Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans. Classical studies that examined the global distributions of human physiological traits such as pigmentation, basal metabolic rate, and body shape and size suggested that natural selection related to climate has been important during recent human evolutionary history. We scanned the human genome using data for about 650,000 variants in 61 worldwide populations to look for correlations between allele frequencies and 9 climate variables and found evidence for adaptations to climate at the genome-wide level. In addition, we detected compelling signals for individual SNPs involved in pigmentation and immune response, as well as for pathways related to UV radiation, infection and immunity, and cancer. A particularly appealing aspect of this approach is that we identify a set of candidate advantageous SNPs associated with specific biological hypotheses, which will be useful for follow-up testing. We developed an online resource to browse the results of our data analyses, allowing researchers to quickly assess evidence for selection in a particular genomic region and to compare it across several studies. Climatic factors like temperature and humidity play an important role in determining species distributions and they likely influence phenotypic variation of populations over geographic space. Several eco-physiological “rules” have been proposed to predict variation in body size, pigmentation and body dimensions as functions of climate or geography Since human populations occupy a wide variety of environments with respect to climate, selective pressures are expected to vary greatly across geographic regions. Adaptations to spatially varying selective pressures are evident in the geographic distributions of many traits. For example, significant correlations exist between body mass and temperature Drosophila melanogaster, variants involved in circadian rhythms, aging and energy metabolism are correlated with climate  www.hapmap.org). The 5 populations we genotyped are especially valuable because they expand information in Africa and Oceania where HGDP population coverage is low and they extend the range of environments to cover more extreme arctic climates. For each of the 61 populations, we gathered environmental data for nine continuous climate variables, chosen to capture those aspects of climatic variation that have a strong impact on human physiology  as a measure of the support for the alternative model relative to the null model, in which the transformed population allele frequency distribution is dependent on population structure alone. In other words, the method asks whether selective pressures correlated with a climate variable have shaped spatial patterns of allele frequencies above and beyond the effect of population structure (as captured by the covariance matrix). As shown in −5.Although several SNPs had very large BFs with climate variables and might be considered “genome-wide significant” using general assumptions see , Text S2We summarized the rank statistics for each SNP by calculating the minimum rank across all nine climate variables. To test for evidence of selection on the climate variables overall, we then calculated the proportion of SNPs likely to be enriched for functional effects (referred to as test SNPs) relative to the proportion of SNPs likely to be neutrally evolving  in the lower tail of the minimum rank distribution. In the absence of selection, equal proportions of these two classes of SNPs are expected to lie in the extreme tail of the BF distribution for any given cutoff. Conversely, if a higher portion of the test SNPs were targeted by selection than the neutral SNPs, an enrichment of test SNPs in the lower tail of the minimum rank distribution is expected. Conducting the analysis on the minimum rank statistic allowed us to assess the evidence of selection from the nine climate variables, overall. We also asked which of the individual climate variables were responsible for the signals we observed. For this analysis, we calculated the rank statistic for each SNP and each individual climate variable and, as in the previous analyses, we looked for an enrichment of large BFs among test compared to neutral SNPs. Finally, for individual SNPs that are discussed below, we quote the BF and the empirical rank specific to their ascertainment and mean frequency bin.As shown in KRT77) (rs3782489), a gene that is specifically expressed in the ducts of eccrine sweat glands, is strongly correlated with summer solar radiation , suggesting that this SNP may influence temperature homeostasis through sweating  and minimum winter temperature   . TRIP6 iDrosophila melanogasterThe plots in Several recent studies have shown that convergent evolution underlies similarities in some phenotypes, e.g., skin pigmentation Despite the smaller number of populations in each subset compared to the worldwide set, we find a significant enrichment overall for genic SNPs with strong correlations with climate relative to nongenic SNPs in all population subsets . We founCORIN gene region  have strong signals with latitude and minimum winter temperature  in the AEA subset, but do not have strong signals with any climate variable in the AWE subset . Three NS SNPs in the toll-like receptor gene cluster  are strongly correlated with winter solar radiation in the AWE subset, but not in the AEA subset . At TLR6 N249S (rs5743810), which is associated with variation in resistance to malaria, the allele that confers resistance occurs at high frequency in Africa and Asia and lower frequency in European populations . We did not find a significant enrichment of signal for this gene set in this analysis . To bettWe also asked how our results compared to those from a recent analysis using the same populations and data, but different environmental variables . We found significant, but weak correlations between the results from this analysis and the previous one  to 0.3 (between relative humidity in the summer and dry ecoregion)) and that the majority of the strongest signals differed across tests  and S6. SLC45A2 (rs28777)  that is associated with hair color and a SNP in OCA2 (rs1667394)  that is associated with eye and hair color. In addition, consistent with the notion that pathogens exerted powerful selective pressures on humans PCDH18  and no excess of overlap with the results from an analysis using the integrated haplotype score (iHS) Here, we presented the results of a genome-wide scan for evidence of positive selection in response to climatic variation. Climate is known to influence the distribution of human pathogens Climate is known to have an important impact on animal physiology and fitness, as a result of both direct and indirect effects. Direct effects include heat and cold stress, dehydration stress, and stress resulting from too much or too little UV radiation. For example, variation in temperature and relative humidity can result in cold or heat stress, i.e. a deviation from the relatively narrow range of body temperatures that is optimal for the coordination of molecular and cellular processes. Likewise, variation in exposure to solar radiation influences vitamin D production in the skin GWAS have helped to clarify the genetic underpinnings of many disease phenotypes, but there are still many outstanding questions. Most human genes appear to be under strong purifying selection TLR6 P249S (rs5743810). The haplotype containing this SNP was previously found to show evidence of positive selection Our results suggest that many loci that appear to be under selection due to climate may yield both positive and negative effects on fitness, and may therefore fit an antagonistic pleiotropy model The results of this genome scan not only increase our understanding of the genetic landscape of adaptation across the human genome, but they may also have a more practical value. For example, they can be used to select candidate genes for common disease risk and to generate specific testable hypotheses regarding the functions of specific genes and variants. While the results of genome-wide scans for association with diseases and other traits are accumulating at a rapid pace, interpretation of these results is often ambiguous because the power to detect all common variants that are important in the etiology of the phenotype is incomplete. This is especially true in the case of complex traits, where variants at many loci may contribute to the phenotype, each with a small effect. By combining the evidence from GWAS with evidence of selection, it may be possible to separate true causative regions from the background noise inherent in genome-wide screens for association. To facilitate this, all of our empirical rank statistics are publically available. Moreover, results of selection scans that detect evidence for spatially-varying selection may be especially relevant to diseases that show substantial differences in prevalence across ethnic groups . In the future, this approach could be extended by including additional populations and aspects of the environment to gain a more complete understanding of how natural selection has shaped variation across the genome in worldwide populations. Furthermore, whereas we relied on linkage disequilibrium between  adaptive variants and genotyped SNPs, whole genome re-sequencing data should give a more complete picture of the variation that underlies adaptation.We used data from 61 worldwide human populations , includihttp://www.hapmap.org/). Total numbers of SNPs and number of genic, non-genic and nonsynonymous SNPs included in the final analysis are shown in Illumina Infinium HumanHap 650Y genotype data for HGDP panel were obtained from Li et al. Climate data were obtained for each population based on the coordinates of the locations where the samples were collected, except for the Vasakela !Kung and the Gujarati, who had recently relocated. For these populations, we used approximate coordinates of their homelands. The individuals who were sampled from the !Kung population were known to have recently relocated to Schmidtsdrift, South Africa from the Angola/Namibia border, and the Gujarati are originally from Gujarat, India. We selected climate variables separately for the summer and winter seasons from the NCEP/NCAR database 10BF is 6.36, which, under general assumptions, is equivalent to a genome wide significant p-value of 8.5×10−9, or a p-value of 5% divided by 650,000 tests across 9 climate variables To calculate correlations between SNP allele frequencies and climate variables, we used a Bayesian linear model method that controls for population history by incorporating a covariance matrix of populations and that accounts for differences in sample size among populations The Illumina genotyping array contains SNPs that were chosen to be tagging SNPs; in addition, they were subjected to ascertainment schemes that differ across the three major subsets of SNPs on the array To reduce the number of tests while assessing the strength of the evidence for selection with climate as a whole, we also calculated the minimum of the transformed rank statistics across all variables for each SNP. We used the rank and minimum rank statistics to look for an enrichment of large BFs (low rank statistics) in one group of SNPs compared to another.To determine whether there was an excess of SNPs with strong signals that are enriched for functional variation, we compared the proportions of nonsynonymous (NS) and genic SNPs in the lower tail of the rank statistic distribution to the proportion of non-genic SNPs in this tail. Given the arbitrary nature of choosing a single cutoff, we set three cutoffs . In other words, we looked at the top 5%, 1% and 0.5% of all BFs and asked whether there is an enrichment of genic and NS SNPs for each tail cut-off. A value of 1 represents no excess and a value greater than 1 represents an enrichment in the tail of the distribution. SNPs below the cutoff are likely to cluster along the genome due to linkage disequilibrium, thus reducing the number of independent signals contributing to an observed enrichment. To account for this possibility, we found the confidence interval for the enrichment using a bootstrap approach. To this end, we separated the genome