM. All input and output data resided on a locally attached 500 GB disk, since prior experience indicated reduced performance when reading and writing to a network drive. Small projects, as well as the demonstration files described below, will run on most ordinary desktop computers, but will require a 64-bit operating system.Software named ‘Haplotag’ was written in the Pascal programming language, implemented as Free Pascal , and ‘SNP alleles’ (the variant bases found at a SNP locus). We then define a ‘tag-level haplotype’ as the combined set of SNP alleles that must exist on a single chromosome due to their recovery in the sequence of a single GBS tag. Although the term haplotype implies the existence of multiple loci, we essentially treat haplotypes as multiple alleles at a single composite locus, which we refer to as a ‘Haplotag locus’, and inferences are made under the assumption that the recombination rate within a tag is negligible. The term ‘heterozygosity’ is used when applying a filter that rejects an inference that two or more haplotypes exist at the same Haplotag locus if those haplotypes occur together more frequently than they would be expected to based on the assumed heterozygosity in the population.http://www.ncbi.nlm.nih.gov/sra/) under project accession number SRP037730, and the GBS key for analysis was presented in Table S4 of File S1), and sample output (File S2 and File S3). Haplotag is available as an executable distribution for recent versions of Windows 64-bit environments . The distribution can be obtained from the site http://haplotag.aowc.ca/, which provides a download links for a compressed file that contains the Windows executable, a user manual , and demonstration files. Future updates will be maintained at this site, and a voluntary registration is provided to monitor interest in this software, and to enable announcements regarding major revisions. The Pascal source code was made available to reviewers of this work, and will be provided by request on an as-is basis for any noncommercial use based on an open source license. The source code is expected to be compatible with any operating system where a Free Pascal compiler is available, although minor modifications to the code may be required to adapt it for the file systems of other operating environments.Data analyzed in this report were deposited in the NCBI short read archive (File S1), which references a set of small simulated input files for demonstration purposes. The input files are archived within the software distribution. When extracted, the demonstration files fall within three separate subdirectories, each containing a complete self-contained set of demonstration files for one of three primary modes in which Haplotag can operate. Within each subdirectory is a master input file with the default name “HTinput.txt,” which contains all relevant parameter specifications as well as a set of pipeline commands that Haplotag will follow in the order listed. Based on these commands, Haplotag can read and process data from three starting points . The result can be a single Haplotag locus within a single cluster, or multiple Haplotag loci within the same cluster. The latter is common in polyploid or recently duplicated genomes. Results of locus prediction and genotype scoring are summarized within a single passport file for each cluster (see below). Although the model selection within clusters does not incorporate sequence divergence, the population filter invariably identifies Haplotag loci in which haplotypes diverge less within the locus than they do among other loci within the same cluster.Another important and unique feature of Haplotag is the automated production of a ‘passport’ file for each cluster. This is illustrated by one passport from the analysis of the included demonstration data . PassporFile S2) to illustrate how Haplotag has inspected all 57 possible combinations (“models”) of two or more tags from the available six tags. This step is referred to as a “population filter”, since it allows the exclusion of inappropriate models based on whether the tags in a model segregate in a diploid manner within the tested population. Parameters for population filtering, reported at the bottom of the details page (File S2) include genotype-completeness, allele frequency, and heterozygosity. Here, each model was evaluated based on whether it would pass this filter (yes or no). Next, the acceptable model having complete data for the greatest number of taxa (Model 42 in S2) was assigned as ‘Locus-1’. All models that overlapped with Model 42 were then removed, and remaining acceptable models were inspected. Of these, the next best model was assigned to a Haplotag locus . The above process is iterated indefinitely until no acceptable models remain. We would then point out that ‘Haplotag Locus-2’ contains only one SNP locus, and, thus, two haplotypes, while ‘Locus-1’ contains two SNPs, which could theoretically form four haplotypes, of which three haplotypes were observed. In practice, it is very rare to observe four haplotypes at a single Haplotag locus with two SNP loci, as this would imply two mutation events at the same SNP locus, or a rare recombination event between two SNP loci in the same tag.For example, in e.g., Taxa TJ, under Locus 1) are scored as heterozygotes. These inferred genotypes are written to a simple text-based file called “HTgenos.txt”. Since many programs for genetic analysis cannot read haplotypes, an alternate genotype file is written, where genotypes are defined by SNP locus calls from within the Haplotag loci. In the example in We would then draw attention to the inferred genotypes and segregation of these five combined haplotypes at two putative Haplotag loci within the population of taxa, which are shown in the table at the bottom of the passport . In thisIt is well known that results of SNP identification, especially in a polyploid without a reference genome, are highly dependent on methods and parameters  of the 4260 UNEAK SNPs filtered at ≥ 80% genotype-completeness were identical to those called by