Pisum sativum L.) is a self-pollinating, diploid, cool-season food legume. Crop production is constrained by multiple biotic and abiotic stress factors, including salinity, that cause reduced growth and yield. Recent advances in genomics have permitted the development of low-cost high-throughput genotyping systems, allowing the construction of saturated genetic linkage maps for identification of quantitative trait loci (QTLs) associated with traits of interest. Genetic markers in close linkage with the relevant genomic regions may then be implemented in varietal improvement programs.Field pea  population. A total of 705 SNPs (91.7%) successfully detected segregating polymorphisms. In addition to SNPs, genomic and EST-derived simple sequence repeats (SSRs) were assigned to the genetic map in order to obtain an evenly distributed genome-wide coverage. Sequences associated with the mapped molecular markers were used for comparative genomic analysis with other legume species. Higher levels of conserved synteny were observed with the genomes of Medicago truncatula Gaertn. and chickpea (Cicer arietinum L.) than with soybean (Glycine max [L.] Merr.), Lotus japonicus L. and pigeon pea (Cajanus cajan [L.] Millsp.). Parents and RIL progeny were screened at the seedling growth stage for responses to salinity stress, imposed by addition of NaCl in the watering solution at a concentration of 18 dS m-1. Salinity-induced symptoms showed normal distribution, and the severity of the symptoms increased over time. QTLs for salinity tolerance were identified on linkage groups Ps III and VII, with flanking SNP markers suitable for selection of resistant cultivars. Comparison of sequences underpinning these SNP markers to the M. truncatula genome defined genomic regions containing candidate genes associated with saline stress tolerance.In this study, single nucleotide polymorphism (SNP) markers associated with expressed sequence tags (ESTs) were developed and used to generate comprehensive linkage maps for field pea. From a set of 36,188 variant nucleotide positions detected through The SNP assays and associated genetic linkage maps developed in this study permitted identification of salinity tolerance QTLs and candidate genes. This constitutes an important set of tools for marker-assisted selection (MAS) programs aimed at performance enhancement of field pea cultivars. Pisum sativum L.) is widely cultivated on a global basis as an important legume crop for human dietary protein intake and livestock forage nutrition[Field pea , such a+ cation, and in Australia, are commonly associated with highly alkaline (pH > 8.5) soils[Soil salinity is a serious global problem due to limitation of plant growth and reduced crop yield. Salinit5) soils,28. In c5) soils and boro5) soils-32 have 5) soils,34, whea5) soils and cano5) soils, due to 5) soils in pea. 5) soils-41, as w5) soils, appear 5) soils and lent5) soils.+ variability in the soil profile, and differential responses according to both growth stage and genotype. However, low-cost and reliable pot-based glasshouse screening methodologies have been developed for a range of crops, including pea[Research on other major dry-land crops such as wheat has demoding pea, which cding pea,46. IdenThe objectives of the present study were: development and characterisation of novel SNP markers and characterisation of existing SSR markers; construction of an SSR- and SNP-based linkage map for a field pea population varying for salinity tolerance; comparative genetic analysis between field pea and other legumes of the sub-family Papilionoideae; and identification of genomic regions and molecular genetic markers associated with salinity tolerance in field pea.2 generation progeny were produced. Single seed descent was undertaken from F2 progeny-derived genotypes for 4 generations in the glasshouse from 2008 to 2011. The resulting F6 mapping population consisted of 134 RILs.Crosses were made between single genotypes of cultivar Kaspa , and Parafield (moderately tolerant). The crosses were performed at DEPI-Horsham in 2007 and FFrozen leaf tissue from each progeny genotype was ground using a Mixer Mill 300 , and genomic DNA was extracted using the DNeasy® 96 Plant Kit . DNA was resuspended in 1 x TE buffer to a concentration of 50 ng/μl and stored at -20°C.GENe software v1.96 . Based on alignment of high-quality sequences from four genotypes (including Kaspa and Parafield[de novo assembly), all base variants were identified. All insertion and deletion (indel) variants were excluded from further analysis. Subsequently, high-confidence SNPs were filtered using the following criteria: (1) base variants in homozygous condition within each genotype; (2) read-coverage equal to or greater than 4; and (3) absence of any other base variants within 20 bp segments flanking each SNP.Putative SNPs were identified from transcriptome sequence data using Nearafield) with thhttp://www.basic.northwestern.edu/biotools/oligocalc.html). PCR reactions contained 10 ng of genomic DNA in a 12 μl reaction with 5 μM of each primer pair. The amplification conditions were as follows: a hot start at 94°C for 15 min, followed by 35 cycles of 95°C for 30 s, 50°C for 30 s and 72°C for 1 min, and a final elongation step at 72°C for 7 min. PCR products were purified in a 15 μl reaction containing 0.5 U exonuclease I (New England Biolabs), 0.5 U shrimp alkaline phosphatase  and 5 μl of PCR product. Sequencing reactions were performed in a total volume of 7.5 μl, each reaction contained 3.2 μM primer, BigDye® Terminator v3.1 , BigDye® sequencing buffer  and were subjected to cycling conditions as described in the BigDye® v.3.1 protocol. Extension products were purified by the ethanol/EDTA/sodium acetate precipitation method, resuspended in 12 μl Hi-Di™ formamide , and separated on the ABI3730xl automated capillary electrophoresis platform. DNA sequence analysis and alignment was performed using Sequencher 4.7, while contig assembly and the SNP validation was performed visually.A sub-set of 48 SNPs was selected for experimental validation by Sanger sequencing. Primer pairs were designed using Sequencher 4.7  and OligoCalc: Oligonucleotide Properties Calculator . A designability rank score (0 to 1) was calculated for each SNP by Illumina. Finally, SNPs with designability scores between 0.7 and 1.0 were selected for development of an Illumina GoldenGate® oligonucleotide pool assay (OPA) for genotyping. Individuals were SNP genotyped according to the manufacturer's instructions using 250 ng of template genomic DNA. The genotyping assays were processed by the Illumina iScan reader. Automatic allele calling was achieved using the Illumina Genome Studio software v2011.1 with a GeneCall threshold of 0.20 and checking the output visually as well for the confirmation of cluster specificity.A preliminary list of SNPs was selected for GoldenGateχ2 score >10 were not included in further analysis. Map distances were calculated using the Kosambi mapping function[M. truncatula[The genetic linkage map was generated using Map Manager Software version QTXb19. Markersfunction at a thrfunction in commoruncatula,53. LGs runcatula.M. truncatula, v3.5 (http://www.medicago.org), G. max v189 (http://www.plantgdb.org), L. japonicus, v2.5 (ftp://ftp.kazusa.or.jp/pub/lotus/lotus_r2.5/pseudomolecule/) and C. cajan v5.0 (http://www.icrisat.org/gt-bt/iipg/Genome_Manuscript.html). BLASTN was used to conduct similarity searches against each genome sequence with a threshold E-value of 10-10.DNA sequences underlying map-assigned SSR and SNP markers were used to perform comparative analysis with genome assemblies of chickpea , -1 from day 9 post-emergence. The concentration of applied NaCl was increased by 3 dS m-1 at each watering time to avoid abrupt osmotic shock, up to a final rate of 18 dS m-1, and maintained at this concentration until assessment. All watering with the nutrient and salt solution was undertaken over 3 day-intervals at a rate of 200 ml per pot applied directly to the growing medium surface. A null-salt application treatment (no added NaCl) was included for control lines  and randomised in the experiment in order to eliminate effects due to other stress factors. Individual plants in each pot were assessed for symptom development (symptom score) as described previously[th day until plant death. Final plant biomass cuts were also obtained and seed set was recorded per genotype pot. Averages for plant symptom score were calculated from individual plant assessments and used to estimate genotype-specific average values for symptom score using REML spatial row-column analysis. An index was used to quantify genotypic salinity tolerance values, and to describe tolerance levels according to sensitivity based on weighted symptom scores and final biomass.The Kaspa x Parafield RIL population was screened for response to NaCl-induced stress applied at the seedling stage. Experiments were conducted during the autumn of 2012 in a semi-controlled (polyhouse) environment at DEPI-Horsham. Screening was undertaken by sowing six plants of each RIL at equidistant spacing in 13 cm diameter pots into a sand and gravel medium (to a depth of 2 cm in two pot replications). This provided 12 plants as replicates for each RIL. The medium was composed from a 1:1 ratio of coarse river sand and 5 mm bluestone chips. Each pot was treated daily with rainwater from sowing until emergence. From 6 days post-emergence, seedlings were watered with a complete nutrient solution , in addition to supplementation with a calcium source . The required NaCl concentration was tested using an electrical conductance (EC) meter and was applied at an initial rate of 3 dS meviously from 28 h2) were calculated for the trait by considering the spatial trends in the experiment using best linear unbiased prediction (BLUP) analysis.Averages for plant symptom score  and salt index were used to generate frequency distribution histograms. Narrow sense heritabilities . Simple interval mapping (SIM) and composite interval mapping (CIM) methods were used to identify and confirm QTLs associated with salt tolerance. The sequences underpinning SNP loci flanking the QTL-containing intervals were BLAST analysed against the ® assay.A total of 36,188 putative SNPs were identified from comparison of transcriptome reads obtained from the mapping parents against the EST sequence database. An average frequency of 1.85 SNPs per kb between two haplotypes was