NR3C1) – a key regulator of HPA axis activity. To determine the causal variant(s), we resequenced the coding region of NR3C1 and found three missense single nucleotide polymorphisms (SNPs). SNP c.1829C>T, leading to a p.Ala610Val substitution in the ligand binding domain, showed large , and highly significant (2.1E-39≤log10(1/p)≤1.7E+0) negative effects on both traits. We were able to replicate the association in three commercial pig populations with different breed origins. We analyzed effects of the p.Ala610Val substitution on glucocorticoid-induced transcriptional activity of porcine glucocorticoid receptor (GR) in vitro and determined that the substitution introduced by SNP c.1829C>T increased sensitivity of GR by about two-fold. Finally, we found that non-coding polymorphisms in linkage disequilibrium with SNP c.1829C>T have only a minor effect on the expression of NR3C1 in tissues related to the HPA axis. Our findings provide compelling evidence that SNP c.1829C>T in porcine NR3C1 is a gain-of-function mutation with a major effect on the activity of the adrenal gland. Pigs carrying this SNP could provide a new animal model to study neurobiological and physiological consequences of genetically based GR hypersensitivity and adrenal hypofunction.Glucocorticoids produced in the adrenal cortex under the control of the hypothalamic-pituitary axis play a vital role in the maintenance of basal and stress-related homeostasis and influence health and well-being. To identify loci affecting regulation of the hypothalamic-pituitary-adrenal (HPA) axis in the pig we performed a genome-wide association study for two parameters of acute and long-term adrenal activity: plasma cortisol level and adrenal weight. We detected a major quantitative trait locus at the position of the glucocorticoid receptor gene ( Glucocorticoids  produced by adrenocortical cells of the In different species, including the pig, glucocorticoid secretion shows large inter-individual variation that has a considerable genetic component nuclear receptor subfamily 3, group C, member 1; NR3C1) was identified as a promising candidate because we found significant association of a non-coding variant (SNP c.*2122G>A) with both traits analyzed NR3C1, mainly non-synonymous, have been shown to generate diversity in GR signaling and to contribute to inter-individual variation in glucocorticoid secretion To identify genes affecting acute and long-term regulation of HPA axis activity in the pig, we previously analyzed polymorphisms in ten functional candidates for potential associations with plasma cortisol level and adrenal weight  in commercial crossbreds. The glucocorticoid receptor gene . This uncovered NR3C1 because they appear to be the main source of inter-individual variation in GR function, and because they are most amenable to functional interpretation. We present here strong genetic and functional evidence that a non-synonymous SNP (c.1829C>T) in NR3C1, leading to the gain-of-function substitution p.Ala610Val in the ligand binding domain of GR, is indeed causally involved.To identify the causal variant(s) we first concentrated on the analysis of non-synonymous polymorphisms of The GWAS was performed in a population of commercial German Landrace pigs (n = 564), which were genotyped using the PorcineSNP60 BeadChip (Illumina) and phenotyped with respect to plasma cortisol level and adrenal weight . After a filtering step, genotype information for 42,359 SNPs that mapped to the 18 autosomes (Sscrofa 10.2) was used.A total of 37 SNPs mapping to nine chromosomes were significantly associated with plasma cortisol level at the genome-wide false discovery rate (FDR) q≤0.05 level , located on seven chromosomes  the ∼3 Mb region flanked by SNPs ALGA0106386 and ALGA0120126 contains eight annotated genes, including NR3C1 with plasma cortisol level and adrenal weight in an additional, purebred, population and suggested that polymorphisms of NR3C1 account for the largest proportion of the variation in adrenal activity.The GWAS thus confirmed association of NR3C1 encoding the predominant isoform of GR–alpha–in a panel of samples from four different breeds  and in pooled samples of crossbred animals  with extreme phenotypes for cortisol secretion and adrenal weight, respectively . We identified a total of six SNPs  in the N-terminal domain in a region of GR-alpha showing a relatively low level of amino acid sequence conservation  We sought to assess frequency of the missense SNPs in the most important commercial breeds and in populations included in association analyses, specifically in the LR population initially used for the GWAS and in LW and PiF1, which were also phenotyped with respect to plasma cortisol level and adrenal weight. We therefore genotyped 21 to 35 unrelated LR, LW, Pi, and Du pigs. While SNP c.1829C>T segregated in all four breeds, SNP c.55G>C segregated only in Pi. Moreover, in contrast to LR, LW, and Du, the Pi breed was fixed at SNP c.39A>C. With the exceptions of c.39A>C in Du and c.55G>C in Pi, all three missense SNPs segregated at relatively low frequency, generally about 10% or less. Genotyping of additional 92 LR and 45 LW pigs  again revealed no carriers of SNP c.55G>C, therefore SNP c.55G>C was assumed to be specific for the Pietrain breed. Consequently, SNP c.39A>C and c.1829C>T were genotyped in all three populations  used for association analyses; SNP c.55G>C was genotyped only in PiF1. In LR and LW the genotype of SNP c.55G>C was assumed to be homozygous GG. Allele distribution of the missense SNPs in the different populations is summarized in Table S4. None of the populations of purebred pigs showed significant deviation from Hardy-Weinberg equilibrium , indicating that the missense SNPs had no negative impact on viability of their carriers. In PiF1 SNP c.55G>C and c.1829C>T showed significant deviation from HWE (p<0.05), which is most likely a result of crossbreeding because we frequently observe deviation from HWE in this cross see .Using the genotypic information collected in LR, LW, and PiF1 we performed population-based single-marker association analysis. Whereas SNP c.39A>C and c.55G>C showed no or only inconsistent associations, SNP c.1829C>T was consistently and, in general, highly significantly associated with decreased cortisol level and adrenal weight in all three populations ,2. The eThe genotype-based analysis was complemented by haplotype-based association analysis. To examine whether the missense polymorphisms account for the previously found association of SNP c.*2122G>A with plasma cortisol level and adrenal weight, we also included genotype information of this SNP in the haplotype analysis  with SNP c.1829C>T.Finally, we performed linkage disequilibrium (LD) and haplotype block analysis of the 3 Mb region between SNPs ALGA0106386 and ALGA0120126 on chromosome 2 using data from the GWAS on the 35 significant and 36 intervening SNPs, and SNP data from genotyping NR3C1 with plasma cortisol level and adrenal weight indicated in the present study by the GWAS and previously by the analysis of SNP c.*2122G>A Together, these results provide strong genetic evidence that SNP c.1829C>T is responsible for the association of As mentioned above, mutagenesis studies of GR indicated that the p.Ala610Val substitution might influence responsiveness of GR to glucocorticoids, which, in view of the key role of GR in feedback regulation of HPA axis activity, might explain phenotypic effects associated with SNP c.1829C>T. To examine the impact of the p.Ala610Val substitution on transcriptional activity of porcine GR, we compared the ability of the two allelic receptor variants to drive luciferase expression from the mouse mammary tumor virus promoter (MMTV) in COS-7 cells after stimulation with various dexamethasone concentrations . The traNR3C1 and, thus, potentially influence adrenal activity. We resequenced the region of NR3C1 encoding the GR-alpha isoform in three additional individuals  homozygous for the A-G-T-A haplotype; apart from SNP c.1829C>T, no other coding polymorphisms (missense or synonymous) were found.To further prove causality of SNP c.1829C>T, we investigated whether other polymorphisms in LD might affect function of cis-acting effects of non-coding polymorphisms in LD with SNP c.1829C>T on expression of NR3C1 in tissues related to the HPA axis, we analyzed its allelic expression imbalance (AEI) in hippocampus, hypothalamus, pituitary, and whole adrenal gland by pyrosequencing using ten samples per tissue (five samples for each diplotype A-G-T-A/A-G-C-G and A-G-T-A/A-G-C-A). In all four examined tissues, expression of the T allele was significantly higher compared to the C allele (2(AEI) = 0.07] and ∼1.10-fold [∼log2(AEI) = 0.13]. Further, allelic ratios in genomic DNA fluctuated tightly around one , indicating that the porcine NR3C1 locus is not affected by copy-number variation  NR3C1, SNPs c.39A>C and c.55G>C, were located on different haplotypes and showed no or only inconsistent effects on adrenal activity. Consequently, supposing that other variants of NR3C1 in LD with SNP c.1829C>T affect adrenal activity, these necessarily have to be functional non-coding polymorphisms. For their detection we followed an experimental approach and analyzed association of SNP c.1829C>T with allelic expression imbalance and splicing of NR3C1 in tissues related to HPA axis. We found no evidence for splicing polymorphisms and only modest allelic expression imbalance of NR3C1, which argues against any significant contribution of polymorphisms in LD with SNP c.1829C>T to the variation in adrenal activity, especially in view of evidence from genome-wide association studies that non-coding variants tend to have weak phenotypic effects However, in contrast to a knock-in mouse model, in an outbred species like the pig variation in a complex trait usually cannot be unambiguously attributed to a particular genetic variant, at least not before contribution of other variants in LD is considered. The only other detected missense polymorphisms of NR3C1 is a gain-of-function mutation with a major effect on activity of the adrenal gland. This SNP is the first genetic variant in the pig for which a causal relationship to adrenal activity has been established and adds to a growing, but still limited, list of genetic variants known to be directly responsible for variation in complex traits , that potentially increase its sensitivity to glucocorticoids have been described, both in humans; the majority of known natural GR variants are associated with glucocorticoid resistance NesCre) in the nervous system display symptoms characteristic of Cushing syndrome, including increased glucocorticoid levels, reduced size, altered fat distribution, and reduced bone density In the mouse, apart from the p.Met610Leu knock-in model, gene-targeting studies have been largely based on glucocorticoid receptor inactivation, which generally leads to an increase