Saccharomyces cerevisiae, we sequenced the genome of a vineyard and oak tree strain and compared them to a reference genome. Among these three strains, 6% of the genome is variable, mostly attributable to variation in genome content that results from large InDels. Out of the 88,000 polymorphisms identified, 93% are SNPs and a small but significant fraction can be attributed to recent interspecific introgression and ectopic gene conversion. In comparison to the reference genome, there is substantial evidence for functional variation in gene content and structure that results from large InDels, frame-shifts, and polymorphic start and stop codons. Comparison of polymorphism to divergence reveals scant evidence for positive selection but an abundance of evidence for deleterious SNPs. We estimate that 12% of coding and 7% of noncoding SNPs are deleterious. Based on divergence among 11 yeast species, we identified 1,666 nonsynonymous SNPs that disrupt conserved amino acids and 1,863 noncoding SNPs that disrupt conserved noncoding motifs. The deleterious coding SNPs include those known to affect quantitative traits, and a subset of the deleterious noncoding SNPs occurs in the promoters of genes that show allele-specific expression, implying that some cis-regulatory SNPs are deleterious. Our results show that the genome sequences of both closely and distantly related species provide a means of identifying deleterious polymorphisms that disrupt functionally conserved coding and noncoding sequences.The abundance and identity of functional variation segregating in natural populations is paramount to dissecting the molecular basis of quantitative traits as well as human genetic diseases. Genome sequencing of multiple organisms of the same species provides an efficient means of cataloging rearrangements, insertion, or deletion polymorphisms (InDels) and single-nucleotide polymorphisms (SNPs). While inbreeding depression and heterosis imply that a substantial amount of polymorphism is deleterious, distinguishing deleterious from neutral polymorphism remains a significant challenge. To identify deleterious and neutral DNA sequence variation within  DNA sequence variation makes an important contribution to most traits that vary in natural populations. However, mapping mutations that underlie a trait of interest is a significant challenge. Genome sequencing of multiple organisms provides a complete list of DNA sequence differences responsible for any trait that differs among the organisms. Yet, distinguishing those DNA sequence variants that contribute to a trait from all other variants is not easy. Here, we sequence the genomes of two strains of yeast and, through comparisons with a reference genome, we catalog multiple types of DNA sequence variation among the three strains. Using a variety of comparative genomics methods, we show that a substantial fraction of DNA sequence variations has deleterious effects on fitness. Finally, we show that a subset of deleterious mutations is associated with changes in gene expression levels. Our results imply that comparative genomics methods will be a valuable approach to identifying DNA sequence changes underlying numerous traits of interest. DNA sequence polymorphism makes a major contribution to phenotypic variation and provides a mean by which natural selection can lead to microevolutionary change and divergence between species. Since the first methods were developed to systematically survey DNA polymorphism within species and divergence between species A number of approaches have been developed to identify and characterize DNA polymorphism or divergence with positive or negative effects on fitness. Applications of these approaches have revealed that many aspects of DNA polymorphism and divergence can be explained by mutation and genetic drift, consistent with the neutral theory of molecular evolution Drosophila melanogaster, wild-caught flies carry deleterious mutations that result in a 60% average reduction in viability and an estimated 97% reduction in net fitness when made homozygous −3 in flies There is abundant evidence that deleterious mutations also make a significant contribution to phenotypic variation and DNA polymorphism Drosophila and humans 3 deleterious nonsynonymous SNPs There are a number of estimates of the fraction of DNA polymorphism that is deleterious. Compared to the frequency distribution of allozymes expected from population genetic theory, there is a vast excess of low frequency alleles in both S. cerevisiae and two of its closest relatives to identify individual instances of conserved transcription factor binding sites Distinguishing deleterious SNPs from those that are neutral is a necessary but difficult step in identifying the molecular basis of quantitative traits and many diseases. Current methods based on protein structure and protein conservation across species show high false positive rates, 10–30% S. cerevisiae and identifying the subset of variation with functional consequences, we sequenced the genome of two strains: M22, a strain isolated from a vineyard in Italy, and YPS163, a strain isolated from an oak tree in the United States of America S. paradoxus, the closest known relative of S. cerevisiae. Combining both population genetics and comparative genomics methods, we find abundant evidence for deleterious coding and noncoding SNPs and we resolve a significant fraction of these differences to single polymorphic sites. Our results imply that comparative genomics can identify polymorphisms that underlie quantitative traits and human diseases.As an initial investigation into cataloging whole-genome DNA polymorphism in S. cerevisiae, M22 and YPS163, were sequenced by whole-genome shotgun and assembled using PCAP Two natural isolates of To identify DNA sequence polymorphism, we separately aligned the M22 and YPS163 assemblies to the S288C reference genome see . ExcludiSSU1To identify any rearrangements between M22, YPS163 and the S288C reference genome, we mapped paired reads to the reference genome. Inversions or translocations should result in read-pairs inconsistent with the reference genome sequence. After removing single inconsistent read-pairs, which could be the result of chimeric clones, we found 5 inconsistent pairs in YPS163 that map to a single break and 11 inconsistent pairs in M22 that map to three breakpoints . One of More than 88 thousand polymorphisms were identified within the combined genome alignments . 93% of Using a Phred quality score cutoff of 20 SNPs and InDels were classified into strain-specific variants for all cases where there were data from all three strains . For botARO3 (4.6%), EHD3 (4.8%), KRS1 (15.1%), ENA5 (16.4%), ENA2 (3.9%), and ENA1 (3.5%). The maximum likelihood tree of EHD3 shows that all strains except YPS163 group with S. paradoxus  occurs on chromosome IV . The higaradoxus . The difS. cerevisiae to divergence between S. cerevisiae and S. paradoxus. Out of 50 genes examined, 12 showed some phylogenetic evidence and 17 showed strong evidence of recent introgression  and NUM1 (30 SNPs). The remaining 349 cases involve SNPs within repeats found at two or more loci, including transposable elements (89), noncoding regions (62), telomeres (28) and a number of multigene families . The frequency of deleterious SNPs in M22 and S288C are not significantly different from one another. In contrast, the proportion of conserved noncoding SNPs that are deleterious is greater in M22 compared to either YPS163 or S288C , the later two not being significantly different from one another.The estimated frequency of deleterious SNPs differs between strains. Because there is only a single unrooted tree for the three strains, each bi-allelic SNP can be assigned to a single lineage and strain-specific substitution rates can be estimated. Although most SNPs occur on the YPS163 lineage, YPS163 contains the smallest proportion of deleterious SNPs in both coding and noncoding sequences . The proSequence conservation provides a mean of identifying functionally constrained sequences and deleterious SNPs that disrupt these sequences. However, distinguishing deleterious from neutral SNPs requires single-base resolution of functional constraint and thus a large set of distantly related species. To identify deleterious SNPs within coding and noncoding sequences, we examined conservation across distantly related fungi.S. cerevisiae, we selected four sensu strictu Saccharomyces species, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus, and six other hemiascomyetes species, Candida glabrata, S. castelli, S. kluyveri, Kluyveromyces lactis, Ashbya gossypii, and Candida albicans. Using alignments from 2046 genes, we found a median synonymous substitution rate of 18 substitutions per site. At this distance, single bases may still show identity across species by chance, but conservation of multiple sites, such as a single codon or transcription factor binding site, should be exceedingly rare.Starting with the genomes most closely related to sensu strictu Saccharomyces species. In contrast, only 813 out of 30,333 (2.7%) high-quality, noncoding SNPs met the same criteria.To generate an alignment for each coding and noncoding SNP, we used BLAST to search for homologous sequences within 100 bp of each SNP in each species. Relative to coding sequences, far fewer homologous noncoding sequences were identified . For codsensu strictu species, SIFT predicted 2,841 (26%) of nonsynonymous SNPs affect protein function.Given a set of protein coding alignments, the SIFT algorithm uses the type of amino acid substitution in combination with average conservation across an alignment to determine whether a nonsynonymous SNP is deleterious sensu strictu Saccharomyces species, we identified 1472 SNPs that disrupt significantly conserved codons (P<0.001), 34% of all nonsynonymous SNPs with distant homologs.To incorporate phylogenetic distance into the likelihood of a SNP being deleterious, we compared the likelihood of a set of aligned sequences under a neutral and a conserved phylogenetic model. Under the neutral model the rate of nonsynonymous substitution within a codon is the same as the rate of synonymous substitution across the genome. Under the conserved model, the nonsynonymous rate within a codon is a fraction of the synonymous rate. Applying a likelihood ratio test to all high-quality SNPs in sequences with homologs in at least two species beyond the ADH1, CDC47, FKS1, IMD4, and SSB1. All five of these genes have paraologs in S288C and have percent identity that is often greater than 85%, implying that any changes in function caused by the SNPs may be buffered by paralogous genes. However, most deleterious SNPs are unlikely to be buffered: only 106 (7%) of the deleterious SNPs occur within sequences with paralogs that show greater than 85% identity within 100 bp of the SNP.The deleterious SNPs are not randomly distributed. Out of the 1472 deleterious coding