observed. A preliminary set of 21,000 SNPs were selected following elimination of indels. After further filtration based on the criteria of homozygous status and absence of other known SNPs in the vicinity, a sub-set of 956 high quality SNPs was obtained. Of these, a total of 953 satisfied the required primer design criteria and a final sub-set of 768 SNP loci with a designability rank of 1 was selected for GoldenGateAnalysis of nucleotide variation revealed that transition substitutions were more predominant 2:1) than transversions. The two most common SNP variants were A/G and C/T, representing 36% and 32% of all changes, respectively. The other SNP variants  accounted for less than 10% of the total  revealed polymorphism between the parental genotypes, of which 78 were selected for screening on the mapping population on the basis of consistency of amplification. A sub-set of 47 SSR markers generated data of sufficient quality to generate a framework genetic map, and 40 loci (85%) were assigned to 9 LGs. These data were then used to select 101 maximally recombinant individuals for use in bin mapping.6 level, so residual heterozygosity was expected to be low (c. 5 - 10%). A total of 705 SNPs (91.7%) produced coherent data, while those generating ambiguous cluster structures were removed from further analysis. A sub-set of 462 SNPs (65%) generated polymorphic clusters within the Kaspa x Parafield mapping population and were used for genetic linkage map construction.A total of 768 SNPs were used to genotype the 101 selected RILs. All SNPs were visually qualified, the majority producing two major clusters in Genome Studio representing the homozygous (AA and BB) genotypic classes, but occasionally a third small cluster of heterozygous (AB) genotypes was also observed  were excluded from linkage analysis due to excessive heterozygosity, missing data, skewed segregation or ambiguity. A final set of 467 markers (53 SSRs and 414 SNPs) was used for linkage map construction. A small proportion of markers were ungrouped, such that 458 (98%), comprising 48 SSRs and 410 SNPs (Table 3.5% wereCorresponding DNA sequences were available for 310 of 458 of the mapped loci (15 EST-SSRs and 295 SNPs), of which 307 detected significant sequence similarity matches to at least one of the reference genome sequences, and 130 sequences displayed similarity to sequences in all five genomes.M. truncatula macrosynteny was observed for 292 (94%) sequences. Among M. truncatula chromosomes, Mt5, 1, 3, and 7 exhibited synteny and colinearity with pea linkage groups Ps I, Ps II, Ps III and Ps V respectively   matches with segmental syntenic blocks rather than whole chromosomal relationships. Field pea – pigeon pea synteny analysis revealed the lowest number of matches (183), short conserved regions being distributed across different chromosomes. In most instances, CcLGs were inverted in order in comparison to PsLGs, apart from CcLG 2 and 11.Despite a large number of matches (294) between field pea and soybean sequencesM. truncatula, but complex relationships were observed with L. japonicus, pigeon pea and soybean, consistent with the pair-wise comparisons. The exception to these general patterns was Ps VI, which displayed complex relationships in all instances.The 130 common orthologous sequences were used to further analyse and confirm the degree of genome conservation Figures  and5. Fh2) for salt index was 0.55. Two different phenotypic measurements, including salt index and mean symptom score (average of symptom scores obtained at up to 35 days) were used to detect salt tolerance QTLs  for salt index score, and 12% and 17% for the symptom score, respectively (Table p (data not shown). The phenotypic data for symptom scores obtained at day 42, 49, 56 deviated from normality, and was consequently not used for QTL analysis based on mean symptom score.Plant symptom response data from salinity screening of the RIL population at the seedling stage indicated that variation for tolerance was normally distributed , diversity of populations under assessment and status (coding or non-coding) of analysed regions. The SNP frequency detected in field pea in the present study is much lower than values reported for cereal crops , but siruncatula and 2.06runcatula).The patterns of nucleotide substitution showed A/G and C/T to be the most common base changes, in agreement with previous studies of legume species such as white clover and chic® SNP assays for genotyping mapping populations and genetic resource collections of pea has been previously demonstrated[The effectiveness and suitability of GoldenGatenstrated. The prenstrated and chicnstrated. Successnstrated, it is nSeveral field pea linkage maps have been previously developed with successive adoption of new molecular marker technologies,12-17. TM. truncatula was consistent with the closer phylogenetic relationship between these species than for the other legumes used in this study. In contrast to results of previous comparative genetic studies between chickpea and other legumes[Extensive conservation of genome structure between field pea and both chickpea and  legumes,69, subsM. truncatula and 7 LGs of field pea, as well as evidence for evolutionary translocations[M. truncatula and field pea[M. truncatula genomes, and such information may be used for candidate gene selection for further application to breeding programs.Broad conservation of chromosome structure was observed between the 8 chromosomes of ocations,70. A nuocations,53 have ocations. Ps VI, ield pea, the extield pea. The larield pea. The resL. japonicus, soybean and pigeon pea genomes. The former is a member of the Galegoid clade of the Papilionoideae sub-family, but more distantly related to pea than M. truncatula and chickpea, while the latter two are members of the Phaseoloid clade, so the observed relationships are in accord with broad phylogenetic affinities[L. japonicus comparison revealed similarities, but was frequently interrupted by chromosomal rearrangements. Similar segmental syntenic relationships were observed between L. japonicus and the Galegoid forage legume white clover[M. truncatula[, again indicating the effects of taxonomic divergence.In contrast, large syntenic blocks spanning entire PsLGs were absent from the comparisons with the finities. For soyfinities. The fiee clover, as alsoruncatula. Extensip. Studies of different physiological traits associated with salt tolerance in M. truncatula identified a total of 19 putative genomic regions, the largest number of QTLs being located on Mt8 followed by Mt5, 1, 3, 4, 7, 6, and 2[M. truncatula sequences associated with markers flanking the QTL intervals. However, the comparative genome analysis revealed macrosyntenic relationships between Ps III and Mt2/3, and Ps VII and Mt4/8. It is hence possible that the QTLs identified in the present study may be conserved between the Galegoid legumes.Plant response to salt tolerance is influenced by various physiological mechanisms, which are likely to be controlled by multiple genes across different environments. The pre6, and 2. A direc+/K+ ratio, an essential process for salinity tolerance[The present study identified candidate genes associated with salt tolerance mechanisms in field pea. Histone deacetylase and glutamine synthetase have a key role in salt stress resistance in plants,82, whilolerance. The genThe QTLs identified in the present study are associated with seedling growth-stage salinity tolerance. Similarly, QTLs for seedling growth tolerance have been identified in numerous grain crops, including rice, barleyImplementation of molecular markers in MAS has rarely been achieved for physiologically complex traits such as salinity tolerance. In suchThe present study describes the development of a multiplexed set of EST-derived SNPs for genetic linkage map construction in field pea. Evaluation of salt tolerance under glasshouse conditions permitted identification of two significant genomic regions. Through use of sequence-associated markers, macrosyntenic relationships were determined between field pea and five other legumes and used to predict candidate genes for salt tolerance. This information may be used for the development of linked and diagnostic polymorphisms for marker-assisted selection (MAS) of salt tolerant cultivars, based on introgression of QTL-containing genomic regions from donor to recipient germplasm. As salinity tolerance is a physiologically complex trait, future research will require evaluation in different screening environments and across varying ontogenic stages to identify additional associated genomic regions. Finally, the genetic resources generated in this study will assist other trait-dissection studies and facilitate transfer of information from related legume crops for future enhanced breeding of field pea.The authors declare that they have no competing interests.AL performed population development, phenotypic assessment, data interpretation and contributed to drafting the manuscript. SS performed marker discovery, map construction, QTL analysis, comparative genomics and contributed to drafting the manuscript. NC co-conceptualised the project, contributed to data interpretation and assisted in drafting the manuscript. PS and MN co-conceptualised the project and assisted in drafting the manuscript. JF, MM and SK co-conceptualised and coordinated the project and assisted in drafting the manuscript. All authors read and approved the final manuscript.Percentage of SNP base variants. This file contains a pie-chart depicting the percentages of each SNP base variant class.Click here for fileDetails of the 768plex SNP-OPA design. This file contains names and sequence information for all SNP markers used for linkage mapping.Click here for fileSNP validation using Sanger sequencing. This file contains an example of an electropherogram generated by Sanger sequencing to demonstrate SNP validation, and showing the occurrence of two arising SNPs between different mapping family parents.Click here for fileRepresentative clustering patterns generated by the Illumina GoldenGate® SNP Genotyping assay. The file contains an example of clustering patterns obtained from SNP genotyping assays on two mapping populations. The data point colour codes represent: red, AA (homozygous); blue, BB (homozygous); purple, AB (heterozygous); black, no call (missing data). A) High-quality polymorphic SNP; B) Monomorphic SNP; C) SNP with a large number of heterozygous individuals; D) Failed SNP.Click here for fileLinkage map statistics. This file contains details of different markers (SSRs and SNPs) and their corresponding positions on different LGs.Click here for fileSynteny analysis statistics. This file details of field pea LGs, number of marker sequences and synteny with chickpea, M. truncatula, soybean, L. japonicus and pigeon pea chromosomes.Click here for fileFrequency distribution histogram. Frequency distribution for salinity index value and qualitative rating (T (tolerant), MT-T (moderately tolerant to tolerant), MS-S (Moderately sensitive to sensitive), S (sensitive), HS (high sensitivity) for Kaspa x Parafield RIL progeny following salinity treatment of 18 dS m-1.Click here for fileFrequency distribution histogram. Frequency distribution for symptom score of Kaspa x Parafield RIL progeny at 7, 14, 21, 35, 42, 49 and 56 days post application of NaCl in watering solution at 18 dS m-1.Click here for file