in the basal activity of the HPA axis Animal care and tissue collection processes followed the guidelines of the German Law of Animal Protection, and the experimental protocol was approved by the Animal Care Committee of the Leibniz Institute for Farm Animal Biology .Three commercial pig populations were used for sample and phenotype collection: a population of purebred German Landrace pigs (n = 834) and a population of German Large White (n = 274) pigs, both consisting only of barrows; and a Pietrain × (German Large White × German Landrace) cross consisting of barrows (n = 291) and females (n = 246). The average age at sampling was ∼170 days.Sample collection was performed in the experimental slaughter facility of the FBN between 0930 and 1130 h. Tissues used for RNA preparation were quickly removed, dissected, frozen in liquid nitrogen and stored at −80°C. For dissection of brain areas of interest  the stereotaxic atlas of the pig brain served as a reference A 50 ml sample of trunk blood was collected from each pig during the exsanguination in a plastic tube containing 1 ml of 0.5 M EDTA. After plasma preparation samples were stored at −80°C until use. Plasma cortisol levels  were determined in duplicate using commercially available enzyme-linked immunosorbent assay  according to manufacturer's protocol. The intra- and inter-assay coefficients of variation were lower than 7.0% and 9.8%, respectively. An overview of analyzed phenotypes is given in Table S1. Before association analysis outliers with cortisol levels ≥200 ng/ml were removed. Remaining data were approximately normally distributed.Samples of Pi and Du animals used for assessment of allele frequency and of Hardy-Weinberg equilibrium were also collected in the experimental slaughter facility of the FBN.Isolation of total RNA was performed using TRI reagent . After DNaseI treatment , RNA was cleaned using the NucleoSpin RNA II Kit . Quantity and purity of RNA were determined using the NanoDrop ND-1000 spectrophotometer , and integrity was checked on 1% denaturing agarose gels.First-strand cDNA was synthesized using SuperScript III MMLV reverse transcriptase  in a reaction containing 1.5 μg RNA, a mixture of 500 ng random hexamers  and 500 ng of oligo (dT)11VN primer, according to manufacturer's protocol.http://www.illumina.com). In brief, 200 ng of DNA were used for genome-wide amplification and subsequent fragmentation. DNA was hybridized to the 62,163 locus-specific 50mers covalently linked to the beads distributed on the surface of the microarray. Single-base extension of oligos on the BeadChip, using captured DNA as a template, was performed, incorporating detectable labels on the BeadChip. Signals of each wavelength were determined using an Illumina iScan that converted the images to intensity data. Intensity data for each SNP were normalized and assigned a cluster position and a resulting genotype with the GenomeStudio software (Illumina Inc.); a quality score was generated for each genotype. Samples with call rates <95% were removed. Markers were excluded if they had low minor-allele frequency (MAF) <5%. The average call rate for all samples was 99.8%±0.2.Genotyping using the PorcineSNP60 BeadChip  was performed in accordance with manufactureŕs protocol for the SNP Infinium HD assay . For resequencing, five overlapping fragments of ∼600 bp covering the target region were designed and amplified in a standard PCR mix containing genomic DNA or cDNA as template, 0.2 µM of each primer (Tables S6 and S7), 50 µM of each dNTP, and 0.5 U SupraTherm Taq Polymerase in 1× supplied PCR buffer containing 1.5 mM MgCl2 . The temperature profile consisted of 40 cycles of denaturation at 95°C for 15°s, annealing at appropriate Ta for 60 s, and extension at 72°C for 60 s. PCR products were sequenced using Big Dye Terminator Cycle sequencing kit V3.1  and analyzed on ABI 3130 automated sequencer. Sequence polymorphisms were detected using the Multiple SeqDoC online tool http://research.imb.uq.edu.au/seqdoc/multi.html).The sequence of porcine MboII and 10 U BglI restriction enzymes, respectively, according to manufacturer's recommendations . Resulting RFLPs were analyzed on 2% ethidium bromide-stained agarose gels.For genotyping SNP c.39A>C and c.55G>C, PCR-RFLP assays were established. Polymorphic regions were amplified in a standard PCR-reaction as described above using primers GRf1 and GRr6, and 10 µl of amplification products were digested overnight using 2.5 U of SNP c.1829C>T was genotyped using pyrosequencing. The polymorphic region was amplified using primers GRf7 (biotinylated) and GRr7 and as described above, except that DreamTaq Polymerase (Fermentas) was used. The sequencing reaction was performed using primer GRseq1 and the Pyro Gold Reagent Kit  in the PSQ 96MA Pyrosequencing instrument  according to manufacturer's instructions.Genotyping of SNP c.*2122G>A was described previously NotI restriction sites, nested in the amplification primers, were used for insertion of the GR-alpha fragment into the pCMV-Tag 1 plasmid . The resulting expression plasmids, pCMV-GRA610 and pCMV-GRV610, were checked by sequencing for orientation and absence of mutations other than SNP c.1829C>T.To construct GR expression plasmids, a 2.38 kb GR-alpha fragment was amplified from cDNA of two individuals homozygous for the c.1829C>T SNP using primers GR_C-Flag_fw and GR_wt_rev (Table S6) and the proofreading PrimeSTAR HS DNA Polymerase . The 4 cells/well in DMEM supplemented with 10% FBS. The next day, cells were co-transfected with 200 ng pCMV-GRA610 or pCMV-GRV610, 100 ng pGL4.36, and 2 ng pRL-SV40 using Lipofectamine 2000.COS-7 cells were seeded in 96-well plates at 2.5×10The pGL4.36 reporter (Promega) expresses firefly luciferase under the control of the glucocorticoid-inducible MMTV promoter. The constitutive expression of Renilla luciferase by the pRL-SV40 plasmid (Promega) was used to normalize for transfection efficiency.Twenty-four hours after transfection, cells were treated with dexamethasone (Sigma) in different concentrations ranging from 0 to 100 nM, and, 24 h later, firefly and Renilla luciferase activities were measured under the use of the Dual-Glo Luciferase Assay System (Promega) in a DTX 880 Multimode Detector . Four separate experiments were preformed in triplicate. The effect of the p.Ala610Val substitution on the normalized expression of the firefly luciferase in the transactivation assay was tested by ANOVA using the MIXED procedure of the SAS V9.2 software package . The model included fixed effects of GR variant, dexamethasone concentration, and their interaction. Estimated least-squares means (LSM) were compared using t-test, and p-values were adjusted by simulation. Dose-response curve and EC-50 were estimated using GraphPad Prism 5 .To analyze allelic expression imbalance, the ratio of alleles of SNP c.1829C>T in cDNA and corresponding genomic DNA (gDNA) samples were measured using pyrosequencing. Pyrosequencing was performed as described above in two separate experiments, in duplicate. Ratios obtained from cDNA were normalized using ratios obtained from gDNA within each experiment. After log2 transformation, allelic expression imbalance was tested by comparing ratios obtained from cDNA with ratios obtained from gDNA using a two tailed t-test.For the analysis of splicing of the GR-alpha isoform, six overlapping amplicons covering the whole open reading frame (ORF) were designed. The sequence corresponding to human exon 1C was retrieved from the sequence of BAC clone CH242-105G5. Reverse-transcription PCR was performed using standard conditions described in the previous paragraph, and products were visualized on 2% agarose gels. Identity of the amplicons was confirmed by direct sequencing. Information on primers and amplicons used for the analysis of the integrity of GR-alpha ORF are summarized in Tables S6 and S7.NR3C1 genotypes were compared by t-test, and p-values were adjusted by Tukey-Kramer correction. Allele substitution effects were estimated by linear regression on the number of copies of the derived allele. FDR , respectively. The model included fixed effect of the SNP genotype and, to account for relatedness, the random effect of sire. In addition, for cortisol data the model included random effect of slaughter date and slaughter order as a covariate. For adrenal weight the model included body weight as a covariate. For both traits gender was included as a fixed effect for the analysis of the PiF1 population. LSM for q-value,  was compHaplotype inference was performed using the expectation-maximization algorithm implemented in the HAPLOTYPE procedure of SAS V9.2. Haplotype trend regression was performed by including one variable for each haplotype indicating the number of copies of the haplotype in question in the models described above.Hardy-Weinberg equilibrium was analyzed using the ALLELE procedure of SAS V9.2. Linkage disequilibrium between SNPs and haplotype blocks were determined using Haploview V4.2 Figure S1Alignment of the glucocorticoid receptor amino acid sequence among different vertebrates. Two regions of the glucocorticoid receptor amino acid sequence are shown where DNA polymorphisms in the porcine NR3C1 introduce amino acid substitutions. The position of the three variable residues found in pigs is indicated by arrows, and the homologous residues of other species by gray bars. Evolutionarily conserved residues are indicated by dots. Residues of helix 5 of the ligand binding domain are underlined.(TIF)Click here for additional data file.Figure S2Linkage disequilibrium analysis of the QTL region on chromosome 2. The values in the boxes show linkage disequilibrium between SNPs (r2) and the box color reflects the degree of linkage disequilibrium. A. Linkage disequilibrium analysis of 71 PorcineSNP60 BeadChip SNPs, including 35 genome-wide significant and 36 intervening SNPs along with three NR3C1 SNPs, c.39A>C, c.1829C>T, and c.*2122G>A. B. Detail of the haplotype block harboring NR3C1 SNPs c.39A>C, c.1829C>T, and c.*2122G>A.(TIF)Click here for additional data file.Table S1Descriptive statistics of the analyzed traits.(DOC)Click here for additional data file.Table S2SNPs with genome-wide significant evidence for association with plasma cortisol level.(DOC)Click here for additional data file.Table S3SNPs with genome-wide significant evidence for association with adrenal weight.(DOC)Click here for additional data file.Table S4NR3C1 in commercial breeds.Frequencies of the three identified missense SNPs in porcine (DOC)Click here for additional data file.Table S5NR3C1 in three different commercial populations.Frequencies of inferred haplotypes of porcine (DOC)Click here for additional data file.Table S6Primer information.(DOC)Click here for additional data file.Table S7NR3C1.Information on amplicons used for the analysis of splicing pattern, resequencing, and genotyping of (DOC)Click here for additional data file.