SNPs, there are many more deleterious SNPs in S288C (618) than in M22 (457) or YPS163 (401), similar to the overall frequency deleterious SNPs in coding sequences . The 147Comparative genomics methods have been used to estimate that 34–43% of noncoding sequences are selectively constrained in yeast sensu strictu Saccharomyces species to reliably identify instances of conserved binding sites Previous studies have shown that there is sufficient divergence among the sensu strictu Saccharomyces species. Out of 16,401 high-quality, Phred quality of 40 or more, noncoding SNPs examined, 2083 (13%) occur in sequences annotated as conserved binding sites (P<0.01), typically two conserved binding site predictions per SNP. For each SNP within a conserved binding site, we calculated the posterior probability of the SNP under a conserved binding site model and a model with loss along the SNP containing lineage Saccharomyces sensu strictu species. Only 13% of positions show both SNP alleles present in other species.To test each SNP, we used 422 models of transcription factor binding sites from various sources In the previous section, we used models of transcription factor binding sites to identify deleterious SNPs. There are a number of concerns with this approach. First, most models of transcription factor binding sites are estimates of the true model and slight errors in these estimates are not accounted for in the binding site loss model GPB2, including the query alignment, the resulting profile alignments, the motif model and the likelihood ratio test for a significant difference in allele frequency.Using the Phylonet algorithm, we identified conserved noncoding sequences similar to those flanking a noncoding SNP of interest. Phylonet is a motif finding algorithm that uses a BLAST-like method to search the genome for sequences with conservation profiles similar to that of a query profile Phylonet identified 4762 SNPs (30% of those tested) as occurring within multiple copy conserved motifs. The median number of alignments per motif was 36. HyPhy was used to implement a likelihood ratio test comparing the probability of the two SNP alleles being equivalent to each other or not. A total of 2452 and 1643 SNPs were significant at a low (P<0.01) and high (P<0.001) confidence cutoff, respectively. Application of the same method to shuffled alignments produced 191 and 104 SNPs, suggesting a false discovery rate of 7.7% and 6.2% for the low and high confidence cutoffs, respectively.The overlap between deleterious nonsynonymous SNPs predicted by SIFT and the likelihood ratio test (LRT) is high, 47% of all SNPs identified . The oveThe overlap between deleterious noncoding SNPs predicted by the binding site model and the Phylonet-based motif model is low, 9% . This isSNPs that disrupt conserved noncoding sequences may often affect the regulation of an adjacent gene. To test whether changes in gene expression are associated with deleterious noncoding SNPs, we used allele-specific expression assays to measure the expression of 190 genes in hybrids of either M22, YPS163 or S288C. Nine of the predicted SNPs occur in divergently transcribed intergenic sequences and so expression of both genes was measured in order to query a total of 181 SNPs. Although four of the genes contain two deleterious SNP predictions and one gene contains three predictions, 60% of the SNPs tested were not confounded with any other noncoding SNP in the same intergenic sequence.GPB2 showed allele-specific expression in five of the nine conditions  were predicted deleterious by the binding site or the Phylonet model. In 86% (32/37) of cases showing allele-specific expression, SNPs were only predicted deleterious by one of the two models. However, not all predictions were associated with allele-specific expression. Expression differences were found for 36% of SNPs predicted by the binding site model but not Phylonet and 41% of SNPs predicted by the Phylonet but not the binding site model. Surprisingly, only 18% of SNPs predicted by both models were found associated with changes in gene expression. No obvious differences were found for SNPs predicted by one method versus those predicted deleterious by both methods, e.g. the median Phastcons posteriori probability is 0.83 for SNPs predicted by both models versus 0.77 for SNPs predicted by only one of the two models.In contrast to SNPs with binding site or Phylonet predictions, 21% of SNPs without any prediction were associated with allele-specific expression. We also tested 34 SNPs predicted deleterious at a lower confidence cutoff (0.001<P<0.01) by either model. While only 6/34 (18%) of the low confidence SNPs were associated with allele-specific expression, 2/3 low confidence SNPs predicted by both models were associated with changes in expression. In comparison to a method that identifies blocks of conserved sequences, only 26% of SNPs with sites predicted to be conserved by Phastcons S. cerevisiae, we found variation in genome content, structure and sequence. Overall, there is substantial evidence for functional variation, based on disruption of sequences annotated in the reference genome, and deleterious variation, based on disruption of sequences conserved across other yeast species.Genome sequencing of multiple organisms from the same species makes it possible to both catalog DNA polymorphism and identify variation with fitness and/or functional consequences. Using genome sequences of two strains of Using whole-genome sequence data, we identified multiple types of polymorphism, including variation in genome content, structure and sequence. Assuming no major skew in the allele frequency spectrum We identified four genome rearrangements and validated one reciprocal translocation that was previously characterized S. paradoxus into YJM789 but not S288C Although genome coverage was low, we found variation in genome content by identifying sequences present in one strain but absent in one or more strains and by identifying insertions or deletions in genome alignments. A total of 261 kb of S288C sequences are present as gaps of greater than 5 kb in length in both the M22 and YPS163 assemblies. Another 273 kb of gaps are present in just M22 or YPS163, but not both. One of the M22-specific gaps spans a 7.9 kb region on chromosome I that was introgressed from S. cerevisiae with genome sequence data. This is likely an underestimate since a total of 323 kb of assembled M22 or YPS163 sequences were not included in the alignments. Many of these sequences are likely M22 and YPS163 duplications since they often matched S288C where other contigs were aligned with higher similarity. The estimates of variation in genome content presented in We also found sequences present in M22 or YPS163 but absent from S288C. A total of 71 kb of sequence was absent from the complete S288C genome but present in more than one of the four other strain of ENA1, FLO9 and TFP1, the two former genes being recently duplicated in S288C. Interestingly, the latter is a YPS613-specific deletion that corresponds exactly to the intein VDE, a site-specific endonuclease that shows similarity to the HO endonuclease When covered by a contig, insertions and deletions were detected. We found 238 kb of large InDels, greater than 100 bp in length, and 21 kb of small InDels. The majority of the large InDels represent transposable elements present in S288C but absent in both M22 and YPS163. In only a few cases were InDels greater than one kb not associated with a transposable element. These cases include As measured by the number of distinct segregating sites, SNPs and small InDels are the most abundant class of polymorphism . SimilarOverall, our results are similar to other whole-genome polymorphism surveys S. paradoxus into S. cereviae  In comparison, we found sequences similar to KHS1, AWA1, and MPR1 in M22 or YPS163. In addition to the SUC, MAL and MEL genes, CUP1 and ASP3 are known to vary in copy number among strains. CUP1 varies in copy number between strains, resulting in substantial variation in copper resistance ASP3 gene lie within a 28 kb gap present in both M22 and YPS163. Previous work has implicated variation in asparaginase gene content to strain differences in the utilization of dipeptides as a nitrogen source The Saccharomyces Genome Database provides updated annotations of the S288C reference genome and includes a list of genes not in the systematic S288C sequence and genes known to vary in copy number among strains HAP1, significantly impairing its function CTR3 and results in loss of copper-dependent transcriptional regulation ATP1, MET17, GRX3, PHO12, POR1, RPS25A, RPT2, YDL038C. Of the 17 InDels that are not related to Ty elements and that include protein coding sequences, 12 involve in-frame tandem repeats: BBC1, BUD27, CHS5, DDR48, EGT2, FIT1, NUP159, SED1, SPA2, YFR016C, YIL169C, YMR317W, some of which have been previously described The majority of large InDels, 83/119, involve transposable elements, primarily Ty1 and Ty2 elements of the Copia family . One of et al. Saccharomyces species, and 210 and 330 cases of different conserved start and stop codons, respectively. Although there is some overlap between previous annotations and variation in M22, YPS163 and S288C, most variation is new: only 41/146 large or shared Indels and 22/157 of the polymorphic start/stop codons occur in genes previously identified as showing variation in gene structure.In addition to variation in gene content, we found a large set of genes with significantly altered protein products, as measured by frame-shifting InDels and SNPs that disrupt or create start or stop codons. Although false positive InDels due to sequencing errors is a concern, 146/503 frame-shifting InDels are unlikely errors since they are either greater than 3 bp in length or are present in both M22 and YPS163 . High quAQY2, splits the gene into two open reading frames and affects water transport and freeze tolerance ENA1 in M22 relative to S288C. ENA1 encodes a P-type ATPase sodium, lithium transporter and a major effect quantiative trait locus for resistance to lithium chloride maps to the tandemly duplicated ENA gene cluster MUC1 has the most frame-shifting InDels in the genome (10) and different MUC1 alleles have been shown to affect biofilm formation MUC1 also has the largest number of nonsynonymous SNPs in the genome, 43, excluding nonsynonymous changes resulting from frame-shifting InDels. The high levels of variation at these loci make positive selection a plausible explanation.The functional significance of variation in gene structure is not easily known. There are, however, a number of compelling examples. An 11 bp deletion in the aquaporin, Drosophila species, we found little evidence that positive selection has made a significant impact on polymorphism or divergence. In Drosophila species, there is a strong correlation between rates of recombination and levels of neutral variation In contrast