When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u2) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects.Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared ( Genomic best linear unbiased prediction (GBLUP) and Bayesian methods are routinely used for genomic selection (GS) and genome-wide association studies (GWAS) in animal and plant breeding, and in human disease studies. The main objective of GWAS is to identify the quantitative trait loci (QTL) with major effect, whereas GS focuses on using genomic information to better evaluate the genetic potential of individuals. Genomic estimated breeding value and single nucleotide polymorphism (SNP) effect or variance can be easily derived from both GBLUP and Bayesian methods. GBLUP assumes a normal distribution for SNP effects, and calculates their effect from phenotypes and a genomic relationship matrix. When not all individuals in a population are genotyped, single-step GBLUP (ssGBLUP) is the method of choice  provides genomic estimated breeding value (GEBV) similar to those of a Bayesian procedure using the same weights  is the weight of SNP i at iteration t+1, i at iteration t, and pi is the minor allele frequency (MAF). They iterated either on SNP alone or on GEBV and SNP. The first procedure gave a good identification of top QTL, and the second procedure provided a slightly higher accuracy of GEBV compared to BayesB, but only at the second iteration. The same study also found that the correlation of simulated and estimated SNP effects was lower than the correlation between simulated SNP and a sum of the effect of 8 SNP clustered around simulated QTL.Wang et al.  evaluateWe hypothesized that efficient SNP weighting methods could considerably increase accuracy of predicting GEBV and help to better estimate SNP effects under WssGBLUP models. Therefore, the objective of this study was to present new procedures to calculate SNP weights individually or for genomic regions (windows) that would improve genomic predictions at animal and SNP level in WssGBLUP compared to other genomic models.−5 was assumed for markers and QTL per generation per locus. The simulated population originated from a historical population with 1000 generations of random mating to create linkage disequilibrium (LD) and establish mutation-drift balance. After that, a recent population was established from 200 males and 2600 females out of the historical population. The simulation was carried out for 205 generations. In every generation, the above number of males and females were selected to mate and produce 1 offspring, forming a population with an effective size (Ne) of 743. For all the analyses, generations 200–204 were treated as a training population and generation 205 as a validation population, with 1240 and 300 genotyped animals respectively. The complete dataset contained 18,400 individuals in the pedigree, of which 13,000 were phenotyped and 1540 were genotyped. Phenotypes were the sum of general mean, true breeding values (sum of QTL effects) and random residuals. Each simulated scenario was replicated 10 times.To test our hypothesis, one trait with a mean of 1.0, a phenotypic variance of 2.0, and an heritability of 0.5 was simulated using QMSim  until Whereas the approach derived by Wang et al.  calculatFor all the 5 new procedures, the changes were done in step (d) where SNP weights are calculated based on SNP effects. Therefore, instead of using Constant: still using the concept of SNP-specific weights, the weight was calculated assi is the number of standard deviations from the mean for each Nonlinear A: using similar approach to VanRaden , SNP-sped3ii with ii from i to i+19 was used as weight for all SNP in the window comprised of 20 SNP; it uses non-overlapping windows;Largest window: uses the concept of SNP-window weights, with weights for a group of 20 SNP asn is the size of the non-overlapping window;Mean window: the weight for a group of 20 SNP was calculated asSummed window: the weight for a group of 20 SNP was calculated asQuality control of genomic data retained SNP with call rates >0.9, minor allele frequencies >0.05, and departures from Hardy-Weinberg equilibrium (difference between expected and observed frequency of heterozygous) <0.15. An average of 36,000 SNP remained after quality control, and the average LD (r2 = correlation between loci pair) in the last generation was about 0.29.For WssGBLUP, the model included population mean, animal effect, and random residual error. All phenotypes , pedigree, and genotypes were accounted for. GEBV was obtained by using the software BLUPF90  were used together with the simulated variance components. The software BLUPF90  following the approach described in Garrick et al.  de-regressed  set to 0.5, 0.9, and 0.99. The simulated values were used as benchmark. Two comparisons were made: (1) accuracy of predicting GEBV defined as the correlation between true simulated breeding value (TBV) and GEBV in the validation population (“accuracy” hereinafter); (2) ability to detect simulated QTL, done by visual inspection of Manhattan plots and by the amount of genetic variance explained. We did not derive significance thresholds or Tables For early iterations (≤5), largest, mean, and summed windows were the most accurate methods. Using the mean weight of 20 SNP in the windows as weight for the entire window improved accuracy by 0.01–0.09 compared to the other new procedures under the 5-QTL scenario. For 100-QTL, the procedure that added a constant outperformed the other new procedures, whereas for 500-QTL both constant and nonlinear A achieved the greatest accuracies that persisted for 10 generations. Overall, we observed that window procedures performed better than procedures with single SNP weights because the uncertainty was smaller . The accuracy for the default procedure was exceeded by the constant only in the 100-QTL scenario (+0.01). Adding a constant to avoid under-weighting in the 5-QTL scenario, where most SNP did not have effect on the trait, was redundant and counterproductive. Still, in the procedure that added a constant, the plateaued accuracies exceeded GBLUP by 0.11, 0.06, and 0.14 under the 5-, 100-, and 500-QTL scenarios  under the 500-QTL scenario, whereas for 5-QTL scenario it had the lowest accuracy among WssGBLUP approaches.The nonlinear A weighting method used in our study was adapted from VanRaden  who defiG in WssGBLUP, but it did not improve the accuracy of GEBV (results not shown). When SNP variance from the literature was used as SNP weight for GBLUP, Zhang et al.  were compared to single SNP weighting (constant and nonlinear A), we observed an average increase in accuracy of 5 percentage points for 5-QTL Tables –4. For 1D = I) and the iteration where the peak accuracy was reached reduced as the number of simulated QTL increased, confirming that weights are more important when traits are influenced by fewer QTL. Thus, for the majority of the traits of interest in livestock breeding, ssGBLUP is able to attain the greatest accuracies without using weights for SNP. For specific traits influenced by few QTL or traits known for being influenced by important QTL  using weights that are common to a SNP window may help to better capture the signal from the QTL in that region. Therefore, using weights for SNP windows helps to improve the accuracy when a relatively small number of markers lose the ability to capture the possible QTL effects. Possibly, differential SNP weighting better depicts the covariance structure of G and reflects the actual contribution of SNP to additive genetic variance of a trait , 6.0 (2.0), and 26.1 (2.8). Figures Ne), where small populations share less independent markers. Perhaps, a Manhattan plot with high resolution (with noisy pattern) is a feature of smaller genotyped populations.Applying common weights for groups of SNP can help to increase the resolution of Manhattan plots; however, the resolution may be limited by the effective number of independent markers as proposed by Li et al. . In theiNew procedures for calculating SNP weights in WssGBLUP can be effective in improving both the accuracy of GEBV and SNP effects. GEBV from WssGBLUP are more accurate than those from BayesB and BayesC, although different priors and π for the latter can change the ranking of the methods. Procedures that consider weights for a SNP window may be the best choice compared to single SNP given that the true number of QTL may not be known in real data. In addition, considering a group of SNP in the same genomic region may be the most appropriate way to capture the signal of an unknown QTL. The WssGBLUP method is especially useful for GWAS and GEBV estimation when only a fraction of the population is genotyped. WssGBLUP with 3 iterations may be enough to reach maximum predictivity of GEBV and SNP effects when a trait is influenced by few QTL.XZ implemented the research, contributed ideas to the algorithms, and wrote the article. DL contributed ideas to the algorithms and revised the article. IA programmed the original version of software, interpreted the algorithms, and revised the article. AL contributed ideas to the algorithms and revised the article. IM lead and designed the project, contributed ideas to the algorithms, and revised the article. All authors approved the final version to be published, and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.USDA's National Institute of Food and Agriculture .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.