Avena sativa L., 2n = 6x = 42) has been hindered by the size and complexity of the genome, the scarcity of robust molecular markers, and the lack of aneuploid stocks. Resources developed in this study include a modified SNP discovery method for complex genomes, a diverse set of oat SNP markers, and a novel chromosome-deficient SNP anchoring strategy. These resources were applied to build the first complete, physically-anchored consensus map of hexaploid oat. Approximately 11,000 high-confidence in silico SNPs were discovered based on nine million inter-varietal sequence reads of genomic and cDNA origin. GoldenGate genotyping of 3,072 SNP assays yielded 1,311 robust markers, of which 985 were mapped in 390 recombinant-inbred lines from six bi-parental mapping populations ranging in size from 49 to 97 progeny. The consensus map included 985 SNPs and 68 previously-published markers, resolving 21 linkage groups with a total map distance of 1,838.8 cM. Consensus linkage groups were assigned to 21 chromosomes using SNP deletion analysis of chromosome-deficient monosomic hybrid stocks. Alignments with sequenced genomes of rice and Brachypodium provide evidence for extensive conservation of genomic regions, and renewed encouragement for orthology-based genomic discovery in this important hexaploid species. These results also provide a framework for high-resolution genetic analysis in oat, and a model for marker development and map construction in other species with complex genomes and limited resources.A physically anchored consensus map is foundational to modern genomics research; however, construction of such a map in oat ( Avena sativa L.; 2n = 6x = 42, AACCDD) is a nutritionally important cereal crop Cultivated hexaploid oat  robust SNP assays; (ii) a new chromosome anchoring strategy; (iii) the first physically-anchored consensus map; and (iv) a comprehensive orthology-based comparison to model grass genomes. These results open a new window of scientific opportunity to explore other complex genomes, and will accelerate the genetic improvement of oat, an important functional food More than 35 million un-filtered SNPs were predicted from cDNA reads using the single-template approach (STA) . StringeThe composite-template approach (CTA) utilized a multi-step procedure : assemblAlleles from 1,311 of the 3,072 SNP assays were clearly discriminated among the mapping progeny and/or a set of 109 diverse oat varieties, indicating a 43.7% conversion rate. Of the converted assays, 991 (44% conversion) were based on the STA, while 144 (43% conversion) and 121 (40% conversion) were derived from cDNA and DArT sequences using the CTA . OverallSeparate linkage maps were constructed within six RIL populations based on 320 to 647 loci . After rA consensus map was constructed from framework SNPs developed in this study and reference loci from previous maps , Fig. S1Twelve SNP loci that mapped to the A genome in at least two populations mapped to the C genome (five) or D genome (seven) in the remaining populations, resulting in two consensus map positions for these markers. Notably, four loci mapped to both 15A and 9D, with conserved ordering between groups . In addi1 deletion hybrids were interrogated with GoldenGate SNP assays and alleles from 234 loci were used to anchor 15 of the 21 linkage groups to chromosomes. Stocks representing 2C, 6C, 9D, 12D, and 16A produced the most robust data  with genomes of model grass species rice and omosomes . AlthougBrachypodium.This work describes novel methods for SNP discovery in polyploids, and a novel physical chromosome anchoring strategy. These methods were applied to develop a new high-throughput SNP platform in oat, and then to produce the first oat consensus map assigned to physical chromosomes. These resources then enabled the first integrative summary of chromosome similarities among oat, rice and Discovery and application of SNPs in polyploid species is a relatively new challenge; therefore, we wished to compare the success of various approaches. The CTA would theoretically allow prediction of non-redundant SNPs indexed to a single reference genome. The success rate of CTA calls (29%) was lower than that of the STA-based SNPs (33%) but this is likely because some of the best CTA-based SNPs that were redundant with selected STA-based SNPs were eliminated. However, the consensus sequences from composite assemblies contained many ambiguities that confounded SNP identification, while the STA approach provided cleaner templates and was more amenable to automation. Therefore the STA was used for the majority of cDNA-based SNPs, and we recommend this strategy for future work.The CTA was used for all DArT-based SNP calls. This choice was made because composite assemblies of DArT sequences were less complex, and because sequence redundancy among varieties was lower due to selectivity of DArT complexity reduction. It was also hoped that DArT sequences might provide a higher rate of polymorphism than cDNA-derived sequences because they contain less coding sequence. Results from the CTA suggested a slightly higher SNP rate in DArT sequences than in cDNA sequences but this was negated by a higher attrition rate after filtering. Although fewer SNPs were called in the DArT sequences, the conversion rate of called SNPs was similar to those based on cDNA.Special attention was given to SNPs based on published DArT marker clones that could provide a bridge to DArT-based maps. Although these markers had the highest success rate, only 66 met the stringent filtering criteria. More disappointing was that only five pairs of DArT/SNP markers could be mapped in the same population; however, all corresponding pairs mapped to identical locations  suggestiin silico to physical assays As demonstrated in previous SNP genotyping studies, homoeology and gene duplication affect hybridization and cluster resolution, complicating genotype calls and decreasing conversion from in silico SNP identification methods used in this study.In this study, approximately 20% of converted SNPs detected alleles with MAF<0.10. For comparison, selected barley SNPs also showed 20% with a MAF<0.08 Until now, it has not been possible to resolve 21 oat linkage groups or to merge individual maps into a single consensus. Previous maps in hexaploid oat have been incomplete, or have resolved to substantially more than 21 linkage groups, and the scarcity of high throughput methods providing reliable single-locus assays that segregate in multiple populations has provided only fragmentary alignment among linkage groups from different populations Framework maps for all six populations were used to build the consensus map consisting of 1,054 loci defining 21 groups with a total length of 1,838 cM. This distance was similar to the previous Kanota/Ogle  and Ogle/TAM O-301  maps, which were about 1,000 cM shorter than an estimated size  The two physically smallest chromosomes, 11A and 18D, produced short genetic maps as expected sativa-type oats, but lacking in byzantina types, including Kanota Integration of genetic and physical map data in other crop species has depended on the availability of cytogenetic stocks. In hexaploid wheat, homoeologous chromosome buffering has allowed development of telocentric chromosomes, sub-arm deletion stocks, and monosomics, all of which have facilitated physical mapping 2 plants to simulate an F1 hybrid and anchored 22 RFLP linkage groups to 16 chromosomes, with no associations for 3C, 4C, 6C, 10D, or 20D. Assignments in the RFLP study were made with a mean of 3.7 markers per chromosome, reflecting the scarcity of available markers at the time, as well as limitations of using segregating F2 progenies to simulate the critical F1 hybrid. In this study, clear data were not obtained for chromosomes 1C, 5C, 7C-17A, and 17A–7C, although linkage group characteristics and available marker data suggest that the Fox assignments are correct. Remaining assignments from Fox were verified by our methods. In addition, chromosomes not previously reported have now been anchored: 4C, 6C, and 20D with SNP deletion analysis and 3C and 10D with DArT dilution analysis.Chromosome assignments of the consensus map based on chromosome deletion SNP anchoring, and to a lesser extent, the DArT dilution approach, were robust and concurred with previous reports. Fox et al. A. magna, hypothesized to carry the C and D genomes, mapped primarily to C-genome chromosomes, again reflecting polymorphism differences between genomes. One A. magna SNP mapped to 20D.Chromosome-linkage group associations highlight characteristic tendencies within each genome. Genetic conservation appeared to be strongest in the D genome, indicated by a lower level of polymorphism and the prevalence of major oat disease resistance genes . PrevaleTwelve SNP loci on the consensus map were assigned to more than one chromosome. In most cases, markers mapped to different genomes, exemplified by the four markers mapping to 9D and 15A. Exceptions to this were markers that mapped to chromosome 5C and two different D genome chromosomes.Brachypodium demonstrates that there are fragmented but common orthologous origins between these two chromosomes  progeny, the most widely-referenced mapping population. Alignment between the recently expanded KO map omosomes . It now Brachypodium provides encouragement for the use of comparative genomics to understand and utilize genome resources in oat and other related species. Although the oat genome is complex, its ancestral origins are likely intermediate between those of rice and BrachypodiumBrachypodiumBrachypodium 2 as it does in the current work. However, there are also inconsistencies . This is likely because former comparisons were based on genomic markers with fewer and weaker orthologies than the 337 highly-informative gene-based anchors in the current work. Comparisons made in this study demonstrate orthology and colinearity on a large scale; however, there are likely to be substantial differences on the micro-synteny scale.Identification of substantial regions of macro-colinearity between oat, rice, and Brachypodium. In addition, the chromosome deletion hybrid SNP anchoring strategy has enabled the first comprehensive anchoring of a genetic map to specific chromosomes. These results are a key resource for gene-based plant improvement approaches such as marker-assisted breeding, and for molecular genetics studies such as candidate gene identification and map-based cloning. This work will enable detailed exploration of genomic similarities among grasses and will contribute to research advances in other orphan crops with complex genomes.SNP development via the discovery pipelines presented here has enabled generation of the first consensus map for the complex hexaploid oat genome, and has provided a new integrative analysis of macro-colinearity among oat, rice and PstI/TaqI protocols on the same 20 genotypes plus an additional five lines PstI PCR primers and resulting amplicons were labeled with standard Roche Multiplex Identifier Tags (MID) and sequenced on the 454 GS-FLX system .Tissues of roots, shoots, pistillate structures, and mature embryos from 20 genotypes  were use1 progenies prior to tissue collection and genotypic analysis.Monosomics for 13A, 14D, and 17A were derived from Kanota A. magna germplasm lines Ba 13-13 and #169. Sequences containing SNPs were submitted for Illumina  GoldenGate assay design, and design scores were incorporated into final SNP filtering.Candidate SNPs were called using three approaches (single-template (STA), composite-template (CTA), and Sanger-template) using cDNA and genomic sequences. The STA  was baseSNP allele interrogation was performed using two 1,536-SNP oligo pooled assays on an Illumina BeadStation using a 32-beadchip platform . Allele http://www.multiqtl.com) rf threshold = 0.15. Iterative resampling was performed to remove unstable markers, and completed groups were merged end-to-end by incrementally increasing rf, with a final rf of 0.25. Iterative resampling was repeated to verify marker order and stability.Individual linkage maps were constructed for subsets of six mapping populations  using Muhttp://www.multiqtl.com). Consensus mapping was performed on a combined raw dataset of six individual map solutions, with data weighted based on sample size. Local analysis was used to resolve pair-wise conflicts, and a global analysis was used to finalize the map solution. The overall consensus solution was compared to an integrated map solution generated using JoinMap v. 4 software A multilocus consensus map was created using the MultiPoint ‘Full Frame’ algorithm, which employs a synchronized traveling salesperson problem approach  and a maternal parent background (Sun II or Kanota) were analyzed across chromosome-deficient hybrids. Alleles present on a critical chromosome and having a hemizygous genotype  were used to anchor linkage groups to chromosomes  and Brachypodium distachyon L.  using nucleotide (BLASTn) and translated (tBLASTx) instances of NCBI BLAST v 2.2.24 [41]. Matches were filtered to retain those with a bit score >100 and/or an expectation <5E-20, and to keep only the best match for each integer-rounded midpoint (Mb) per chromosome. SNPs matching more than three Brachypodium chromosomes were ignored, and remaining matches were used to infer regions of oat chromosomes having sequential orthology to those of the model genomes.Design sequences from all mapped SNPs were matched against pseudomolecule representations of complete genomes from rice Click here for additional data file.Figure S2Colinearity of SNP loci mapping to chromosomes 9D and 15A.(TIF)Click here for additional data file.Figure S3SNP deletion analysis of monosomic hybrid stocks representing chromosomes 6C and 9D.(TIF)Click here for additional data file.Table S1Correspondence of consensus chromosomes with published KO linkage groups.(DOCX)Click here for additional data file.Dataset S1Pedigree data for 20 genotypes selected for transcriptome sequencing and SNP identification and for 109 genotypes selected to represent genetic diversity in North American oat germplasm.(XLSX)Click here for additional data file.Dataset S2Consensus map and component maps from six bi-parental mapping populations, including original SNP genotype calls for component mapping populations.(XLSX)Click here for additional data file.Dataset S3Details for regions of sequence similarity between SNP markers on 21 chromosomes from an oat consensus map and chromosomes from the sequenced genomes of Brachypodium distachyon and Oryza sativa.(XLSX)Click here for additional data file.Dataset S4Sequences for in silico SNP assays analyzed with the GoldenGate genotyping platform.(XLSX)Click here for additional data file.