to other species, particularly Drosophila species, McDonald-Kreitman style tests have indicated that adaptive substitutions are common in both coding and noncoding sequences S. cerevisiae and D. melanogaster (see below). The absence of evidence for positive selection in both inbred and outcrossing species of Arabidopsis suggests that special considerations may be needed to interpret the Drosophila rather than the yeast data In D. melanogaster, 27% Our estimate of the proportion of nonsynonymous SNPs that are deleterious, 36%, is similar to estimates from The frequency of deleterious SNPs differs among strains. One explanation is differences in selective constraint, caused by either differences in effective population size or differences in the selective regime Using a likelihood ratio test, we identified 1472 nonsynonymous SNPs that are deleterious. The likelihood ratio test differs from SIFT and other heuristic methods based on protein structure or sequence conservation Distinguishing deleterious and neutral SNPs in noncoding sequences is much more difficult than in coding sequences. First, even though 34–43% of noncoding sequences are selectively constrained A number of methods have been developed to identify conserved noncoding sequences, e.g. END3 since it occurs in a repetitive sequence and only closely related sensu strictu homologs were identified even after removing BLAST filters. Of the 1472 deleterious nonsynonymous SNPs, 393 have a likelihood ratio test P-value within the range of the cases listed in The phenotypic effects of some deleterious SNPs may be large. Out of eleven quantitative traits that have been mapped to single nucleotide sites, eight alter amino acids that are significantly conserved and are easily identified by the likelihood ratio test implemented in this study . The tesRHO2 contributes to a high temperature growth phenotype but shows no detectable effect on RHO2 mRNA expression levels RAS2 affects sporulation but shows no effect on RAS2 mRNA expression level Assessing the phenotypic effects of deleterious noncoding SNPs is more difficult. First, the majority of deleterious SNPs occur upstream of genes with no detectable allele-specific differences in expression. It is possible that some of the deleterious SNPs affect temporal aspects of gene regulation or disrupt sequences with functions other than gene regulation. For example, polymorphism in the 3' UTR of The ability to sequence multiple organisms of the same species has the potential to revolutionize the analysis of quantitative traits and human diseases. Realizing this potential depends on our ability to link phenotypes to genotypes without being limited by recombination. Our analysis of DNA polymorphisms among three strains of yeast outline two general approaches to identifying candidate genotypes for any given phenotype. First, candidates can be selected from DNA polymorphisms that disrupt sequences that have been experimentally annotated, e.g. frame-shift and nonsense polymorphisms. Second, candidates can be selected from DNA polymorphisms that are deleterious and disrupt evolutionarily conserved coding or noncoding sequences. Although not all traits of interest may be caused by polymorphisms that disrupt annotated and/or conserved sequences, the abundance of candidates present in the genomes of three strains of yeast implies that they may impact a considerable number of traits. Our results show that probabilistic evolutionary models can be used to distinguish deleterious and neutral SNPs in both coding and noncoding sequences, an important step in identifying SNPs that underlie quantitative traits and human diseases.− derivatives of S. cerevisiae strains YPS163, isolated from an Oak tree in Pennsylvania, and M22, isolated from a vineyard in Italy. Rho- strains were obtained by growth in minimal medium with 25 ug/ml ethidium bromide DNA was extracted from rhoGenome assemblies were generated using PCAP Genome rearrangements were identified by paired reads with unique but inconsistent matches to the reference genome. Unique matches were defined as those with a BLAST E-value less than 1.0e-90 and no secondary hit greater than 1.0e-50. Out of 16,074 read-pairs from M22 and 18,381 from YPS163, 37 YPS163 pairs and 27 M22 pairs were inconsistent with the reference genome either because they matched different chromosomes or matched greater than 20 kbp apart from one another on the same chromosome.Contigs from both M22 and YPS163 were aligned to the S288C reference genome using BLAST. Alignments were subsequently filter to ensure one-to-one alignment for both genome alignments: M22-S288C and YPS163-S288C. The two genome alignments were combined using S288C as a reference. Variation in genome content was identified by sequences not present in the genome alignments. SNPs and InDels were identified within the genome alignments. A more detailed description of methods and results can be found in Supporting Information . A tallyRecombination rates were obtained from genetic mapping data S. paradoxus). The two trees were simultaneously maximized and shared all parameters except those measuring selective constraints. For coding sequences, selective constraint was measured by the ratio of nonsynonymous to the synonymous substitution rate. For noncoding sequences, constraint was measured by the ratio of the substitution rate in conserved and unconserved noncoding sequences. To avoid bias, conserved noncoding sequences were defined by sequences identical in S. mikatae, S. kudriavzevii and S. bayanus or identical any two of the three species if one sequence was missing. Unconserved noncoding sequences were defined as the complement of the conserved sequences. Transposable elements and other noncoding genes were eliminated from the analysis. The likelihood ratio test was also applied to each coding and noncoding region individually.Modified McDonald-Kreitman tests The fraction of coding or noncoding SNPs that are deleterious was estimated by the difference between constraint estimated from polymorphism data and constraint estimated from divergence data A total of 11,075 nonsynonymous and 16,164 noncoding SNPs have a Phred quality score of 40 or more, excluding those in sequences within overlapping annotations. Homologs in 10 yeast genomes were identified using TBLASTX  for coding SNPs and BLASTN  for noncoding SNPs. For nonsynonymous SNPs we used the codon containing the SNP along with 33 codons upstream and downstream and for noncoding SNPs we used 100 bp upstream and downstream. Multiple alignments were generated by combining the ungapped BLAST alignments using S288C as a reference.Deleterious SNPs were identified by a likelihood ratio test. For coding sequences we compared the likelihood of the codon containing the SNP under two models: dN equal to dS and dN less than dS, where dN and dS are the nonsynonymous and synonymous substitution rate respectively. A dS of 18 substitutions per site was obtained from the median synonymous substitution rate of 2046 genes using a set phylogeny S. cerevisiae sequence with a log-odds score greater than 2 for a match to the binding site model versus background sensu strictu Saccharomyces species besides S. cerevisiae. To identify binding sites conserved across species, we calculated the likelihood under a neutral and conserved binding site model Using 125 transcription factor binding site models from chromatin immunoprecipitation data The Phylonet algorithm is described in Allele-specific expression was measured in nine different environmental conditions: rich medium, glycerol, amino acid starvation, nitrogen starvation, heat shock, stationary phase, and three time points of sporulation. For rich medium, cells from an overnight YPD  culture were diluted in fresh YPD and sampled after eight hours of growth. For glycerol, cells from an overnight YPD culture were diluted into YPG  and sampled after eight hours of growth. For amino acid starvation, cells from a mid-log phase culture of YPD were resuspended in minimal medium  and sampled after 45 minutes. For nitrogen starvation, cells from a mid-log phase YPD culture were resuspended in 2% dextrose with 0.17% yeast nitrogen base without ammonium sulfate and sampled after 8.5 hours of growth. For heat shock, cells from a mid-log phase YPD culture growing at 20C were transfered to 37C for 20 minutes and then sampled. For stationary phase, a YPD culture was grown for 54 hours and then sampled. For sporulation, cells from an overnight YPA  culture were resuspended in 1% potassium acetate and sampled after 4, 8 and 12 hours. For the S288C-M22 hybrid, samples were taken at 8, 12 and 20 hours to account for the slower rate at which tetrads formed compared to the M22-YPS163 and S288C-YPS163 hybrids.RNA was extracted using the Qiagen RNEasy kit from the nine environmental conditions for three biological replicates of each hybrid strain. cDNA was synthesized for each RNA sample using a poly-T primer and the Qiagen Omniscript RT kit. Genomic DNA was extracted from each hybrid for a reference. The samples were diluted to a concentration of 2.5 ng/ul and quantitatively genotyped using the Sequenom MASSArray method following the manufacturer's protocol . The allele frequencies of each SNP were obtained from the Sequenom allele frequency data report.Significant differences in allele-specific expression were identified using an analysis of variance on the ratio of alleles across all conditions. Both cDNA and DNA samples were included in the analysis of variance. The same results were obtained if cDNA samples were analyzed alone after normalization to the DNA ratios. For the 86% of the cases where there was informative data from two hybrids, a term for the hybrid effect was included in the ANOVA model. The significance of was estimated by permutation resampling of the expression ratios, shuffling the column labels 20,000 times. A P-value cutoff of 0.019 corresponds to a false discovery rate of 5% .Table S1Rearrangements identified by read-pairs inconsistent with the S288C reference genome.(0.12 MB XLS)Click here for additional data file.Table S2Summary of Insertion/deletion polymorphism greater than 100 bp in length.(0.11 MB XLS)Click here for additional data file.Table S3Strain-specific variation.(0.11 MB XLS)Click here for additional data file.Table S4S. cerevisiae compared to divergence between S. cerevisiae and S. paradoxus in different classes of sites.Rate of polymorphism in (0.12 MB XLS)Click here for additional data file.Table S5M22 and YPS163 sequences that do not align to the S288C genome.(0.08 MB XLS)Click here for additional data file.Text S1Supplemental results.(0.17 MB PDF)Click here for additional data file.Dataset S1Tabulated results.(0.44 MB XLS)Click here for additional data file.Dataset S2Genome polymorphism data.(11.32 MB XLS)Click here for additional data file.Dataset S3Deleterious coding and noncoding SNPs.(3.33 MB XLS)Click here for additional data file.