Haplotag at the same filtering level. In contrast, UNEAK identified only 24% of the 17,117 Haplotag SNPs filtered at ≥ 80% genotype-completeness. In general, Haplotag called most of the same SNP loci discovered by UNEAK, because these represented the clusters in Haplotag with exactly two haplotypes having only a single SNP difference. The small number of UNEAK SNPs that were missed by Haplotag are a result of rare haplotypes, and/or sequencing errors that were aligned into a large cluster by Haplotag. In rare cases, this resulted in a complex cluster that was excluded from the Haplotag project because it exceeded the threshold for the maximum number of tags per cluster. The UNEAK pipeline has a different network-based strategy that is intended to exclude rare haplotypes because it is designed to seek models with only two haplotypes and a single SNP. While it is possible to adjust Haplotag parameters to increase the coverage of UNEAK SNPs, this would be at the expense of a greater number of multi-haplotype models that are called by Haplotag.Haplotag was also tested in production mode, which required only 11 min in our test environment. As shown in File S3. This table provides links to the passport files for the 7343 Haplotag loci called in the production mode and filtered at ≥ 80% genotype-completeness. We have chosen this output because it contains legacy SNPs and nomenclature (from https://triticeaetoolbox.org/oat/). Since passport files can also be saved and opened without the need for a web server, an individual passport file can be shared easily with a collaborator when there is an interest in inspecting the sequence and genotypes of a specific Haplotag locus.Each Haplotag run produces a complete index of passport files, linking each Haplotag locus to a passport file for the cluster where that locus was called. While this index is written in HTML format, it can be easily manipulated into a table, which we have demonstrated in 2 populations, or in populations of outcrossing species), a heterozygosity filter that was set higher  could still be effective in excluding nonsegregating haplotypes from paralogous locus, but complications could arise if multiple paralogous loci are segregating simultaneously. We initially considered the application of a Fishers’ test of contingency tables, but extending this test to an arbitrary number of haplotypes was beyond our programing skills. In future, we may consider adding additional population filters to expand the genetic scenarios in which Haplotag can be used, and we welcome suggestions in this regard.Haplotag was developed primarily to solve problems of genotyping in self-pollinating allopolyploid species without a reference genome. It will also function well in a self-pollinating diploid species. When paralogous loci exist, such that they are aligned together within the same cluster, Haplotag depends on a simple heterozygosity filter to build models of Haplotag loci that exclude haplotypes from nonhomologous loci. Typically, this is very effective in self-pollinating populations where heterozygotes are rare and this filter can be set at a low level . In populations where high rates of heterozygosity are expected (in F


The aim of the present study was to compare the power of single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) and haplotype-based GWAS for quantitative trait loci (QTL) detection, and to detect novel candidate genes affecting economically important traits in a purebred Duroc population comprising seven-generation pedigree. First, we performed a simulation analysis using real genotype data of this population to compare the power (based on the null hypothesis) of the two methods. We then performed GWAS using both methods and real phenotype data comprising 52 traits, which included growth, carcass, and meat quality traits.Sus scrofa chromosome (SSC) 1 at 304 Mb; and on SSC7 at 35–39 Mb, 41–42 Mb, and 103 Mb. The vertnin gene (VRTN) in particular, was located on SSC7 at 103 Mb and was significantly associated with vertebrae number and carcass lengths. Mapped QTL regions contain some candidate genes involved in skeletal formation  and fat deposition .In total, 836 animals were genotyped using the Illumina PorcineSNP60 BeadChip and 14 customized SNPs from regions of known candidate genes related to the traits of interest. The power of SNP-based GWAS was greater than that of haplotype-based GWAS in a simulation analysis. In real data analysis, a larger number of significant regions was obtained by SNP-based GWAS than by haplotype-based GWAS. For SNP-based GWAS, 23 genome-wide significant SNP regions were detected for 17 traits, and 120 genome-wide suggestive SNP regions were detected for 27 traits. For haplotype-based GWAS, 6 genome-wide significant SNP regions were detected for four traits, and 11 genome-wide suggestive SNP regions were detected for eight traits. All genome-wide significant SNP regions detected by haplotype-based GWAS were located in regions also detected by SNP-based GWAS. Four regions detected by SNP-based GWAS were significantly associated with multiple traits: on Our results show that a multigenerational pig population is useful for detecting QTL, which are typically segregated in a purebred population. In addition, a novel significant region could be detected by SNP-based GWAS as opposed to haplotype-based GWAS.The online version of this article (doi:10.1186/s12863-016-0368-3) contains supplementary material, which is available to authorized users. SCD) gene for fatty acid composition [LEPR) gene for fat accumulation [Even before performance testing in pig breeding, single nucleotide polymorphisms (SNPs) associated with economically important traits  can be useful in the selection of young animals. Several local animal experiment stations in Japan have performed closed-line breeding for more than five generations of purebred pigs , 2. To iposition  and the mulation . These Q2 intercross pig populations [Recent studies have reported genetic loci that are associated with complex traits using genome-wide association studies (GWAS) with