into 500kb segments and then we bootstrap resampled a number of segments equal to the length of the genome divided by 500 kb. Just as we did for the observed data, for each of 1000 bootstrap replicates, we calculated the ratios of the relative proportions of genic and NS SNPs to the proportion of non-genic “neutral” SNPs in the tail of the minimum rank distribution. In this and other analyses described here, we considered an enrichment significant (with a one-tailed test) if at least 95% of the bootstrap replicates were enriched .We calculated correlations between each SNP and each climate variable for geographically defined subsets of populations using the same Bayesian linear model method we used for the worldwide populations. For these analyses, we re-estimated the covariance matrices for each of the population subsets and for each SNP ascertainment panel. We defined two population subsets. One subset included the populations in sub-Saharan Africa as well as the populations in the Middle East and Western Eurasia (AWE) and the other included populations in sub-Saharan Africa and the populations in East Asia and Oceania (AEA). Populations included in each subset are shown in −5. This database contained entries for 800 unique autosomal SNPs found on the Illumina HumanHap650Y platform and 61 traits. From this list of SNPs, we identified a set of SNPs with extremely low rank statistics (less than 5×10−4) for each of the nine climate variables included in our analyses. Because most GWAS are performed in populations of European ancestry, the SNPs in the Illumina panel were binned based on the allele frequency in Europeans rather than the global allele frequency.We downloaded the information in the Catalog of Published Genome-Wide Association Studies http://www.ncbi.nlm.nih.gov/dbGaP) Lists of genes implicated in major classes of disease were obtained from the Genetic Association Database (http://www.broadinstitute.org/gsea/msigdb), we obtained 438 sets of genes involved in canonical pathways and 1168 sets of genes that were up or down-regulated in response to chemical or genetic perturbations.From the Molecular Signatures Database , and linked to the HGDP Selection Browser (http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP) to allow for the comparison of selection signals from different types of analyses.The results of these analyses will be made available through a searchable online data base, dbCLINE  absolute latitude, (B) maximum summer temperature, (C) minimum winter temperature, (D) winter precipitation rate, (E) summer solar radiation, (F) summer relative humidity, and (G) winter relative humidity. Transformed allele frequencies were computed by subtracting the mean allele frequency across populations. SNPs with rank statistics less than 1e-5 are included in the plots. Population names and means are colored based on membership in one of seven major geographical regions  and ordered so that the climate variable values increase from left to right across the x-axis. Each gray dot represents an individual SNP and fitted lines for each region are shown in color. The range of each climate variable across the geographic region is shown in each section of the plot.(6.30 MB TIF)Click here for additional data file.Figure S2Transformed allele frequency plotted against each of eight climate variables for SNPs with the strongest signatures of selection in the AWE population subset. Panels include: (A) absolute latitude, (B) maximum summer temperature, (C) minimum winter temperature, (D) summer precipitation rate, (E) winter precipitation rate, (F) summer solar radiation, (G) summer relative humidity, and (H) winter relative humidity. Transformed allele frequencies were computed by subtracting the mean allele frequency across populations. SNPs with rank statistics less than 1e-5 are included in the plots. Population names and means are colored based on membership in one of seven major geographical regions  and ordered so that the climate variable values increase from left to right across the x-axis. Each gray dot represents an individual SNP and fitted lines for each region are shown in color. The range of each climate variable across the geographic region is shown in each section of the plot.(4.50 MB TIF)Click here for additional data file.Figure S3Transformed allele frequency plotted against each of eight climate variables for SNPs with the strongest signatures of selection in the AEA population subset. Panels include: (A) absolute latitude, (B) summer maximum temperature, (C) winter minimum temperature, (D) summer precipitation rate, (E) winter precipitation rate, (F) summer solar radiation, (G) summer relative humidity, and (H) winter relative humidity. Transformed allele frequencies were computed by subtracting the mean allele frequency across populations. SNPs with rank statistics less than 1e-5 are included in the plots. Population names and means are colored based on membership in one of seven major geographical regions  and ordered so that the climate variable values increase from left to right across the x-axis. Each gray dot represents an individual SNP and fitted lines for each region are shown in color. The range of each climate variable across the geographic region is shown in each section of the plot.(4.50 MB TIF)Click here for additional data file.Figure S4Two SNPs implicated in pigmentation phenotypes that have strong correlations with winter solar radiation in the AWE population subset. (A) rs1667394, a SNP in OCA2, and (B) rs28777, a SNP in SLC45A2.(3.00 MB TIF)Click here for additional data file.Figure S5SNPs with transformed rank statistics less than 10-4 with any climate variable are listed in the figure. For each SNP, the strength of the transformed rank statistic (TRS) with all climate variables from this analysis as well as all ecoregion, diet and subsistence variables from the previously published Hancock 2010 analysis are shown using color-coding. Red represents a TRS < 1e-5, dark orange represents a TRS < 1e-4, orange represents a TRS < 1e-3, dark yellow represents a TRS < 1e-2 and light yellow represents a TRS < 1e-1. SNPs that were not analyzed in the previous study are colored gray. All other SNP-environmental variable combinations are colored white.(0.43 MB EPS)Click here for additional data file.Figure S6SNPs with transformed rank statistics less than 10-5 with any climate variable are listed in the figure. For each SNP, the strength of the transformed rank statistic (TRS) with all climate variables from this analysis as well as all ecoregion, diet and subsistence variables from the previously published Hancock 2010 analysis are shown using color-coding. Red represents a TRS < 1e-5, dark orange represents a TRS < 1e-4, orange represents a TRS < 1e-3, dark yellow represents a TRS < 1e-2 and light yellow represents a TRS < 1e-1. SNPs that were not analyzed in the previous study are colored gray. All other SNP-environmental variable combinations are colored white.(0.18 MB EPS)Click here for additional data file.Figure S7Signals for SNPs implicated in pigmentation and tanning in the worldwide, AWE and AEA analyses.(0.59 MB EPS)Click here for additional data file.Table S1Proportion of genic relative to nongenic and nonsynonymous relative to nongenic SNPs in the tails of the minimum rank distribution for population subset analysis with individual climate variables. Symbols *, ** and *** denote support from >95%, >97.5% and >99% of bootstrap replicate, respectively.(0.02 MB XLS)Click here for additional data file.Table S2Reanalysis of enrichment of correlations with climate for SNPs in an energy metabolism gene set (0.03 MB XLS)Click here for additional data file.Table S3Canonical pathways and sets of genes differentially expressed in response to chemical and genetic perturbations enriched in the 1% and 5% tails of the minimum rank distribution. Symbols *, ** and *** denote support from >95%, >97.5% and >99% of bootstrap replicate, respectively.(0.03 MB XLS)Click here for additional data file.Table S4Numbers of SNPs in each category  and in each population set.(0.02 MB XLS)Click here for additional data file.Text S1Descriptive information about populations included in this study.(0.76 MB DOC)Click here for additional data file.Text S210 BFs for each variable and for each population set.Manhattan plots showing the log(0.05 MB DOCX)Click here for additional data file.Text S3Descriptive information about population subsets and comparison to worldwide sample.(2.20 MB DOC)Click here for additional data file.


Secale cereale L.) cultivar improvement have been hampered by the lack of genome sequence information. To overcome this limitation we sequenced the transcriptomes of five winter rye inbred lines using Roche/454 GS FLX technology.The improvement of agricultural crops with regard to yield, resistance and environmental adaptation is a perpetual challenge for both breeding and research. Exploration of the genetic potential and implementation of genome-based breeding strategies for efficient rye  resource. From sequence comparisons 5,234 single nucleotide polymorphisms (SNPs) were identified to develop the Rye5K high-throughput SNP genotyping array. Performance of the Rye5K SNP array was investigated by genotyping 59 rye inbred lines including the five lines used for sequencing, and five barley, three wheat, and two triticale accessions. A balanced distribution of allele frequencies ranging from 0.1 to 0.9 was observed. Residual heterozygosity of the rye inbred lines varied from 4.0 to 20.4% with higher average heterozygosity in the pollen compared to the seed parent pool.The established sequence and molecular marker resources will improve and promote genetic and genomic research as well as genome-based breeding in rye. Triticum aestivum L.) and barley (Hordeum vulgare L.). As a genetic research system, rye is intriguing due to its high genetic variability. In addition to being an economically important crop for Middle and Eastern Europe, rye provides valuable traits for other crops, as a parent of the amphiploid triticale, and as a donor of translocated chromosome segments in wheat , rice , sorghuf barley  and wheaf barley  were perf barley  and the f barley .Brachypodium protein sequences. Sce_Assembly03 sequences were mapped onto the Brachypodium genome by using a sliding window approach with a window size of 0.5 Mb and a shift of 0.1 Mb along the Brachypodium chromosomes. The number of BlastX hits and the percent bp coverage of the respective Brachypodium genes were determined. These density values were corrected for the number of Ns per window, if the N content exceeded 60% the value was set to zero. Density values were extrapolated to genes , maize hypodium ], and sohypodium ]. Due to sorghum ,55. The ast hits , suggestast hits . This mir barley ,57 and wr barley  and explr barley  has beenp-value < 0.05) exceeds this theoretical expectation. This might be in part explained by the allogamous behaviour of rye resulting in remaining heterozygosity [Sequence information of the five rye inbred lines was used to detect sequence variation that was transferred into more than 1,300 SSRs and about 5,000 SNPs. Molecular markers have been developed for a range of crop species and play an essential role in modern plant breeding. They have been used to monitor DNA sequence diversity within and among species, to identify genes responsible for desired traits, to disclose sources of genetic variation that allow for the production of new varieties by introducing favorable traits from landraces and related grass species, and to manage backcrossing programs . Togethezygosity . DespiteIn conclusion, the Sce_Assembly03 provides a new and comprehensive EST resource that integrates rye in the comparative analysis between small grain cereals. The Rye5K SNP array allows the analysis of large sets of individuals to obtain genotyping data for association studies, estimating linkage disequilibrium, and population genetic approaches. Our genomic resources comprise 115,400 EST sequences, 1,385 SSRs, more than 5,000 SNPs, and the Rye5K SNP array for large-scale genotyping that will improve and promote genetic and genomic research as well as genome-based breeding in rye.Brachypodium chromosomes. NS and RZ developed and examined