There is a need for software scripts and modules for format parsing, data manipulation, statistical analysis and annotation especially for tasks related to marker identification from sequence data and sequence diversity analysis.Here we present several new Perl scripts and a module for sequence data diversity analysis. To enable the use of these software with other public domain tools, we also make available PISE (Pasteur Institute Software Environment) wrappers for these Perl scripts and module. This enables the user to generate pipelines for automated analysis, since PISE is a web interface generator for bioinformatics programmes.A new set of modules and scripts for diversity statistic calculation, format parsing and data manipulation are available with PISE wrappers that enable pipelining of these scripts with commonly used contig assembly and sequence feature prediction software, to answer specific sequence diversity related questions. Validation of predicted SNP(s) through wet lab experiments is the next step to convert the identified SNP into a genetic marker. Although more than 30 SNP genotyping platforms are currently available, these are both expensive and demand considerable expertise. One solution for validating the identified SNP(s) through cost effective SNP genotyping platform is development of CAPS (cleaved amplified polymorphic sequences) marker by predicting the restriction enzyme that can use the identified SNP as a recognition site.Single Nucleotide Polymorphisms (SNPs) are commonly found throughout the genome and provide dense maps over small chromosomal regions. The recent advances in sequencing and genotyping have made large scale SNP diversity analysis possible in several crop species. This helps assess genome variation that can then be harnessed for crop improvement. Sequence diversity information may be desirable across defined groups of sequences, such as candidate gene transcripts from different genotypes, or assembled transcripts for a particular marker from more than one genotype. The grouping could be based on the objective of the study – across race, location, genes or regions within genes. Sequence data analysis usually involves steps such as clustering of sequence data, to determine redundancy levels. Sequence assembly is carried out to generate consensus sequences or contigs and singlets. The user then processes this output to determine presence of microsatellites or SNPs. Along with SNP identification it is also desirable to obtain other aspects from the alignment; such as SNP and  are used for sequence assembly. Similarly, there are many freely available software programmes for the identification of SNPs[divest.pm).There are several available software solutions for sequence clustering, and a few popular ones for assembly. The popular group of Clustal programmes , d2-clusn of SNPs-10. DnaSn of SNPs. None ofSequence analysis involves pipelining of data from one software to another and often also includes branched flows such as when annotation of sequences with putative function is also a requirement. Format conversion scripts to convert output of one program to input of another are needed when the user wants to pipeline several tools and modules. Along with output parsing scripts, some degree of automation can be achieved in data analysis tasks. The availability of software environments for pipelining and workflow management help the user to create custom analysis pipelines.The PISE programme ,13 is a , where file manipulations and redirection of output files to another programme are also specified. The scripts are also available as a soaplab service and can so be called through the Taverna workflow tool.The source code for the programs has been written in Perl 5.8. For the diversity estimator module, Bioperl modules have been used. The scripts have been tested within the PISE environment and are being used independently as well, on machines running either Linux or Windows OS. An example pipeline that makes use of the scripts written is indicated Figure . The moddivest.pm is the diversity estimator module that reads an input file derived from assembly programmes like cap3 or the alignment file output from multiple sequence alignment programmes like ClustalW or BioEdit. The file contains the assemblies of reads for each user defined group. The user can choose to calculate diversity statistics for all groups or one particular group. The statistics calculated include nucleotide diversity, number of SNPs, and type of variant whether transition or transversion, SNP frequency, number of haplotypes, the PIC (polymorphism information content) of SNP and haplotype, haplotype frequency and also number and frequency of indels.The module SNP and indel frequency are simply calculated as the total length of the sequence in base pairs/number of SNPs or indels. The count of number of haplotypes and haplotype frequency is based on the standard methods . An impoThe nucleotide diversity π is calculated using the formula where k is the number of SNPs identified in an alignment of 'n' genotypes, L is the number of basepairs and The PIC of SNP is calculated using the formula pi is the frequency of the ith allele at a given SNP locus.where The haplotype diversity is calculated using the formula ,18i is the frequency of haplotypes and n is the total number of reads.where K is the number of haplotypes, p. Network helps generate evolutionary trees from various data including haplotype and haplotype frequency data.The statistics generated is exported in Excel format, besides the script also generates input file to Network 4.502 Scripts that enable the conversion of a multiple sequence alignment into the diversity estimator input file have been currently written for the ClustalW and cap3 programmes. Another script that enables concatenating cap3 contig and singlets file is also available, the output of this file generates a 'unigene' file that can serve as input to the MISA microsatellite analysis programme. An example analysis pipeline is provided in Figure , all mentioned scripts are available under assembly, format parsing, statistics and data manipulation directories. A sample ACD file for the divest module is also given, this provides meta-data regarding the input and output files for this module and can be used by soaplab for deploying divest.pm as a web service.Figure The modules and scripts being provided through the PISE environment can be used for various purposes (a) identifying microsatellites in a set of unigenes, (b) calculating the diversity between two or more groups of data based on geographical location of the genotypes , regions of the gene (e.g. intronic and exonic regions) being sampled, (c) sequence diversity features in a candidate gene across several genotypes and (d) identifying SNPs and finding out how many of these are convertible to CAPS, (e) functionally annotate sequences. The ability to chain scripts to generate pipelines reduces the file management burden for the user. A part of the pipeline indicated in Figure As the modules and scripts are being released as open source code, interested users can continue to use as well as make improvements to them.Modules and scripts that facilitate sequence diversity analysis, format parsing and data manipulation are being made available along with source code. The module/scripts can be chained together with other freely available sequence analysis software to generate pipelines for automated data analysis. The pipelining has been currently made feasible through the PISE web interface generator and a part of it can also be executed on the Taverna workbench. The module and scripts are available to interested users..Modules available from: Can be accessed and used at Soaplab service: Operating System: platform independentProgramming language: PerlPISE SNP2CAPS ; )MISA, cap3 (optional; Any restrictions to use by non-academics: noneACD: Ajax Command Definition; SNP: Single Nucleotide Polymorphism; PIC: Polymorphism Information Content; XML: Extensible Markup Language; PISE: Pasteur Institute Software Environment; CAPS: Cleaved Amplified Polymorphic markers; EST: Expressed Sequence Tag; PCAP: Parallel Contig Assembly Program; CPAN: Comprehensive Perl Archive Network; MISA: MIcroSAtellite Identification toolThe authors declare that they have no competing interests.BA, AJ coded for the Perl modules and scripts, PSR and SN used the programmes on their datasets, RKV articulated the use cases, indices for the diversity estimator module while JB developed the pipeline architecture and wrote the manuscript.


Technological advances have lead to the rapid increase in availability of single nucleotide polymorphisms (SNPs) in a range of organisms, and there is a general optimism that SNPs will become the marker of choice for a range of evolutionary applications. Here, comparisons between 300 polymorphic SNPs and 14 short tandem repeats (STRs) were conducted on a data set consisting of approximately 500 Atlantic salmon arranged in 10 samples/populations.ST ranged from 0.033-0.115 and -0.002-0.316 for the 14 STR and 300 SNP loci respectively. Global FST was similar among 28 linkage groups when averaging data from mapped SNPs. With the exception of selecting a panel of SNPs taking the locus displaying the highest global FST for each of the 28 linkage groups, which inflated estimation of genetic differentiation among the samples, inferred genetic relationships were highly similar between SNP and STR data sets and variants thereof. The best 15 SNPs  gave a similar level of self-assignment to the best 4 STR loci , however, addition of further STR loci did not lead to a notable increase assignment whereas addition of up to 100 SNP loci increased assignment.Global FWhilst the optimal combinations of SNPs identified in this study are linked to the samples from which they were selected, this study demonstrates that identification of highly informative SNP loci from larger panels will provide researchers with a powerful approach to delineate genetic relationships at the individual and population levels. The characterisation and availability of single nucleotide polymorphisms (SNPs) in non-model organisms is increasing rapidly ,2, and wHighly polymorphic short tandem repeat loci (STR), commonly known as microsatellites, have been the primary molecular tool of choice for addressing evolutionary questions for nearly two decades. However, these markers display several negative characteristics including size homoplasy , complexIn contrast to STR analysis, SNP genotyping reveals polymorphisms directly in the DNA sequence, circumventing the need for between laboratory calibration. Furthermore, development of high through-put genotyping platforms permits simultaneous genotyping of thousands of loci, enabling the identification of highly diagnostic panels . SNPs ocST, may influence and potentially bias genetic relationships among the populations being studied. Furthermore, few studies have compared the power of SNPs and STRs to perform genetic assignment of individuals to populations , but samples originating from a number of fish farms in Norway, and, a number of escaped farmed fish. This species was chosen due to the fact that a large number of verified SNPs have previously been identified and mapped [The aim of the present study was to compare the performance of a large resource of SNPs (388) and a panel of STRs 15) to perform individual genetic assignment and delineate population structure. This was achieved through genotyping a set of Atlantic salmon , in addition to 50 farmed escapees (RF) that had been recaptured in the vicinity of these farms. The recaptured fish were distinguished as farmed salmon based upon morphological characteristics. The farms, locations and exact dates of collection remain anonymous for legal reasons.AY372820), SSsp2210, SSspG7, SSsp2201, SSsp1605, SSsp2216 [DNA extraction and STR analysis was performed at the Institute of Marine Research (IMR) in Bergen. DNA extraction was carried out in 96 well format using a Qiagen DNAeasy kit according to the manufacturers' instructions. Each plate contained a minimum of two negative controls. DNA was extracted twice for 48 of the 50 escapees (separate dates). The following fifteen STR loci were amplified in three separate multiplex PCR reactions; SSsp3016  using the MassARRAY™ Analyzer (Autoflex mass spectrometer) from Sequenom. Genotypes were assigned in real time [SNP genotyping was performed using the MassARRAY platform from Sequenom . A total of 388 SNPs were included in the study. Map position and flanking sequence of the majority of these SNPs are from Moen et al.  and Loreeal time  by usingST for each of the 28 linkage groups), and 4) 195 mapped SNPs with minimal distance of > 1 cM to nearest SNP . For some tests, loci were ranked prior to computation. Ranking of loci was carried out by three methods including number of alleles displayed through samples A-I (STR loci only), global FST across samples A-I (STR and SNP loci), and with the locus assignment power program BELS across samples A-I [In order to compare the two classes of markers, the STR and SNP data sets were mixed in various combinations. These are described in the results section as some combinations were test specific. However, the following four data sets were used as the start point for the majority of the analyses within: 1) 14 STR loci, 2) 300 SNP loci, 3) 28 SNP loci  represented by the data set. Following pilot analysis, main runs assuming k = 1-6, each with 3-5 iterations (depending upon data set), were conducted in order to estimate the most likely k and to assign individuals to these groups without using prior information about their sample of origin using correlated