high-density SNP arrays , and havulations –11, and The purpose of the present study was (1) to compare the power between SNP-based GWAS and haplotype-based GWAS for QTL detection in simulation and real phenotype data analyses, and (2) to detect novel candidate genes affecting economically important traits. We used real genotype data of a purebred Duroc population comprising seven-generation pedigree in simulation analysis. These genotypes and 52 quantitative traits related to growth, carcass, and meat quality were used in real phenotype data analysis. We performed the linear mixed model association test at each SNP locus for LD-based GWAS (referred to as SNP-based GWAS), and at each haplotype locus for linkage-based GWAS (referred to as haplotype-based GWAS). Haplotypes were constructed based on pedigree and LD information .2 (for LEA), 2.6 cm (for BF), and 6 % (for IMF content). DG, LEA, and BF were measured in all pigs, and IMF content was measured in slaughtered sib-tested pigs. Boars (16) and gilts (22) were mated in the first generation, and 22 gilts from among their offspring and nine boars were then mated in the second generation. Pigs in the first and second generations were regarded as the base population. Closed-line breeding was performed from the third to the seventh generation, and phenotypes were determined in each generation. Pig in each generation were mated to account for inbreeding and to prevent rapid loss of genetic diversity, and a new generation was obtained each year. The inbreeding coefficient was estimated by the algorithm of Meuwissen and Luo [All procedures involving animals followed the Guidelines for the Care and Use of Laboratory Animals established by the National Livestock Breeding Center. A total of 836 Duroc purebred pigs at the National Livestock Breeding Center in Japan were used for GWAS. This population comprised one family from the first to the seventh generation; therefore, all individuals were related. Table  and Luo , and thes. Boars 6 and gils. Boars 6 and gilGenomic DNA of 836 animals was extracted from ear tissue of 10-day-old pigs using proteinase K and the phenol method. Sample DNA was quantified and genotyped using the Illumina PorcineSNP60 BeadChip  according to the manufacturer protocol. Image data were analyzed with the iScan (Illumina) and genotype data were then called using the genotyping module contained in the GenomeStudio software (Illumina). Autosomal chromosomes were used and SNP quality control was assessed using the PLINK software . The excFourteen SNPs were chosen based on the current knowledge of physiological roles or the known candidate genes affecting pig meat quality and carcass traits, as shown in Table r2) values, which are a measure of LD, for all pairs of SNPs less than 10 Mb apart by PLINK software [r2 values for a given intermarker distance, with marker distances grouped in 1 kb bins, were estimated in each autosome, and the average r2 values among chromosomes were then calculated. Average r2 values were then plotted against intermarker distance, as shown in Additional file r2 value = 0.20) extended to about 1.0 Mb in this population. The threshold for useful LD was assumed to be r2 value ≧ 0.20 for the application of GWAS in this study, and the extent of LD (1.0 Mb) was applied for simulation study and QTL detection.We estimated LD coefficient  and generation (seven classes) effects as a fixed effect; this generation effect is the environmental effect at each generation; u represents polygenic effects distributed as N; and e represents the residual effect distributed as N. A is the numerator relationship matrix, σu2 is polygenic variance, I is the identity matrix, and σe2 is residual variance. The ASREML program [yadj) were then derived bywhere  program  was usedThe adjusted phenotypes were then used as the dependent traits in a linear mixed model approach for each SNP:βi is the allele substitution effect; wi is a vector of SNP genotypes ; and e’i is the random residual at the i-th SNP distributed as N. The regression coefficient and p-values tested by the Wald test were obtained by Genome-wide mixed-model association (GEMMA) software [i-th SNP effects was calculated using the formula:where software . The propi is MAF of the i-th SNP and VP is the adjusted phenotypic variance [where variance .Haplotype-based GWAS was performed on the basis of pedigree and LD information. Haplotypes were constructed using the hidden Markov model with DualPHASE , which a1n is a vector of n ones; n is the number of animals with phenotype; μ is the mean; hi is a vector of haplotype random effect distributed as N; Hi is the incidence matrix of haplotype genotypes for the individuals at the i-th haplotype locus; The element of Hi (Hjk) is equal to the number of copies (from 0 to 2) of ancestral hapotype k carried by j-th animal; σh2 is the haplotypic variance; and the covariance between ancestral haplotype effects is assumed to be zero. For this analysis, the polygenic effects (u) distributed as N and the residual effects (e’i) distributed as N were derived in a similar manner as they were for the SNP-based GWAS. Using the GLASCOW software, a test statistics T based on the score tests was calculated at every tested position as follows;where T score can be interpreted as a sum over the ancestral haplotypes groups of the sum of the squared difference between observed  and expected  values of the phenotypes for each group. The significance of the T scores can be obtained using the distribution of the T score under the null hypothesis (σh2 = 0) that could be approximated using a gamma distribution. Parameters of the gamma distribution were estimated for each tested position.The P = 1.31 × 10−6). The extent of LD in this population was about 1.0 Mb, and the genome-wide significance thresholds defined by the Bonferroni correction was too conservative. Therefore, the genome-wide suggestive threshold was also defined as; P = 5.0 × 10−5. SNP maps were updated according to the SNPchiMp v.2 database [r2 values between the SNPs of the significant region were