Thrips tabaci is a major pest of high-value vegetable crops and understanding its population genetics will advance our knowledge about its ecology and management. Mitochondrial cytochrome oxidase subunit I (COI) gene sequence was used as a molecular marker to analyze T. tabaci populations from onion and cabbage fields in New York. Eight COI haplotypes were identified in 565 T. tabaci individuals collected from these fields. All T. tabaci were thelytokous and genetically similar to those originating from hosts representing seven plant families spanning five continents. The most dominant haplotype was NY-HT1, accounting for 92 and 88% of the total individuals collected from onion fields in mid-summer in 2005 and 2007, respectively, and 100 and 96% of the total in early fall in 2005 and 2007, respectively. In contrast, T. tabaci collected from cabbage fields showed a dynamic change in population structure from mid-summer to early fall. In mid-summer, haplotype NY-HT2 was highly abundant, accounting for 58 and 52% of the total in 2005 and 2007, respectively, but in early fall it decreased drastically to 15 and 7% of the total in 2005 and 2007, respectively. Haplotype NY-HT1 accounted for 12 and 46% of the total in cabbage fields in mid-summer of 2005 and 2007, respectively, but became the dominant haplotype in early fall accounting for 81 and 66% of the total in 2005 and 2007, respectively. Despite the relative proximity of onion and cabbage fields in the western New York landscape, T. tabaci populations differed seasonally within each cropping system. Differences may have been attributed to better establishment of certain genotypes on specific hosts or differing colonization patterns within these cropping systems. Future studies investigating temporal changes in T. tabaci populations on their major hosts in these ecosystems are needed to better understand host-plant utilization and implications for population management. Thrips tabaci Lindeman is a major insect pest of multiple crops worldwide including two high-value vegetable crops, onion, Allium cepa L., and cabbage, Brassica oleracea capitata (L.) Thrips tabaci is known to have multiple biotypes with different reproduction modes, host preferences and virus-transmission competencies T. tabaci is a complex of cryptic species T. tabaci populations from tobacco and populations from onion and leek have been documented T. tabaci populations in cropping systems will advance our knowledge about its population ecology, which can be important for developing effective management strategies T. tabaci infestations in onion and cabbage fields are routinely managed with applications of insecticides. Insecticide resistance in T. tabaci populations in onion fields has been widespread T. tabaci in cabbage T. tabaci infestations in onion T. tabaci populations in onion fields to insecticides have also been documented T. tabaci populations and that they may change during the season.In New York (USA), T. tabaci population genetics is important for developing management strategies, especially those that mitigate insecticide resistance. Mitochondrial DNA markers have been successfully used to identify genetic differentiation among T. tabaci populations T. tabaci populations collected from onion and cabbage fields in New York to examine 1) the differentiation between T. tabaci populations from these crops and how they relate to conspecifics from other reproductive modes, hosts and locations, and 2) seasonal changes in T. tabaci population genetic structure from onion and cabbage cropping systems. Our results revealed a commonality in the most abundant T. tabaci haplotypes from onion and cabbage fields and these haplotypes were genetically similar to those originating from an array of hosts and locations around the world. All haplotypes from onion and cabbage fields were in the same lineage as those that reproduce via thelytoky; none were in the arrhenotokous or “tobacco type” clades. The population genetic structure of T. tabaci populations in onion fields was similar between mid-summer and early fall, whereas the population structure in cabbage fields changed substantially between these periods.Knowledge about Vegetable growers gave us verbal permission to collect thrips on their privately owned land and no permits were required to collect this common insect. Sampling did not involve regulated, endangered or protected species.Thrips tabaci adults were collected from commercial onion and cabbage fields in western New York in 2005 and 2007. Onion and cabbage fields were sampled from two of the largest onion and cabbage production regions in New York , and incubated at 65°C for 30 min and then heated at 95°C for 15 min to inactivate the protease K activity in the lysate. The lysate was used as the DNA template for PCR amplification of a 706 bp fragment of mtCOI, using the universal COI primer pair for insects, LepF: ATTCAACCAATCATAAAGATATTGG and LepR: TAAACTTCTGGATGTCCAAAAAATCAThrips preserved in ethanol were rinsed with de-ionized water to remove ethanol and then DNA from individual thrips was isolated with a rapid genomic DNA preparation method The PCR products were purified for DNA sequencing using a one-step enzymatic purification method T. tabaci COI haplotypes identified in this study and from public databases, a BLASTN search T. tabaci populations from cabbage and onion fields collected during the two seasons, the population fixation index FST was calculated using the software ARLEQUIN 3.5.1.2 P<0.01.DNA sequences from thrips individuals were assembled and aligned to identify single nucleotide polymorphisms (SNPs), using the DNASTAR Lasergene software suite . For analysis of the phylogenetic relationships of the T. tabaci individuals sequenced, 10 SNP sites were identified within the 655 bp COI fragment (excluding the primer sequence regions) (From 565 regions) , yieldinregions)  and S2. regions) , resultiT. tabaci, arrhenotoky and thelytoky T. tabaci individuals sampled in this study were all thelytokous  sequences were combined and were chosen for this study. A phylogenetic tree of the eight T. tabaci COI haplotypes and the 43 sequences from GenBank was generated with the COI sequence from T. imaginis as an outgroup, by maximum likelihood analysis  and 0.244 (P<0.01), respectively. However, the FST values for the populations in onion were very low, 0.033 and 0.034 in 2005 and 2007, respectively, showing minimal population structural change between the two seasons. Pairwise comparisons of FST values between the T. tabaci populations indicated that T. tabaci populations in cabbage and onion had different genetic structures  and 0.551 (P<0.01).The population structural change of T values . The paiructures . The greT. tabaci , numerous similar analyses from around the world have followed T. tabaci in our study were from a single leek clade that included individuals collected from an array of hosts representing seven plant families and multiple locations spanning five continents  during mid-summer and early fall, whereas populations in cabbage fields included several common haplotypes that changed considerably from mid-summer to early fall. The haplotype NY-HT2 was noticeably abundant in cabbage fields in mid-summer, but became a minor type in early fall , it is possible that T. tabaci populations from onion may colonize cabbage and other crops late in the season following onion harvest in August and September. If so, this may explain the late-season occurrences of NY-HT1 in cabbage fields.While onion and cabbage fields were grown in proximity , habitatT. tabaci has a wide host range T. tabaciT. tabaci and other cultivated hosts. The extent to which T. tabaci populations restrict themselves to specific crop hosts in space and time will likely influence the population dynamics related to the development and spread of traits such as insecticide resistance within and among different cropping systems in the same area. Future studies investigating population structures of T. tabaci on their major hosts in these ecosystems through time are needed to better understand host-plant utilization and its potential implications for population management.Although Figure S1DNA sequence of the 655 bp COI fragment from Thrips tabaci collected in New York. Eight COI haplotypes were identified from 565 T. tabaci individuals, based on the SNP site . The nucleotides G and T boxed are specific for thelytokous T. tabaci(TIF)Click here for additional data file.Table S1Frequency of Thrips tabaci haplotypes collected from onion fields, Allium cepa L, during mid-summer and early fall in western New York in 2005 and 2007.(DOCX)Click here for additional data file.Table S2Frequency of Thrips tabaci haplotypes collected from cabbage fields, Brassica oleracea, during mid-summer and early fall in western New York in 2005 and 2007.(DOCX)Click here for additional data file.


Glycine max (L.) Merr.] is a primary source of vegetable oil and protein. We report a landscape analysis of genome-wide genetic variation and an association study of major domestication and agronomic traits in soybean. A total of 106 soybean genomes representing wild, landraces, and elite lines were re-sequenced at an average of 17x depth with a 97.5% coverage. Over 10 million high-quality SNPs were discovered, and 35.34% of these have not been previously reported. Additionally, 159 putative domestication sweeps were identified, which includes 54.34 Mbp (4.9%) and 4,414 genes; 146 regions were involved in artificial selection during domestication. A genome-wide association study of major traits including oil and protein content, salinity, and domestication traits resulted in the discovery of novel alleles. Genomic information from this study provides a valuable resource for understanding soybean genome structure and evolution, and can also facilitate trait dissection leading to sequencing-based molecular breeding.Cultivated soybean [ Glycine max (L.) Merr.), a leguminous crop of major economic importance, is a main source of oil and protein267Legumes account for 27% of the world’s primary crop production, and the legume seeds are an essential source for food and feed and also provide nitrogen fixation through symbioses with microorganismsGlycine soja, the undomesticated ancestor of Glycine maxG. max genome, and the comparative genomic analysis shows significant differences between the genomic compositions of the 2 soybean lines12As a commercial cash crop in both the developing and developed world, the genomic information of soybean is essential for discovering traits for crop improvement. This information will help to further investigate the genetic bottle-neck that causes a change in allele frequencies and causes low genetic diversity and high linkage disequilibrium (LD), eliminating rare alleles in the selected populations/lines. In 2010, the first cultivated soybean genome was sequencedOryza sativa)Zea maize)Cucumis sativus)Sorghum bicolor)Phaseolus vulgaris)819208132223G. soja, has been collected from a wide geographical distribution of a multitude of ecological and environmental conditions. Along with the wild species, soybean landraces also have an extensive geographical distribution. Adaptation to a wide variety of environmental condition makes these lines a valuable (untapped) reservoir for adaptive traits and associated genes that can be used in soybean breeding programs for various agronomic traits including stress tolerance and yield improvement.Advances in the next generation sequencing (NGS) technologies have made crop genome sequencing easier and more cost effective, and as a result, several crop genomes were sequenced, for example, rice  than any previous reports on soybean. Comparison of genomic sequences has revealed approximately 35.34% new SNPs that have not been previously reported913We selected a total of 106 soybean accessions representing different countries of origin and domestication. The collection includes seven wild, 43 landraces, and 56 elite soybean cultivars associated with different traits as well as maturity groups for whole genome re-sequencing using the Illumina platform . Most ofWe analyzed the SNP position relevant to the gene annotation, and also identified non-synonymous SNPs. The majority of SNPs (82.6%) were located in intergenic regions, with