allele frequencies and an admixture model. Each run consisted of a burn-in of 100,000 MCMC steps, followed by 500,000 replications. STRUCTURE 2.2 was also used to perform a modified self-assignment procedure by removing 10 individual salmon at random from samples A, D, F and G, then assigning these individuals to the baseline which did not include those individuals. This was conducted by using the prior population information for the baseline samples and no prior population information for these 40 individuals. The results obtained from this analysis were compared to an identical procedure in Geneclass for these 40 individuals .Bayesian clustering analysis implemented in STRUCTURE 2.2 ,48 was uDNA was isolated from a total of 512 salmon that were analysed for 15 STR loci at IMR and 388 SNP loci at CIGENE. In the two laboratories, 13 and 14 of the individual DNA extracts failed to yield PCR products for any of the loci, leaving STR and SNP data sets consisting of 499 and 498 individual fish respectively. Individuals failing to yield PCR products for the STR loci were spread among samples, whereas all complete amplification failures in the SNP data set were observed within the sample of escapees.Within the STR data set, a total of 87 individuals displaying PCR failure in ≥ 2 loci were selected for re-amplification for all STR loci in order to increase the scoring percentage in the data set. These individuals represented the majority of, but not all the individuals displaying PCR failure at ≥ 2 loci. As a result of re-analysing these 87 individuals, in addition to analysis of the second DNA isolate for 48 of the 50 escapees, > 1000 genotypes in 135 fish were independently scored on two occasions. Of these, no genotyping inconsistencies were observed between original and re-analysis. The resultant overall genotyping success in the STR data set (n = 499 × 15 markers) was > 99% . A total of 294 tests were not computed due to some loci being monomorphic in some samples. Following application of Bonferroni correction in a conservative manner (α = 0.05/300 loci = 0.00017), only 6 of the observed deviations remained significant. These were observed in (locus:sample) 74:A, 202:E, 202:F, 202:G, 207:D, 300:C. Within the STR data set, 16 out of 140 tests of HWE were significant at α = 0.05 (11.4%). These deviations were spread among loci and samples, with all being implicated in a minimum of one significant deviation except loci Ssa289, SsaF43, SSspG7, and samples E, F. Following application of Bonferroni correction in a conservative manner (α = 0.05/14 = 0.0036), none of these deviations remained significant.Within the SNP data set, 104 out of 2706 tests of HWE were significant at A summary of the allelic variation and expected average heterozygosity per sample are presented Table . In totaST values per locus ranged from 0.033-0.115 among the 14 STR loci, and -0.002-0.316 among the 300 SNP loci  exceeded a global FST of 0.1 (= 14%). Despite the considerable differences in FST among the SNP loci, an analysis using the Bayesian simulation-based test by Foll and Gaggiotti [p < 0.01; data not shown). Hence, there was limited evidence to suggest that the loci might be under diversifying selection in the analysed set of samples.Global Faggiotti  only ideST per SNP locus varied greatly . Analysis with a parametric ANOVA gave a similar result (P = 0.4). The majority of tightly linked loci  displayed very similar global FST values to each other, however, this was not universally true. For example, whilst the three loci located at 13.7 cM on linkage group d02 displayed global FST values of 0.026-0.034, and the two loci located at 3 cM on linkage group d14 displayed global FST values of 0.0002 and 0.0003, the two loci located at 61.8 cM on linkage group d06 displayed highly contrasting global FST values of 0.05 and 0.27.Within each linkage group, global FST, displayed greater among-sample differentiation than other data sets, however, among sample relationships were remarkably similar for all data sets , overall assignment reached 58% which is similar to the value reported for the best 25 SNP loci selected by global FST irrespective of linkage group. However, as the SNP loci displaying highest global FST values were spread between linkage groups . For all data sets starting with different numbers of STR loci, addition of up to 50 SNP loci increased assignment when selecting loci with BELS .Addition of 1-4 STR loci increased assignment for data sets starting with 5, 10 and 25 SNPs selected by BELS, however, for the data set starting with 50 SNPs, addition of STR loci lead to a reduction in assignment  revealed identical results between the programs Geneclass and STRUCTURE for data sets consisting of 28 SNPs (58%), and 195 SNPs (78%). The latter is an important as STRUCTURE used a marker linkage model, taking marker distance into the computations, whereas Geneclass treated the loci as independent. STRUCTURE outperformed Geneclass for self-assignment of these 40 individuals using 14 STR loci (73% contra 65%), and all 300 SNPs (88% contra 80%).The absolute accuracy of assignment was lower when computed using a distance based calculation , howeverP = 0.01 indicated that more loci were required for exclusion of the alternative samples, and, this trend was evident for both marker classes and marker selection criteria (data not presented).Direct assignment Table  using alk was estimated at 4 or 5 (data not presented), and consequently, assignment of the individuals is presented for k = 3-5  and sample I into a single cluster separate from all other samples, confirming the assignment results conducted above.For the data sets consisting of 14 STR, 28 SNP, 195 SNP and 300 SNP loci, Onchorhynchus keta) [Comparisons between marker classes to perform genetic assignment can be conducted in a number of ways, although locus by locus, total number of alleles, or cost per information unit comparisons are commonly applied. In this study, it took 15 of the best SNP loci  to match the accuracy of self-assignment achieved by the four most polymorphic STR loci 83 alleles), clearly demonstrating that although some of the STR loci out-performed the SNP loci on a single locus basis, combinations of SNPs outperformed the STRs based upon total number of alleles. The latter observation is consistent with a recent comparison between the two marker classes in chum salmon (us keta) . Whilst us keta) ,21,36, aus keta) , the sel3 allelesThe drop in accuracy of self-assignment conducted for the entire data set in Geneclass when adding 100-195 SNP loci based upon selection by BELS Figure  represenAlthough the drop in self-assignment observed over 100 SNP loci may have been linked to the conditions presented above, it is also suggested that this may be due to the inclusion of weakly and/or non-informative loci, and the manner in which Geneclass deals with such (large numbers) data. This idea is supported by the fact that no increase in self-assignment was observed from 195 to 300 loci (which did not include any locus ranking in BELS) when assigning the 40 individuals removed from the baseline with Geneclass (195 to 300 SNP loci = 78% to 80%), which contrasts with the fact that a large increase was observed using STRUCTURE (195 to 300 SNP loci = 78% to 88%). Clearly, STRUCTURE was able to utilise data from addition of extra loci whereas Geneclass was not. This may be linked with the different computation methods implemented in the programs, and therefore, this topic requires further investigation.In) [ST as opposed to the 195 loci. Therefore, use of the program BELS to identify a reduced panel of informative markers from a very large set of loci  should be conducted with caution.The ability for loci to perform individual genetic assignment can be evaluated by a number of criteria and programs, for example various distance based methods   and modiIn) . Whilst In) ), and itST values represents a way to create a better discrimination between pairs of populations. For population geneticists who are interested in highly discriminatory genetic markers, the approach described here, by taking those markers displaying highest FST values may provide a way to differentiate very similar populations, as is often the case for marine organisms where there is a large degree of gene flow and little differentiation.With the exception of the 28 unlinked SNP data set which overinflated the differences observed between samples Figure  and BayeMost statistical tests have conditions which need to be fulfilled in order to avoid violation of the underlying principles. Some of the data sets investigated in the present study, for example analysis of all 300 SNPs combined, violated some of the tests performed. The 300 SNP data set for example, included a number of markers which were tightly linked, i.e., came from the same contig. For both the individual assignments tests, and the genetic relationships among the samples however, the violations incurred in the present study did not appear to have any effect on the results of these tests, and gave more or less identical results to the data sets tested which did not violate the tests . Whilst deliberate violations of tests is not recommended, here, we computed these tests in order extract the maximum amount of information from the SNP data set as possible. However, the simulations also indicate that moderate violation of the underlying principles of genetic assignment and phylogeny may not lead to erroneous results. In a test of individual assignment, Narum et al.,  also repIndividual genetic assignment is based upon matching or excluding an individual's multilocus composite genotype to the group genetic profiles of potential source populations. A number of statistical methods to test this exist . HowResults of this study demonstrate that the identification of a highly informative set of SNPs from a larger panel gave significantly more accurate individual genetic self-assignment compared to any combination of STR loci. Furthermore, once a set of 50 or more diagnostic SNP loci were included in the self-assignment analyses, addition of even the most informative STR loci did not increase the accuracy of self-assignment, whilst addition of informative SNPs to any combination of STR loci increased self-assignment. These results clearly demonstrate that identification of highly informative SNP markers from the screening of larger pools represents a powerful approach to create molecular tools to study individual ancestry.KAG conceived and designed the study, supervised STR analysis, performed all statistical analysis, and wrote the first draft of the manuscript. MMH helped design the study and contributed to statistical analysis. SL contributed to the design of the study and supervised analysis of SNPs. TDA contributed to the design of the study and contributed to statistical analysis. BH contributed to the design of the study. ØS contributed to the both the conception and design of the study. All authors contributed to the writing and approved the final version of the manuscript.Summary statistics for 388 SNP and 15 STR loci. Data set consists of approximately 500 farmed Atlantic salmon arranged in 10 samples/populations.Click here for fileST values  for 300 polymorphic SNP markersGlobal F. Each linkage group is represented by a single figure, with associated SNPs and their global FST values plotted on them. Unmapped SNPs are ranked by global FST and placed in the bottom figure.Click here for fileAssignment of individual fish to samples A-I and RF. Figures based upon information from 14 STR loci (top), 28 unlinked SNPs (upper middle), 195 mapped SNPs (lower middle), and 300 SNPs (bottom), each for K = 3, 4 and 5.Click here for file