Bacillus anthracis is one of the most monomorphic pathogens known. Identification of polymorphisms in its genome is essential for taxonomic classification, for determination of recent evolutionary changes, and for evaluation of pathogenic potency.Bacillus anthracis genome are compared and previously unpublished single nucleotide polymorphisms (SNPs) are revealed. Moreover, it is shown that, despite the highly monomorphic nature of Bacillus anthracis, the SNPs are (1) abundant in the genome and (2) distributed relatively uniformly across the sequence.In this work three strains of the Bacillus anthracis strains. In the case when the data is of still lower quality, a new DNA sequence fingerprinting approach based on recently introduced markers, based on combinatorial-analytic concepts and called cyclic difference sets, can be used.The findings support the proposition that SNPs, together with indels and variable number tandem repeats (VNTRs), can be used effectively not only for the differentiation of perfect strain data, but also for the comparison of moderately incomplete, noisy and, in some cases, unknown  I have deeply regretted that I did not proceed far enough at least to understand something of the great leading principles of mathematics; for men thus endowed seem to have an extra sense.Charles DarwinBacillus anthracis strain differentiation poses a particularly difficult challenge [B. anthracis strains are highly monomorphic, sequence typing must rely on subtle differences between genomes, sampled at multiple loci [This research is part of an effort to develop novel techniques for the interrogation of pathogenic genomes. In this domain the task of hallenge -4. Sinceple loci . The comple loci .B. anthracis genome in the three strains considered here was recently investigated in [The principal genomic markers used in sequence typing are VNTRs, indels and SNPs. The occurrence of VNTRs and indels in the gated in . Here, wgated in . The advgated in .B. anthracis genome: Ames Ancestor, Ames and Sterne. It is shown that SNPs are abundant in the B. anthracis genome and that they are distributed relatively uniformly throughout the sequence. These findings demonstrate that the B. anthracis SNPs can be used effectively as part of an increased resolution, multi-tier strain differentiation scheme for the analysis of moderately incomplete, noisy or uncertain data. The SNP detection approach used here is based on an advanced design theory construction known as the cyclic difference set [In this work the occurrence of SNPs is investigated in the three main strains of the ence set . In thisence set ; the priB. anthracis includes [The work described here intersects several application domains. Prior work on includes ,1,5,11,3includes -14. Prioincludes -18. Prioincludes ,1, and [includes . Prior wincludes  and [21-includes .B. anthracis genome is made up of chromosomal DNA and two plasmids, pXO1 and pXO2. We analyzed the chromosomal sequences of Ames Ancestor GenBank: NC_007530.2, Ames GenBank: NC_003997.3, and Sterne GenBank: NC_005945.1, the pXO1 plasmid sequences of Ames Ancestor GenBank: NC_003980 and Sterne GenBank: NC_001496, and the pXO2 plasmid sequences of Ames Ancestor GenBank: NC_003981.1 and Pasteur GenBank: NC_012659.1. For brevity, we refer to Ames Ancestor, Ames, Sterne, and Pasteur as AA, A, S, and P.The essential to establish such a definition, so that confusion can be avoided in analysis, in comparison of results and in discussions. In this work a SNP is defined as a single letter difference between two sequences flanked on the left and on the right by at least one letter that is identical in both sequences. For example, in the stringsThere is no standard, mathematically consistent definition of the term SNP . We consC G T A CG TA A G G A TT TA the second and fourth letters are SNPs but the sixth and seventh letters are indels, as the letter differences are adjacent. This convention is different from general practice, which sometimes permits adjacent letter differences to be regarded as SNPs . We insensSNPs). In a three-way comparison a coding SNP is considered non-synonymous when at least one of the pair-wise SNPs is non-synonymous. For example, there are two pair-wise SNPs in letters A-C-C in the three-way comparison of AA-A-S, one for the pair of strains AA-A and one for the pair of strains AA-S. If either of these pair-wise SNPs is non-synonymous then the three-way SNP is declared an nsSNP.The definition of SNP must be further disambiguated when more than two sequences are considered. In this case two or more distinct letters might appear at a putative SNP position, raising the possibility of counting each pair-wise mismatch as a separate SNP. We will ignore this multiplicity. For example, both triples A-C-T and A-C-C will be considered instances of a single SNP. We will distinguish between coding and non-coding SNPs, and between synonymous and non-synonymous SNPs GGGG and (CAC)GG.The rationale for using DSs as sequence markers is that when DNA sequences are highly homologous, so are the sequences of DS locations. Conversely, in regions where DNA sequences differ, so do the DS sequences. This is convenient as the analysis of DNA sequences can then be replaced by the analysis of much sparser, and therefore easier to compute, DS sequences. Since a difference in DS sequences marks the occurrence of an indel, mismatching segments are removed from the DS sequences.In the second stage of the algorithm, the DS sequences are mapped back to "new", indel-free DNA sequences. These DNA sequences differ only by nucleotide mismatches. Once adjacent mismatches are filtered, SNPs are easily identified by a point-wise comparison of the modified nucleotide sequences. In the example given above this yields the indel-free sequencesTTACACCACCCACAGTCCTCTTT...ACCGCATACACCACCCACAGTCCTCTTT...ACCGCth bp.Point-wise comparison of these sequences reveals a SNP T/A at the 6associated with, not identical to, the special binary strings considered here. However, for convenience and by abuse of language in this text we will refer to the relevant strings as DSs. While motivating the technical approach, for brevity, we mention here only the computational complexity reason for the utility of DSs.Several comments are necessary here to make statements precise. First, while a more natural acronym for a cyclic difference set would be CDS, to avoid potential confusion with a coding sequence we settle for DS. Second, DSs are combinatorial designs that are 2 n logn complex operations. For a more extensive discussion of the role of DSs in DNA sequence analysis the reader is directed to [Specifically, the computational advantage of the method as compared to a direct approach not relying on DSs is proportional to the abundance of DSs in genomes (1 in 500 nucleotides in the B. anthracis genome). This advantage is further enhanced by the suitability of the method for implementation using Fast Fourier Transform algorithm, which requires only ected to .B. anthracis genome are summarized in Tables The results of the SNP analysis of the The chromosomal analysis included the three pair-wise comparisons of AA-S, AA-A and A-S. These comparisons revealed 131, 19 and 150 SNPs, respectively Table . The minThe plasmid analysis included pair-wise comparisons of strains AA-S for pXO1 and AA-P for pXO2. Given their relatively short sequence lengths, the pXO1 and pXO2 plasmids are polymorphism-rich, containing 14 and 21 SNPs each, respectively. Of these SNPs, 7 and 16 are coding SNPs. Of the coding SNPs 6 and 9 are nsSNPs. The minimum, average and maximum distance between subsequent SNPs in the pXO1 plasmid are 3, 12977 and 84568 bp. The minimum, average and maximum distance between subsequent SNPs in the pXO2 plasmid are 94, 4516 and 13884 bp. The density of SNPs decreases in the pXO1 and pXO2 plasmids when indels are removed from the sequences , capture all sequence differences in pan-genomes  in the sampling rate, this approach can be expected to be effective in the case of closely related strains whose sequence data is of moderate quality or partly unavailable . Exceptionally complex tasks, such as detection of data manipulation or revelation of unknown distant strains, will require the use of even more dense, uniform and flexible sequence sampling schemes. One such scheme is offered by the DS-based sequence homology assessment procedure [Results of the SNP investigation undertaken here together with the prior work on DSs  both infrocedure . In thisThe authors declare that they have no competing interests.AKB conceived the approach. AKB and JF implemented and tested the method and wrote the manuscript. Both authors read and approved the final manuscript.Tables of SNPs. Tables of SNPs for chromosomal and plasmid sequences of B. anthracis strains Ames Ancestor, Ames, Sterne, and Pasteur. The GenBank reference numbers of sequences are given in the Data section.Click here for file


Studies to detect associations between DNA markers and traits of interest in humans and livestock benefit from increasing the number of individuals genotyped. Performing association studies on pooled DNA samples can provide greater power for a given cost. For quantitative traits, the effect of an SNP is measured in the units of the trait and here we propose and demonstrate a method to estimate SNP effects on quantitative traits from pooled DNA data.To obtain estimates of SNP effects from pooled DNA samples, we used logistic regression of estimated allele frequencies in pools on phenotype. The method was tested on a simulated dataset, and a beef cattle dataset using a model that included principal components from a genomic correlation matrix derived from the allele frequencies estimated from the pooled samples. The performance of the obtained estimates was evaluated by comparison with estimates obtained using regression of phenotype on genotype from individual samples of DNA.For the simulated data, the estimates of SNP effects from pooled DNA are similar but asymptotically different to those from individual DNA data. Error in estimating allele frequencies had a large effect on the accuracy of estimated SNP effects. For the beef cattle dataset, the principal components of the genomic correlation matrix from pooled DNA were consistent with known breed groups, and could be used to account for population stratification. Correctly modeling the contemporary group structure was essential to achieve estimates similar to those from individual DNA data, and pooling DNA from individuals within groups was superior to pooling DNA across groups. For a fixed number of assays, pooled DNA samples produced results that were more correlated with results from individual genotyping data than were results from one random individual assayed from each pool.Use of logistic regression of allele frequency on phenotype makes it possible to estimate SNP effects on quantitative traits from pooled DNA samples. With pooled DNA samples, genotyping costs are reduced, and in cases where trait records are abundant this approach is promising to obtain SNP associations for marker-assisted selection. Genotyping with dense single nucleotide polymorphism (SNP) assays is now common for studies on humans, laboratory animals, wild animals and livestock. In all cases, power increases with increasing numbers of genotyped individuals. Recent studies on quantitative traits in humans and livestock suggest that, for many traits, genetic variation is mainly due to a large number of regions in the genome, each having a small effect ,2. This Research on the statistical analysis of data from pooled DNA experiments has included sources and effects of errors in allele frequency estimates, e.g. -5, experIn this paper, we propose and demonstrate a more general method to analyze pooled DNA samples when the phenotype is a quantitative trait with a continuous distribution. The effect of an SNP on a quantitative trait is measured in the units of the trait. For quantitative traits and with DNA assayed on individuals, it is usual to regress phenotype on genotype and to estimate the effect of each haploid copy of the SNP allele. As with selective genotyping , this mee2, then the distribution of the haploid phenotypes is a mixture of two normal probability density functions f - and f +, with means μ - α/2 and μ - α/2, and variance σe2/2, where μ is one half the diploid population mean. Let Y be a vector of haploid phenotypes, and let Zi be a random variable that takes the value 1 if haploid DNA copy i contains the SNP allele associated with effect + α/2, and the value 0 if haploid DNA copy i contains the SNP allele associated with effect -α/2, and let πi be the probability that Zi = 1. Then, using the same derivation as Henshall and Goddard [Each SNP was analyzed independently to estimate SNP effects and significance levels. Conceptually, for a single SNP, a pooled sample of DNA consists of multiple haploid copies of each of the two SNP alleles, and associated with each haploid copy is a contribution to the overall pooled phenotypes. Our aim was to estimate the effect of the ratio of SNP alleles in the pooled DNA on the pool of phenotypes. Each individual contributes two haploid DNA alleles to the pool and, assuming an additive model for SNP effects, half of its phenotype to the pool of phenotypes for each haploid DNA copy. The variance of the haploid phenotypes is half the variance of the diploid phenotypes (since the diploid phenotypes are the sum of two random haploid phenotypes). If the effects associated with the diploid SNP genotypes are , and if we assume a normally distributed error with diploid variance σ Goddard This can be written asb = 2α/σe2 and g = -2αμ/σe2. Both g and b can be estimated using standard logistic regression software, and the estimate of b is an estimate of the effect of the phenotype on α, the effect of a SNP allele