calculated, and the haplotype block pattern was visualized using Haploview software [For both methods, the Bonferroni correction was applied to determine the 5 % genome-wide significance thresholds  was generated based on data from the parent. If both parents were unknown , then uj was derived as follows: uj ~ N. If both parents were known, uj was derived as follows: uj ~ N(0.5(usj + uDj), 0.5(1 − 0.5(FSj + FDj)σu2)), where uSj and uDj are the additive genetic values of sire and dam of the j-th animal, respectively, and FSj and FDj are the inbreeding coefficient for the sire and dam of the j-th animal, respectively. The residual value of the j-th animal (ej) was derived from ej ~ N. The heritability of the base phenotype was set at 0.30, and phenotypic variance was assumed to be 1. The base phenotype was under the null hypothesis of no phenotype-SNP correlation  . The SNP selected as the QTL was then masked. In each replicate, SNP-based GWAS and haplotype-based GWAS were performed, and the power to achieve 5 % genome-wide significance (P = 1.31 × 10−6) was calculated as the proportion of replicates with a significant SNP within ± 0.5 Mb of the selected QTL. In addition, we also evaluated which method is practical for finer mapping in this population. The power to achieve 5 % genome-wide significance was calculated in three different regions, which ranged from ± 0.5 Mb, ± 0.5–1.0 Mb, and ± 1.0–2.0 Mb, apart from the selected QTL. In this scenario, the QTL heritability was set to 0.10.Some missing genotypes were imputed by DualPHASE , and werTable Carcasses were scalded and dehaired, and chilled overnight. Carcass measurements were then recorded as follows: weight (CWT); lengths I (CL1), II (CL2), and III (CL3); thickness (CT); and vertebrae number. CL1, CL2, and CL3 denote the lengths from the first cervical vertebra to the pubic bone; from the first rib to the pubic bone; and from the first rib to last lumbar vertebra, respectively. Depth of backfat over the midline was measured with a ruler at the first rib (shoulder: SSFT), at the thinnest depth over the ribs (back: BSFT), and at the first lumbar vertebra (loin: LSFT). One side of the carcass was split between the fourth and fifth ribs (at 45r), and at half-body length (at HBL). The longissimus muscle was traced on acetate paper and the area was determined using computerized morphometric planimetry. Cross sections of each carcass were then recorded photographically. The fat area ratios for subcutaneous fat (SFA), intermuscular fat (IFA), and all fat area (ALLFA) ratios were measured at 45r and at HBL, using the ImageJ ver 1.48 software . The clag for 30 min at 2 °C. The pH of the longissimus muscle was measured using a pH meter  equipped with a needle-type electrode . The appearance of meat and fat  were assessed immediately after cutting, using a Minolta spectrophotometer CM-2002 . All measurements were performed in duplicate, and means and standard errors were calculated.The weight of each wholesale cut of the remaining side of the carcass was then recorded. Moisture (MOS), IMF, protein (PROT), cooking loss (COOK), water holding capacity (WHC), and shear force value (SF) were measured as described by Okumura et al. . The MOSThe powers to detect QTL by SNP-based GWAS and haplotype-based GWAS in simulation analysis are presented in Fig. For the practicability of finer mapping by SNP- and haplotype-based GWAS, the power among three different region apart from the selected QTL were calculated by SNP- and haplotype-based GWAS, and are shown in Fig. In real data analysis, SNP-based GWAS and haplotype-based GWAS were performed for 52 traits related to growth, carcass, and meat quality. We summarized the genome-wide significant and suggestive SNP regions for these traits in Fig. Sus scrofa chromosome (SSC) 1 at 304 Mb and DG (P = 1.22 × 10−8) was evident in both SNP-based and with haplotype-based analyses (Table P = 6.30 × 10−10) and CC (P = 6.82 × 10−9), respectively, were observed in both SNP-based and haplotype-based analyses (Table P = 1.17 × 10−7) was only detected with SNP-based GWAS. Each copy of effect allele A at rs81352969 was associated with increases in BF of 0.18 cm; CW of 0.45 cm; and CC of 0.87 cm. Both SNPs, rs81352969 and rs81352956 on SSC1 at 304 Mb, were located within close proximity (a distance of 27 kb) of each other.The SNPs showing genome-wide significant association with growth, body measurements, carcass measurements, and fat area in SNP-based GWAS and haplotype-based GWAS are presented in Table es Table . Each coVRTN) gene on SSC7 at 103 Mb with CL (P = 3.64 × 10−9), CL1 (P = 1.56 × 10−11), CL2 (P = 2.96 × 10−14), CL3 (P = 3.10 × 10−18), and thoracic vertebrae number  were observed in both SNP-based and haplotype-based analyses  were detected with SNP-based GWAS only  was detected (Table 2. Significant association between rs81257576 on SSC9 at 46 Mb and LSFT (P = 7.36 × 10−7) was detected. Each copy of the A allele (with MAF 0.017) was associated with an estimated increase in LSFT of 0.65 cm. Significant association between rs81415869 on SSC9 at 124 Mb and LEA at 45r (P = 1.94 × 10−7) was detected and accounts for 10 % of the phenotypic variance. Each copy of the G allele (with MAF 0.023) was associated with an estimated increase in LEA at 45r of 3.98 cm2. Significant association between rs81422289 on SSC10 at 27 Mb and BF (P = 7.04 × 10−7) was detected. Each copy of the G allele was associated with an estimated increase in BF of 0.17 cm. Significant association between rs330963199 on SSC11 at 53 Mb and CL3 as longissimus muscle length (P = 3.32 × 10−7) was detected and accounts for 10 % of the phenotypic variance. Each copy of the G allele was associated with an estimated decrease in longissimus muscle length of 1.00 cm. Significant association between rs81345146 on SSC18 at 7 Mb and BSFT (P = 4.37 × 10−7) was detected and accounts for 10 % of the phenotypic variance. Each copy of the A allele was associated with an estimated decrease in BSFT of 0.19 cm.Significant SNPs were detected within six regions with SNP-based GWAS only. Significant