only a small portion (3.83%) being located in the coding sequences (CDS) (6% of the genome); the remainder were present in the 5′ and 3′ un-translated regions (UTR) and introns . Lower d922Total indels  were detected using the SOAPindel pipeline. A total of 745,814 indels were identified in all germplasm lines . ApproxiLarge-effect SNPs are mutations that affect gene splicing and polypeptide chain initiation and termination. These large-effect SNPs have a major impact on gene function, and a total of 12,029 large-effect SNPs in 7,632 genes were identified, including 6,236 premature stop SNPs, 1,072 stop to non-stop code SNPs, 1,000 start to non-start code SNPs, and 3,721 splice sites SNPs . This reWe detected gene content variation by assembling the unmapped sequences and annotating them to identify the presence and absence variations (PAVs) in different soybean lines. In total, 7,902 PAVs gene fragments longer than 1,000 bps were identified, and approximately 994 PAVs in the coding sequence have homology to plant proteins . We haveTo investigate the phylogenetic relationship and population structure of the selected 106 soybean lines, we generated the population structure using 79,632 4-fold degenerate-site SNPs without missing genotypes using STRUCTURE28I locus was examined to evaluate the difference in seed coat color because the dominant forms (I and ii) of the I locus inhibit pigmentation of the seed coat in a spatial manner resulting in a completely yellow seed  or a yellow seed with pigmented hilum I, ii, and i) in the form of haplogroups for seed color. The second haplogroup further divided into 2a (majority with buff hilum) and 2b (majority with black hilum). The allelic variation present in the CHS genes I locus is a major contributor for seed coat color.The phylogenetic relationships among 106 soybean germplasm lines were analyzed using both 4-fold degenerate-site SNPs, and all 10,417,285 SNPs were obtained from the sequence comparisons. The results of 2 SNP data sets show almost the same structure as in All of the US cultivars were grouped into the elite group, and most of Chinese cultivars were grouped into the landrace group, which confirms the fact that the cultivated soybean was domesticated in China and then introduced to the United States. Individuals from the same geographical region tended to cluster together, which reflected isolation by distance during evolution and/or parallel selections in similar ecological habitats accompanied by gene flow.r2) of alleles to measure the LD level in the 3 populations. The wild, landraces and elite germplasms showed a high LD level . The total number of SNPs and the number of non-synonymous SNPs was 15% higher in wild soybeans than in the landrace and elite groups. Also the number of large effect SNPs was nearly the same between landraces and elite groups  and almost 57% of the non-synonymous SNPs  from wild soybeans were shared with the landrace and elite groups, which indicated that approximately 43% of the SNPs were distinct between the wild and other 2 groups . This obThe number of SNPs, which was different between the landraces and elite cultivars , was about one-quarter (27%) of the total number of SNPs that occurred during the domestication process . SimilarFst) between wild, landrace, and elite soybean was calculated. This measurement helped to localize genomic regions with a higher degree of diversification between wild-landrace, wild-elite and landrace-elite soybeans   in 50-kb sliding windows with a step size of 5 kb was used to identify regions with significantly lower levels of polymorphisms in the cultivated groups. We have identified 159 putative domestication sweeps, which include 54.34 Mbp (5.7%) and 4,414 genes.We combined the landrace and elite groups as a single cultivated gene pool to identify the domestication sweeps. The regions and genes under domestication sweeps should have significantly lower diversity in cultivated soybeans compared to wild soybeans. The ratio of genetic diversity by comparing the wild group to cultivated groups (πwww.soybase.org) was gathered to develop a gene list containing 15,502 genes associated with 96 QTL regions for the most important soybean agronomic traits. These genes were used for the diversity analysis. Approximately 22 sweep regions (496 genes underlying 26 QTL) were overlapped in the 15,502 gene regions  were identified. Approximately 15 artificial selection regions (270 genes) were associated with a QTL region of major traits when compared to the list of 15,502 genes .e1-nl allele. In addition, this line showed all of the major mutant alleles for other maturity (E2 and E3) and Dt genes  in high oil lines, whereas the negative regulators  of lipid biosynthesis genes showed more copies in low oil lines , moderate oil lines (17 to 19.9%), and high oil lines (20 to 24%), comprising 26, 38, and 37 lines respectively. All the US PIs from elite backgrounds contain more than 19% oil. In contract, most of the wild relatives contain less than 14% oil, except for 2 lines . Candidail lines . The difGlyma20g19680; ammonium transporter, Glyma20g21030; ethylene receptor, and Glyma20g21780) were associated with protein content  in wild soybean using a combination of WGS approaches, and showed CHX1 gene is associated with salt tolerance. The GmCHX1 gene (Glyma03g32900) possesses an approximately 3.3 kb Ty1/copia retrotransposon in the third exon, leading to a sensitive phenotype, and another variant without a retrotransposon leads to a tolerant phenotype. In the present investigation, we studied the CHX1 locus to verify the allelic variation in the 106 soybean genomes. Alignment of 13 kb consensus genomic region in the whole genome sequences of 106 soybean lines resulted in 3 major groups G1, G2, and G3. The groups G2 and G3 were reported by Qi et al.st and 2nd intron result in their inclusion in the sensitive group. These variants can be confirmed by phenotyping and association with allele specific marker assays. The tolerant line, S-100, is the ancestor of popular cultivars in the US germplasmCHX1 gene reconfirms that GmCHX1 is the major gene controlling salt tolerance in soybean via ion homeostasis.Recently Qi Soybean yield improvement is changing at a slower pace and this major challenge needs to be addressed to meet the global demand for food, feed and fiber. Natural genetic variation analysis of soybean germplasm using the genome sequence information of individuals will greatly help to identify genes and rare alleles that are associated with major traits and components for soybean improvement. In the present investigation, we provided a significant genomic variation data set for wild, landrace, and elite soybean lines. Deeper sequencing, high-quality data, and millions of SNPs in the representative soybean lines provide an unprecedented opportunity for us to understand the landscape of soybean diversity and domestication. We identified a number of candidate genes and genetic loci that may have been selected as hot spots of domestication in soybean. Our population structural analysis and phylogenetic analysis support the common hypothesis that soybean was domesticated in the China subcontinent and then introduced to the US and other parts of the world.Our findings showed that the SNP variation and PAV between the wild and elite lines were 43% and 50%, respectively; and this finding explains the diversification of elite lines after domestication based on climatic zones, plant architecture, and agronomic traits. The genes identified in the domestication sweep regions were also correlated with the previously identified traits. We also studied agronomically important traits, such as oil and protein content, maturity, plant architecture, seed coat color, and salinity, and provide genomic variation information that can be used for developing trait specific genetic markers. Genetic and genomic resources generated in this study will provide a strong foundation for trait discovery, next generation breeding tools, and genomic selection strategies for the development of improved soybean cultivars.ftp://ftp.jgi-psf.org/pub/JGI_data/phytozome/v7.0/Gmax. We joined the unanchored scaffolds to a pseudo-chromosome “GmU” then PE reads of each line were independently mapped to the reference genome using the program SOAP2 . The parameters were set as “-m 100 -x 888 -s 35 -l 32 -v 3 -p 4”, 3 mismatches were allowed in a single read while mapping to the genome. The output of mappings were split into chromosomes, and sorted by the ninth column i.e., location of first bp on the reference. Approximately 95 to 99% of the reads were mapped, which covered 97.48% of the genome at a 17× depth. All of the mapping results were used to detect variation.We extracted genomic DNA from 106 soybean lines, constructed paired-end (PE) sequencing libraries with insert sizes of approximately 500 bp, and generated approximately 2 Tb of clean data after filtering low quality reads and duplicate reads. The soybean reference genome and annotation were downloaded from http://soap.genomics.org.cn/soapsnp.html) with parameters “-L 100 -u -F 1”. Based on the maximum-likelihood estimation of site frequency, we used GLF multi to generate the mapping depth, allele, allele frequency and quality of each site using the consensus sequence information from 106 lines. Sites were filtered by “>50x and < 3000x” in depth, “>15” in quality and “<1.5” in copy-number. SOAPsnp outputs were used to obtain accurate genotype SNPs of the filtered sites. We generated a final SNP set containing 10,417,285 SNPs after removing the sites with the heterozygote allele ratio larger than 0.88, or the missing allele ratio larger than 0.88.Consensus sequence for the genome of each line was assembled based on Bayes’ theorem using the program SOAPsnp 1.04 (http://soap.genomics.org.cn/soapindel.html) by “-m 1 -p 0.01 -k 5 -c 3 -h 0.5”.All of the reads were mapped to the reference using a parameter “–g 5”, which allowed a less than 5 bp gap within the hit of one single read to detect small insertions and deletions. Indels (1–5bp) were called by the SOAPindel 1.09 , and filtered for scaffolds with lengths less than 1 kb. Retained scaffolds were then annotated using the mapping protein to genome method based on all available proteins, and the gene set was generated using Copy number variations were detected according to the depth distribution of each line, having a minimum length of >2 kb, with a mean depth of less than half of the sequence depth, or larger than double of the sequence depth. The initial and final minimum probability to merge the adjacent breakpoints were set to 0.5 and 0.8, respectively.The program STRUCTURE 2.3.1Using a subset of 79,632 4-fold degenerate SNPs, a phylogenetic tree was constructed using neighbor (Version: EMBOSS:6.6.0.0) based on the Neighbor-joining methodPCA of the SNPs was performed using the software EIGENSOFTθπ), the Watterson’s estimator (θw) and Tajima’s D were estimated for the whole genome of 3 soybean populations using a sliding windows of 50 kb. In each window, these parameters were calculated using the Bio::PopGen package of PERL and FST was calculated to measure the differentiation between the two populations.The average pairwise divergence within a population (2) of alleles to measure the LD level in 3 populations. The parameters were set as: -maxdistance 250 -minMAF 0.1 -hwcutoff 0.001 -dprime -memory 2,096. The average r2 value was calculated for each length of distance and LD decay figures were drawn using an R script.We used Haploview. 4.2 to calculate correlation coefficient values (rw/πc) in 50-kb sliding windows, the ratio of genetic diversity was used to identify regions with lower levels of polymorphisms in cultivated groups. After removing the windows lower than 0.002 πw, windows in the top 5% of πw/πc values were considered as candidate regions with significantly lower diversity in cultivated lines, and windows with a distance of ≤50 kb were merged into a single selected locus.We combined the landrace group and elite group into a single cultivated gene pool to detect the regions and genes underlying domestication sweeps. These regions should have lower diversity in the cultivated group compared to the wild group. Thus, by comparing the wild group to the cultivated group .