on the phenotype, and we obtain this by noting that σ2 = σe2 + 2p(1-p)α2, where σ2 is the diploid phenotypic variance and p is the frequency of one of the SNP alleles. Solving for α in terms of b and p giveswhere p = 0.5, this formula is very similar to that of Henshall and Goddard [p was not necessary in their analyses of paternal half-sib data, since within large families, the allele frequency of the paternally derived allele is always close to 0.5. In practice, a more general model α by regressing individual phenotypes on genotypes (i.e. without pooling), using the model G is a vector of genotypes for individuals and ε is an error term. For this regression we used the R lm function.When  Goddard . Includited in R  using thJust as with individual samples, a potential problem in estimating SNP effects from pooled DNA is the presence of hidden population stratification . To acco2, which incorporates components due to environmental variation and all genetic effects other than the SNP under consideration. We repeated the simulation 1000 times, each time sampling and analyzing a single SNP. For each replicate, we sampled a population-wide frequency for the first SNP allele (p) from a uniform  distribution and a population-wide SNP effect α from a uniform  distribution. Diploid SNP genotypes were then sampled for each individual, and genotypic effects of 0, α or 2α were assigned to individuals with zero, one or two copies of the first SNP allele, respectively. Phenotypes comprised the genotypic effect plus a random error term, with a new error term for each individual sampled from a normal distribution with mean = 0 and variance σ2 = 1.0. Individuals were ranked on phenotype and assigned to pools based on rank. To explore the effect of the number of pools, we used 2n pools for n = 1:m, with m chosen such that 2m was less than 3000, i.e. the number of individuals in the population. We calculated the mean phenotype and SNP allele frequency for each pool, with either no error simulated in the frequency estimates, or with an error component added to each allele frequency estimate. The error component was sampled from a normal distribution with mean = 0 and standard deviation = 0.05. Since frequencies are only meaningful when between 0 and 1, if adding the error resulted in an allele frequency that was less than 0 or greater than 1, frequencies were set to 0 or 1, respectively.We simulated data on a population of 3000 individuals. No relationships between individuals were assumed and phenotypes consisted of the effect of a single SNP and an error term with variance σBos indicus) and Tropical composite (predominantly B. taurus). Tropical composite cattle are widely used in northern Australia, with founder genetics from a variety of breeds, including British B. taurus , but sometimes also Sanga (African B. taurus) and Brahman (B. indicus). We analyzed hip height (adjusted for age), measured in cm at around three years of age. Hip height is known to have a high heritability, for example Vargas et al. [Records were available on 1041 female cattle born over three years and originating from two genetically diverse breeds, pure bred Brahman  of genotype on phenotype using the significance of estimated SNP effects , abbreviated here by MLP) and the magnitude of the estimated SNP effect α, estimated using equation (1). In each case, the comparison is done with the MLP or α obtained using least squares (LS) regression of phenotype on genotype with SNP data from all individuals. For clarity in the presentation of results, generally we do not explicitly state the method (LR or LS). Where the data are referred to as "pooled" it is implicit that the analysis method is LR, even when the number of pools is equal to the number of individuals . Where the data are referred to as "individual" it is implicit that the analysis method is LS. Selected comparisons are presented graphically, by plotting the MLP or α from pooled data against the MLP or α from individual data, for the 1000 replicates, in the case of simulated data, and for 1000 randomly selected SNP and all SNP with a significance value less than 1.0E-4, in the case of the cattle hip height data.2) and the slope from a regression of MLP or α estimated from pooled data on the MLP or α estimated from individual data are presented. For the estimates obtained when no error in estimated allele frequency was simulated . Thus, only results for individual samples and for 64, 16 and four pools are shown graphically. With individual samples, the effect estimated using LR is smaller than the effect estimated using LS, indicating that the methods produce different estimates even when given the same (un-pooled) data. Most of the plotted points for 64 pools are masked by, or very close to those from individual samples . As the number of pools decreases, the R2 falls but the slope of the regression remains fairly constant. Figure For the simulated data, summary statistics from various pooling scenarios are shown in Table d Figure , the est2 between results from pooled samples and individual samples is partly due to the wide range of SNP effects simulated in the data (from 0.0 to 0.5 phenotypic standard deviations). Figures 2 for α and MLP 0.998 and 0.999 respectively, regression slope equal to 0.998 for both α and MLP). This means that the estimated effect and significance level for an SNP will be almost exactly the same regardless of whether all 3000 individuals are genotyped or 64 pooled samples of DNA are genotyped. Even with four pools only, SNP that are significant with individual samples will be identified as significant (R2 = 0.9), and with 16 pools the precision will be sufficient for most purposes (R2 = 0.98).The high RThese are best-case results, since no error in estimating allele frequency for the pools was simulated. As expected, introducing an error with a standard deviation of 0.05 (a 5% error) reduces the correspondence between the LR and LS results Table . This is2 from the regression of the results from pooled data on the results from individual data. The results from individual data were obtained using concurrent estimation of contemporary group, population stratification and SNP effects, with data from contemporary groups excluded when the within-group MAF was less than 0.01. This model is more complicated to set up because which individuals are included in the analysis varies between SNP, but we judged that this model would be less affected by any confounding of contemporary group and SNP allele frequency.Minor allele frequency (MAF) affected results more for pooled samples than for individual data, probably because pooling reduces the number of genotype observations in each contemporary group. Consequently, results are presented for SNP with a population-wide MAF > 0.20, MAF > 0.05 and MAF > 0.01. To summarize results, we present the R2 are lower than for the simulated data, and fall rapidly as the number of pools is reduced. In most cases, there is little difference between the R2 for both breeds analyzed together and for each breed analyzed separately. The R2 obtained from pre-adjusted data are generally similar to those obtained from concurrent estimation, except when SNP with a MAF < 0.05 are included. For both concurrent estimation and pre-adjusted data, estimates of α from pooled data in Brahman with a MAF > 0.01  have a very low correlation with estimates from individual data. Further analysis of the results revealed that this is due to a small number of SNP with a low MAF and with very large effects when estimated using LS on individual data, so the slope of the regression of α estimated from pooled data on α estimated from individual data is close to zero.Results for selected analyses are presented in Table 2) between parameters (effect (α) and minus the log p-value (MLP)) estimated using a logistic regression  and least squares regression ; SNP effects were either estimated concurrently with contemporary group and population stratification effects, or estimated on data pre-adjusted for those effects; in all rows except for the last, pooling was within contemporary group; results are presented for SNP with minor allele frequency (MAF) greater than 0.20, 0.05 and 0.01, for combined breed analyses and within breed analysesBeef cattle hip height data, 940 individuals, BovineSNP50 Array; coefficients of determination . For 940 individuals, these models differ only in the order in which fixed effects are fitted: the eigenvectors from the principal component analysis are identical. With less than 940 pools the eigenvectors from the principal components analysis differ according to whether pooling is performed before or after adjustment, and results for adjustment for contemporary groups before pooling are worse than for pooling within contemporary groups.We evaluated an alternative approach to the analyses described above, by pre-adjusting the phenotypes for contemporary groups, and then allocating animals to pools across breeds and contemporary groups. Clearly, this is not appropriate for such data because of the presence of two genetically divergent breeds; however a comparison of models is interesting since in some data, the existence of a genetic effect such as breed may not be known to the researcher. Even without pooling  and only including SNP with a MAF > 0.20, estimates were worse under this model than when pre-adjustment was performed after pooling  in estimating pool allele frequencies is introduced, the results are not nearly as good, and it is clear that controlling error will be very important in assembling pools and genotyping the pooled samples.With the beef cattle hip height data, as the total number of pools increases, and therefore also as the number of pools per contemporary group increases, the results converge toward those for 940 pools , suggesting that the strategy of pooling within contemporary groups and fitting contemporary group in the model is effective. This is important, as it may also be the most efficient way to organize an experiment. For example, it makes it possible to increase the size of an existing study by including estimated allele frequencies from pooled samples from additional contemporary groups, rather than having to pool the new samples with samples that have already been genotyped in the existing study. Furthermore, pre-adjusting the data for contemporary group and pooling based on adjusted phenotypes did not perform well in our study. In most cases, it made little difference whether phenotypes were pre-adjusted for contemporary group and stratification effects (after pooling) or whether estimation was done simultaneously. The only exception was for SNP with a small MAF and even then it was not the pre-adjustment itself that caused the difference, but the inclusion of data from contemporary groups in which only one SNP allele was present. A weakness of our analysis of the beef cattle data is that SNP allele frequencies for pools are estimated from individually assayed DNA samples, instead of from assays of pooled DNA. As such, our results are upper bounds for the performance of methods with pools, achievable only if the allocation of DNA to pools and subsequent assay introduces no error. However, our approach allowed us to consider a greater range of pooling strategies.The motivation for assaying pooled samples of DNA rather than individual samples is to reduce costs. However, pooling DNA places restrictions on the experimental design and on the parameters that can be estimated, as discussed by Craig et al.  and otheWith pooled DNA, only the effects of haploid SNP alleles can be estimated, so the estimates cannot be used in models with dominance or epigenetic (imprinting) effects. If SNP are sufficiently dense and all possible haplotypes are known in advance, then haplotype frequencies for pools can be estimated, as suggested by Barratt et al. . The metFor livestock quantitative traits, individual SNP effects are often small and an estimate of the breeding value or sum of the SNP effects for an animal is required. Methods currently used to estimate breeding values using high-density SNP genotype data apply distributional constraints to the estimated SNP effects to avoid over-fitting . Some moWith pooled DNA samples, an estimate of the effect of an SNP on a quantitative phenotype can be estimated using a logistic regression of the pooled allele frequencies on the pooled mean phenotypes. For a simulated dataset with 3000 individuals and no error in estimating allele frequencies for pooled samples, there was little benefit in having more than 128 pools, unless all individuals were genotyped. With the livestock data, consisting of 940 individuals from two breeds distributed over 29 contemporary groups, having more pools was always an advantage. However, for a fixed number of assays, pooled samples produced far better results than one random individual assayed from each pool. Models that include the relationship between pools derived from a genomic correlation matrix can be used to account for hidden population stratification. Pooling across contemporary groups based on adjusted phenotypes with relationships between pools included in the model was for our data inferior to pooling within contemporary groups. This suggests that choosing the model and understanding and correctly fitting contemporary group structure are important in studies with pooled DNA data.The authors declare that they have no competing interests.JH conceived the study, participated in the design and statistical analysis of the study, and drafted the manuscript. RH carried out the molecular genetic analyses of the study, and participated in its design. SD contributed to the statistical analyses and helped to draft the manuscript. WB contributed to the conception of the study and participated in its design. All authors read and approved the final manuscript.