association between rs81398418 on SSC8 at 25 Mb and IFA at 45r (P = 1.09 × 10−5) was detected with SNP-based GWAS only. Each copy of the A allele was associated with an estimated decrease in IMF content of 0.59 %. Suggestive association between rs336391107 on SSC7 at 83 Mb and IMF content (P = 5.78 × 10−6) was detected with SNP-based GWAS only. Each copy of the A allele was associated with an estimated decrease in IMF content of 0.58 %  gene (which is involved in c-Myc regulation) is located. In humans, this locus is associated with height [FUBP3 may affect skeletal formation as it relates to both height and bone mineral density. A region on SSC7 at 103 Mb was associated with CL and TVN. Mikawa et al. [VRTN gene in this region is responsible for the QTL for the TVN in commercial breeds of pigs. Two of nine polymorphic sites, an insertion of the PRE1 element in intron 1 and an SNP in the promoter region, may have effected changes in expression of the VRTN gene at the embryonic stage. Fan et al. [VRTN affects the TVN, CL, and the length of the longissimus muscle. These findings are consistent with those of Nakano et al. [VRTN gene polymorphisms greatly contributed to the TVN and CL-related traits. Two regions on SSC7 at 35–39 Mb and 41–42 Mb had genome-wide significance with multiple traits. In the region at 35–39 Mb, an LD block from 35.0 to 37.5 Mb was observed, which is consistent with the report of Guo et al. [HMGA1) gene  is located. A QTL for the CCB in Duroc pigs has been reported in this region [HMGA2 is one of the strong biological candidates for height [HMGA1 is possibly also a strong candidate gene that might improve leg strength in Duroc sires that are used in terminal crosses. The region at 41–42 Mb including rs80928067 was associated with SFA at HBL and was in close proximity to the locus at which the triggering receptor expressed on myeloid cells 2 (TREM2) gene (which is involved in osteoclast development and the anti-inflammatory response) is located. In humans, this gene is identified as one with transcripts exhibiting differential patterns of expression in abdominal subcutaneous fat between obese and normal pregnant women [TREM2 is required for adipocyte differentiation and promotes adipogenesis by upregulating adipogenic regulators and inhibiting the Wnt10b/β-catenin signaling pathway [TREM2 gene may be a candidate for the quantitative trait of fat deposition.In the population of the present study, four regions were associated with multiple traits. One region on SSC1 at 304 Mb that included rs81352956 was associated with DG, BF, CW, and CC. This region was in close proximity to the locus at which the far upstream element (FUSE) binding protein 3  gene (which is involved in thiamine metabolism) is located. TPK1 is a cellular enzyme that catalyzes the transfer of the pyrophosphate group from ATP to thiamine, to form thiamine pyrophosphate (TPP). TPP is an active cofactor for enzymes involved in glycolysis and energy generation. In humans, QTL for birth weight have been mapped on chromosome 7, using linkage analysis [TPK1 has been tested as a candidate gene [TPK1 may be a candidate gene for the quantitative trait LEA at 45r. The region on SSC10 at 27 Mb including rs81422289 was associated with BF and was in close proximity to the zinc finger protein 281 (ZNF281) gene, which was predicted to be a potential target of microRNA-33 (miR-33) [FUBP3 and ZNF281 may be candidate genes for the quantitative traits DG and BF. The region on SSC18 at 7 Mb including rs81345146 was associated with BSFT and was in close proximity to the locus at which the zyxin (ZYX) gene  domains in its C-terminal half [ZYX is involved in a number of important signaling pathways that regulate cell differentiation, proliferation, and morphology. Proline-rich and LIM domains interacted with a number of proteins and specific protein partners, respectively. One of those partners, the Homeodomain-interacting protein kinase 2 (HIPK2) gene, identified as an essential regulator of white fat development, might have functions within the adipocyte cell compartment of the skin in the mouse [ZYX may be a candidate gene for quantitative trait of fat deposition.In the present study, six regions had genome-wide significance, and three of those six regions contained potential candidate genes. The region on SSC9 at 124 Mb including rs81415869 was associated with LEA at 45r and was in close proximity to the locus at which the thiamine pyrophosphokinase  is locanal half  suggestehe mouse . TherefoASAP1) gene  is located. DEF-1 is involved in the differentiation of fibroblasts and possibly that of other cell types. In vitro, this gene encoded a Src SH3 binding protein, increased expression of DEF-1, promoted adipogenesis in cultured fibroblast cell lines, and was detected in the adipose tissue of obese and diabetic mice. In addition, the ubiquitous expression pattern of DEF-1 implies that its function is not restricted to adipogenesis [METTL3) gene (which is involved in messenger RNA modification of N6-methyladenosine (m6A) methylation) is located. Adenosine methylation in RNA is a reversible modification that is widespread throughout the transcriptome, and plays an important role in RNA biology, which influences a wide variety of biological pathways and physiological processes [6A RNA–methylation by METTL3 suppression reduced RNA processing efficiency. In porcine adipocytes, overexpression of METTL3 significantly reduced the intracellular triglyceride content, significantly increased glycerol content in the medium, and significantly downregulated relative mRNA expression of proliferator-activated receptor γ and fatty acid synthase genes [METTL3 also enhanced m6A levels and inhibited adipogenesis. Therefore, we suggest that METTL3 and DEF-1 may be candidate genes for the quantitative trait of IMF content.In the present study, we observed no genome-wide significant association with IMF. However, two regions with genome-wide suggestive association contained potential candidate genes. The region on SSC4 at 10 Mb including rs80793147 was associated with