the SSR markers. EB, GH, and TS developed the Rye5k SNP array. CCS, EB, KFXM, NS, and US designed the study. EB, GH, MS, RZ, and TS drafted the manuscript. All authors read and approved the final manuscript.GH prepared the sequencing samples, participated in the bioinformatic analyses, conducted the genotyping, and evaluated the genotyping data. TS, MM, and US carried out the processing and assembly of 454 reads and gave the descriptive statistics for them. KFXM and MS performed the BLAST analyses, functional annotations, and sequence comparisons along the Set of plant tissues for RNA extraction. RNA of each rye inbred line was extracted from plant tissues exposed to various stress treatments and harvested at different developmental stages.Click here for filein silico SNP miningEstablishment and description of the Sce_Assembly02 generated for . The Sce_Assembly02 was performed in three steps using the MIRA assembler V2.9 on integrated standard settings: Firstly, raw sequence reads surpassed a quality filtering process where 454 sequencing adapter and cDNA synthesis primer sequences as well as low quality reads were removed. Secondly, the cleaned and trimmed sequence reads were subjected to a line-specific assembly where reads of each inbred line were aligned in a separate assembly run. Non-aligned reads in the line-specific assemblies, i.e. singletons, were rejected. Thirdly, those reads that merged into contigs in the line-specific assemblies were moved further to the Sce_Assembly02 starting again with the cleaned and trimmed reads, but now from all five inbred lines. This strategy resulted in contig sequences that were used for SNP detection and subsequently for the design of the high-throughput genotyping SNP array. With regard to SNP discovery this assembly allowed the deduction of critical information about the SNP position like allele coverage.Click here for fileGigaBayes parameters. Only parameters different from GigaBayes program default settings are listed.Click here for fileBrachypodium chromosomes Bd1 to Bd5Association of multi-line and single-line contigs of the Sce_Assembly03 to the . The four heatmaps per chromosome are depicting the density of Brachypodium genes, homologous rye sequences, contigs represented on the Rye5k SNP array, and SNPs that were heterozygous among 59 rye inbred lines (from top to bottom) by going along the Brachypodium chromosomes in a sliding window with 0.5 Mb window size and a 0.1 Mb shift and determining for each window the number and percent bp coverage of the respective tagged genes. The density values were corrected for the number of Ns per window, if the N content exceeded 60% the value was set to zero and drawn in white color. The number was extrapolated to number per Mb to facilitate comparisons. The heatmaps were created from density values using the Python pylab module in combination with the jet colormap . Minimum, maximum, and mean number of genes/Mb in Brachypodium and hits/Mb in rye, respectively, were given on the left of each map. The ruler on top gives the chromosome length in Mb.Click here for fileGO categories found in the Sce_Assembly03 multi-line and single-line contig sequences on Blast2GO level 2. Categories with an occurrence less than 0.05% were summarized in "others".Click here for fileSSR motifs detected in 338,536 contigs of the five line-specific assemblies. Mononucleotide repeat motifs were discarded. Mixed motifs describe two close SSR motifs which are separated by less than 100 bp.Click here for fileDescription of the Rye5K SNP array. SNP containing contigs represented on the Rye5K SNP array were listed including candidate SNP position, probe design sequences provided to Illumina Inc. , and GO annotations.Click here for fileObserved residual heterozygosity of 54 rye inbred lines representing the two heterotic pools. Heterozygosity was calculated based on genotyping data obtained with the Rye5K SNP array. Lines from the pollen parent pool were in generations F3 to F4, lines from the seed parent pool were in generation F6.Click here for file


P<1.52×−610) were detected for 6 Single Nucleotide Polymorphic (SNP) loci for carcass weight on chromosome 14. The most significant SNP was BTB-01280026 (−11P = 4.02×10), located in the 25 Mb region on Bos taurus autosome 14 (BTA14). The other 5 significant SNPs were Hapmap27934-BTC-065223 (−11P = 4.04×10) in 25.2 Mb, BTB-01143580 (−11P = 6.35×10) in 24.3 Mb, Hapmap30932-BTC-011225 (−10P = 5.92×10) in 24.8 Mb, Hapmap27112-BTC-063342 (−9P = 5.18×10) in 25.4 Mb, and Hapmap24414-BTC-073009 (−8P = 7.38×10) in 25.4 Mb, all on BTA 14. One SNP  lies independently from the other 5 SNPs. The 5 SNPs that lie together showed a large Linkage disequilibrium (LD) block (block size of 553 kb) with LD coefficients ranging from 0.53 to 0.89 within the block. The most significant SNPs accounted for 6.73% to 10.55% of additive genetic variance, which is quite a large proportion of the total additive genetic variance. The most significant SNP  had 16.96 kg of allele substitution effect, and the second most significant SNP  had 18.06 kg of effect on carcass weight, which correspond to 44% and 47%, respectively, of the phenotypic standard deviation for carcass weight in Hanwoo cattle. Our results demonstrated that carcass weight was affected by a major Quantitative Trait Locus (QTL) with a large effect and by many SNPs with small effects that are normally distributed.This genome-wide association study (GWAS) was conducted to identify major loci that are significantly associated with carcass weight, and their effects, in order to provide increased understanding of the genetic architecture of carcass weight in Hanwoo. This genome-wide association study identified one major chromosome region ranging from 23 Mb to 25 Mb on chromosome 14 as being associated with carcass weight in Hanwoo. Significant Bonferroni-corrected genome-wide associations ( Korean beef producers select for meat yield and quality to increase their income from steer feedlots and sold calves. Estimated breeding values (EBVs) for eye muscle area (EMA), carcass weight (CWT), marbling score (MAR), and backfat thickness (BF) after adjustment for age (30 months) are commonly used as selection criteria in attempts to increase meat yield and quality, which determine profitability for the Korean beef industry. However, current gain-based long-term programs targeting improvement of meat yield and quality in the Korean finishing farm sector have resulted in inefficient meat production because the animals produce excessive fat . More targeted meat production, such as improved genetic gain for EMA and CWT and more desirable MAR, needs to be achieved in Hanwoo beef industry by genetic improvement. To this end, investigations have been made for incorporation of DNA information into genetic evaluation models, which may result in improved accuracy of estimated breeding value (EBV) and lead to increased selection response in cattle PLAG1-CHCHD7. Nishimura et al. (2012) revealed three major QTLs for carcass weight on chromosomes 6, 8, and 14, including the PLAG1-CHCHD7 QTL for stature in Japanese Black cattle. Most of the agriculturally important traits are complex in their genetic architecture due to their polygenic nature as many genes have small effects and it makes it difficult to identify and characterize individual QTLs for genetic prediction The recently established genome-wide SNP panel for cattle enables the mapping of quantitative trait loci (QTL) and the prediction of animal's genetic merit, without using phenotypic and pedigree records . This approach has been used for fine mapping of QTL for milk traits in dairy cattle 2r = 0.1) levels of LD have been found in Hanwoo of up to 100 kb An important prerequisite for unbiased QTL mapping is homogeneity of the mapping population No ethics statement was required for the collection of DNA samples. DNA was extracted either from AI bull semen straws or from blood samples obtained from different veterinary practitioners in the Hanwoo Improvement Center of the National Agricultural Cooperative Federation with the permission of the owners. Both the semen and the blood samples were collected for routine veterinary procedures and not explicitly for the purpose of this study.ad libitum ration of a total mixed diet of concentrates and rice straw with ratios in total feed of about 1.5∶1, 2∶1, and 4.5∶1 for the growing period (4–12 months), finishing period I (13–18 months), and finishing period II (19–23 months), respectively. Percentage of crude protein (CP) and total digestible nutrients (TDN) of the concentrates were, respectively, 14–16% and 68–70% for the growing period, 11–13% and 71–73% for finishing period I, and 11% and 72–73% for finishing period II. Phenotype data in this study included carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MAR). BF, EMA, and MAR were measured at the 12th and 13th rib junction after a 24-hour chill. MAR was assessed on a one-to-nine point scale, and the degree-of-marbling score was evaluated based on the Korean Beef Marbling Standard from the Animal Product Grading Service in Korea Data were obtained from 1,011 Hanwoo steers derived from 118 sires from progeny testing in the Hanwoo Improvement Center of the National Agricultural Cooperative Federation in Seosan, Chungnam province, Korea. The sample comprised paternal half-sib pedigrees from 118 proven Korean sires. The steers were born from spring of 2005 through fall of 2007 and weaned at 5 or 6 months of age, and each group of 10 steers were raised in a pen. The steers received an Genomic DNA for genotyping assays was extracted from the blood sample, and the DNA was isolated from the blood using DNeasy 96 Blood and Tissue Kit . DNA quantification was performed using a NanoDrop 1000 . DNA samples were submitted for genotyping with total DNA of 900 ng, 260/280 ratio >1.8, and DNA concentration of 20 ng/ul.The genotyping for 1,020 animals was done by the Animal Genome & Bioinformatic Division of the National Institute of Animal Science, RDA, Korea, using a BovineSNP50 BeadChip Ver. 1 and Bovine HD 777 K Chip  and allele frequencies for six significant SNPs among breeds, five European cattle breeds  and five Northeast Asian cattle breeds  were used. The 6 SNPs' data for the 10 breeds were selected from the bovineSNP50 BeadChip data. The sample size for the 10 breeds was 20 animals for each. The haplotypes for the 6 significant SNPs in the 25 Mb region on BTA14 were reconstructed using default parameters in PHASE K) ranged from 2 to 14, with 10 repeat runs for each K value under the admixture model.The STRUCTURE software was applied to study genetic structure, the degree of population uniformity, and admixture in the chosen cattle breeds To annotate SNPs with respect to their functional classification on genes, we downloaded bovine gene annotation data sets (gene annotations and FASTA sequences) from the UCSC Genome Browser. The SNPs were annotated using the ANNOVAR tool (version 2012) In this genome-wide association study, the 1,011 steers descended from 118 sires were used as a discovery data set to identify the QTL for carcass traits . The paternal half-sib families had up to 118 members. The wide-spread use of a limited number of sires in Hanwoo cattle results in extensive linkage disequilibrium (LD), however it also causes a subtle form of admixture due to relationships among the animals. If unaccounted for, the relationship can lead to a large number of false positives in the GWAS. The carcass traits of the samples had divergent phenotypes; for example, carcass weights ranged from 183 kg to 488 kg  but were−3 for carcass weight revealed a large deviation from the distribution under the null hypothesis, indicating that strong associations were obtained (P<1.52×−610) were detected for 6 SNPs for carcass weight on chromosome 14 (BTB-01280026 (P = 4.02×−1110) located in the 25 Mb region on BTA14 (Hapmap27934-BTC-065223 (P = 4.04×−1110) in the 25.2 Mb region, BTB-01143580 (P = 6.35×−1110) in the 24.3 Mb region, Hapmap30932-BTC-011225 (P = 5.92×−1010) in the 24.8 Mb region, Hapmap27112-BTC-063342 (P = 5.18×−910) in the 25.4 Mb region, and Hapmap24414-BTC-073009 (P = 7.38×−810) in the 25.4 Mb region, all on BTA 14.The GWAS identified one major chromosome