Bos taurus autosome (BTA) 24. That region is flanked by U1  and NCAD . In addition, the SNP at 58,500,249 bp on BTA 16 explained 0.08% and 0.11% of the SNP variance for 2- and 3-SNP analyses, respectively. That contig includes SNORA19, RFWD2 and SCARNA3. Other SNPs associated with RT were located on BTA 16 (close to CEP170 and PLD5), BTA 5 (near SLCO1C1 and PDE3A), BTA 4 (near KBTBD2 and LSM5), and BTA 26 . In conclusion, there are QTL for RT in heat-stressed dairy cattle. These SNPs could prove useful in genetic selection and for identification of genes involved in physiological responses to heat stress.Heat stress compromises production, fertility, and health of dairy cattle. One mitigation strategy is to select individuals that are genetically resistant to heat stress. Most of the negative effects of heat stress on animal performance are a consequence of either physiological adaptations to regulate body temperature or adverse consequences of failure to regulate body temperature. Thus, selection for regulation of body temperature during heat stress could increase thermotolerance. The objective was to perform a genome-wide association study (GWAS) for rectal temperature (RT) during heat stress in lactating Holstein cows and identify SNPs associated with genes that have large effects on RT. Records on afternoon RT where the temperature-humidity index was ≥78.2 were obtained from 4,447 cows sired by 220 bulls, resulting in 1,440 useable genotypes from the Illumina BovineSNP50 BeadChip with 39,759 SNP. For GWAS, 2, 3, 4, 5, and 10 adjacent SNP were averaged to identify consensus genomic regions associated with RT. The largest proportion of SNP variance (0.07 to 0.44%) was explained by markers flanking the region between 28,877,547 and 28,907,154 bp on  ATP1A1Heat stress in dairy cattle can compromise a variety of physiological functions including milk yield Core body temperature during heat stress is a heritable trait in dairy cattle, with early estimates varying from 0.15 to 0.31 The objective of the current study was to perform a GWAS for rectal temperature during heat stress in lactating Holstein cows and identify SNPs that serve as QTL for that trait.Use of animals was approved by the University of Florida Institutional Animal Care and Use Committee .The study was conducted with lactating Holstein cows located in Florida dairies at nine locations in Bell , Hague , Okeechobee  and Trenton . The following dairies allowed access to cows and records: Alliance Dairy , Hilltop Dairy , Larson Dairy , McArthur Dairy , North Florida Holsteins  and the University of Florida Dairy Unit . All except the University of Florida Dairy Unit are private. For seven locations, cows were housed in free-stall barns equipped with fans and sprinklers. In the other two locations, some cows were housed in free-stall barns and other cows were housed in tunnel-ventilation barns. Cows were fed total mixed rations. Data on RT were collected from a total of 11,128 cows. A subset of these data have been analyzed previously Rectal temperature was measured between 1500 and 1700 h during the months of June to September in 2007 and between 1400 and 1600 h in the months of June to September in 2010, 2011 and 2012. Each cow was measured once during the experiment. Rectal temperature was measured using a digital GLA M750 thermometer . Measurements were recorded while cows were under shade and, generally, while in head locks  or while resting (for tunnel-ventilation barns).db), relative humidity (RH), dew point temperature (Tdp) and black globe temperature (Tbg) inside the barn and Tbg outside the barn were measured at 1 min intervals during the period of data collection using a HOBO-U12 data logger  and HOBO Water Temp Pro V2 data loggers (Tbg) . Measurements were taken at a position that was 2 m from the ground at a position in the center of the barn where cows were housed. Rectal temperature was matched with measurements of Tdb, RH, Tdp, and Tbg to the nearest minute at which environmental variables were recorded. Temperature humidity index (THI) db = dry bulb temperature (°C), and RH = relative humidity (%).Measurements of dry bulb temperature variance components were estimated by restricted maximum likelihood A single-step genomic BLUP (ssGBLUP) method that integrates phenotypes, genotypes, and pedigree information was used to simultaneously estimate genomic predicted transmitting ability (PTA) and allele substitution effects y is a vector of RT phenotypes, b is a vector of fixed effects, a is a vector of additive genetic effects, pe is a vector of permanent environmental effects, and e is a vector of random residual effects. Fixed effects included parity, year of data collection, stage of lactation , location of cows at measurement (resting or in headlocks), farm type , technician who measured RT, and farm. THI and milk yield at the time of measurement were included as covariates. bX, aZ, and peZ are incidence matrices relating a record to fixed environmental effects in b and to random animal and permanent environmental effects in a and pe, respectively. Results were calculated using a Bayes A model as implemented in the BLUPF90 program The single-trait model used for ssGBLUP analysis of RT wasManhattan plots were created using ggplot2 0.9.2 Wang et al. The proportion of SNP variance explained by each marker individually is shown in The 10-SNP results  are simiBos taurus autosome (BTA) 24. That region is flanked by a U1 spliceosomal RNA (U1) on the left  and a cadherin-2 (NCAD) gene on the right . The U1 small ribonucleoprotein is involved in postranscriptional modification and regulation of mRNA length NCAD was more highly expressed in paratuberculosis-infected cattle than in noninfected animals NCAD and stress responses.The 20 largest explanatory loci for RT for each of the analyses is listed in SNORA19) from 58,520,021 to 58,520,149 bp; a ubiquitin-protein ligase (RFWD2) from 58,600,678 to 58,838,844 bp; and small Cajal body specific RNA 3 (SCARNA3) from 58,628,128 to 58,628,268 bp. Small nucleolar RNA like SNORA19 identify sites of pseudouridine formation SCARNA3 gene also encodes a small nucleolar RNA similar in structure to SNORA19. The RFWD12 gene encodes an E3 RING-type ubiquitin-protein ligase, which selects proteins for proteasomal degradation Two regions of interest were flagged on BTA 16. The 2- and 3-SNP analyses  and 2 foCEP170) and inactive phospholipase D5 (PLD5). Neither gene has an obvious relationship with physiological response to heat stress.The second region of interest on BTA 16 is located at about 35,272,426 bp. The closest genes to this area are centrosomal protein of 170 kDa (SLCO1C1) and a phosphodiesterase (PDE3A). In humans, SLCO1C1 mediates the Na+-independent high-affinity transport of thyroxine and reverse triiodothyronine A region of BTA 5 at approximately 89,500,000 bp was consistently identified across analyses. Two genes flank the consensus region, solute carrier organic anion transporter family member 1C1 (KBTBD2) and U6 snRNA-associated Sm-like protein LSM5 (LSM5). KBTBD2 encodes for a protein that participates in ubiquitination of proteins LSM5 is likely to participate in RNA processing and to form part of the stress granule seen in stressed-cells that contains mRNAs stalled in translation The 4- and 5-SNP analyses showed a peak near 64,400,000 bp on BTA 4 that explained 0.09–0.11% of the observed variance  and 3. TGOT1) gene. GOT1 is involved in synthesis of sulfur dioxide, which has been shown to reduce formation of reactive oxygen products and protect rat myocardial tissue in response to isoproterenol A region centered on the SNP at 20,290,497 bp on BTA 26 explained 0.10% to 0.22% of the observed variance in the 2-SNP to 5-SNP analyses .In an earlier study, Hayes et al. There are small but significant genetic correlations of RT during heat stress with 305-d milk, fat, and protein yields, productive life, net merit, somatic cell score and daughter pregnancy rate SLC01C1, which is involved in regulation of metabolic rate through transport of thyroxine, plays a known role in processes controlling body temperature. More commonly, candidate genes play roles that are important for stabilizing cellular function during stress. Among these are GOT1, which synthesizes the cytorotective compound sulphur dioxide, genes involved in protein ubiquitination (KBTBD2 and RFWD12), and genes involved in RNA metabolism .In conclusion, these data demonstrate that QTL exist to predict some of the genetic variation in RT. These QTL may prove important for genetic selection of thermotolerance. The challenge of performing GWAS for traits with low heritability when the number of phenotyped animals is low means that confirmation of the utility of these QTL is essential. In addition, several candidate genes for regulation of RT in dairy cattle were identified and one or more of these may play an important role in physiological adaptation to heat stress. Only one candidate gene, Figure S1Proportion of SNP variance explained by individual SNP effects for rectal temperature from a single-step GBLUP analysis.(PNG)Click here for additional data file.Figure S2Proportion of SNP variance explained by 2-SNP moving windows for rectal temperature from a single-step GBLUP analysis.(PNG)Click here for additional data file.Figure S3Proportion of SNP variance explained by 4-SNP moving windows for rectal temperature from a single-step GBLUP analysis.(PNG)Click here for additional data file.Figure S4Proportion of SNP variance explained by 10-SNP moving windows for rectal temperature from a single-step GBLUP analysis.(PNG)Click here for additional data file.Table S1The 20 loci with the largest single-SNP proportion of SNP variance explained for rectal temperature.(PDF)Click here for additional data file.Table S2The 20 loci with the largest proportion of SNP variance explained for rectal temperature using 2-SNP sliding windows.(PDF)Click here for additional data file.Table S3The 20 loci with the largest proportion of SNP variance explained for rectal temperature using 4-SNP sliding windows.(PDF)Click here for additional data file.Table S4The 20 loci with the largest proportion of SNP variance explained for rectal temperature using 10-SNP sliding windows.(PDF)Click here for additional data file.File S1The name, chromosome number, SNP location (BP), and proportion of SNP variance for rectal temperature explained by each SNP from a single-step GBLUP analysis in CSV format. Marker locations are based on the UMD 3.1 assembly of the bovine genome.(CSV)Click here for additional data file.