Corn borers are the primary maize pest; their feeding on the pith results in stem damage and yield losses. In this study, we performed a genome-wide association study (GWAS) to identify SNPs associated with resistance to Mediterranean corn borer in a maize diversity panel using a set of more than 240,000 SNPs.r2 < 0.10) rapidly within short distance, suggesting high resolution of GWAS associations.Twenty five SNPs were significantly associated with three resistance traits: 10 were significantly associated with tunnel length, 4 with stem damage, and 11 with kernel resistance. Allelic variation at each significant SNP was associated with from 6 to 9% of the phenotypic variance. A set of genes containing or physically close to these SNPs are proposed as candidate genes for borer resistance, supported by their involvement in plant defense-related mechanisms in previously published evidence. The linkage disequilibrium decayed  contains supplementary material, which is available to authorized users. Corn borers are the primary maize pest in many environments ,2. Corn Ostrinia nubilalis Hubner in North America, Europe and North Africa; Ostrinia furnacalis Guenée in Asia; Diatraea saccharalis Fabricius from USA to Argentina; Chilo partellus Swinhoe in Southern USA, Central America and the Caribbean; Diatraea lineolata Walker in Central America, the Caribbean region and South America; and Diatraea grandiosella Dyar in North and Central America. Within the Noctuidae family, the main maize borers are: Sesamia nonagrioides Lefebvre in the Mediterranean region, Busseola fusca Fuller in sub-Saharan Africa, and Sesamia calamistis Hampson in West Africa. This study focuses on the noctuid Mediterranean corn borer (MCB) Sesamia nonagroides Lefebvre, the most important insect pest of maize in the Mediterranean region that includes Southern Europe [There are different species of borers that attack maize in different parts of the world. The most economically important species are classified into two families: Crambidae and Noctuidae. Within the Crambidae family, the species with economic importance are: n Europe ,5,6.Bacillus thutingiensis (Bt) toxins is a good method for controlling these pests, but transgenic crops are not authorized in several European countries under any agricultural system [Bt transgenes caused by evolved resistance of some important pests [The use of transgenic corn which produces l system  and are l system . In addint pests -11. The nt pests . Naturalh2 = 0.50 to 0.78) [In Spain, there are three MCB generations per year and the second and subsequent generations are able of making significant damage on the stem and, secondarily, on the ear. Following artificial infestation, the level of maize host resistance to stem borer is measured by the tunnel length made by the larvae in the stem as well as by a visual scale for kernel damage. These traits have a complex genetic architecture because resistance depends on the plant-insect relationship, which is influenced by environmental conditions and the developmental stage of the host plant ,14. The to 0.78) -18, depeS. calamistis and African sugarcane borer . Sandoya et al. [At present, commercial materials with high levels of native resistance to these insects are not available even though breeding for increasing maize resistance to corn borers has been conducted during the last three decades in different regions around the world. Klenke et al.  reduced a et al.  achieveda et al.  when sela et al. . In summGenetic effects for resistance against borer attack fit an additive – dominant model, although additive effects appear to be the most important in determining resistance to tunnel length and kernel damage ,24-28. TSeveral studies performed with segregating biparental populations have reported genomic regions containing minor and major quantitative traits loci (QTL) for resistance to stem and leaf attack by European corn borer  based on linked disequilibrium (LD) in diverse genetic samples is a relatively new approach which offers higher resolution mapping that under optimal conditions can pinpoint causal genes underlying quantitative trait variation. Exploiting advances in genotyping and sequencing technology, this approach has been successful in detecting genes associated with diseases in humans -38, animFusarium ear rot resistance [Many single-nucleotide polymorphism (SNP) markers have been identified and scored on a maize diversity panel (composed of 302 inbred line) that represents the diversity available in public breeding sector around the world ,50. The sistance .To date, no GWAS for insect resistance in maize has been reported, although a few GWA studies that deal with plant defense mechanisms against insect attack have been reported in other plant species ,55. In tP < 0.01) for all resistance  and agronomic traits ; while significant (P < 0.05) genotype by environment (G × E) interactions were observed for all traits as shown in Additional file h2 = 0.60) and KR (h2 = 0.52) across the three years, whereas a low heritability value was obtained for SD  phenotypic correlation was observed between TL and SD. Genetic correlation coefficients were also significant and high between TL and SD and between TL and PH  by each SNP associated with TL and SD ranged from 7 to 9%. Eleven SNPs were significantly associated with KR  explained by each of them ranged from 6 to 8%. The minor allele for those SNP reduced from 0.15 to 0.40 points the ratio that accounts for kernel resistance.Ten SNPs were identified as significantly associated with length of tunnels made by MCB Figure , Table 3R Figure  and the Nineteen SNPs were significantly associated with PH and 48 and 43 SNPs were significantly associated with DTA and DTS, respectively  between significantly associated SNPs and SNPs around them decays (r2 ≤ 0.10) rapidly; but, in some cases, the LD spans as much as 0.5 Mb at an r2 value greater than 0.20  of those obtained in other works with biparental crosses under infestation with MCB or ECB [The heritabilities for resistance traits ranged from low to moderate while PH and flowering time were higher as expected. The heritability for TL estimated herein is within the range  in a panel of diverse origin. Nevertheless, we did not find a single SNP or linked SNPs associated with both traits, suggesting that it is primarily due to the polygenic background. This relationship has also been important in some previous experiments with MCB and ECB [A significant genetic relationship was observed between TL and PH  and between KR and PH were found, which suggest that late and taller genotypes will be the healthiest; but these results have to be taken with caution because infestation was made simultaneously for all genotypes placing the MCB eggs close to the ground and the higher distance between the eggs and the ear of the taller and later plants could have impeded the larval arrival to the ear. In addition, when stem tissue is more lignified at time of infestation (as would be the case for earlier flowering genotypes), the preference of MCB larvae for the stem tissue compared to ear tissue could be less evident.A high genetic correlation coefficients between KR and flowering time (c = 1.1 to 1.6), except for TL, which had the highest value for compression level (c = 8.3). However, Zhang et al. [There was a minimal variation in model fit of the compressed MLM among different traits because similar compression level values were observed for all traits  by each SNP was comparable to that explained by QTLs for TL made by ECB and MCB (R2 = 3.5 – 15.7%) in biparental crosses [SNPs located on chromosomes 3 and 7 for TL co-localized with previously reported QTLs for TL by corn borers in genome bins 3.02 and 7.03 . The pro crosses -18,64. N crosses . Therefohttp://www.maizegdb.org/) to identify genes that either include or are close to the significantly associated SNPs. A region of approximately 0.2 Mb around the SNP was checked for annotated genes putatively involved in plant response to wounding and/or damage by microbes (insects or pathogens) based on bibliographic records.We used the maize B73 genome v2 (RefGen_v2) available from the Maize GDB   gene is relatively close to this SNP. It is known that some transcription factors from the Ovate family protein interact with other transcription factor families such as NAC domain protein1, MYB transcription factors, and KNOX homeodomain protein to regulate the synthesis of the three major components of secondary cell wall  in A. thaliana [The candidate gene adjacent to the significant SNP associated to TL on chromosome 2 encodes a Tetratricopeptide repeat (TPR) protein containing. The TPR is one of many repeat motifs that form structural domain mediating protein-protein interactions in several cellular process including translocation and degradations of proteins ,69. A resistance -73. A gestresses . Furtherthaliana -78 and pthaliana .2) which plays a very important role in signal transduction in plants since it is the precursor of oxylipins and jasmonated acid, two hormones which regulates defense genes against herbivores [The candidate gene for the other significant SNP on chromosome 4 encodes a Phospholipase A2  contains the second SNP at chromosome 1 significantly associated to SD. One of the functions of APF4 protein is remodeling the actin of cytoskeleton under different stimulus, including wounding and pathogen attacks . Some stignaling -95. The ignaling ,97. On tell wall ,99. In aell wall .ystatin1 gene, whose product is the corn kernel cysteine proteinase inhibitor//cysteine proteinase inhibitor I (psei1), an anti-metabolic protein synthetized and stored in the maize kernel [cysteine1 gene, the effect of this polymorphism would be expected to be especially large because the linkage between the significant SNP and SNPs in the cystatin1 gene is low, although significant.The SNP on chromosome 3 significantly associated to KR was located nearby the ce kernel . The expe kernel , and it e kernel ,104. LD Another significant SNP associated to KR was located close to a gene encoding a basic Helix-Loop-Helix (bHLH) transcription factor family member. Recent studies in other plant species have demonstrated that this family gene protein has an important role in regulating jasmonic acid response -109. TheThe two significant SNPs associated to KR on chromosome 4 are adjacent to a gene that encodes for an Ubiquitin thiolesterase. These proteins form complex systems of selective protein degradation ,126 and The candidate gene containing the significant SNP associated to KR on chromosome 5 encodes a Phytosulfokine receptor (PSK), a recognition hormone whose level of expression has been increased by pathogens elicitors ,130.2O2 which is a key component in signal transduction pathways leading to stress responses. In Zea mays, the function of PAO in wound-healing is likely due to increased lignin and suberine deposition as consequence of H2O2 release [2O2 [The candidate gene, adjacent to the significant SNP located on chromosome 6, encodes a Polyamine oxidase  (PAO). PAO plays an important role in stress tolerance by generating H release . In addi release . The canase [2O2 ,134, andase [2O2 .ipt4 (isopentenyl transferase4) gene, which is involved in the regulation of cytokinin biosynthesis pathway [itp gen (fused with a wound-inducible promoter) in transgenic plants of Nicotiana plumbaginifolia decreased leaf consumption by Manduca sexta (lepidopterous) and reduced survival of Myzus persicae (aphid) [A candidate gene for the significant SNP on chromosome 7 at position 19,347,596 is adjacent to the SNP that encodes a Kinase associated protein phosphatase. This gene family has been proposed to regulate the response to different type of stresses including pathogens and herbivore attack . It is i pathway . The exp (aphid) . Althoug (aphid) .Unlike markers linked to QTLs for resistance to corn borers, the SNPs associated with resistance in the present study could be incorporated in whole-genome predictor models in order to improve genomic selection . Marker–R2 = 6-9%). No co-localized SNPs were found for resistance and agronomic traits that could underlie the genetic correlations found between these traits.We conducted a genome wide association study for resistance traits to MCB with more than 245 kb SNP distributed through the whole genome. We found a set of significant SNPs associated to the three resistance traits to MCB attack. Of which 10 SNPs were significant associated to TL, 4 SNPs were associated to SD, and 11 SNPs were associated to KR. In general, each of these SNPs explain a considerable proportion of the phenotypic variance  to each significantly associated SNP.cystatin1 gen and the defensin proteins.Most of the candidate genes proposed herein are part of the signaling pathway, others act as regulator of expression under biotic stress condition, and a few genes are encoding enzymes with antibiotic effect against insects such as the The identification of these polymorphisms associated to resistance traits to MCB attack can be useful to understand the molecular mechanisms that affect resistance and susceptibility of host plants to insect attack, in order to contribute to advance in the understanding of plant-insect interactions. Nevertheless further studies are necessary to validate the candidate genes identified herein.The maize diversity panel (composed of 302 inbred lines) represents much of the diversity available in public breeding sector around the world. A subset of the maize diversity panel (henceforth we will refer to this population as “association panel”) was evaluated for resistance to MCB attack at Pontevedra , Spain, through three years . A subset of 270 inbred lines was assayed in an 18 × 15 α-lattice design with two replications in 2010 and 2011. In the third year a subset of 255 inbred lines was assayed (because we did not have enough seed for the remaining15 lines) in a 17 × 15 α-lattice design with two replications.