IMF content and was in close proximity to the locus at which the ArfGAP with SH3 domain, ankyrin repeat, and PH domain 1 . The effects of candidate genes depend on the genetic background of the population. Further study is necessary to evaluate the effects of candidate genes in different genetic backgrounds. Nevertheless, we identified multiple novel regions related to growth, carcass, and meat quality traits, which were separate from the regions of known candidate genes. The annotation of these genomic regions exhibited several genes that function in the regulation of c-Myc, cell growth and differentiation, thiamine metabolism, and adipogenesis. These findings provide new insights for investigations into altered gene functions that result from modifications of RNA and/or micro-RNA, in addition to DNA polymorphisms. Our results also suggest that the detected region could be useful as a means of improving marker-assisted selection in pigs.As we expected that the  for TVN , 31, onlFUBP3, a gene implicated in c-myc regulation, is highlighted as novel candidate gene for skeletal formation associated with average daily gain, and METTL3, a gene implicated in messenger RNA modification, is novel candidate gene for fat deposition associated with IMF content. Further research could identify the causal genes involved in growth, carcass, and meat quality traits.SNP-based and haplotype-based GWAS were performed in a Duroc multigenerational population. A comparison was drawn between the power of SNP-based GWAS and that of haplotype-based GWAS, using simulation and real data; SNP-based GWAS demonstrated greater power than haplotype-based GWAS in the population under investigation. In real data analysis, larger numbers of significant regions were obtained and some regions had genome-wide significant association with multiple traits. In these significant regions, genes that serve specific functions were located. Among them, All procedures involving animals followed the Guidelines for the Care and Use of Laboratory Animals established by the National Livestock Breeding Center, and this research project was approved by the laboratory animals committee (23–9) on the National Livestock Breeding Center.Not applicable.The datasets supporting the conclusions of this article are included within the article and its additional files.


Publically available SNP array increases the marker density for genotyping of forage crop,Lolium perenne. Applied to 90 European ecotypes composed of 716 individuals identifies a significant genetic–geographic correlation.Lolium perenne) was created and validated in a broad ecotype collection of 716 individuals sampled from 90 sites across Europe. Genetic diversity within and between populations was assessed. A strong correlation of geographic origin to genetic structure was found using principal component analysis, with significant correlation to longitude and latitude (P < 0.001). The potential of this array as a resource for studies of germplasm diversity and identifying traits underpinning adaptive variation is highlighted.Grassland ecosystems are ubiquitous across temperate and tropical regions, totalling 37 % of the terrestrial land cover of the planet, and thus represent a global resource for understanding local adaptations to environment. However, genomic resources for grass species  are relatively poor. The advent of next-generation DNA sequencing and high-density SNP genotyping platforms enables the development of dense marker assays for population genetics analyses and genome-wide association studies. A high-density SNP marker resource (Illumina Infinium assay) for perennial ryegrass (The online version of this article (doi:10.1007/s00122-015-2556-3) contains supplementary material, which is available to authorized users. Lolium perenne L.) is a dominant species of temperate grassland ecosystems, covering a broad range of environmental conditions . Understanding the patterns and magnitude of genetic diversity in the allogamous forage grass species L. perenne is thus a useful first step towards identifying loci under selection for multiple ecological traits, and also serves as a gateway for gene discovery in other grasses, with which it shares considerable synteny  has previously been studied in Studies of natural populations are increasingly turning towards high-density, genome-wide approaches to understanding genetic diversity  by the researchers referenced below. The five genotypes selected were: AberMagic ; a Chromosome 3 substitution line with Festuca pratensis , a single individual was grown for each genotype. Each individual was harvested at the young (3–4 weeks post-germination) stage and total RNA isolated from both total above and below ground biomass using Trizol extraction (Sigma Aldrich). The above/below ground extracts were pooled for each individual genotype at equimolar concentrations prior to Illumina RNAseq library construction, to provide as much coverage of the transcriptome at equivalent life history stages (flowering tissue was ignored as the genotypes used display significant variation). Aliquots of 2 µg of total RNA per genotype were used to prepare libraries as per the Illumina mRNA-seq protocol (mRNA-Seq 8-sample Prep Kit (RS-100-0801). Each library was sequenced in a single lane of an Illumina GA-IIx platform at GenePool (University of Edinburgh) using paired-end 2 × 56 bp sequencing. Read count averaged 41 million reads per genotype (20.5 million pairs), with the lowest output being the amenity genotype with 13 million pairs and the highest AberMagic (50.5 million pairs). Raw FASTQ data for these libraries are available through the NCBI short read archive (http://www.ncbi.nlm.nih.gov/sra), accessions SRR2034619–SRR2034623.To identify putative SNP loci which could be used to construct an Infinium assay, we conducted Illumina RNAseq of five diverse genotypes of Reads were imported into the Genomics Workbench version 4.5.1 package (CLC Bio Ltd.) and a reference transcriptome was assembled de novo using the reads from AberMagic, since it generated the highest read