region ranging from 23 Mb to 25 Mb on chromosome 14, which is associated with carcass weight in Hanwoo . The quaobtained . Signifiosome 14 . The moson BTA14 . The othP = 0.001 and −6P = 10 explained only 2 to 5% of additive genetic variance, which is a quite small proportion of the total additive genetic variance. However, the most significant SNPs accounted for 6.73 to 10.55% of the additive genetic variance, which is quite a large proportion of the total additive genetic variance  had 16.96 kg of allele substitution effect, and the second most significant SNP  had an effect of 18.06 kg for carcass weight, which corresponds to 44% and 47%, respectively, of the phenotypic standard deviation for carcass weight in Hanwoo (−6P<10) in this study. In addition, there were no highly associated signals for fat traits such as BF and MAR. For these traits, it is possible that a large number of genes with small effects are scattered across the whole genome in Hanwoo  lies independently from the other 5 SNPs  that span the 0.6 Mb region  is more prominent than other two of the associated SNPs  for the major region of the 1.1 Mb interval (from 24. Mb to 25.4 Mb) which harbored 29 SNPs (2r) ranging from 0.1 to 0.94 within each LD block  was located in block 5 (block size of 234 kb) along with the other 2 significant SNPs .We investigated LD structure  were not polymorphic. This might be because Brahman cattle have a different genetic background as a bos indicus. In this study, we also compared favorable allele frequencies for three Hanwoo breeds . Interestingly, the favorable allele frequencies of the 6 SNPs in Korean brown cattle were different from those in Korean brindle and Jeju Black Hanwoo cattle  were not detected, and the other two SNPs (BTB-01280026 and Hapmap24414-BTC-073009) were observed at very low allele frequencies . This may be because crossbreeding of brown Hanwoo with European breeds in the past was the source of the favorable allele  with relatively small body stature. The favorable allele frequency was different in European (from 25% to 77%) and Northeast Asian (from 0% to 14%) breeds in all SNPs. In Brahman, two SNPs  lay independently from the other 5 SNPs in Korean brown, Angus, Holstein, Chosun, and Yanbian cattle  gene was for calving ease that is highly correlated with fetal growth traits in cattle. According to long range LD results . In particular, carcass weight is genetically correlated with EMA (rG = 0.45), but no corresponding SNPs achieving the Bonferroni-corrected threshold (P<1.52×−610) were associated with EMA on chromosome 14, although there is an association signal with EMA in a corresponding region on chromosome 14  that lay independently from the other 5 SNPs in Korean brown, Angus, Holstein, Chosun, and Yanbian cattle. The other 5 SNPs showed a large LD block (block size of 553 kb) with LD coefficients ranging from 0.53 to 0.89 within the block in these breeds. However, the LD structure of the 6 SNPs for Korean brown Hanwoo was quite different from that of two other Korean cattle breeds (brindle and Jeju black). As for the allele frequencies for the 6 significant SNPs, the favorable allele frequency was about 25 to 77% in European and 0 to 14% in Northeast Asian breeds. Interestingly, the favorable allele frequencies of the 6 SNPs in Korean brown were different from Korean brindle and Jeju black Hanwoo, as were the LD structures. Korean brown Hanwoo had around 11–14% favorable allele frequencies, whereas Korean brindle had about 3%. In Jeju black, the favorable alleles for 4 SNPs  were not detected, and the other two SNPs (BTB-01280026 and Hapmap24414-BTC-073009) were observed with very low allele frequencies . Thus, the favorable allele was common (25 to 77%) in European breeds, while they showed much lower frequencies in Northeast Asian cattle. Recent genetic introgression has been performed to improve growth rate and meat yield in the Hanwoo population. A Holstein-Angus-Charolaise sire was used within the Korean brown Hanwoo population from the early 1970s to 1980s to improve growth rate and meat yield Another key factor for mapping QTL is carefully corrected animal relatedness in the study population. In the Hanwoo production system, the use of artificial insemination (AI) from a few elite sires has created not only an extensive LD structure but also a subtle form of admixture of relationships among the animals, which causes LD between SNP markers even if they are unlinked Our finding of major QTL at 24.3–25.4 Mb on BTA14 is supported by several previous studies in cattle. Kneeland et al. (2004) PLAG1-CHCHD7. Pleomorphic adenoma gene 1 (PLAG1) and CHCHD7 as possible causative genes for bovine stature. PLAG1 gene is known to affect levels of IGF2 and other growth factors. The two QTL mapped to an intergenic region of PLAG1-CHCHD7 modulated bidirectional promoter strength and affected binding of nuclear factors. Nishimura et al. (2012) PLAG1-CHCHD7 QTN for stature in Japanese Black cattle. In addition, Pausch et al. (2011) ribosomal protein S20 (RPS20). Unlike the QTL for bovine stature and carcass weight reported by Karim et al. (2011) and Nishimura et al. (2012) FAM110B, TOX, and SDCBP). However, the SNPs located close to PLAG1-CHCHD7 did not show significant associations with CWT. Fortes et al. (2011) TOX from this genomic region as a key transcription factor responsible for the molecular regulation of puberty in Brahman cattle.Recent studies have revealed a major QTL for bovine stature on chromosome 14  Improving carcass weight and eye muscle areas along with marbling scores is a major objective of the Hanwoo breeding program. Animals known to carry favorable alleles for the chromosome 14 QTL could be more stringently selected without antagonistically compromising marbling score. Therefore, the small carcass weight of the Hanwoo cattle could be improved by selection on favorable alleles of the major QTL. This study helps to explain the genetic architecture for carcass weight in Hanwoo cattle, and it provides useful information for marker-assisted selection.Figure S1Association of 32,696 SNPs with the eye muscle area (EMA), backfat thickness (BF) and marbling score (MAR) in the Hanwoo breed. Manhattan plot. Significance threshold was set up at P<1.5×10−6 (Bonferroni corrected significance level). Quantile-quantile plot. The red line represents the 95% concentration band under the null hypothesis of no association. The black dot represent the P-values of the entire study, upper six dots represent SNPs with P<1×10−8 in this study.(TIF)Click here for additional data file.Figure S2The -logP value for the association between SNP and carcass traits across genome (grey colour) and additive genetic variance (red colour) that significant SNPs (P<0.001) account for in single marker regression analysis. (A) Eye muscle area, (B) Back fat thickness and (C) Marbling score (MAR).(TIF)Click here for additional data file.Figure S3Linkage disequilibrium of the 29 SNPs surrounding 1.1Mb BTA14 in Hanwoo.(PNG)Click here for additional data file.Figure S4STRUCTURE result between Asian and European cattle breeds.(TIF)Click here for additional data file.


The major bottle neck in genetic and linkage studies in tomato has been the lack of a sufficient number of molecular markers. This has radically changed with the application of next generation sequencing and high throughput genotyping. A set of 6000 SNPs was identified and 5528 of them were used to evaluate tomato germplasm at the level of species, varieties and segregating populations.S. lycopersicum ‘Moneymaker’ from all its distantly related wild relatives. Clustering and neighbour joining analysis among varieties and species showed expected grouping patterns, with S. pimpinellifolium as the most closely related to commercial tomatoesearlier results.From the 5528 SNPs, 1980 originated from 454-sequencing, 3495 from Illumina Solexa sequencing and 53 were additional known markers. Genotyping different tomato samples allowed the evaluation of the level of heterozygosity and introgressions among commercial varieties. Cherry tomatoes were especially different from round/beefs in chromosomes 4, 5 and 12. We were able to identify a set of 750 unique markers distinguishing Our results show that a SNP search in only a few breeding lines already provides generally applicable markers in tomato and its wild relatives. It also shows that the Illumina bead array generated data are highly reproducible. Our SNPs can roughly be divided in two categories: SNPs of which both forms are present in the wild relatives and in domesticated tomatoes (originating from common ancestors) and SNPs unique for the domesticated tomato (originating from after the domestication event). The SNPs can be used for genotyping, identification of varieties, comparison of genetic and physical linkage maps and to confirm (phylogenetic) relations. In the SNPs used for the array there is hardly any overlap with the SolCAP array and it is strongly recommended to combine both SNP sets and to select a core collection of robust SNPs completely covering the entire tomato genome. Landraces and wild relatives constitute a vast genetic resource that can be tapped to introduce novel traits into tomato breeding programmes . During http://www.kbioscience.co.uk) , [BB], [AB] or a non-call for each SNP. Advanced assembling within each correspondent analysis was performed and manual inspection and adjustment were performed in order to optimize call rates in the case of questionable SNPs. In particular those cases, and based on the knowledge on segregation patterns within the material, clustering errors were identified and amended .Before further analysis, markers that were more than 98% monomorphic, were removed, as well as markers with more than 25% heterozygosity in accessions or breeding lines. Finally, also markers with a large number of NCs were removed. For this two thresholds for the percentage NCs were used: more than 20% NCs among the commercial hybrids and/or more than 50% among wild relatives.When specific populations were evaluated, synchronization of parental lines together with the corresponding offspring was performed. This means that, for each analysis alleles were sorted according to the parent lines and replaced by a specific allele designation (A or B) for each parent.For cluster analysis the genotype calls were converted into numerical values: [AA]=1, [AB]=2, [BB]=3. Cluster analyses were done using the Jukes-Cantor similarity measure with 1000 bootstraps. Neighbour joining analysis using the Manhattan similarity measure with an out-group rooting and 1000 bootstraps was performed using the statistical package PAST version 2.12 . The BioData visualization heat maps were made in GeneMaths XT 2.12 (Applied Maths). Linkage maps were constructed using JoinMap® version 4.1 (Kyazma©) . The defThe authors declare that they have no competing interests.MVZ carried out the analysis of the SNP data and wrote the manuscript. BV carried out the experimental set up, participated in the data analysis and co-wrote the manuscript. HvdG carried out the SNP search and helped in the bioinformatics support. AB managed the sampling of the tomato material and assisted the data analysis. RF carried out quality controls and the initial data analysis. RGFV was involved in the writing of the manuscript. AWvH conceived the study, coordinated the experimental set-up, helped in the SNP search, and co-wrote the manuscript. All authors read and approved the final manuscript.Example of genotyping graphs in GenomeStudio®. SNP marker within one population in which two different groups were clustered automatically by the program in one group (the heterozygous group) due to an outlier sample (NTC). The right grouping is in Figure 1B, this was confirmed by flanking markers in a segregating population. The red circle exemplifies an outlier sample.Click here for fileS. lycopersicum used for comparisons . Table S2: Table with the accessions used in analysis. Table S3: Table with the introgressions known to be present in the initial breeding lines.Varieties from Click here for fileAll SNP scores in the varieties from S. lycopersicum used for comparisons .Click here for fileHeat map representation of polymorphisms found in the TMV region of chromosome 9.Solanum lycopersicum allele - gray background), yellow heterozygous and homozygous wild relative allele – green background. (PPTX 536 kb)Click here for fileBioNJ tree with 1000 bootstrap analysis showing an implicit relation of the available species according the different tomato groups.Click here for fileHeat map of the genotype call of 188 markers distributed along Chromosome 12 of 100 RILs  from a cross between S. lycopersicum cv Moneymaker (red) and S. pimpinellifolium G1.1554 (green). Heterozygous calls (yellow) and NCs (black) are also included. Certain loci marked for reference as: sequence name / position (Mbp). The positions were blasted towards the published tomato genome version 2.4 [16].Click here for file