Accurate pedigree information is critical to animal breeding systems to ensure the highest rate of genetic gain and management of inbreeding. The abundance of available genomic data, together with development of high throughput genotyping platforms, means that single nucleotide polymorphisms (SNPs) are now the DNA marker of choice for genomic selection studies. Furthermore the superior qualities of SNPs compared to microsatellite markers allows for standardization between laboratories; a property that is crucial for developing an international set of markers for traceability studies. The objective of this study was to develop a high throughput SNP assay for use in the New Zealand sheep industry that gives accurate pedigree assignment and will allow a reduction in breeder input over lambing. This required two phases of development- firstly, a method of extracting quality DNA from ear-punch tissue performed in a high throughput cost efficient manner and secondly a SNP assay that has the ability to assign paternity to progeny resulting from mob mating. A likelihood based approach to infer paternity was used where sires with the highest LOD score (log of the ratio of the likelihood given parentage to likelihood given non-parentage) are assigned. An 84 “parentage SNP panel” was developed that assigned, on average, 99% of progeny to a sire in a problem where there were 3,000 progeny from 120 mob mated sires that included numerous half sib sires. In only 6% of those cases was there another sire with at least a 0.02 probability of paternity. Furthermore dam information  was absent, highlighting the SNP test’s suitability for paternity testing. Utilization of this parentage SNP assay will allow implementation of progeny testing into large commercial farms where the improved accuracy of sire assignment and genetic evaluations will increase genetic gain in the sheep industry. To make genetic gain in animal breeding programs, pedigree information is required to estimate breeding values accurately. The use of incorrect pedigree information has the potential to reduce the rate of genetic gain Traditionally pedigree information has been achieved by breeder records and more recently via DNA marker tests, for example, using microsatellites (MS), also known as either simple sequence repeats or short tandem repeats The New Zealand (NZ) sheep industry has had the potential to utilize a MS marker test for parentage analysis. Although SNP markers have less polymorphic information  compared to MS markers which can have many alleles, this can easily be overcome and superseded by utilizing multiple SNP markers simultaneously. In addition SNPs are superior to MS markers in that, due to utilizing the biallelic SNPs, they are more robust with respect to use in the lab, subsequent interpretation of data and have a lower mutation rate. In addition, short amplicons (<100 bp) can be achieved and high throughput genotyping technologies are applicable to SNP markers. Due to these qualities, we selected a set of SNP markers that were multiplexed with the aim of producing a reproducible, low cost, high throughput genotyping test that is effective in accurately assigning paternity for the NZ sheep industry. Encouraging the use of DNA markers for correct sire assignment will enable accurate breeding values to be estimated and accelerate the rate of genetic gain To establish a SNP based marker test for sire assignment, substantially more markers are required compared to the traditional MS marker tests. For paternity exclusion, it has been estimated that approximately 4 SNPs with allele frequencies of 0.5 give the same power of exclusion as for 1 MS marker and that variations in the allele frequencies between 0.2–0.8 did not substantially affect the probability of exclusion in paternity cases in human studies The practical use of DNA markers to establish a molecular pedigree in livestock breeding programs has a number of issues to address prior to implementation into the industry. If the breeding program is able to sample all parents (i.e. a closed breeding program) and high power of exclusion can be achieved (i.e. many variable loci) without genotyping errors, parents can be identified using Mendelian inheritance of alleles to exclude the incorrect parents. However, the use of pure exclusion methods to assign parentage may fail if information is limiting  and genotyping errors are present. To overcome these limitations, several statistical approaches have been developed that rely on likelihood methods whereby probabilities of parentage assignment are determined from simulations or Bayesian methodologies 2 for the jth SNP, where MAFbj is the allele frequency in the bth breed) in the HapMap samples genotyped with the 1536 SNP assay jI value together with SNPs that had a MAF>0.25 for Romney, Perendale and Coopworth animals (n = 250) were subjected to preliminary assay design (MassARRAY software) to select the top 300 SNPs that would be submitted to the Sequenom iPlex assay development process. The location of the SNPs was also considered so that relatively equal chromosome spacing throughout the genome would be achieved. In addition to the 300 proposed Parentage SNPs, 19 “production trait” SNPs were also submitted to Sequenom  for inclusion in the iPlex assay along with the Trio animals supplied as a 50 µl sample (20 ng/µl) in a 96 well plate for development of a parentage SNP assay. The various steps of the design process were validated by genotyping the Trio animal set. The resulting parentage SNP plus production SNP Sequenom iPlex assay, hereafter referred to as the Parentage SNP assay, that was generated by Sequenom consists of a two plex 115 SNP (58 and 57 SNPs) product. Information on the 98 parentage SNPs in the Parentage SNP assay are located in The SNP sequences that were considered for inclusion in the Parentage SNP assay had been previously validated on both the Ovine 1536 Golden Gate SNP assay and the Ovine SNP 50K Illumina chip (n = 454). To establish the SNP set to be submitted to Sequenom Inc. , the SNPs were given an index value (www.sheephapmap.org). These are presented in To provide further information to the international community, SNPs were identified in the 100 base pair flanking regions of the 98 parentage SNPs for the International Sheep Genomics Consortium (ISGC) diversity panel of 73 sheep samples that were sequenced to ∼12 X coverage , 0.1 µM each PCR primer, 0.5 U of HotStarTaq enzyme, and 1.5 µl DNA. The thermal cycling conditions consisted of a first denaturation step at 95°C for 2 min, followed by 45 cycles of denaturation at 95°C for 30 s, annealing at 56°C for 30 s, and extension at 72°C for 1 min, with a final extension step at 72°C for 5 min. To neutralize unincorporated dNTPs, PCR products were treated with 0.5 U shrimp alkaline phosphatase by incubation at 37°C for 40 min, followed by enzyme inactivation by heating at 85°C for 5 min. By adding 2 µl of an iPLEX Gold extension reaction cocktail to the purified PCR products, the Plex 1 and Plex 2 extension reaction was carried out in a final volume of 9 µl containing 0.222×iPLEX buffer, 1×iPLEX termination mix, 1×iPLEX enzyme, and the SBE primer mix that contained the Plex 1 and Plex 2 extension primers. The iPLEX extension reaction was performed under the following thermal conditions: an initial denaturation step at 94°C for 30 s, followed by 40 cycles of a denaturation step at 94°C for 5 s, 5 cycles of annealing at 52°C for 5 s and extension at 80°C for 5 s and a final extension step at 72°C for 3 min. After desalting of the products by using SpectroCLEAN resins following the manufacturer’s protocol, cleaned extension products were dispensed onto a 96 SpectroCHIP array using an RS1000 Nanodispenser, and finally, the array was introduced into a MassARRAY Compact 96 mass spectrometer. Spectra were acquired using SpectroAcquire software, and data analysis, including automated allele calling, was done using MassARRAY Typer software, version 4.0.5.All DNA samples (12.5 ng/µl) were transferred into 384 well PCR plates for genotyping. They were arranged by plex and are referred to as Plex 1 (58 SNPs) and Plex 2 (57 SNPs). The oligonucleotides were supplied by Sequenom as combined primer pools . The oligonucleotide sequences for the 98 parentage SNPs are located in nd best sire match (LOD2) from that of the best Sire match (LOD1). Note that for this first approach if the 2nd best LOD is <0 then Δ is defined to be LOD1 (rather than LOD1-LOD2). This differs from that of the Cervus 3.03 program also utilized in this study where LOD2 is used even if <0.The first approach taken to assign paternity in this study is based on the methods described by Marshal et al. For the paternity assignment using likelihood methods implemented through the Cervus 3.03 computer program, allele frequencies were determined as above for each locus prior to performing simulation analyses to establish a confidence threshold for assignment. This software package is marketed as an easy-to-use and practical tool to establish parent-offspring relationships when some genotypes are incomplete, incorrect or missing. It assumes that markers are autosomal and that the species is diploid. It also assumes that markers are inherited independently of each other (not in linkage disequilibrium). In the simulation, the identity of the true parent is known for each offspring. Cervus compares the distribution of LOD or Δ scores for tests in which the most likely candidate parent is the true parent with the distribution of LOD or Δ scores for tests in which the most likely candidate parent is not the true parent. Confidence in assignment is defined as the proportion of all candidate parents with LOD or Δ scores exceeding a given LOD or Δ score and are the best LOD or Δ that are true parents A simulation was also carried out using the genotypes from Flock D assuming each sire produced the number of progeny that was estimated from the initial analysis. The progeny genotypes were generated by randomly sampling alleles from the true genotypes of the sires and the alleles from the dams were obtained by random sampling based on the proportions given in the allele frequency files. The simulated genotypes for the offspring were then combined with the true genotypes of the sires and modified to mimic the missing and mistyped loci observed in the original data. These genotypes were then processed through Cervus using the same approach as for the real genotype data.The high throughput ear-punch tissue DNA extraction method established produces good quality and quantity of DNA suitable for various downstream genotyping platforms including next generation sequencing technologies. On average, 36 µg of DNA (mean 260/280 ratio = 1.81) was extracted from an ear-punch tissue collected in an AllFlex TSU. When genotyped using the Sequenom system call rates of 96–98% were obtained. In addition to utilizing the DNA extraction method for the Sequenom Parentage SNP iPlex assay developed in this study, the extracted DNA has also been used to genotype samples with the Illumina Ovine 5K SNP chip . When the accepted Illumina genotype call rate was set to greater than 95%, 0.4% of samples failed and when a more stringent call rate of at least 98% was set, 1.5% failed (data not shown). Furthermore, the DNA is suitable for both whole genome and reduced representational next generation sequencing techniques. Sequencing Tru-seq paired end libraries on the HiSeq 2000 produced an average of 48 Gb of raw sequence/lane which when trimmed  reduced to 41 Gb of sequence . For theThe developments in iPLEX Sequenom chemistry for multiplexing of up to 60 SNPs in a single multiplex allow for a cost competitive high throughput parentage assay to be established. Following the selection of 300 SNPs it was anticipated that 2 iPLEX assays of approximately 60 SNPs each would