Shimofuri, is an economically important trait of beef cattle in Japan. The c17-25 expressed sequence tag (EST) has been previously shown to possess expression difference in musculus longissimus muscle between low-marbled and high-marbled steer groups, and to be located within genomic region of a quantitative trait locus for marbling. Thus, the akirin 2 (AKIRIN2) gene containing the c17-25 EST sequence was considered as a positional functional candidate for the gene responsible for marbling. In this study, we explored single nucleotide polymorphism (SNP) in the AKIRIN2 and analyzed association of the SNP with marbling.Marbling defined by the amount and distribution of intramuscular fat, so-called AKIRIN2, referred to as c.*188G>A, was the only difference detected between high- and low-marbled steer groups. The SNP was associated with marbling in 3 experiments using 100 sires (P = 0.041), 753 paternal half-sib progeny steers from 4 sires heterozygous for the c.*188G>A (P = 0.005), and 730 paternal half-sib progeny steers from 3 sires homozygous for the A allele at the c.*188G>A (P = 0.047), in Japanese Black beef cattle. The effect of genotypes of the SNP on subcutaneous fat thickness was not statistically significant (P > 0.05).A SNP in the 3' untranslated region of the AKIRIN2 SNP polymorphism is associated with marbling and may be useful for effective marker-assisted selection to increase the levels of marbling in Japanese Black beef cattle.These findings suggest that the  The bovine AKIRIN2 gene spans 22,844 bp and comprises 768 bp of exon 1 including 533 bp of 5' untranslated region (UTR) sequence, 144 bp of exon 2, 150 bp of exon 3, 72 bp of exon 4, 823 bp of exon 5 including 812 bp of 3' UTR sequence, and 4 introns, based on genomic sequence from the NCBI database.We screened the NCBI bovine genome sequence database  with the c17-25 EST sequence, and obtained 1,210,178-bp bovine genomic sequence [GenBank:AKIRIN2 gene [AKIRIN2 gene.For single nucleotide polymorphism (SNP) detection, we used two Holstein steers and two somatic nuclear-derived cloned steers  from a JIN2 gene . The detIN2 gene . We usedAKIRIN2 gene between two low-marbled Holstein steers and two high-marbled cloned steers. PCR amplification and direct sequencing were performed as described in [PCR primers were designed to target ~8-kb proximal promoter and exon regions, to screen polymorphisms in the ribed in . Nucleotribed in . Primer AKIRIN2 gene between the 2 steer groups: a G to A transition located 22,220 bp downstream of the transcription initiation site in the 3' UTR (c.*188G>A). The c.*188G>A corresponds to the rs41255597 in the dbSNP [G allele at the c.*188G>A site, whereas the two cloned steers homozygous for the A allele.This sequence analysis revealed only one SNP in the c.*188G>A with marbling and subcutaneous fat thickness. We used 100 Japanese Black sires in experiment 1. The sires were or are used in the Oita Prefectural Institute of Animal Industry . There was no strong bias for a specific father or a specific maternal grandfather of the sires, and the sire panel likely represents a variety of the sire lines. In experiment 2, 753 paternal half-sib Japanese Black progeny steers (56 to 546 steers per sire) produced from 4 sires heterozygous for the c.*188G>A, with dams considered to represent a random sample of the female population, were used. In experiment 3, we used 730 paternal half-sib Japanese Black progeny steers (59 to 454 steers per sire) produced from 3 sires homozygous for the A allele at the c.*188G>A, with dams considered to be a random mating population. The progeny steers in experiments 2 and 3 were fattened and shipped to the carcass market in the Oita prefecture. Semen or blood of the sires and adipose tissues of the progeny steers were collected for SNP genotyping. DNA samples were prepared from the materials according to standard protocols.We performed 3 experiments for association of the The predicted breeding values of the sires and the progeny steers for marbling score and subcutaneous fat thickness were used as phenotypic values in this study. The details of breeding value estimation and scoring methods for marbling and subcutaneous fat thickness were described in .This study conformed to the guidelines for animal experimentation of the Graduate School of Agriculture, Kyoto University .c.*188G>A was genotyped using PCR-restriction fragment length polymorphism (RFLP) method. PCR primers used for PCR-RFLP were 5'-TCTTAGGCAGCAACCGGATT-3' and 5'-GAAGGGCATGTTCTTAGAATACCAG-3'. Nucleotide positions relative to the transcription initiation site of the AKIRIN2 gene were 22,167 to 22,186 and 22,336 to 22,312, respectively. PCR amplifications were carried out as described in SNP detection section, using a final volume of 20 μl and the annealing temperature of 56°C. An aliquot of PCR-amplified products was digested at 37°C for 1 h with restriction enzyme FokI, and electrophoresed on a 3.0% agarose gel. Agarose gels were stained with ethidium bromide and photographed under an ultraviolet light. Using this method, 170-bp PCR fragments containing the SNP site were amplified, and FokI-digested into 65- and 105-bp fragments for the G allele, but not for the A allele: the GG homozygotes, the AA homozygotes, and the GA heterozygotes resulted in 2 bands (65 and 105 bp), 1 band (170 bp), and 3 bands , respectively.The c.*188G>A revealed 26 animals homozygous GG, 48 animals heterozygous GA and 26 animals homozygous AA. Statistically significant differences among the genotypes of the SNP were detected for marbling (P = 0.041), but not for subcutaneous fat thickness (P = 0.165), by the analysis with the model that included the SNP genotype as the fixed effect and the sire (father of the sire) as the random effect  in the model that included the SNP genotype and the sire as the fixed effects and their interaction, and was excluded from our statistical model. In the model without the interaction effect, the SNP genotype had the statistically significant effect on marbling (P = 0.005), but not subcutaneous fat thickness (P = 0.070)  in the model that included the SNP genotype and the sire as the fixed effects and their interaction, and was excluded from our statistical model. In the model without the interaction effect, the SNP genotype effect reached statistical significance (P = 0.047) for marbling, but not for subcutaneous fat thickness (P = 0.455) .Based on 3 experiments, we showed that the c.*188G>A SNP with marbling, on the expression difference in the c17-25 EST between low- and high-marbled steer groups, and on the co-localization of the marbling QTL with the AKIRIN2 gene, we hypothesize that the SNP in the 3' UTR might influence gene expression and marbling by affecting the AKIRIN2 mRNA stability. However, a more likely event is that the AKIRIN2 SNP is in linkage disequilibrium with an unidentified and truly relevant mutation, rather than functional and a causal mutation for marbling, from a view of biological function that the AKIRIN2 is involved in innate immune responses [Based on the association of the esponses .P > 0.05) for subcutaneous fat thickness. Further, the marbling QTL corresponding to the chromosomal position of the AKIRIN2 did not show a statistically significant effect on subcutaneous fat thickness [AKIRIN2 SNP is not associated with subcutaneous fat thickness in Japanese Black beef cattle. This might be supported by the fact that Japanese Black breed exhibits low genetic correlation between marbling and subcutaneous fat thickness [The effect of genotypes of the SNP was not statistically significant  gene [titin (TTN) gene [We have recently reported that SNPs in the 5' UTR and the 3' UTR of the G1) gene , and a STN) gene  were assAKIRIN2 SNP obtained in this study, as well as the EDG1 SNPs and TTN SNP, may be applied to effective marker-assisted selection to increase the levels of marbling in Japanese Black beef cattle. Further studies will be needed to examine whether the effects of the favourable marbling alleles at each locus are additive, and whether animals homozygous for favourable alleles at the 3 loci have the highest marbling scores.The information on the The authors declare that they have no competing interests.TY carried out the statistical analyses and drafted the manuscript. SS, SS, TO, and YT carried out the SNP detection and genotyping. TF, MM, HM, and FM participated in the sample and data collection. TM carried out further statistical analyses. YS participated in the design and coordination of the study and helped to draft the manuscript. All authors read and approved the final manuscript.


The ultimate goal of QTL studies is to find causative mutations, which requires additional expression studies. Given the limited amount of time and funds, the smart option is to identify the most important QTL with minimal effort. A cost-effective solution is to genotype only those animals with high or low phenotypic values or DNA-pools of these individuals. A two-stage genotyping strategy was applied on samples in the tails of the distribution of breeding values.The tail-analysis approach identified eight out of the 19 QTL in the first stage, explaining about half of 98% of the genetic variance. Four additional QTL with small effects were found in the second stage.The two-stage genotyping strategy with selective genotyping detected regions with highly significant QTL useful for further fine-mapping. The large reduction in costs allows for follow-up expression and functional studies. Discovery and subsequent validation of causative mutations affecting complex traits require identification and fine-mapping of QTL followed by expression and functional studies. Given the limited amount of time and funds, the challenge is to identify the most important QTL with minimal effort.A cost-effective strategy is to reduce genotyping costs by only genotyping individuals with high and low phenotypic values, or to genotype pools of these individuals. Tail analysis, bulked segregant analysis and selective DNA pooling have been advocated by Hillel et al. , MichelmCommercial breeding pedigrees present a situation where phenotypes are abundant, across many generations. In such a situation, selective genotyping is an important step in setting up a cost effective QTL study. This study implements a two-stage strategy. First, genotypes on a large SNP panel are obtained for highly informative individuals, that is, individuals with extreme breeding values. High and low phenotype animals are selected within each sire-dam pairing in order to control for stratification.The objective is to identify major segregating QTL in a simulated pig-type pedigree with minimal effort both in terms of genotyping and analysis.th QTLmas workshop [In a four generation pedigree, 45 sires produced 100 offspring each. Each sire was mated to 10 dams with 10 progeny each. Sires and dams of the base generation were unknown. All 4665 animals were phenotyped for a quantitative trait (TRT). Six thousand equally distributed (0.1 cM) SNPs were available for genotyping, located on 6 chromosomes of 100 cM each. A full description of the dataset can be found at the website of XIIworkshop .For each sire, the offspring with the highest and the lowest EBV within a set of full sibs (i.e. per dam) were included into the high tail (H-tail) and the low tail (L-tail) respectively. Since there were 10 dams per sire there were 10 animals in either tail for each sire. Only sires with progeny that have phenotype records were used.2 (1) test. SNPs with a Pearson statistic exceeding 10  were considered putative. A Pearson χ2 value exceeding 10 required that the counts of the allele in either tail differed by at least 10. A difference of 10 alleles suggested linkage between a QTL and this SNP in the sire, assuming equal contributions of the dam's alleles to both tails. A Chi-square test was appropriate under the null hypothesis of no association and the assumption that both sires and dams were sampled randomly from the population with respect to their SNP genotypes.For each SNP and for each sire, the frequencies of the '1' and '2'-alleles in the high and low tail were determined and submitted to a χWhen multiple segregating SNPs occur in a small region then this region was considered likely to contain a QTL. Genotypes of all putative SNPs were subsequently obtained for all animals with phenotype records and an association was determined by applying the following model:Where:TRT = trait valueμ = overall meanZ = incidence matrix linking polygenic effects to individualsN with A as the additive genetic relationship matrixu = polygenic effect ~SNP = effect of single SNP N; with I as the Identity matrixe = residual effects ~Model selection, i.e. which SNP(s) needs be included in the model, was determined by region. Forward stepwise regression was applied to identify markers with a large effect.For fine-mapping LDLA-software was used . was app114 putative markers significantly (p < 0.0016) differed in frequency between the high and low tail in at least one sire family . Table Subsequently LDLA was applied to these 114 markers and the profiles of the likelihood ratio test are shown in Figure Two very obvious candidates for further study were the regions between SNP 403 and SNP 466 on chromosome 1 and between SNP 3007 and SNP 3091 on chromosome 4. Both regions had a maximum log likelihood ratio greater than 80. QTLs with smaller effects are expected on chromosome 1 (to the left of SNP 232), on chromosome 2 (between SNP 1326 and 1483) and on chromosome 4 (between SNP 3646 and 3766 and around 3965).The region on chromosome 3 around SNP 2185 did not show a peak in the LDLA-analysis. In this region sire 1 and 1034 were segregating  were included in the high tail and the 10 worst (one per dam) were in the low tail. By choosing high/low within dam instead of across dams within sires, the chances of picking up false putative markers are reduced. Many more were found choosing across dams (data not shown). An illustration is the box-plot of estimated breeding values of progeny of sire 389 shown in Figure The data allowed for 45 high/low tails to be made because there were 45 sires with 100 progeny each. All tails were analyzed but only 21 sires showed segregation of at least one marker, nine of which were segregating for one marker only. A relevant question is whether the segregating sires could have been identified beforehand. It would decrease the work load for preparation and testing considerably. An analysis of higher moment statistics in the distribution of the phenotypes in the offspring might prove useful.True positions of QTL were revealed after the workshop had taken place . In TablThe 2 stage approach reduced the number of genotypes from 28 million in the whole data set to 5.4 million in stage 1 plus 0.43 million in stage 2; a reduction of almost 80%. If SNP-genotyping allows for sufficient accurate estimation of allele frequency in pooled DNA, then only 540.000 genotypes have to be determined in the first stage, reducing the genotyping effort with another order of magnitude. The number of individuals to put into a pool depends on the accuracy of determining the allele frequency, which in turn depends on the method applied. With AFLP-markers the typical choice is to put 10 individuals in each pool .The two-stage genotyping strategy with selective genotyping detected regions with highly significant QTL useful for further fine-mapping. Large reduction of genotyping efforts saves costs which could be used for subsequent expression and functional analyses.The authors declare that they have no competing interests.HH and JB conceived the project. HH and SvdB analyzed the data. All took part in writing the paper.