coverage. De novo assembly in Genomics Workbench uses the de Bruijn graph method with a k-mer value assigned based on the scale of data input . The maximum bubble size for conflict resolution within the graph was set at 50. Repeat regions within the graph were resolved using scaffolding based on paired-end sequences. Following initial contig assembly, reads were mapped back to contigs, requiring 50 % match at 80 % similarity across the read. Ambiguous read mappings (reads mapping to more than one contig) were discarded from the mapping. Insertion and deletion penalties were set at 3 and mismatch penalty at 2. Contigs from the initial assembly were removed if no reads mapped. This step was included to resolve conflicts by generating a consensus based on the most common base for each position.This assembly produced a total of 55,181 contigs which were used as the reference for read mapping of the five genotypes. This Transcriptome Shotgun Assembly project has been deposited at DDBJ/EMBL/GenBank under the accession GDAT00000000. The version described in this paper is the first version, GDAT01000000. BLASTx annotation of contigs .Despite the high number of putative SNPs identified, not all the putative SNPs identified were suitable for construction of Infinium probes: firstly, we needed to maximise the likelihood that markers would be informative across a broad range of material. Secondly, we needed to account for the possibility of misassembly during the de novo contig construction and remove sequences which might be present in high copy number. To address the first issue, we subselected markers which showed evidence of polymorphism in two or more of the accessions, reducing the possibility that a particular marker might not show polymorphism in wider g et al.  because With regard to the possibility of misassembly in the contig data, we, therefore, excluded any contigs displaying evidence of frameshift (multiple hits to the same match) within their BLASTx result. Further filtering on BLAST identifier was then applied to remove likely organellar or retroelement sequences (as suggested by Illumina), which are likely to be present in high copy number or overrepresented in DNA extracts used for genotyping.Finally, a minimum flanking sequence of 50 bp is required around the SNP for Infinium probe design (60 bp preferred), which would exclude some SNPs positioned close to the ends of contigs. Although Infinium technology is more tolerant of the presence of other SNPs within the probe sequence, we decided to err on the side of caution and also eliminate any SNPs within 50 bp of each other. Custom PERL scripts were designed to mine the contig FASTA file based on the SNP report tables produced by Genomics Workbench and isolate SNPs with sufficient flanking sequence which were >50 bp away from any other SNP. These filters reduced the number of possible SNPs to 4513 (spread across 2943 contigs). A custom PERL script was then employed to extract the flanking 50–60 bp around each marker and annotate the SNP itself with the format [allele1/allele2]. This provisional SNP probe set was uploaded to the Illumina Assay Design Tool (ADT) and the SNPs assessed for probe designability. SNPs with designability scores of 0.6 or higher were selected for inclusion in the final array design, producing an initial assay of 3775 putative SNPs in total. Subsequent validation steps (described below) reduced the final marker set to 2185 SNPs.A bi-parental mapping population  was genotyped using the custom Infinium assay and the data used to produce a cluster file for allele calling. Clustering was initially performed using automated cluster assignment within Illumina’s Genome Studio software. However, comparison of the clustering of the SNPs was inconsistent and manual reassignment of cluster position was required. This was independent of the original GenTrain score and, therefore, we were unable to manually reassign the cluster position to only SNPs with a GenTrain score below a certain threshold. Therefore, although laborious, all SNPs were visually inspected by one person for their original automated AA/AB/BB cluster positions and manually reassigned where appropriate. This also included the exclusion of SNPs with poor performance in this genetically diverse population. Markers were excluded where the average intensity (R mean) for each cluster was below 0.2 or cluster separation was less than 0.3. Markers were reviewed where cluster separation ranged between 0.3 and 0.45 . Markers were also excluded where there were missing data for more than 10 % of 716 samples, leaving 2501 markers at this stage. The wide range of genotypes used at this stage will maximise the general utility of the selected probes for further studies, because any interference due to genetic polymorphism resulting from genetic distance between the sequenced plants and the tested individuals will lead to exclusion from the array at this stage.Genotyping was performed as per the manufacturer’s guidelines using the Illumina Infinium iSelect custom assay . There was a 91 % assay conversion rate resulting in 3425 putative SNPS on the final array (2334 in unique contigs). The Although the original 5 individuals that were sequenced for the identification of SNPs were genotyped on the array, the comparison of the RNA sequence data to the genomic DNA SNP array proved difficult due to lack of information on allelic expression bias. Therefore, to further validate the markers, the cluster positions of the 2501 markers on the ecotype samples were exported and applied to the biparental mapping population that had also been genotyped on the array. This enabled use of heritability of alleles within a segregating population to be employed as confirmation of marker behaviour. Of the 2501 markers, 43 markers had more than four parent–parent–child heritability errors and were subsequently excluded, leaving a total of 2458 markers for further analysis on the ecotype population.http://www.ncbi.nlm.nih.gov/SNP/) under accessions ss1751856902–ss1751859086 and are due for public release in Autumn 2015. Probe details are thus also provided in Supplementary Data Table 2. Marker names follow the convention “ContigX_Y” where X is the contig number as in the NCBI Transcriptome Shotgun Assembly (accession GDAT00000000) and Y is the base position of the SNP within that contig. Users may freely employ these probes in their own assays; alternatively IBERS offer access to the existing array as a genotyping service (contact corresponding author).Thus, following reassignment of cluster positions or exclusion of markers using all 716 samples 2458 loci were exported. 