be sufficient for paternity assignment. The resulting SNPs in the Parentage SNP assay are autosomal and span all but chromosome 16 and 26 . There wThe initial validation of the Parentage SNP assay identified 12 SNPs that gave pedigree errors. The DNA samples used for the initial validation had been extracted from both whole blood and ear-punch tissue with the resulting call rates of 99% and 96–98% obtained, respectively. The reproducibility of this test was 99.8%. The concordance level between the Sequenom iPLEX and the Illumina platforms  was found to be 96.1% for the 98 parentage SNPs in common between both platforms. However, upon removal of the 14 non-performing SNPs  are presented in The Parentage SNPs selected for assay design were required to have an estimated MAF greater than 0.25 in the predominant NZ breeds; Romney, Perendale and Coopworth. The observed MAF following analysis of the parentage SNP genotypes obtained from Flocks D, E and F during the beta test are presented in nd best parentage assignments, of 31.91 was obtained . Summary statistics for the sire assignment-best pedigrees (i.e. the sire with the highest parentage probability) for the industry flocks utilized in the initial validation are presented in obtained . When paobtained . AlthougIn addition to genotyping Flock A with the Parentage SNP assay, the genotypes from a commercial MS marker test were also available for these animals and analyses compared and presented in In addition, as a measure of the relative power of the marker sets, exclusion probabilities A total of 7,875 animals were genotyped  from three industry flocks where the genotype call rate ranged between 94.98% and 98.36%  with assTo establish a confidence threshold for assignment, parentage inference was performed on the six data sets from Flocks A–F using the likelihood methods implemented in Cervus. Similar to the AgR method, Cervus also calculates likelihood ratios allowing for the possibility that the genotypes of parents and offspring may be mistyped.The results for Flock A from the Cervus analysis are presented in nd best sire assignments are presented in nd best sire assignment when genotyped with the MS markers (Flock A) and also for Flock D using the SNP markers. The latter result may be indicative of the lower genotype call rate obtained for this flock or that there is higher degree of relatedness between putative sires in this flock compared to the others. The mean LOD score for the 2nd most likely sire in the other flocks and also Flock A using SNP markers is negative.To further illustrate the superior power of the Parentage SNP assay compared to the MS markers in paternity assignment, the mean LOD scores for the best and 2The aim of this study was to produce a high throughput, reproducible, cost effective and accurate parentage test for implementation into the NZ Sheep Industry. The use of DNA markers to establish parentage, a requirement for estimating breeding values accurately and managing inbreeding, has aided in the progress of genetic gain in animal breeding programs. Misidentification rates of 7–15% have been reported which have led to a 2.5% to 15% decrease in genetic gain In this study, the power of the Parentage SNP assay was found to be superior to that of the MS marker test alone. This is to be expected as the Parentage SNP assay has 34 more SNPs than the number estimated to achieve the same power as the MS marker test (∼50 SNPs were required to achieve the same exclusion power as set of 11 MS markers). This estimation of 4.5x SNPs to one MS marker is in agreement with previous studies The observed increased power of the Parentage SNP assay is most evident from the ‘uniqueness’ values where, when both the dam and sire information was also included in the analysis of the flock examined, only 50% of progeny assigned were reported to be unique for the MS test, however, this increased to 99% for the SNPs analysis. For paternity only assignment situations, the MS test often reported multiple possible paternities (only 37% unique assignment) which greatly improved to a unique assignment of 89% for the SNPs assay, further highlighting the higher power of the latter test. A similar finding was also observed when the Cervus program was utilized to assign paternity at a given confidence level. More than half of the progeny could not be assigned at the 99% confidence level when genotyped with the MS markers compared to less than 1% for the SNP markers.The use of DNA markers in extensively farmed livestock have generally been used to either verify or exclude potential parentage. In this study categorical assignment has been utilized that can be used when parentage information is incomplete, allows for genotyping errors, and is able to assign putative paternity  The initial validation carried out on Flocks A to C provided evidence for the power of unique assignment to sire in a flock of up to 300 lambs from mob mating of rams. The finding suggested that flock management and/or recording systems could be altered to reduce breeder involvement. To further investigate this, several large paternity test situations were undertaken, without knowledge of dam genotype or lambing or mating groups. The progeny and potential sires from three industry Flocks  were genotyped with the Parentage SNP assay in a commercial lab environment with subsequent paternity assigned. On average 99% of progeny were assigned a sire with 93% uniquely assigned with an average Δ value of 13.01 obtained using the AgR method. These results indicate that the SNP based parentage test has the power to uniquely assign 3,000 progeny that result from mob mating and without additional information such as the subset of all sires that the true sire belonged to (which might be known if different mating groups were retained to lambing). Dam information  was unavailable during this validation phase, highlighting the SNP test’s suitability for paternity testing. When the same data sets were analysed using the Cervus software program between 93.8 and 98.4% of the progeny were confidently assigned a Sire at the 99% confidence level.The use of categorical assignment of paternity in this study using the highest likelihood of putative parent-progeny pairs given their multiple SNP genotypes and observed allele frequencies in the flock has resulted in at least 98% of progeny assigned to a sire. The accuracy of parentage assignment analysis tests differ depending on factors such as the number of loci and polymorphic content, ability to sample all candidate parents and the presence of genotyping errors. Recently the relative accuracy of different methods of parentage analysis was investigated, albeit in natural populations using MS markers The Parentage SNP assay presented in this study delivers paternity assignment with confidence; however, it may not be adequate for use in product tracing. This would require further investigation as the risk of incorrectly assigning a sire from a genetic data set that contains many putative sires that are highly likely to include relatives  still needs to be determined. Hill et al. www.sheephapmap.org). In addition to the advantages of SNP markers for parentage testing compared to MS markers , SNPs are easy to standardise between laboratories. The selection of an international set of parentage markers that is recommended to be included in all sheep parentage SNP assays globally would greatly aid traceability of meat products internationally. The 84 parentage panel that has been developed in this study has an overlap of 48 and 34 SNPs in common between the 163 “diverse breed” and 109 “ North American” SNPs sets, respectively Although the Parentage SNP assay in this present study was trialled on flocks from the NZ sheep industry, the panel is also highly likely to be successful internationally. Sheep populations from NZ, Australia and North America clustered together with the European breeds despite their geographical separation, when genotyped with the ∼1,500 SNP panel Another important aspect of this study was the development of a DNA extraction method from ear-punch tissue samples required to produce high quality DNA for downstream applications such as the Sequenom system utilized in for the Parentage SNP assay, but also for the Illumina iScan-bead chip and next generation sequencing platforms. This was achieved with more than 2,000 samples genotyped with Illumina iScan-bead chip technology resulting in 98.5% of samples producing call rates of at least 98% and more recently with the production of quality whole genome sequencing data using an Illumina HiSeq 2000. The ability to extract high quality DNA in a low cost high throughput manner suitable for multiple genotyping platforms is an essential component for the uptake of genomics technology by the livestock industry, an area that is evolving rapidly. Furthermore, the SNPs that were selected for inclusion in the Parentage SNP assay are also present on the Illumina Ovine 5K SNP chip allowing integration of the genomic tools utilised by the industry; sires that are genotyped with the Ovine 5K SNP chip for genomic selection therefore will not be required to be re-genotyped with Parentage SNP assay for assignment of progeny.The utilization of an accurate, robust and high throughput cost competitive Parentage SNP assay will allow implementation of progeny testing on large commercial farms where the improved accuracy of sire assignment and genetic evaluations will increase genetic gain in the sheep industry. When multiple-sire mating is used in farm production systems, paternity testing is essential to identify the sires that are producing progeny with the desired and superior traits. It is also important with single sire mating to avoid incorrect progeny assignment, eg incorrect bonding at birth resulting in misallocation of dam (where lambing occurs in the field). Use of correct pedigree is crucial in obtaining accurate estimates of heritabilities and genetic correlations required for developing selection programs in the NZ sheep industry. This will be of even more importance with breeding operations moving to more extensive environments. The performance of progeny in such environments may have different genetic parameters from those currently used by the industry and therefore there is a requirement for estimating these parameters.This study has developed a parentage SNP assay for use in the NZ sheep industry for accurate pedigree assignment that will allow a reduction in breeder input over lambing and has advantages over existing microsatellite based parentage tests. In addition, the SNP assay is suitable for paternity testing. The utilization of an accurate, robust and high throughput cost competitive parentage SNP assay will allow implementation of progeny testing into large commercial farms where the improved accuracy of sire assignment and genetic evaluations will increase genetic gain in the sheep industry and allow better management of inbreeding.Figure S1Whole genome sequence data utilizing DNA extracted from the ear-punch tissue high throughput method. The total bases (A) and coverage (B) obtained for 15 individual samples for the raw (solid bars) and trimmed (hatched bar) data obtained from sequencing Illumina Tru-seq paired end libraries (2x 100 bp reads) on a Illumina HiSeq2000 .(TIF)Click here for additional data file.Table S1The 98 parentage SNP panel included in the Sequenom Parentage 2 iPlex SNP assay.(XLSX)Click here for additional data file.Table S2The oligonucleotide sequences for the Sequenom 2x iPlex 98 Parentage SNP assay.(XLSX)Click here for additional data file.Table S3The genotypes observed in the ISGC 73 sequenced animals (diverse breed panel) for the 98 parentage SNPs in Sequenom iPlex assay and neighboring SNPs identified in the 100 nucleotide 5′ and 3′ flanking regions.(XLSX)Click here for additional data file.