P < 0.05) for each trait. SNPs significantly associated with each trait were used in a forward selection algorithm to identify genomic regions putatively harbouring genes with effects on each trait. A total of 53, 66 and 68 SNPs explained 54.12% (24.10%), 62.69% (29.85%) and 55.13% (26.54%) of the additive genetic variation (when accounting for the genomic relationships) in steer breeding values for AFI, RFI and ADG, respectively, within this population. Evaluation by pathway analysis revealed that many of these SNPs are in genomic regions that harbour genes with metabolic functions. The presence of genetic correlations between traits resulted in 13.2% of SNPs selected for AFI and 4.5% of SNPs selected for RFI also being selected for ADG in the analysis of breeding values. While our study identifies panels of SNPs significant for efficiency traits in our population, validation of all SNPs in independent populations will be necessary before commercialization.Estimated breeding values for average daily feed intake , residual feed intake  and average daily gain  were generated using a mixed linear model incorporating genomic relationships for 698 Angus steers genotyped with the Illumina BovineSNP50 assay. Association analyses of estimated breeding values (EBVs) were performed for 41 028 single nucleotide polymorphisms (SNPs), and permutation analysis was used to empirically establish the genome-wide significance threshold ( Expected progeny differences  have enabled beef cattle breeders to make rapid genetic progress in several economically important traits i.e.  since th0.75) as:The strong, positive phenotypic relationship between growth rate and gross feed efficiency has been exploited for at least the last 30 years through selection for increased growth rate, especially increased yearling weight. However, this approach to increasing feed efficiency has several negative consequences, including an increased mature cow size with a concomitant increase in maintenance energy costs for fast-growing, large-framed cattle  and MFA, Inc.  were individually fed a commercial feedlot ration using Calan gates (Circle A Ranch in Iberia) or GrowSafe feeding systems . This population was further described in n = 1057) were included in the analysis. Filtering was also applied to eliminate animals with >5% missing genotypes. FastPHASE  was utilized to account for multiple hypothesis testing and reduce the reporting of spurious associations by controlling the type I error rate  to perform the GWA analysis in three steps. First, F-statistics were formed for each SNP under an additive model to test the effect of each SNP and to identify the set of SNPs determined to be statistically significant for each trait × model combination. Second, this set of candidate SNPs was included in a forward selection analysis which was performed independently for each chromosome. In this analysis, the SNP with the highest F statistic was sequentially added to an additive effects model, and an association analysis was performed for the remaining SNPs on the chromosome until no additional SNPs on the chromosome reached the significance threshold determined by the permutation analysis. Finally, after a set of SNPs had been selected for each of the chromosomes by the forward selection algorithm, all selected SNPs were included into a final model to estimate the amount of variance explained by the selected SNPs in the data set  analysis was performed on EBVs weighted by their corresponding accuracies, and permutation analysis  analysis was used as a secondary measure of the joint variance explained by all markers used in the final models. The GBLUP was implemented in FORTRAN using a similar procedure as described above. Two models were fitted, one which fitted only a fixed mean and the GRM for all animals to estimate the total additive genetic variance and a second that fitted SNP allele substitution effects as covariates along with the GRM to allow estimation of the residual additive genetic variance. The proportional differences between these variance estimates are reported in To characterize the genomic regions identified by the forward selection analysis and to identify candidate genes influencing biological pathways related to the efficiency of growth, a pathway analysis was performed. Because of the more extensive functional annotation of the human genome and because only about 8% of bovine genes have no human othologs  were theP < 0.05 correspond to F values of: AFI 23.7163; RFI 23.7750; ADG 23.7011) and the number of SNPs included in the final models for each analysis are shown in R2 (% EBV Variance) and GBLUP analyses. Additional information pertaining to the SNPs included in the final model for each trait can be found in The correlation between ADG and RFI breeding values was not significant. AFI EBVs were more highly correlated with RFI EBVs than ADG EBVs. The number of significant candidate SNPs  for these traits were much lower than literature estimates  and metabolic disorders, were identified. Other categories such as signal transduction were also considered, because of their involvement in energy transportation or energy-requiring processes . Many ofThis study has identified BovineSNP50 SNPs that are associated with variation in feed efficiency and are validated by previous independent studies. Other associated SNPs that could not be validated by the literature should be tested in independent populations prior to the development of commercial assays. Selection indices incorporating molecular breeding values, for example, for AFI, ADG, carcass traits and mature size, should be used to appropriately select for increased profitability while constraining undesirable correlated responses on mature size.


It is strict epistasis if no proper subset of the interacting loci exhibits a marginal effect. For many diseases, it is likely that unknown epistatic interactions affect disease susceptibility. A difficulty when mining epistatic interactions from high-dimensional datasets concerns the curse of dimensionality. There are too many combinations of SNPs to perform an exhaustive search. A method that could locate strict epistasis without an exhaustive search can be considered the brass ring of methods for analyzing high-dimensional datasets.The interaction between loci to affect phenotype is called SNP pattern is a Bayesian network representing SNP-disease relationships. The Bayesian score for a SNP pattern is the probability of the data given the pattern, and has been used to learn SNP patterns. We identified a bound for the score of a SNP pattern. The bound provides an upper limit on the Bayesian score of any pattern that could be obtained by expanding a given pattern. We felt that the bound might enable the data to say something about the promise of expanding a 1-SNP pattern even when there are no marginal effects. We tested the bound using simulated datasets and semi-synthetic high-dimensional datasets obtained from GWAS datasets. We found that the bound was able to dramatically reduce the search time for strict epistasis. Using an Alzheimer's dataset, we showed that it is possible to discover an interaction involving the APOE gene based on its score because of its large marginal effect, but that the bound is most effective at discovering interactions without marginal effects.A We conclude that the bound appears to ameliorate the curse of dimensionality in high-dimensional datasets. This is a very consequential result and could be pivotal in our efforts to reveal the dark matter of genetic disease risk from high-dimensional datasets. Biologically, epistasis likely arises from physical interactions occurring at the molecular level. Statistically, epistasis refers to an interaction between multiple loci such that the net effect on phenotype cannot be predicted by simply combining the effects of the individual loci. Often, the individual loci exhibit weak marginal effects; sometimes they may exhibit none. We say it is a pure epistatic interaction if no single locus exhibits a marginal effect, and we say it is a strict epistatic interaction if no subset of the interacting loci exhibits a marginal effect.In Mendelian diseases, a genetic variant at a single locus may give rise to a disease The ability to identify epistasis is important in understanding the inheritance of many common diseases. For example, studying genetic interactions in cancer is essential to further our understanding of cancer mechanisms at the genetic level. Many cancer-associated mutations and interactions among the mutated loci remain unknown. For example, highly penetrant cancer susceptibility genes, such as BRCA1 and BRCA2, are linked to breast cancer single nucleotide polymorphism (SNP) that results when a nucleotide that is typically present at a specific location on the genomic sequence is replaced by another nucleotide. In most cases a SNP is biallelic, that is it has only two possible values among A, G, C, and T (the four DNA nucleotide bases). The less frequent (rare) allele must be present in 1% or more of the population for a site to qualify as a SNP The most common genetic variation is the genome-wide association studies (GWAS). A GWAS involves genotyping representative SNPs in individuals sampled from a population. A GWAS dataset can concern millions of SNPs and may soon concern billions. Data in which each record has such a large number of attributes is called high-dimensional. In case-control GWAS we identify the disease status along with the values of the SNPs. Such studies provide researchers unprecedented opportunities to investigate the complex genetic basis of diseases. By looking at single-locus associations, researchers have identified over 150 risk loci associated with 60 common diseases and traits The advent of high-throughput technologies has enabled SNP pattern is a Bayesian network (BN) model that describes the relationship between However, single-SNP investigations could not detect complex epistatic interactions in which each locus by itself exhibits little or no marginal effect. To fully exploit genomic data and possibly reveal a great deal of the dark matter of genetic risk, it is critical that we analyze such data using multi-locus methods, which investigate Multifactor Dimensionality Reduction (MDR) Bayesian score, which is the probability of the data given the pattern.When investigating SNP patterns, in some way we must score the patterns to determine which patterns are most noteworthy. Standard techniques such as linear regression may not work well because both the predictors and the target are discrete. One well-known technique is curse of dimensionality. For example, if we only investigated all 0, 1, 2, 3 and 4-SNP patterns when there are 500,000 SNPs, we would need to investigate A difficulty when learning SNP patterns from high-dimensional GWAS datasets concerns the Each of these methods has at least one of the following shortcomings: 1) It only investigates two-locus interactions and still requires quadratic time; or 2) It has only been shown to detect interactions in which only one interacting locus has no significant marginal effect. Many of the methods proceed in stages, using the first stage to identify promising SNPs, which in some way are investigated further in the second stage. Strict epistasis constitutes the worst-case in terms of detecting disease associations because such associations are only observable if all interacting SNPs are included in the disease model. None of these two-stage methods made any progress