239 had a minor allele frequency less than 5 % and were, therefore, excluded. Markers were also tested for observed heterozygosity (Ho) excess using GenePop (Raymond and Rousset Data exported from Genome Studio (Illumina) were converted to allele specific presence. For the A alleles for each SNP, AA individuals were coded as 1, AB as 0.5 and BB and missing data were 0, and vice versa for the B alleles. Missing data were, therefore, coded as 0 for both the A and B alleles and were, therefore, not imputed. Allele frequencies for each marker within accessions were calculated by summing the values for the genotypes (as described above) for each individual within an accession for each SNP and divided by the number of individuals in the accession. Principal component analysis (PCA) was performed on these relative allele frequencies using R (version 2.15.3). Markers contributing the most to PC1 and PC2 were identified via the absolute loading of each marker to the respective PC. For each PC, the BLASTx annotation for the top 50 markers was investigated Tables .PT. PhiPT is used for codominant data as it suppresses intra-individual variation  (Earl and VonHoldt Population structure was inferred using an unbiased Bayesian approach Markov chain Monte Carlo (MCMC) clustering of samples via STRUCTURE v2.3.4 , however, are representative of those in the sampled location. Allele frequency was, therefore, used to represent the sample locations across Europe in analyses.The Infinium platform was used to quantify the diversity present in 90 geographically referenced ecotype accessions, represented by 716 individual genotypes, spanning 21 countries and across a range of geographical conditions in Europe . The regions were defined by groupings observed in the PCA plot . Whilst some genetic variation was partitioned between regions, the greatest diversity (68 %) was attributed to between individuals within an accession (Table PT showed greater variation between populations within regions (PhiPR), compared to between regions (PhiRT). A more focused analysis of the distribution of variation between the different regions found similar levels (73–74 %) of within accession variation in the East and West group. The greatest within-population variation was found in the North group, at 76 %, and the least variation in the South (69 %). The regional PhiPT values reflect the between-population variation and indicate that this is highest in among accessions in the Southern group.The distribution of the genetic variation was also considered and partitioned based on the outcome of the PCA and geographic–genetic correlations  of 0.798 was found for PC1 to longitude (P < 0.001) and 0.712 for PC2 to latitude (P < 0.001) . Ecotypes from the UK were found to cluster in the upper left quadrant of the PCA plot, with particular similarity of accessions originating from England and Ireland. Accessions from Scottish islands and Wales were more divergent. A strong Iberian cluster was observed, with exception of one Portuguese accession  and the inclusion of an Italian accession . The centre of the PCA plot shows divergence of accessions along PC2 approximately split by the Alps Mountain range. Accessions originating from Eastern Europe are found on the right hand side of the plot, with particular extremity shown by those collected from Bulgaria.To understand the broad genetic diversity and distribution across Europe, unbiased PCA was performed on the allele frequency for each of the 2185 SNPs within each of the 90 sample locations (accession) . The notable outlier  was PT4, which was expected given the clustering in the PCA plot  and latitude . The probability of an individual’s group membership was averaged for each region, as defined by the PCA plot . High probabilities were found for each group  suggesting that the secondary peak at K = 4 was reflective of the PCA plot.The population structure of the European ecotypes was also examined using STRUCTURE. The optimal number of subgroups (K) within this large collection of individuals was found using Structure Harvester to be two  for PC1 and PC2 were identified Tables , 4. MarkL. perenne. This significantly increases the number of SNPs assayed on a single array from the previously published 768-plex Illumina GoldenGate array : potentially a mixture of both. Divisions of the L. perenne population across both latitudinal and longitudinal gradients have been proposed previously in limited sample population sizes and with a reduced marker set using isozyme analysis   Table .Evidence has also been previously provided to support the migration from South to North Europe via comparisons of geographic groups using AMOVA, whereby no variation was found between Near Eastern and Southern European ecotypes, nor Western and Southern European ecotypes .Markers contributing to PC2, or the North–South axis, tell a similar story. Markers were identified in transcripts linked to plant growth/development: cysteine proteinase . The next steps will be to identify the extent of linkage disequilibrium within L. perenne to determine the power of this marker set to perform genome-wide association studies (GWAS) of adaptive traits, as well as to determine the amount of ecological diversity which has been captured within existing breeding programmes. Large-scale genotyping has the potential to significantly improve the rationale of conservation, characterisation and utilisation of crop genetic resources Supplementary material 2 (EPS 1829 kb)Supplementary material 3 (XLSX 229 kb)