CAPN1), calpastatin (CAST), leptin (LEP), growth hormone receptor (GHR) and acylCoA:diacylglycerol acyltransferase 1 (DGAT1), are included in various commercial tests for tenderness, fatness, carcass composition and milk yield/quality.The purpose of this study was to evaluate the effects of eight single nucleotide polymorphisms (SNP), previously associated with meat and milk quality traits in cattle, in a population of 443 commercial Aberdeen Angus-cross beef cattle. The eight SNP, which were located within five genes: μ-calpain (A total of 27 traits were examined, 19 relating to carcass quality, such as carcass weight and fatness, one mechanical measure of tenderness, and the remaining seven were sensory traits, such as flavour and tenderness, assessed by a taste panel.CAPN1 gene, CAPN316, was significantly associated with tenderness measured by both the tenderometer and the taste panel as well as the weight of the hindquarter, where animals inheriting the CC genotype had more tender meat and heavier hindquarters. An SNP in the leptin gene, UASMS2, significantly affected overall liking, where animals with the TT genotype were assigned higher scores by the panellists. The SNP in the GHR gene was significantly associated with odour, where animals inheriting the AA genotype produced steaks with an intense odour when compared with the other genotypes. Finally, the SNP in the DGAT1 gene was associated with sirloin weight after maturation and fat depth surrounding the sirloin, with animals inheriting the AA genotype having heavier sirloins and more fat.An SNP in the The results of this study confirm some previously documented associations. Furthermore, novel associations have been identified which, following validation in other populations, could be incorporated into breeding programmes to improve meat quality. LEP) gene, involved in the control of appetite and energy metabolism, which have been shown to be associated with carcass fat [CAPN1) and calpastatin (CAST) genes, which are known to play a key role in post mortem tenderisation of meat and have been associated with meat tenderness [GHR) gene, which have been associated with drip loss [Meat quality is of great importance to the beef industry where the consumer is willing to pay more for superior products . Traditicass fat -8, body cass fat , and grocass fat ; polymornderness -13; and rip loss , body werip loss ,16 and mrip loss . Before The aim of this study was to test such associations between eight single nucleotide polymorphisms (SNP) from five different genes and meat quality traits in a sample of Aberdeen Angus-cross animals collected in Scotland. All the tested SNP have been previously reported to be associated with various carcass and meat quality traits in cattle or pigs and have been incorporated into commercially available tests for meat or milk quality.i.e. farms where animals are bred and finished on the same farm) and were selected to be representative of British commercial cattle slaughtered for beef production, being a mix of heifers and steers ranging between 408 and 912 days old at kill, with the age differences depending largely on the farm. The 443 animals used in the experiment included 135 females and 308 males. The sires for all animals were pure-bred Aberdeen Angus whilst the dams were a mixture of purebreds of various breeds and crossbreeds including Aberdeen Angus, Aberdeen Angus-cross, Simmental-cross and Limousin-cross.Commercial crossbred beef cattle  with purebred Aberdeen Angus sires were sourced through the Scotbeef abattoir . Cattle originated from 14 breeder finisher farms  . ConformAt deboning, weight of the hindquarter and sirloins were recorded. Sirloins were vacuum-packed and stored below 4°C for 21–30 d to mature, then removed from the vacuum pack, patted dry to remove excess moisture and weighed. Three steaks were cut from the centre of the sirloin as follows: for tenderometer testing 3–4 cm thick, for sirloin measurements 1–2 cm thick and for sensory testing 2 cm thick.For tenderometer testing, steaks were trimmed to 200–220 g of eye muscle and placed in a water bath at 100°C until the centre of the sample reached 82°C. Samples were left to cool to 7°C then tested using a MIRINZ Tenderometer machine .A full list of analysed carcass quality traits can be found in Table Taste panel members were chosen among workers at the Scotbeef meat processing plant in East Kilbride, Scotland. Members of staff (n = 38) were tested using the Triangle and Matching tests  with 10 Prior to assessment sirloin steaks were cooked using a Lincat Lynx 400 electric griddle  until a thermometer placed in the centre of the steak reached 74°C. The six panellists then scored the steaks on a 1–8 scale for seven sensory traits, a full list of which can be seen in Table Due to the possibility of multiple sire mating and discrepancies between the recorded and the true sire, paternity was determined using genetic markers. Details are described in full in: Gill, Matika, Williams, Worton, Wiener, and Bishop: Consistency statistics and genetic parameters for taste panel assessed meat quality traits in a commercial population of Angus-sired beef cattle (submitted).Briefly, genotypes were obtained for each sample for a panel of 15 unlinked microsatellite markers. Genotypes for each offspring and all possible sires were analyzed with the program Cervus , which aSamples were genotyped at eight SNP from five different genes by Orchid Cellmark Ltd .CAPN1 (2 SNP), CAST (1 SNP), leptin (3 SNP), GHR (1 SNP), and acylCoA:diacylglycerol acyltransferase 1 (DGAT1) (1 dinucleotide substitution). All animals with phenotypes were genotyped, as were all available sires.The SNP locations in each gene, Genbank accession number and the positions (intron/exon etc) are listed in Table 2, 1× primer mix, 1 U Hot Start Taq  and 0.1 μg/μL BSA. PCR conditions consisted of 15 min at 95°C followed by 32 cycles of 30 s as 94°C, 1 min at 67°C and 1 min at 72°C. Following amplification, 4 μL of a solution containing 4 U Exonuclease I , 1.4 μL of 10× Antarctic Phosphatase buffer  and 2 U Antarctic Phosphatase  was added to the PCR product. Samples were then incubated for 60 min at 37°C followed by 15 min at 72°C.A single PCR was used to generate 12 amplicons. Each 10 μL reaction volume contained 5 μL DNA, 100 μM of each dNTP, 1 × QIAGEN PCR buffer which contained 1.5 mM MgCl®  reactions included 5 μL of Antarctic Phosphatase/Exonuclease I treated PCR product, 2 μL of SNaPshot Multiplex Ready Reaction Mix , 2 μL H2O and 1 μL probe mix. Thermocycling conditions consisted of 30 s at 94°C and 20 s at 67°C. Following single base extension, 4 μL of CIP solution containing 0.4 μL of 10× NEBuffer 3 , and 2 U CIP  was added to each sample. The samples were then incubated at 37°C for 60 min followed by 85°C for 20 min.SNaPshot® v4.0 was used to interpret the genetic profiles.Five μL of the SNaPshot/CIP product was added to 10 μL of Hi-Di™ formamide  and the samples were electrophoresed on a 3100 Genetic Analyzer . Genemapperχ2 tests [P < 0.05). Pairwise genotype combinations of the SNP were also tested for linkage disequilibrium (LD), the degree of non-random association of alleles at two or more loci, using the Haploview program, version 4 [r 2 values and LOD scores.Genotype frequencies of each polymorphism were tested for deviations from Hardy-Weinberg equilibrium by χ2 tests   provided in Genstat, release 10 . The staThe general model used for carcass traits was as follows:where:ijklmn is the trait measured on the individual iYμ is the overall mean for the traitj is the fixed effect of farm j (14 levels)Fk is the fixed effect of SNP genotype k (3 levels)Gl is the fixed effect of sex l (2 levels)Skl is the interaction between the k-th SNP genotype and the l-th sex(G × S)m is the random effect of kill-date mKjn is the random effect of the n-th sire on the j-th farmMjklmno is the residual term associated with the observationei.e. where effects were estimated to be negative they were set to zero.Additional interaction terms between sire, kill-date, genotype and sex groupings were fitted as random effects. Variance components were constrained to be non-negative, For the taste panel traits the general model was as follows:where additional terms are:m is the random effect of taste panel date mPio is the random effect of the o-th steak (A or B) for the i-th animalTijklmnop is the residual error associated with the observation.eAgain, additional interaction terms between sire, taste panel date, genotype and sex groupings were fitted as random effects, and variance components were constrained to be non-negative.The effects of several covariates  were also examined in separate analyses. The percentage Aberdeen Angus (% AA) was based on dam breed so that each animal was assigned a value of 100% (if the dam was AA), 75% (if the dam was AA-cross) or 50% (if the dam was neither). Statistical significance for the fixed effects was determined using approximate F-statistics with denominator degrees of freedom  estimateAdditive effects and dominance deviation were also calculated using a re-parameterized model. The additive effect was estimated as the difference between the mean of the two homozygotes divided by two, and dominance was estimated as the deviation of the heterozygote from the mean of the two homozygotes .P value of 0.01 for the 5% significance level.To allow correction for the fact that a large number of traits were analysed with a large number of SNP, and hence a high probability of false positive results, a Bonferroni correction was applied. The three leptin SNP and the two μ-calpain SNP were found to be in partial or strong LD so that the effective number of SNP tested was estimated as five. The correction for multiple SNP testing resulted in an adjusted CAPN1 gene out of the 443 genotyped animals. In order to determine whether the haplotype information accounted for additional variation beyond the SNP genotype analysis, we nested the haplotype group (a combination of the two haplotypes) within a SNP model i.e. the model was the same as the genotype model but with additional fixed terms accounting for the variation between the haplotype groups within the SNP groups in the fixed model. This analysis was carried out for each of the traits found to be significantly affected by either the μ-calpain or leptin SNP, i.e. tenderometer, weight of hindquarter and tenderness for the μ-calpain markers and overall liking for the leptin markers. Statistical significance of the extra variation accounted for by the presence of the haplotype groups in the model was determined using approximate F-statistics derived from Wald statistics with denominator degrees of freedom estimated in the Genstat REML procedure [Haplotypes were reconstructed for the genes that contained more than one SNP using software that determines the gametic haplotypes for each animal where phase is known with certainty  based onrocedure .UASMS1 where 17 animals were missing genotypes , including consideration of the reliability of taste panel measurements and genetic parameters for traits of interest. In brief, a total of 27 traits were measured, seven of which