towards detecting strict epistasis. So, Evans et al. An exhaustive search is not possible when there are millions of SNPs. So some researchers turned their efforts to reducing the search space based on ancillary knowledge. You et al. brass ring of methods for analyzing high-dimensional datasets.However, once the search space is reduced, we can still be left with a large number of SNPs, prohibiting an exhaustive search of even the pruned dataset. Furthermore, in an agnostic study we are searching for possible interactions for which we have no previous knowledge. Therefore, a multi-stage technique that can effectively locate strict epistatic interactions could still be considered the true pattern (the one representing the interjected interaction) was as little as 0.0004.Initially it might seem that it is not possible to successfully prune our search for strict epistatic We developed both simulated and semi-synthetic datasets based on models of strict epistasis. We compared the performance of the bound and Bayesian score in their ability to efficiently locate the true pattern. After providing background on Bayesian networks, we discuss specialized Bayesian networks called SNP patterns that represent relationships among SNPs and a disease. Then we show the algorithm used for the comparisons, and we describe the datasets we developed.directed acyclic graph (DAG) where Markov condition if for each variable Bayesian network (BN).Let It is a theorem Methods have been develop both for learning the parameters in a BN and the structure (DAG) from data. Pierrier et al. DAG model consists of a DAG model selection. As an example, if we had data on a large number of individuals concerning lung disorders we might be able to learn the DAG in A generative probability distributionIn the constraint-based structure learning approach Bayesian score, is the probability of the DAG model given the A straightforward score, called the K2 score.The likelihood in Markov equivalent if they entail the same conditional independencies). For example, the DAGs prior equivalent sample size. If we want to use a prior equivalent sample size and represent a prior uniform distribution for each variable (not parameter) in the network, for all Bayesian Dirichlet equivalent uniform (BDeu) score. That score is as follows:However, Heckerman et al. The Bayesian score does not explicitly include a DAG penalty. However, the penalty is implicitly determined by the hyperparameters The Bayesian score decomposes into the product of local scores, one for each node If in we fix the value of  for each , then  is maximized if for each ,  for some . That is, for every combination of values of the parents of , all data items that have those values have the same value of This theorem could be used to obtain a bound on the local score that could be obtained by adding parents to a given parent set. However, the bound is a very loose bound (i.e. the bounds are much greater than the scores), and therefore the bound has not proven to be useful in pruning the search space.SNP patterns. Examples of such patterns appear in We could develop a DAG model that represents many factors that affect phenotype including inheritable allele variation, somatic mutations in alleles, environmental factors, and epigenetic phenomena such as DNA methylation. A subnetwork of that model contains only variables that represent inheritable allele variation . If, for example, we have a 5-way interaction, two 3-way interactions, and two 2-way interactions, there are 15 SNPs in this subnetwork, all which have edges to the disease node In the case of a SNP pattern eu score  is as foUsing Theorem 1, we can obtain a bound on the local score that could be obtained by adding parents to a given parent set. Given a SNP pattern and the score in As noted earlier, this is a very loose bound and would not be useful for provably pruning the search space. However, in the case of searching for strict epistatic interactions, perhaps it is asking too much to hope for provable results. We would achieve valuable progress if we could just often heuristically locate such interactions without an exhaustive search. We conjectured that the bound might enable us to do that, and that is what is investigated here.Suppose we have a dataset concerning SNPs and a disease, and our goal is to find a 2-SNP pattern with a particular BDeu score . This paThis strategy extends readily to search for Algorithm 1 Find_Patternif   if   output   halt; endifelse for      endforendelseAlgorithm 1 constructs each candidate pattern in the array heritability and the minor allele frequency (MAF). We wanted to test a full range of MAFs (0.05 to 0.4) and relatively low and high heritabilities (0.05 and 0.2). However, we found that to obtain models with a low MAF, it was sometimes necessary to decrease the heritability. The actual MAFs and heritabilities used in the simulations are shown in Fisher et al. For each of the 30 combinations of MAF and heritability, we developed datasets in which there were 100 SNPs and 1000 SNPs. Each dataset had 1000 cases and 1000 controls. For each of the 60 variations, 100 datasets were generated, making a total of 6000 datasets. We used the BDeu score with We did not vary the number of cases and controls because that number does not have a significant effect on the running time. The data needs to preprocessed to obtain the counts needed in the computation of the bound and the score. The only effect that the number of cases and controls has on the running time is that the pre-processing time increases linearly with the total number of cases and controls.http://www.tgen.org/neurogenomics/data concerning this dataset. Hunter et al. http://cgems.cancer.gov/ concerning this dataset.Reiman et al. We developed 2-SNP, 3-SNP, and 4-SNP models of strict epistatic interactions using GAMETES, and used the models to inject interacting SNPs into each of the real GWAS datasets resulting in semi-synthetic datasets. The models generated have the properties shown in We used the BDeu score with For the simulated datasets developed from the 2-SNP models, These tables and figures show that the bound usually performs much better than we would expect by chance, and also performs substantially better than the score. When the MAF is small (0.05) the average fraction of patterns checked by the bound is at most around 0.008. By chance alone we would expect that average to be around 0.5. In general, the average fraction increases as the MAF increases. On the other hand, the heritability and the dimension of the dataset have little effect on the average fraction. It is somewhat surprising that the bound performs about as well when the heritability is low as when it is high. Since unknown genetic risk might confer low heritability, this is an encouraging result.The other variable that affects the performance of the bound is the number of SNPs involved in the epistatic interaction. That is, the performance improves as the number of SNPs increases. In order to make these results more transparent, we did an analysis using actual times. As noted above, the performance does not seem to degrade as the dimension of the datasets increases . If we assume that this result holds true for larger dimensions, then Notice that the bound always performs about an order of magnitude better than the exhaustive search as far as the dimension that it can handle in an acceptable amount of time. That is, in the case of the 2-SNP models the bound can handle 100,000 SNPs whereas the exhaustive search can only handle 10,000 SNPs; in the case of the 3-SNP models the bound can handle 10,000 SNPs whereas the exhaustive search can only handle 1000 SNPs; and in the case of the 4-SNP models the bound can handle 1000 SNPs whereas the exhaustive search cannot.These results indicate that in the case of pure epistasis the bound can often locate the true pattern much sooner than would be expected by chance, and offers a substantial improvement over using the score to locate that pattern.For the LOAD datasets, The results are similar to those for the simulated datasets. In the case of both the LOAD and the breast cancer datasets, when the MAF is small (0.05 or 0.1) the bound never checks more than 5% of the patterns before finding the pattern representing the interacting SNPs, and it always checks substantially fewer patterns than the score.Consider the following example. In the case of the LOAD dataset, there are GAB2 gene is significantly associated with LOAD in the APOEIt is well known that the APOE gene is associated with LOAD Using this same dataset, Jiang et al. This discovery of a possible interaction between GAB2 and APOE was achieved because of APOE's high marginal effect. rd and 4th SNPs, Algorithm 1 would discover the pattern containing them after investigating only 6 patterns.It might seem odd that the average bounds for the interacting SNPs are around −18 when the MAF is 0.2 see , which iFinally, note that the bound is a very loose bound. Therefore, it would not be useful in a best-search first algorithm that prunes SNPs based on their bounds and which is able to guarantee that we discovered the highest scoring pattern. However, as we have seen, the bound can be quite effective for guiding a heuristic search for high scoring patterns. In general, when we are searching for possible epistatic interactions, our concern is with finding likely patterns that we can then further investigate for biological plausibility. It is not necessary that we know that a discovered interaction has the highest score of all patterns.We identified a bound on the Bayesian score of any SNP pattern that could be obtained by expanding a given SNP pattern. Using simulated datasets based on models of strict epistasis, we showed that the bound can locate the true pattern, when searching moderate-dimensional datasets, much faster than can be expected by chance. Using semi-synthetic datasets based on models of strict epistasis, we showed that the bound can locate the true pattern, when searching high-dimensional GWAS datasets, much faster than can be expected by chance. The average fraction of patterns checked before finding the true pattern was as little as 0.0004. These results indicate that the bound can be an extremely useful tool in algorithms that search high-dimensional datasets for strict epistatic interactions.We used an algorithm that sorts the SNPs by their bounds and by their scores to test the effectiveness of the bound. Although this was an effective way to compare the bound to the score, in practice it may not be the most effective way to use the bound in a heuristic search. First, the algorithm assumes we know the number of interacting SNPs up front. Second, if, for example, we were looking for a 4-SNP interaction, we would be able to visit very few different SNPs. We plan to incorporate the bound into other algorithms we have developed such as MBS


Merluccius merluccius), one of the most important demersal resources of European fisheries. Two sequencing platforms, the Roche 454 FLX (454) and the Illumina Genome Analyzer (GAII), were used for Single Nucleotide Polymorphisms (SNPs) discovery in the hake muscle transcriptome. De novo transcriptome assembly into unique contigs, annotation, and in silico SNP detection were carried out in parallel for 454 and GAII sequence data. High-throughput genotyping using the Illumina GoldenGate assay was performed for validating 1,536 putative SNPs. Validation results were analysed to compare the performances of 454 and GAII methods and to evaluate the role of several variables . Despite well-known differences in sequence length and throughput, the two approaches showed similar assay conversion rates (approximately 43%) and percentages of polymorphic loci . Both NGS platforms therefore demonstrated to be suitable for large scale identification of SNPs in transcribed regions of non-model species, although the lack of a reference genome profoundly affects the genotyping success rate. The overall efficiency, however, can be improved using strict quality and filtering criteria for SNP selection .The growing accessibility to genomic resources using next-generation sequencing (NGS) technologies has revolutionized the application of molecular genetic tools to ecology and evolutionary studies in non-model organisms. Here we present the case study of the European hake ( Merluccius merluccius, Linnaeus, 1758; Merlucciidae, Actinopterygii) is a widely distributed species inhabiting the North-Eastern Atlantic Ocean and the Mediterranean Sea, whose respective stocks are managed by the International Council for the Exploration of the Seas (ICES) and the General Fisheries Commission for the Mediterranean (GFCM). It represents one of the most important demersal target species of Western European and Mediterranean commercial fisheries i.e. the Northern and Southern stocks which extend northward and southward to the Cape Breton submarine canyon in the Bay of Biscay, respectively) and one in the whole Mediterranean, from the Gibraltar Strait to the Levantine Sea. Population genetic structure assessed at six potentially neutral microsatellite loci supported a distinction between Atlantic and Mediterranean stocks The European hake  using the Zebrafish (Danio rerio) repeat library and then cleaned for short sequences using SeqClean . Sequence clustering was performed using CLC GenomicsWorkbench  and after parsing the resulting large ace file respective sequences were then assembled ‘per contig’ using the assembly program CAP3 et al.The sequences were de-multiplexed based on the specific barcoding tags and binned per individual sample. In order to obtain an optimal de novo using Abyss http://compbio.dfci.harvard.edu/tgi/software/; l = 100). MosaikBuild was used to build a database of reads  and MosaikAligner was applied  to map the reads (database) against the Abyss contigs. After running MosaikSort the MosaikAssembler was used to generate the (.gig) input files for the SNP detection by GigaBayes .After renaming and trimming of the first base, GAII short reads were assembled −5), against all annotated transcripts in the draft genomes of Danio rerio, Gasterosteus aculeatus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis, and Homo sapiens available at the Ensembl Genome Browser, and against all unique transcripts for D. rerio, H. sapiens, O. latipes, T. rubripes, Salmo salar, Oncorhynchus mykiss stored in the NCBI UniGene databases. The Blastx option was used  to search against the entire UniProtKB/SwissProt and UniProtKB/TrEMBL protein databases as well as against the annotated proteins from the transcriptomes of D. rerio, G. aculeatus, O. latipes, T. rubripes, T. nigroviridis, and H. sapiens available through the Ensembl Genome Browser.In order to characterize 454 and GAII contigs several approaches were explored, using the Basic Local Alignment Search Tool (BLAST) against various protein and nucleotide databases. The Blastn option was used against the NCBI nucleotide database  terms  were recovered using the Blastx search tool implemented in the software Blast2GO G. aculeatus, T. nigroviridis, O. latipes, T. rubripes, D. rerio and Swissprot database) using the Blastx algorithm . The best match was selected and the aligned sequence portions of the query were saved as fasta files and then formatted as a Blast database. A fasta file containing 120 bp SNP-flanking sequences was prepared  and a Blastx analysis was performed against the previously formatted database . The aligned sequence portion of the two alleles for each SNP was compared for the presence of a synonymous or not-synonymous mutation.A pipeline was developed to characterize the SNP mutations at the amino acid level. To obtain the putative reading frame all SNP-containing contigs were compared against six peptide sequence databases , the analysis of putative intron-exon boundaries within each contig, and visual inspection of flanking region sequence quality. SNP scores obtained with the ADT take into consideration template GC content, melting temperature, uniqueness, and tendency to form hairpin loops. All SNPs with ADT scores below 0.4 were discarded, while SNPs with an ADT score higher than 0.7 were preferentially selected.After SNP detection, G. aculeatus, T. nigroviridis, O. latipes, T. rubripes, and D. rerio) using Blastn . After parsing Blast results, the best match was listed including information on alignment length, as well as the start and end of the aligned region. In the case of a positive match, the position of the candidate SNP was evaluated in the framework of the aligned region. If the 60 bp up- and downstream of the SNP position were present in the alignment, the candidate SNP was considered embedded in a single exon, otherwise an intron was assumed to be present in the 121 bp target region for SNP assay design. The same process was repeated against five different databases (see above) and each SNP was assigned a code, either “1” (when in at least one comparison the candidate SNP and its flanking regions were located on a single exon), “0” (an intron was predicted to disrupt the candidate region), or “no” (no significant match against any of the five reference fish genomes). The second approach was designed in order to further increase the likelihood of a positive match and the reliability of intron-exon boundary prediction. SNP-containing contigs were used as a query in a Blast search  against the transcriptome of each of the five model species as above. In the case of a positive hit, the matching transcript for each fish model species was downloaded from the Ensembl database, and the nucleotide position in the downloaded sequence corresponding to the candidate SNP in the original hake contig was identified based on the start-end positions of the Blast alignment between the hake contig and the fish model transcript. Then the putative homolog transcript was compared to its own genome sequence using Blast. Based on the inferred SNP position on the fish model transcript, SNPs were assigned a code as above (“1” for SNP candidate region located on a single exon or “0” if SNP region was assumed to be disrupted by an intron). In the case of no matches between the original hake contig and any of the five fish transcriptomes, the SNP was scored “no”. A flowchart for the intron-exon pipeline is depicted in Intron-exon boundary prediction was performed using fish genome and transcriptome sequence resources following two parallel approaches. In the first, SNP-containing contigs were compared against five high-quality draft fish genomes (Ensembl genome assemblies for http://compbio.dfci.harvard.edu/tgi/software/), with the aim of ranking candidates within each contig by integrating information on the overall contig assembly quality, depth and length, the quality of flanking regions (number of ambiguous sites), distance and clustering of polymorphic sites. SNPs with highest rank values were selected within each contig.A final evaluation step of putative SNPs was performed by direct visual inspection of contigs using the assembly viewer software Eagleview A total of 1,536 candidate SNPs were selected to be validated by high-throughput genotyping. Genomic DNA was extracted from fin clip tissues of 207 individuals sampled from the same four locations of origin of specimens used to derive the libraries  if Illumina SNP assay did not yield a reliable genotype for the examined individuals, either because no clear clustering was observed or due to lack of signal, value 1 (successful) if consistent clustering was obtained, irrespective of the observed genotype(s). SNP_genotype has value 0 (monomorphic) if all scored individuals are homozygous for the same allele, value 1 (polymorphic) if two alleles are observed. SNP_score reports ADT score for individual SNPs. I_E_test and I_E_species_match refer to the outcome of the intron-exon boundary analysis pipeline, I_E_test = “no” means no significant match could be obtained, value 1 signifies that at least one significant match was reported, with the SNP candidate region putatively contained in a single exonic region, while I_E_species_match counts the number of fish model species showing a significant Blast match. Depth is a quantitative variable reporting the number of sequence reads supporting individual SNPs, Individuals and Geosites counts respectively the number of individuals and geographical sites from the original discovery panel that contributed with at least one sequence read to a specific SNP. MSAF  represents the frequency of the minor allele detected at the SNP discovery stage. Rank is an ordinal variable referring to an order of choice of SNPs within each contig, arbitrarily assigned after a visual inspection (see above). Q reports the number of mismatches in the flanking regions of SNP positions based on comparison of sequence reads within a contig. SNP discovery results obtained using 454 sequencing technology were compared with those produced through GAII sequencing using either parametric T-tests for two independent samples  or non-parametric tests, a χ2 test for categorical variables  and a Mann-Whitney U test for ordinal variables  or quantitative variables not following a normal distribution . All tests were carried out in SPSS ver. 12.0 with Monte Carlo simulation  to estimate confidence intervals for Mann-Whitney tests.Different variables were defined to analyze results of SNP discovery and genotyping. The first two are categorical variables that refer to the outcome of individual SNP assays. SNP_assay_conversion and SNP_genotype. For both data sets (GAII and 454), positive conversion/clustering of individual SNP assay was analyzed first, assigning single SNPs into two classes (successful-failed). Second, DNA polymorphism was evaluated by filtering out failed SNPs and dividing positive loci respectively into two groups based on observed genotypes . For 454 data, eight ordinal/quantitative variables  and one categorical variable (I_E_test) were considered. For GAII data, six ordinal/quantitative variables  and one categorical variable (I_E_test) were examined. Predictor variables were either all included in the predicting model (option “enter” in SPSS) or best predictors were selected using a stepwise deletion approach (option “backward”). In the latter approach, the Wald χ2 statistic was used to estimate the contribution of each predictor.Binomial logistic regression, implemented in SPSS ver. 12.0, was used to evaluate several predictor variables on two dependent dichotomous variables: The Receiver Operating Characteristic (ROC) curve, a widely used method for evaluating the discriminating power of a diagnostic test, was used to assess the significance of specific variables. ROC analysis was implemented in MedCalc ver. 11.5.1.0.http://www.ebi.ac.uk/ena/data/view/ERP000950).Approximately 100 Mega base pairs (Mbp) were obtained using 454 sequencing technology, whereas nearly 4,000 Mbp were produced using the GAII sequencer . About 6de novo into 5,702 separate contigs of at least 100 bp length . GAII sequence assembly resulted in 9,258 contigs, of which 60% were discarded, having a length shorter than 100 bp. The remaining 3,756 contigs were further processed for SNP discovery. Results on contig length are summarized in e.g. use of a single tissue library and different genome size of the target species) are likely contributing factors to the observed differences.After pre-processing steps , 462,489 454 reads were assembled 2 = 14.83, p<0.001) than 454 contigs. A potential explanation for this observation lies in the shorter average length of GAII contigs, which likely reduces the overall probability of obtaining positive Blast hits. In any case, the percentage of annotated contigs is higher for both 454 and GAII data when compared to other studies reporting transcriptome sequencing in teleost fish , therefore increasing the chance of finding a positive Blast match. A total of 120 454-contigs (2.1%) and 67 GAII-contigs (1.78%) were identified as mitochondrial sequences. It was possible to associate one or more GO terms to 1,606 454-contigs (28.16%) and 884 GAII contigs (23.53%). Results from level 2 GO assignments within the three categories are summarized in A total of 4,221 454-contigs (74.02%) showed a significant Blast match against at least one species sequence database among those searched. Annotation by similarity was possible for 2,644 GAII contigs (70.39%), significantly less (χin silico in 889 454-sequenced contigs (15%), with an average of 0.73 SNPs per 100 bp surveyed in the assembled transcriptome, or 41.1 SNPs per generated Mbp. Approximately 60% of 454-contigs contained one or two SNPs (i.e. having sufficient flanking regions) were analysed with the Illumina ADT software, and SNPs with ADT score >0.4  were further evaluated. After filtering for the intron-exon boundary prediction results, 2,173 SNPs in the 454 data, presumed to be located in single exons (851 SNPs) or without a Blast match , were selected to be visually inspected. For GAII SNPs, 3,857 loci out of 4,637 with ADT score >0.4 passed the filtering step based on the intron-exon boundary prediction   .2 = 72.4, p<0.0001). Blast searches also identified 218 candidate SNPs (positioned in 62 different contigs) that are presumably located on the hake mitochondrial genome. GO term analysis showed a significant enrichment of specific GO terms when comparing the annotations of SNP-containing contigs against all unique transcripts obtained for the hake muscle transcriptome  are significantly over-represented across both 454- and GAII-SNPs, while cytoskeletal components  are enriched in GAII-SNPs. As mentioned before, the use of non-normalized cDNA libraries likely favored a higher representation of abundantly expressed genes. In the skeletal muscle, a high level of protein synthesis is required to fulfill the need for abundant contractile fibres, therefore over-representation of ribosomal/translation components as well as cytoskeletal proteins is expected. Such over-representation likely translates into greater sequence coverage and ultimately in a larger proportion of SNPs being identified in specific functional groups of genes.Annotation by similarity of SNP-containing contigs further confirmed that 454-contigs show a higher percentage of putatively annotated sequences . In total, 817 454-SNPs distributed on 516 contigs, 557 GAII-SNPs distributed on 463 contigs, 127 common (454 and GAII) nuclear SNPs, and 35 mitochondrial loci were included in the panel of 1,536 candidate loci to be validated by high-throughput genotyping. The common set of 127 nuclear SNPs was genotyped only once, but all these loci have been independently identified twice and descriptive variables could be estimated in both data sets (454 and GAII). For this reason, they have been considered as independent observations and analysed separately.A set of 1,536 SNPs  was sele2 = 0, p = 1). This leads to the question of how do SNP conversion rates compare to those observed in other studies that reported on high-throughput SNP discovery and validation in non-model species. This remains largely unexplored, and the few available data are not homogeneous. Most studies report in silico SNP detection in next-generation sequencing data from non-model species with limited, if any, experimental validation of discovered polymorphisms  and 296/684, 43.27% (GAII), χsms e.g.. In a resms e.g.. PCR-bas2 = 1.18, p = 0.272) of truly polymorphic sites was detected in GAII-SNPs  in the percentage of polymorphic “unmapped” SNPs  and “mapped” ones  in the turkey data set Of the successfully converting assays, a slightly (non-significantly) higher percentage  – 17.9% (GAII), χ2 = 68.05 p<0.0001). Also continuous/ordinal variables were significantly different between the two data sets (data not shown), with average I_E_species_match and MSAF being higher in 454 SNPs, whereas mean SNP_score, Geosites, and Depth were larger in GAII data. Higher mean number of species matching with 454-SNPs is linked to the significantly larger I_E test rate, which in turn likely correlates with the higher percentage of annotated 454-contigs, while greater Depth in GAII-SNPs reflects the overall much deeper sequence coverage obtained with GAII technology. Likewise, differential coverage likely explains the more complete representation of sequence variation across all four collection sites in GAII-SNPs. It is not clear why higher average SNP_score for GAII-SNPs was observed. It might be an effect of different contig length and/or sequence quality as the parameters that the ADT uses for estimating SNP scores are likely influenced by these factors. The higher MSAF estimated for 454-SNPs appears to be the effect of over-estimation of allele frequencies based on sequence data, again putatively related to the lower coverage of SNP sites obtained with 454 technology. When sequence-based and Illumina Golden Gate genotypic results are compared for the same loci in a paired-samples Wilcoxon signed rank test, Minor Allele Frequency (MAF) is significantly lower than MSAF   , whereas<0.0001) . Neither<0.0001) , whereas<0.0001) .2 = 34.944, degrees of freedom (df) 9, p<0.001) and marginally for GAII data . The ability to predict the outcome of individual SNP assays is relatively low, with an overall correct classification rate of 60.1% for 454 data and 56.7% for GAII data. Backward stepwise deletion of predictor variables identifies five best predictors  for 454 data and three predictors  for GAII data as summarized in per se is less relevant than the level of coverage across different individuals. The outcome of the intron-exon boundary pipeline is significant as a predictor only for 454 data, with a higher number of successful assays for SNPs that are verified to be located in a single exon compared to “unknown” SNPs. This is in agreement with what reported by Wang and et al. MSAF in 454-SNPs and the negative correlation with number of geographic areas in GAII-SNPs, while the negative correlation of the variable Q, which reports the number of mismatches in the flanking regions of SNP positions based on comparison of sequence reads within a contig, can be easily explained as a result of poor sequence quality on assay performance. This parameter was estimated only on 454-SNPs because the much greater sequencing depth of GAII data did not allow visual estimation of Q. Since this variable appears to convey valuable information, it might be useful to develop an automated pipeline to measure it in the future. Finally, for both data sets, and in particular for 454-SNPs, the ADT score proved to be a significant predictor  analysis) was carried out to evaluate the significance of ADT score as predictive variable. The estimated area under the ROC curve is 0.539±0.015, which is significantly different  than expected by chance (0.5), confirming ADT score as a significant predictor of assay conversion. The optimal ADT value, which has the best specificity/sensitivity ratio, is 0.735. It should be noted that, while significant, the overall performance of ADT as diagnostic factor is rather limited.i.e. to predict polymorphic and monomorphic loci. Including all predictor variables, model fitting improved significantly for 454 data  as well as for GAII data , with overall rates of correct classification of 70.9% (454) and 70.6% (GAII). Stepwise deletion of independent variables reduced the list of contributing predictors to I_E_species_match, Rank, Individuals, Geosites, and Q in the case of 454 data, while I_E_test, I_E_species_match, Individuals, and Geosites best predicted SNP_genotype for GAII data  with the degree of polymorphism, similar to what was observed for assay conversion rate. This evidence is in agreement with the positive predictive value of Rank, a subjective measure of overall candidate SNP suitability, which also takes into account contig sequence quality. While Rank cannot be translated into an objective, operator-free score, Q appears a promising predictor, which might be possible to automatically estimate for a very large number of SNPs and could provide useful information on assay conversion as well as SNP polymorphism. In fact, ROC analysis shows that the area under the ROC curve . Optimal trade-off between specificity (56.7%) and sensitivity (72.7%) is obtained with Q>1.Binomial logistic regression analysis was also implemented to evaluate the outcome of successful SNP assays, AII data  and 7. T2 15.06, df 1, p<0.0001). Closer examination of non-synonymous SNPs shows that, if amino acids are divided into five classes , the majority of amino acid substitutions are conservative replacements (i.e. occurring within the same class), but there are 12 non-conservative mutations, which might cause significant functional changes in the encoded protein.Finally, all experimentally validated SNPs were analysed using a bespoke pipeline, which was developed to compare SNP-containing contigs with known protein sequences and to predict protein-coding regions and the corresponding putative reading frame. It was possible to obtain this information reliably for approximately half of the validated SNPs  for bothde novo assembly, due to the higher sequencing output, we believe results on SNP discovery and validation, primarily influenced from sequencing error profiles, would be proportionately comparable to our results. In order to evaluate and compare costs of SNP discovery, several aspects should be taken into account. However, considering only the raw cost of reagents per Mb updated to 2011 in silico analysis of sequence reads  and the high-throughput genotyping assays. While the latter showed similar conversion rates, analysis of GAII reads might be more laborious and produce less reliable assemblies Q values (number of mismatches in the flanking regions).In the present study, two different NGS methods were applied to high-throughput SNP discovery in the muscle transcriptome of a non-model fish species. Overall, the comparison revealed that despite substantial differences in sequence throughput, average sequencing depth, and sequence read length, similar results were obtained after SNP experimental validation in terms of assay conversion rate and percentage of polymorphic loci. GAII technology yields a larger number of candidate SNPs, but the majority of them are not suitable for SNP genotyping due to short flanking regions. On the other hand, 454-SNPs are less numerous, but are located on longer contigs, which are more easily annotated and screened for putative introns in the candidate region using a comparative genomic approach. Although the platforms we have evaluated have been recently upgraded and superseded by later versions, still our findings should remain valid and relevant. Indeed, during the past few years Illumina and 454 platforms have experienced rapid progress towards the enhancement of reads length and yield in terms of Mb per run produced, mainly resulting in increased throughput; however, raw sequencing error rates have not decreased along with the improvement of instruments and chemistry The SNP markers developed in the current study represent novel tools for a broad range of future applications in population genetic studies focusing on the European hake. A deeper understanding of ecological and evolutionary dynamics of European hake populations across the entire distribution range provides the necessary means for a proper management and conservation policy, aimed at promoting sustainable fishery and preventing overexploitation and illegal fishing activities.Figure S1Gene Ontology (GO) assignment (2nd level GO terms) of hake 454 contigs.A “Cellular Component”. B Molecular Function”. C “Biological Process”.(PDF)Click here for additional data file.Figure S2Gene Ontology (GO) assignment (2nd level GO terms) of hake GAII contigs.A “Cellular Component”. B Molecular Function”. C “Biological Process”.(PDF)Click here for additional data file.Figure S3Flowchart describing the intron-exon pipeline.(PDF)Click here for additional data file.Table S1Results of GO enrichment analysis performed using all 454 contigs as reference set, and 454 SNP-containing contigs as test set. Significantly overrepresented GO terms are listed, together with the respective category , FDR  and Fisher's Exact Test p-value.(PDF)Click here for additional data file.Table S2Results of GO enrichment analysis performed using all GAII contigs as reference set, and GAII SNP-containing contigs as test set. Significantly overrepresented GO terms are listed, together with the respective category , FDR  and Fisher's Exact Test p-value.(PDF)Click here for additional data file.Table S3List of 395 SNPs validated as polymorphic in this study  together with the corresponding flanking sequences of approximately 120 bp .(XLSX)Click here for additional data file.