−1. The evaluations were performed under artificial infestation with eggs of MCB. The eggs for inoculation were obtained at the Misión Biológica de Galicia by rearing the insect as described by Eizaguirre and Albajes [The trials were hand-planted and each experimental plot consisted of one row spaced 0.8 m apart from the other row with 29 two-kernel hills spaced 0.18 m apart. Plots were overplanted and thinned, obtaining a final density of ~70,000 plant ha Albajes . Five plData collected were: tunnel length (TL), the mean length of stem tunnels made by borers on the five infested plants, which were longitudinally split at the time of harvest; stem damage (SD) as the percentage of the stem damaged by MCB larvae on the five infested plants; kernel resistance to borer attack (KR) recorded at harvest as the damage on the main ear of the five infested plants according to a subjective visual resistance scale of 1 to 9 in which 1 indicates completely damaged and 9 indicates no damage; days to anthesis (DTA) and days to silking (DTS) as the days from planting to the date on which 50% of plants were shedding pollen or showing silks, respectively; and plant height (PH) on five representative plants as the distance from the ground to the top of the plant.We used a set of unique SNP markers derived from a Illumina maize 50 k array  and a geA genetic kinship matrix (K) previously published by Olukolu et al.  was usedEach trial was analyzed separately with the SAS mixed model procedure (PROC MIXED) in SAS software version 9.3  considerĥ2) for each year were estimated for each trait on a family-mean basis as described previously by Holland et al. [rg) and phenotypic (rp) correlations between traits were computed using REML estimation in SAS mixed model procedure following Holland [Heritabilities , β is a vector of fixed effects, including the SNP marker tested, u is a vector of random additive effects (inbred lines), X and Z represents matrices, and e is a vector of random residuals. The variance of random line effects was modeled as K is the n × n matrix of pairwise kinship coefficient and Genome-wide association analysis based on mixed linear model (MLM) was performed in Tassel 4.1.26 . The MLMvariance .n) total individuals into (s) groups based on their realized genomic relationships, allowing the original K matrix to be replaced by a smaller relationship matrix. The P3D option uses iteration to estimate population parameters such as genetic and residual variance only once in a model with no fixed marker effects, then uses those estimates without iteration in subsequent association tests for each marker. The combination of these two methods reduces computational time and improves model fit [Restricted maximum likelihood estimates of variance components were obtained by using the optimum compression level (compressed MLM) and population parameters previously determined options (P3D) in Tassel . The optodel fit .P < 1 × 10−4 and subsequently detected in ≥ 30 subsamples, i.e. resample model inclusion probability (RMIP) threshold of 0.30, were considered as significantly associated to the trait under study.To identify SNPs with the most robust associations with traits, a subsampling or subagging procedure was employed in GWAS analysis ,149. Eacr2) with each SNP significantly associated with resistance traits in a region of 60 SNPs (ranging from 0.15 to 1 Mbp). For each trait, the linkage disequilibrium between significant SNPs was also calculated. The genes containing or adjacent to SNPs significantly associated with traits were identified and characterized by the use of MaizeGDB genome browser [We examined the linkage disequilibrium (LD) measure ( browser . We exam


The genetic markers associated with economic traits have been widely explored for animal breeding. Among these markers, single-nucleotide polymorphism (SNPs) are gradually becoming a prevalent and effective evaluation tool. Since SNPs only focus on the genetic sequences of interest, it thereby reduces the evaluation time and cost. Compared to traditional approaches, SNP genotyping techniques incorporate informative genetic background, improve the breeding prediction accuracy and acquiesce breeding quality on the farm. This article therefore reviews the typical procedures of animal breeding using SNPs and the current status of related techniques. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, along with their potential are highlighted. Examples in pig and poultry with different SNP loci linked to high economic trait values are given. The recommendations for utilizing SNP genotyping in nimal breeding are summarized. In recent years, breeding programs for farm animals have dramatically evolved from visual and phenotypic evaluations with subjective judgments to quantitative selections with genetic technology. While the breeding selection based on phenotypic evaluation can be slow, difficult and often not completely accurate for quantitative traits , marker-assisted selection (MAS) techniques, which allow breeders to select farm animals with high breeding values early and effectively, have gained high popularity. It is a selection process that focuses on the traits of interest based on the linked genetic markers . When itMAS typically utilizes three types of genetic markers: (i) direct marker, for causative mutations, (ii) linkage disequilibrium (LD) maker, for population-wide linkage disequilibrium with the QTL, and (iii) linkage equilibrium maker (LE), for population-wide equilibrium with the QTL within pedigree . The MASMoreover, SNP markers are abundant in the genome, genetically stable, capable of effectively distinguishing different alleles, and amenable for automated high-throughput analysis ,13. The A variety of SNP genotyping techniques have been developed to improve their detection accuracy, time, and cost . These gad-hoc select candidates for high value breeding.Utilizing SNP genotyping techniques to select the species of farm animals with high economic trait values, including reproduction, growth rate, milking yield, and egg production, have become increasingly popular . RecentlThe purpose of this article is to discuss the procedures of SNP discovery and genotyping techniques in regard to selective animal breeding. The associated SNP information and genotyping techniques, including microarray and Lab-on-a-Chip based platforms, are highlighted. The recommendations of utilizing SNP genotyping for animal breeding are summarized.SNP markers have been utilized by breeders to seek for high breeding values of economic traits . They arin silico study on sequenced species. A SNP marker pool, from public database or Single Nucleotide Polymorphism Database (dbSNP), to represent informative SNP markers, is found at a given representative population. Based on the phenotypic differences and genetic background from the population with the traits of interest, the candidate SNPs are selected. Next, the primary selection is conducted to identify the association of the SNP markers and breeding germplasms. Bead-based assay and microarray are popular tools during this selection step. Finally, in the secondary selection step, the SNP markers validated from the primary selection are used during the association analysis. Additional samples from farms are exploited to evaluate their SNP genotypes. Commercial tools of TaqMan® and Invader® assays are commonly employed.In the first step of SNP discovery, SNP marker information is obtained through sequencing techniques, targeting local lesions in genomes (TILLING) analysis or To establish the large numbers of SNPs, two strategies have been employed in a given population. The first strategy is sequence-independent, and it detects unknown sequence variants or those species whose whole genome sequence are not available . This stThe second strategy is sequence-dependent, and it detects known sequence variants, in which whole genome sequence information is utilized to discover SNPs of interest. Commercial genotyping assays can simultaneously genotype up to 2 million SNP per DNA sample . Other genotyping platforms involving multiplex, loci-specific PCR or whole genome amplification followed by primer extension or allele-specific hybridization can distinguish between bi-allelic SNPs in different fluorescence colors.To interpret the sequencing data and to accurately identify the SNPs of interest, bioinformatics algorithms for searching SNPs have been developed, including Tablet , PyrobayA variety of SNP genotyping techniques have been developed and reviewed ,66,67,68In this section, SNP genotyping techniques will be briefly discussed according to three categories, based on the number of samples or SNP sites to be genotyped. “Large throughput” genotyping is defined as genotyping of thousands of individuals for hundreds/thousands of SNP sites, or even conducting genome-wide association studies (GWAS). Likewise, “medium throughput” genotyping is an intermediary option, which detects hundreds of samples for hundreds to thousands of SNP sites. “Small throughput” methods only detect one to tens of samples for tens of targets ,69. DepeLarge throughput genotyping methods deal with a large-scale project, where hundreds to thousands SNP sites on a few individuals or a few SNP sites on many individuals are simultaneously queried. These genotyping methods provide a great deal of SNP genotyping data, thereby enabling conducting whole-genome association studies in a given population.Microarray technology is an ideal method for large throughput analysis of multiple SNP sites. Two detection principles are widely used in microarray-based methods, which include allele-specific oligonucleotide (ASO) hybridization and allele-specific primer (ASP) extension, as shown in ® II (Illumina), GoldenGate® (Illumina), Genome Wide SNP Array  and OpenArray® (Applied Biosystems). Although these tools can provide massive information for SNP discrimination, two issues need attention: (i) the assay may have a high failure rate and (ii) it can only be conducted on model species because of the requirement of oligonucleotide probes or primers attached physically on the array [Commercial tools derived from the concept of microarray have been developed; they include Infiniumhe array ,68. In oet al. [Despite the limitation in using microarray technologies, many advantages still exist, including large throughput data analysis and improving accuracy in accelerating animal breeding. For example, Jiang et al.  used