are taste panel assessed sensory traits, one a mechanical measure of tenderness with the remaining 19 being carcass and sirloin measurements. Trait means, coefficients of variation and heritability estimates are given in Tables Genotypes for four of the eight SNP tested did not significantly influence any of the 27 traits measured and 20 of the traits were not affected by the genotypes of any of the SNP tested. Tenderometer score, sirloin weight after maturation, sirloin fat depth, hindquarter weight, odour, overall liking and tenderness showed significant additive effects of at least one of the remaining four SNP.CAPN1 gene SNP, CAPN316, the C allele was associated with reduced tenderometer values, increased hindquarter weight and an increase in taste panel assessed tenderness compared to the G allele  such that the C allele was dominant to the T allele.The T allele of the leptin SNP, ts Table . There wDGAT1, the A allele was associated with an increase in sirloin weight after maturation and an increase in sirloin fat depth when compared to the G allele . Animals with the heterozygous genotype had values close to the homozygous GG animals, indicating a strong degree of dominance of the G allele over the A allele.For le Table . The estGHR SNP was significantly associated with an increase in taste panel-assessed odour, with the estimated difference between the homozygous genotypes AA and TT equal to 0.48 taste panel units . Percentage Aberdeen Angus made no difference to the genotypic effects for all of significant traits. As expected, when hot carcass weight was fitted as a covariate, the significance of the associations with weight traits was reduced. Age at kill made no difference to the effects of genotype on tenderness, hindquarter weight, overall liking, sirloin fat depth or odour. The only SNP trait association affected by age at kill was the association between CAPN316 association with tenderometer, weight of hindquarter and taste panel tenderness; the GHR association with odour; and the DGAT1 association with complete sirloin weight and sirloin fat depth. When the genotype means of each sex were assessed using pair wise comparisons it became apparent that the majority of significant genotype-trait associations mentioned previously were stronger in the female animals. Females with the CC genotype at the CAPN316 SNP had significantly lower tenderometer values, higher hindquarter weights and higher taste panel tenderness scores than CG or GG animals. In contrast, the CC males had higher tenderometer values than the other genotypes although this difference was not significant.There were significant genotype-by-sex interactions for six of the significant trait SNP genotype pairs Table . These wGHR SNP. Here, AA animals had significantly higher scores than either AT or TT animals. For the male animals, those with the TT genotype received the highest score for odour, although again, the differences between genotypes were not significant. Finally, female animals with the AA genotype at the DGAT1 SNP had significantly heavier sirloins after maturation and sirloin fat depths than AG or GG animals. For males the AA animals also had heavier sirloins although the GG animals had higher values of fat depth, however, differences between genotypes were not significant.Additionally there was a significant difference in odour score assigned to female animals with the AA, AT and TT genotypes at the CAPN1 gene ), and four haplotype possibilities for SNP in the leptin gene )  and the CAST SNP make up the Igenity TenderGENE panel [CAPN1 SNP forming the basis of the GeneSTAR tenderness test [OptiGRID test, whilst the SNP in the DGAT1 and GHR genes comprise the Igenity OptiYIELD test [The primary objective of this study was to test previously identified associations between SNP from five genes and economically important meat quality traits. The SNP tested are located in the NE panel , with thess test . AdditioELD test .CAPN316, CAPN4751 and UoGCAST SNP are in agreement with previous studies [CAPN316 SNP, the C allele was found to have a frequency of only 0.10 in the sires (data not shown), as opposed to 0.22 in the offspring. This is due to the absence of sires with the CC genotype at this SNP.The allele frequencies found for the  studies -31. AlleCAPN316 SNP in the CAPN1 gene, the UASMS2 SNP in the leptin gene, the DGAT1 SNP and the GHR SNP.Using single-marker, mixed-model association analysis four of the SNP were found to be associated with one or more of the traits tested in a significantly additive manner. These include the CAPN1 gene, mapped to BTA 29 [CAPN1 were evaluated; CAPN316, situated in exon 9 of the gene, results in an amino acid substitution from alanine to glycine for the C and G alleles respectively; and CAPN4751, situated in the intron between the 17th and 18th exons of the gene. A significant effect of CAPN316 genotype was found on the hindquarter weight; animals with the CC genotype at this locus had significantly heavier hindquarters (by 3.8 kg) than GG animals (P = 0.04). However, some of this effect could be explained by effects on overall carcass weight. Additionally, animals inheriting two copies of the C allele at the CAPN316 SNP had meat that was more tender than animals with one or zero copies, when measured by both the tenderometer machine and the taste panel. The association with mechanical tenderness is consistent with work done in experimental herds of crossbred cattle [The o BTA 29 , encodeso BTA 29 . It is to BTA 29 ,31,34,35d cattle ,35.CAPN1 SNP, CAPN4751, and WBSF values in an experimental population and a Bos indicus-influenced crossbred population [CAPN4751 SNP on the tenderness trait . Polymorphisms in CAST, an inhibitor of the CAPN1 protease mapped to BTA 7 [Previous work has shown an association between genotypes at the other pulation  where CCto BTA 7 , have prto BTA 7 ,11,31. HUASMS2, was found to be significantly associated with any of the traits tested. Panellists gave animals with the TT genotype significantly higher overall liking scores than animals with CC or CT genotypes. A significant, non-additive association was also observed between the UASMS2 SNP and sirloin fat thickness, such that animals inheriting the CC genotype had significantly less fat surrounding the sirloin when compared to either CT or TT animals. This is in line with previous reports where UASMS2 was significantly associated with both backfat thickness and marbling score, with TT animals having higher values for both traits [Three SNP in the leptin gene, which has been mapped to BTA 4  and prodh traits . It may DGAT1 gene, which has been mapped to BTA 14 [K (lysine) to A  amino acid substitution (K232A) in the protein [DGAT1 were found to have an increase in intramuscular fat content [K232A genotype and carcass fat traits [The o BTA 14 , plays ao BTA 14 . The SNP protein . This po protein . The pre content . In contt traits .GHR polymorphism studied here is an A to T substitution in exon eight of the gene which results in a non-conservative replacement of phenylalanine with a tyrosine residue (F279Y) [GHR gene  . Whilst  (BTA 20 ) have pr . As discussed above, the present study observed a significant, non-additive association between sirloin fat depth and UASMS2 genotype but no association was found with carcass or sirloin weight related traits. Previous reports also indicate associations between UASMS1 and fat yield and Exon2FB with fat and lean yield and grade fat [UASMS1 or Exon2FB genotype and any of the traits tested. These contrasting results may be due to the different populations studied and indicate the importance of multiple validation studies in different breeds and populations.The present study confirmed some well-known associations and identified novel significant trait-genotype associations. However, there were some traits where associations were expected but not observed. Specifically, associations between carcass and sirloin related traits and the SNP in the leptin gene were expected but were not found. Serum levels of the leptin hormone have previously been found to be significantly associated traits such as marbling, backfat depth and kidney, pelvic and heart fat . Additiolaughter , carcasslaughter , althougrade fat , howeverThe analyses of the mean trait value for each genotype in each sex for those trait-SNP pairs where there were significant sex interactions indicated that, for the majority of associations, the effect was primarily in the female animals. Differences between male and female genotype effects were seen in five traits: taste panel assessed tenderness, weight of hindquarter, odour, sirloin weight after maturation and sirloin fat depth. The reason for the difference between male and female animals is unknown. Whilst differences in meat quality between male and female cattle have been reported ,47, therThe haplotype analyses tested whether incorporating information on combinations of SNP  led to an improvement over models with only single SNP genotypes. We found that using haplotypes in addition to SNP genotypes in the analysis accounted for no extra variation for any of the SNP/trait combinations. This may be explained by the fact that only one of the SNP from each of the genes  had a significant effect on any of the traits in the single SNP analyses. Thus incorporating haplotype information would not improve the performance of marker-assisted selection for this population.CAPN316 genotype and tenderness, the most important quality trait for consumers. Furthermore, novel associations have been identified which, following validation in other populations, could be incorporated into breeding programmes to improve meat quality. Finally, whilst some previously noted associations were not replicated in the current study, it is important to note that validation is dependent on the specific nature of the population screened and that genetic background may influence the size of the effect of a polymorphism. Validation failure may be due to a lack of true associations between the trait and marker but could also be caused by differences in SNP frequencies, different marker-causative mutation linkage phases, genotype-by-environment interactions or epistasis as well as sample size effects and the way the trait is measured. Nevertheless, for those associations confirmed here, the additional validation instils confidence in using these markers in selection programmes for improved meat quality.The results presented here confirm some of the previously documented associations, for example, the association between The authors declare that they have no competing interests.JLG carried out data analysis and drafted the manuscript. SCB participated in the design of the study and statistical analysis and helped draft the manuscript. CMcC participated in the design of the statistical analysis and helped to draft the manuscript. JLW conceived of the study, and participated in its design and coordination and helped to draft the manuscript. PW conceived of the study, and participated in its design, coordination and statistical analysis and helped to draft the manuscript. All authors read and approved the final manuscript.