Bov® OpenArray®, Restriction site-associated DNA sequencing (RAD sequencing) and Pyrosequencing. These techniques are flexible, high throughput and low cost. TaqMan® OpenArray® is the derivative of TaqMan product to discriminate multiple SNP genotypes [PHKG1 with SNP markers. A total of 53 SNPs are first identified from Illumina PorcineSNP60 chip and further applied as an OpenArray® for genotyping 140 pigs. A SNP site is found to affect PHKG1 gene splicing during post-transcription and the mutant causes high glycogen content and low meat quality in skeletal muscle [Other large throughput SNP genotyping techniques include TaqManenotypes . It has l muscle . RAD seql muscle . A totall muscle .The second category is medium throughput SNP genotyping tools for a slightly smaller number of individual or SNP sites—hundreds of samples for hundreds to thousands of SNP sites. The commercial products in this category are iPLEX, SNPstream, SNaPshot, PCR-APEX (Arrayed primer extension), Luminex100™ and TaqMan assay. The first four methods are based on primer extension , while the latter two employ oligonucleotide ligation and exonuclease mechanism to achieve multiplexing in a single tube PCR. Similar concerns with large throughput methods remain—including limited applicability and high failure rate ,68.For small throughput projects, the technical platform for DNA molecular markers detection includes PCR-free genotyping methods, single-step homogenous method, homogeneous detection with fluorescence polarization, DNA chip/Array based assays, bead-based methods, mass spectrum based genotyping assays and dynamic allele-specific hybridization (DASH) ,81. AmonPCR-RFLP is another technique for small throughput projects in which individual SNPs are differentiated by analyzing the patterns derived from cleavage of their amplified DNA. If a sample with different nucleotides in the same SNP site differs in the distance between sties of cleavage of a particular restriction endonuclease, then the length of the fragments produced differ when the DNA is digested with a restriction enzyme.Recently, miniaturized devices have brought many advantages over their analogues at the macroscale, including portability, reduced sample consumption, rapid reaction times, and high throughput ,35,36,37Modern livestock production requires powerful technology for a fast, reliable and effective breeding protocol. For example, superior sows are selected based on their genetic background for high-yield traits. Once the piglets or poultry are selected at early stage, the breeders could save more money and increase the economic values. Lab-on-a-Chip technology, which implements genetic biotechnology onto a monolithic platform, enables the automation of the complete laboratory procedures of the genetic analysis protocol ,85. Use Escherichia coli (E. coli) and hepatitis B virus [For example, a Lab-on-a-Chip genotyping system, which integrated the functions of PCR and FRET-based melting curve analysis onto a microchip was developed to genotype SNPs—CYP3A4*15B allele and CYP3A5*3 allele . Saliva  B virus , human d B virus , and manThe development of the Lab-on-a-Chip technology for SNP detection has been rapidly progressing. Various working principles had been utilized. A digital microfluidic system was developed for SNP detection . A droplATM) gene from Landrace sow was successfully shown. Another bead-based method was shown in a micromachined chip combined with multiple bio-molecules detecting system [Due to its simplicity and low cost, melting curve analysis were widely applied for SNP genotyping in Lab-on-a-Chip. A rapid melting curve analysis system was demonstrated via immobilizing microbeads on the surface of a microheater chip with rapid temperature controller capabilities as shown in g system . Four tyg system . Only whThe utilization of SNP genotyping techniques in promoting QTL analysis among farm animals will permit breeders and farmers to predict traits and estimate breeding values. The traits of economic interest in general include growth, body composition, carcass, meat quality, reproduction, and disease resistance capability ,13,97,98In swine, (1) reproduction and (2) carcass and meat quality are traits with high economic values.ATM gene has been discovered by using GoldenGate® in differentially expressed genes between morula and blastocyst [ATM genes had been detected by using Lab-on-a-Chip platforms [The traits related to swine reproduction usually include litter size, litter number, number weaned, age at puberty, weaning to estrus interval, farrowing interval, and total number of pigs born (TNB) . Among tastocyst . These glatforms ,95.ESR1 and ESR2) are found to be involved in ovarian follicular growth [ESR2 polymorphism. Furthermore, the Polish sow with AA genotype is found to have the largest litter size compared to other genotypes of AB and BB [Another important trait for reproduction is litter size, which is highly influenced by ovarian follicular growth. Two estrogens receptors (ESR)  gene, which controls luteal and follicular steroidogenesis of Large White sows (n = 301), has been tested by using the PCR-RFLP method. The results suggest that PRLR/AluI gene improves reproductive performance traits of sows [PRLR and ESR play different roles in sow reproduction, without knowing the correlation with the traits of interests. In addition, a commercial PCR-APEX chip, which contains 45 SNPs, is used to determinate PSS-porcine stress syndrome (a single-gene disease) in four QTL genes, including protein kinase adenosine monophosphate-activated γ3-subunit (PRKAG3), calpastatin (CAST), Melanocortin 4 receptor (MC4R) and ESR—these four genes are exploited for the marker-assisted selection. The SNPs’ genotyping technique used in this selection procedure is PCR-APEX (Arrayed primer extension) technique—a medium throughput method [On the other hand, the polymorphism in Prolactin receptor , MC4R, Phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) and vertnin (VRTN) genes analyzed [ITIH) genes as well as retinol binding protein 4 (RBP4) and follicle stimulating hormone (FSHB) genes with porcine reproductive traits have been reported [Besides, there have been significant interest lately in several populations and in different approaches for leptin receptor  gene, located in chromosome 2 of pig, has been found to positively control skeletal muscle growth, which is discovered by using RFLP. An SNP (G>A) of IGF2 intron 3 (g.3072) disturbs a repressor zinc finger BED-type containing 6 (ZBED6) binding onto IGF2 and causes a three-fold overexpression of postnatal skeletal muscle IGF2 mRNA. While this mutant leads to the increase of carcass lean yields, it reduces backfat deposition [IGF2 plays an important role in the pig industry due to the 15%~30% and 10%~20% of phenotypic variation in muscle mass and backfat thickness, respectively [Meat quality is important in swine-breeding programs. The related traits, including muscle growth, tenderness, color and oxidative stability, are mostly quantitative . They arSTN gene ,45,113. position ,115. Furectively .PIT1) has been reported to be associated with growth and carcass traits in pigs [RYR1) mutant pigs induce a high rate of glycolysis, leading to a low pH value of meat after slaughtering. The low pH value of meat could produce pale, soft, and exudative muscles, which have an unacceptable meat appearance for customers. RYR1 gene was reported to be associated with porcine stress syndrome (PSS) and functions at regulating calcium transport across skeleton muscle [MRF) gene family, which encoded a basic helix-loop helix protein and was responsible for myotubular transformation. Two genes, Myf5 and Myf6, in MRF family were found to regulate the development of skeletal muscle fibers and postnatal growth hypertrophy, which affected meat quality [SNP marker of pituitary-specific transcription factor  gene has a 6.5 kb receptor transcript and is expressed in the brain and ovary. The LRP8 gene plays a role in cholesterol supply for steroid biosynthesis, thereby enabling folliculogenesis [Genetic selection in superior chicken improves the breeding speed and accuracy. Several important traits have been addressed in Taiwan chicken breeding programs, which include improvement of egg production, egg quality, and eggshell. The traits of egg production, egg quality and eggshell are possibly controlled by multiple genes. For instance, low density lipoprotein receptor-related protein 8 , KIAA1462, Rho GTPase activating protein 21 (ARHGAP21), acyl-CoA synthetase family member 2 (ACSF2) and astrotactin 2 (ASTN2) are found to be promising candidate MAS markers for egg number in geese.Also, the reproduction trait of poultry can be related to the laying performance. Due to low heritability, the selection in laying performance imperatively requires the MAS strategy. Yu et al. has 139,013 SNPs obtained from 42,291,356 RADseq tags by using RAD sequencing method . Five noPIT1 gene has shown a significant association with growth traits in chicken. PIT1 affects the expression of growth hormone, thyroid-stimulating hormone chain, and prolactin. The PIT1 SNP marker has been used to determine the genotypes of 10 chicken populations (n = 662), including six Chinese indigenous breeds, White Leghorn, paternal/maternal lines of brown egg layer and a paternal line of broiler by using the PCR-SSCP method. Two PIT1 SNP haplotypes, AA and TT types, have a significant difference between their body weights at eight weeks, making the PIT1 SNP marker a potential marker for MAS selection of early growth rates in chicken [The haplotype of the  chicken . Similar chicken .MC4R) belongs to G protein–coupled receptors (GPCR) super family and is a transmembrane neuron receptor, which controls the appetite, body weight and energy metabolism. The polymorphism of MC4R in the population of crossing Broiler and Chinese Silky chicken has been genotyped for the carcass traits in body weight and growth by using the PCR-SSCP technique [In addition, the melanocortin 4 receptor (echnique .Finally, all these candidate genes with SNP markers discussed in this section for economic traits of swine and poultry are summarized in As the traits described in the last section are generally lowly heritable, traditional breeding selections with phenotypic evaluations are insufficient. Quantitative MAS techniques with reliable markers constitute a feasible and effective approach. In practice, depending on the number of individuals and SNP sites, choosing appropriate SNP genotyping techniques for animal breeding will involve tradeoffs between reliability, sample preparation, reagent/sample expense, instrument depreciation and procedure complexity. For instance, large throughput SNP methods require a series of complicated steps and special instruments. They are high cost, labor intensive and time-consuming.Moreover, even though the rapid progress of sequencing technology and the “$1000 genome” project may be soon possible and can significantly reduce the cost , it may Animal breeding by using phenotypic selection is insufficient.Utilization of genetic markers for breeding has become mainstream in the livestock industry.In particular, breeding using SNP markers is an effective and economical approach as it possesses the advantages of abundance and wide distribution in genome, and is easy to analyze.SNP markers can be discovered by using sequence-dependent and sequence-independent methods. The former one is large throughput but expensive, while the latter one is small throughput but robust.Appropriate SNP genotyping techniques for animal breeding will be adapted addressing concerns regarding throughput, reliability, associated expense, and procedure complexity.A combination of whole-genome sequencing for a small population of individuals and a SNP chip for the genotyping of the majority of individuals will be a feasible and economical approach for animal breeding.SNP genotyping techniques have received considerable attention in selective breeding. We have discussed the procedures of SNP discovery and genotyping techniques for animal breeding. The associated SNP information and various genotyping techniques are presented. General findings may be summarized as follows: