Obesity and the metabolic syndrome are systemic inflammatory diseases reaching epidemic proportions. Contemporary changes in human nutrition occurred characterized by increased consumption of fat and of vegetable oils rich in n-6 polyunsaturated fatty acids (PUFAs) together with decrease in n-3 PUFA-rich foods, resulting in an n-6/n-3 ratio of 10–20/1 in Western diet for a ratio around 1/1 in the diet of our ancestors. The literature provides compelling evidence for the health benefit of n-3 PUFA consumption on inflammation and metabolic syndrome prevention and treatment. Such evidence led to the establishment of comprehensive recommendations. However, we show here that, both in collective catering proposed to children and in hospital diet, it is not straightforward to meet such recommendations. Willingness of governments to institute changes, with accountable decisions on catering, nutritional education, and food processing, is required to face our neglected responsibility in promoting balanced diet and consumption of foods rich in essential nutrients in the general population. The metabolic syndrome (MetS) is defined as a cluster of symptoms such as visceral obesity, insulin resistance, elevated blood pressure, and dyslipidemia, associated with increased risk of type 2 diabetes, cardiovascular disease , nonalcoDuring the past 15 years, research efforts have focused on the primary factors responsible for the MetS and its increasing prevalence worldwide. Contemporary to the rise in MetS prevalence, important changes in human nutrition and dietary habits were observed Figures  in paralIn particular, a steady increase in dietary refined sugar and fructose, that parallels the rise in obesity and diabetes, is observed since the 70s due to the increased consumption of soda, soft drinks, and manufactured candy and pastry  and use κB, protein kinase C, and mitogen-activated protein kinases which induce the expression of inflammatory genes in adipose tissue and immune cells, (iii) the decrease of peroxisome proliferator-activated receptor (PPAR) α and adiponectin levels and consequent decreased oxidation of FA and glucose, and (iv) recruitment of immune cells in adipose tissue and muscle .–43.41–43Importantly, genetic polymorphisms in Δ5 and Δ6 desaturase-encoding genes (FADS1 and FADS2) are associated with variation in n-6 and n-3 PUFA content in serum phospholipid fractions and tissues . Also, pThis clearly underlies that, beside nutrition, genetic factors, concurrent pathological conditions, and epigenetic modifications also play an important role in the regulation of LC-PUFA metabolism and may thus influence the development of inflammatory and metabolic diseases. It also suggests interindividual variations for requirements in n-3 PUFA.α, IL6, CRP) in many populations , di adults -–64. Despite a relatively low accumulation in adipocytes , LC n-3 Human trials confirmed that LC n-3 PUFA from either fish or fish oil supplements as well as ALA enrichment significantly reduce blood triglyceride levels in patients with MetS in a dose-dependent manner , 68, an A body of evidence demonstrates that n-3 PUFA are involved in the control of glucose homeostasis and insulin sensitivity . In muriDiet interventions with increased n-3 PUFA clearly demonstrated therapeutically, reliability in lowering mortality in subjects with cardiovascular diseases or the MetS , 72, an NAFLD, now recognized as the hepatic complication of the MetS, might trigger development of T2DM. Low dietary n-3 PUFA content induces hepatic desaturase activity . In addiAs important, emerging evidence indicates that incidence and tumour growth of some cancers associated with the MetS can be attenuated by n-3 PUFA .From the above, it is obvious that there is a need for recommendations for n-3 PUFA nutritional supplies both for the prevention of MetS and associated disorders in the general population and for secondary prevention or treatment. Establishing such guidelines represents a complex issue for three main reasons. First, there is a large interindividual variability in n-3 PUFA metabolism based on genetic determinants, gender and age, further magnified by concurrent associated diseases or epigenetic modifications. Therefore, ideal requirement for physiological effect needs to be tailored for a specific individual –54, 78. Deduced from ancestral nutrition, in an ideal balanced diet, fat should represent no more than 20%–30% of total energy intake amongst which 5-6 g/d of n-3 PUFA with a great proportion of EPA+DHA and the n-6-to-n-3 ratio should average 1 , 30. To Previously, health organizations and government agencies in most western countries recommended daily consumption of 0.6 to 1 g n-3 PUFA from which 100–200 mg of LC n-3 PUFA (EPA+DHA). However, in intervention studies reporting a beneficial health effect, the consumption of fish oils or their derivatives resulted in LC n-3 PUFA daily intakes well above those “recommended” 200 mg/day and ranged from 0.5 to 9 g/d. Indeed, in a meta-analysis, a 37% reduction in the relative risk of coronary heart disease in the general population was seen with a daily intake of EPA/DHA of 566 mg. Therefore, this justifies readjustments of nutritional guidelines to an upper level. Governments  and health organizations  now recommend dietary intakes for total n-3 PUFA of 1.4 to 2.5 g/d, with EPA and DHA ranging from 140 to 600 mg/d depending on the authority issuing guidelines, FOA/WHO making a relatively low recommendation of 250 mg/d, the average being around 500 mg/d , 86, 87.To address this question, we calculated FA composition in meals proposed by nutritionist coordinated collective caterings to which health is of concern: first, in lunches supplied by the township of Lille (France) to healthy pupils (4–6 and 6–9 y) and adults and second, in meals proposed to patients hospitalized in St. Luc University Hospital . Total content in FA and specific contents in SFA, MUFA, PUFA, n-6, n-3, and LC n-3 PUFA were calculated in menus over 6 representative weeks for the township collective catering of Lille and in 4 weeks winter menus and 4 weeks summer menus, proposed in rotation along the year by the university hospital. Three types of menus were analyzed: normal, for diabetic patients, and low fat. We used (i) the official table of composition in saturated, mono- and polyunsaturated FA of foodstuffs provided by the French Agency for Food Safety , (ii) thResults are presented in In the meals proposed at St. Luc University Hospital, total FA were relatively low (66%–74% of DRI and 48% of DRI in low fat menus), SFA were in the recommended range or below, but MUFA were dramatically low (38% of DRI and 27% for low fat). Recommended amounts of total and n-6 PUFA were supplied in classical and low-fat diets, but they were outleveled in the diabetic regimen, owing to the addition of 2 safflower-based dressings per day for lunch and evening salads. Regarding n-3 PUFA, the content in total n-3 PUFA was between 1.8 and 1.9 g/d and that of LC n-3 PUFA (EPA+DHA) of 460 mg/day. Those are close to or within the recommendations . Thus, nThus, consistent with other reports , 30, 77,Fish and oils rich in ALA  represent the main sources of n-3 PUFA. As conversion rate from ALA to EPA/DHA is low in humans, a minimum part of the recommended nutritional supplies in n-3 PUFA should be provided as marine LC PUFA (500 mg/d). The first effective measure for increasing n-3 PUFA intakes should consist in actively promoting fish consumption, to reach 35–40 g fish/d. Ideally, wild fish should be given the preference as some species have a higher n-3 PUFA content and/or a lower n-6/n-3 ratio than farmed ones, which usually contain more n-6 PUFA –28, 92 pAn additional measure is to use ALA rich oils, as envisaged in school catering. For example, replacement of dressing oils rich in n-6 PUFA (mainly safflower oil) by oils rich in n-3 PUFA  provides a substantial additional n-3 PUFA supply  while decreasing n-6 intake. However, such modifications require nutritional education and education to different tastes (oils rich in n-3 PUFA have pronounced tastes), probably easier to integrate onto the developing palette of taste during infancy. As edible wild plants provide higher amounts of ALA and antioxidants than intensively cultivated plants , encouraConsideration has also to be brought to the cooking methods as n-3 PUFA are highly sensitive to oxidation by oxygen, light, and heat, leading to production of deleterious free radicals. Indeed, cooking fish might reduce by up to 50% its content in n-3 PUFA . Thus coC. Elegans, an enzyme missing in mammals which catalyses conversion of n-6 to n-3 PUFA [Food enrichment is emerging as perhaps the best long-term solution to the chronically low intake of n-3 PUFA that plagues western cultures . First, n-3 PUFA . This reThe literature provides compelling evidence for the health benefit of n-3 PUFA consumption not only on the MetS, cardiovascular risks, and associated comorbidities but also on other conditions such as neuroinflammatory and neurodegenerative diseases. This evidence must be taken into consideration, and efforts have to be made to promote increased n-3 PUFA consumption together with lowering intakes of high glycemic food, fructose, and fat, in particular SFA and n-6 PUFA when clearly excessive, and increasing ingestion of fruits, vegetables, whole grains, and nuts. Despite this awareness, what is still needed is an educational program for professionals and for the public  as well


LAP3, encoding for a leucine aminopeptidase involved in the oxytocin hydrolysis; NCAPG, encoding for a non-SMC condensin I complex, which has been associated in cattle with fetal growth and carcass size; and LCORL, which has been associated to height in humans and cattle. To further confirm the results of the genome-wide scan we genotyped additional SNPs within these genes and analyzed their association with direct calving ease. The results of this additional analysis fully confirmed the findings of the GWAS and particularly indicated LAP3 as the most probable gene involved. Linkage Disequilibrium (LD) analysis showed high correlation between SNPs located within LAP3 and LCORL indicating a possible selection signature due either to increased fitness or breeders’ selection for the trait.Calving in cattle is affected by calf morphology and by dam characteristics. It is described by two different traits: maternal calving ease, which is the ability to generate dams with good physiological predisposition to calving, and direct calving ease, which is the ability to generate calves that are easily born. The aim of this study was to identify regions of cattle genome harboring genes possibly affecting direct calving ease in the Piedmontese cattle breed. A population of 323 bulls scored for direct calving ease (EBV) was analyzed by a medium-density SNP marker panel  to perform a genome-wide scan. The strongest signal was detected on chromosome 6 between 37.8 and 38.7 Mb where 13 SNPs associated to direct calving ease were found. Three genes are located in this region:  Reproductive health in cattle is an economically important trait and calving is a key element to improve productivity and animal welfare. Calving difficulty, also known as dystocia, has negative effects on the sustainability and profitability of a herd by adding considerable costs for veterinary care, hampering the next conception, and worsening the animal welfare. Calving is influenced by many environmental and genetic factors: in cattle it is affected by calf morphology The aim of this study was to identify regions of the bovine genome possibly affecting calving performance. We used most of the records registered in the Piedmontese breed, which is known to be at risk of dystocia like all breeds that carry harmful mutations in the myostatin gene Three of the 323 sires were excluded for having a genotype call rate lower than 95% and two for having a too high autosomal heterozygosity. As for markers, 506 out of the 54,001 SNPs of the chip were excluded because the ratio of missing genotypes was higher than 5%; 10870 because showed minor allele frequency below 2%; 1372 because they were out of Hardy-Weinberg Equilibrium . The final dataset was thus formed by 318 sires and 41333 SNPs. 10(P*) values can be observed on the first half of BTA (Bos taurus autosome) 6, between 37.8 and 38.7 millions of base pairs (Mbp) . The inflation factor λ obtained for the test was 1.03 showing that, given the number of markers, residual inflation was acceptable; a qq-plot of P-values prior to λ normalization is shown in −7 also after the correction for multiple testing. Allelic effects of the minor allele  are located in the LCORL gene. Average Linkage Disequilibrium (LD) estimation for all markers in the selected region yielded r2 = 0.426 but higher correlation was observed between SNPs within the LCORL gene (r2 = 0.637) and between SNPs located in LCORL and LAP3 (r2 = 0.571).Distribution of genomic kinship values is shown in http://www.ncbi.nlm.nih.gov/snp/) and from the literature LAP3 (only one marker from the Illumina BovineSNP50 panel was located in this gene), 5 in NCAPG, and 5 in LCORL , all SNPs shared the same P* and β implying that these markers are in high LD. The average LD estimation among LAP3 and LCORL SNPs was r2 = 0.653  but this value dropped to r2 = 0.383 if all significant SNPs located in the peak on BTA6  were included. It is also worth to observe that SNPs located in LAP3 gene showed the highest LD (r2 = 0.783) in agreement with the GWAS results. The significant SNP in the region explained about 22.8% of the EBV variance .Based on the results of genome wide scan we decided to genotype additional SNPs in the genes located in the region where we found a strong association of the Illumina Bovine SNP50 with direct calving ease. We therefore selected, from NCBI dbSNP . Pausch and collaborators identified by genome-wide association two major loci on BTA 14 and 21 affecting calving ease and growth-related traits, respectively Today, selection objectives in agriculture pay a great deal of attention to robustness, longevity, welfare and, more generally, sustainability of production. Calving ease is a functional trait having a large impact on animal health and farm profitability, and it also make feasible to breeders the exploitation of sustainable livestock systems LAP3, encoding for a leucine aminopeptidase, which is responsible of the oxytocin hydrolysis NCAPG, encoding for a non-SMC condensin I complex, which has been associated in cattle with foetal growth and carcass size LCORL, encoding for a ligand dependent nuclear receptor co-repressor-like, which affects height in humans and also controls stature in cattle in high correlation with NCAPGLCORL were also found associated with carcass weight in Japanese Black steers LAP3, NCAPG and LCORL as potential positional and functional candidate genes on BTA6 for direct calving ease. Thus, we chose to enrich the SNP set in these genes to be genotyped on the population. Two out of five SNPs in NCAPG show a weak association to direct calving ease and a positive effect linked to the homozygote genotypes for the major allele (T/T in rs109570900 and C/C in rs110251642). Recently it has been shown that a polymorphism in the NCAPG gene  is significantly associated to an increase in bovine carcass weight at puberty LCORL (rs109572301) was significantly associated with direct calving ease. LCORL is immediately adjacent to the NCAPG gene; the LCORL/NCAPG locus has been associated with human height LAP3 is the one showing the most suggestive involvement with the trait. It results significant considering both SNPs in the Illumina BovineSNP50 chip and the further genotyped SNPs. Moreover, the SNPs within its sequence show the highest Linkage Disequilibrium, which is probably due to an increased fitness or to breeders selection. It is worth to observe that these results add relevant information to those obtained in the GWAS since the Bovine SNP50 v1 panel contains only one marker in LAP3 and the strongest signal was obtained for the LCORL gene. However, it is not possible to draw definitive conclusions about the relative importance of LCORL or NCAPG as compared with LAP3 since most markers genotyped within these genes were not usable as being monomorphic or with MAF<0.05. We cannot rule out that the region was subject to selective sweep that dropped out one allele. LAP3 is involved in oxytocin metabolism and hence in maternal calving effects, nevertheless we did not find an association with this trait (data not shown). This lack of association is probably due to the scarcity of data since EBV records for maternal calving effects were available for less than one hundred sires and with lower accuracy since calves outnumber dams.In this study, we performed a genome wide scan of Piedmontese cattle using a medium density chip featuring about 54K SNPs. The chip analysis clearly showed a cluster of 13 SNPs in a 0.9 Mb wide region of BTA6 significantly associated (P*<0.001) with direct calving ease. Three interesting genes are located in this region: Finally, our genome-wide association study shows that EBVs of traits with low heritability are still able to capture the molecular features of the genome. These can be exploited by a Gene Assisted Selection (GAS), that is comparably much faster and more efficient than the selection based on EBV. Furthermore, traits with low heritability should not be disregarded because their relative objectives of selection can only be reached in a long time, indeed they may be successfully dissected to reveal important insights at molecular level.http://www.anaborapi.it/). The sample includes almost all Piedmontese bulls for which data were available and thus it represents the most complete population available for the association analysis for this breed.Genomic DNA was extracted from semen of 323 Piedmontese bulls using the NucleoSpin Tissue kit (Macherey & Nagel) according to manufacturer’s instruction. DNA was checked for quality on agarose gel and quantified using a DTX microplate reader (Beckman Coulter) after staining with Picogreen (Invitrogen). This Italian breed has been selected for beef production and almost all individuals carry muscular hyperplasia www.geneseek.com). SNP calls accurate to more than 99.8% were obtained on average. The SNP positions within each chromosome were based on the Bos taurus genome assembly Btau_4.0 In total, 323 individuals were genotyped using BovineSNP50 BeadChips . The Illumina BovineSNP50 array contains 54,001 SNPs distributed across the entire genome, with an average SNP spacing of 51 Kb and a proportion of known chromosome positions of about 97%. Genotyping was outsourced to Geneseek, USA  <2.5%. Sires were checked for abnormally high autosomal heterozygosity (FDR 1%) LAP3 gene, 5 in the NCAPG gene and 5 in LCORL (LAP3 three markers were described by Zheng in silico NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/). Three SNPs in NCAPG gene were described by Setoguchi LCORL gene were all selected in NCBI dbSNP. The 22 SNPs were genotyped on 247 bulls from the whole population from which we could recover biological samples. SNP genotyping was outsourced to Kbiosciences (www.kbiosciences.co.uk). Data were filtered using the same criteria illustrated above for the SNP chip data. Association between these SNPs and EBVs for direct calving ease was performed using the same statistical model employed for the genome-wide scan but without correcting for inflation factor or for multiple testing since in this case SNPs were limited; however the full kinship matrix obtained with data from the BovineSNP50 panel and the bulls for which additional genotyping was available was used in the calculations.Since the genome wide analysis pointed to a ∼0.9 Mb region associated to direct calving ease (see Results) we searched for further SNPs not represented in the Illumina BovineSNP50 panel in genes included in this region to validate and enrich the results of the genome wide scan. We selected 12 SNPs in the in LCORL . For LAPThe variance explained by the SNPs was evaluated using a Principal Component Analysis regression to cope with correlation (Linkage Disequilibrium) among markers Figure S1QQ-plot of significance levels for the GWAS scan prior to normalization.(PNG)Click here for additional data file.Table S1Additional SNPs genotyped in the LAP3, NCAPG and LCORL genes. The rs number, the gene region and the reference are shown.(PDF)Click here for additional data file.


Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring ( Gadus morhua) Merluccius merluccius) Population genomic approaches have been revolutionised by Next Generation Sequencing (NGS) technologies such as 454 (Roche) and Illumina sequencing. These developments facilitate genome-wide analyses of genetic variation across populations of non-model organisms Ailuropoda melanoleura) Theobroma cacao) de novo assembly of larger, repeat-rich or polyploid genomes requires additional information  and extensive bioinformatic capacity in order to build the large, computationally intensive, structured sequence scaffolds de novo contig assembly and SNP prediction, as only a small percentage of the genome is accounted for by the transcriptome. Another advantage of transcriptome sequencing is the information produced concerning functional genetic variation in specific genes which may be under selection; these can then be targeted to evaluate gene expression profiles. The ability to examine both neutral variation and genomic regions under selection provides researchers with unprecedented tools for understanding local adaptation of wild populations at the molecular level.The strategy used for SNP development in non-model organisms is dependent on the availability of genomic information from closely related species. If such resources are available, PCR amplicons (homologous to regions in the reference genome) can be sequenced and SNPs identified . Without a reference genome, three principal strategies for genome-wide SNP discovery can be applied; whole genome sequencing and assembly, genome complexity reduction and sequencing methods (e.g. RRL and RAD-seq) and cDNA sequencing (RNA-seq). While whole genome sequencing has now been completed for species with large complex genomes (for example: panda (Clupea harengus) is an abundant and ecologically highly diverse species, occurring with a more or less continuous distribution in the North-Atlantic benthopelagic zone. Habitats are distributed across highly diverse environments, from temperate (33°N) to arctic (80°N) and at salinities from oceanic (∼35 ppt) to brackish (down to 3 ppt). In spite of its large ecological range, studies using “neutral” microsatellites have unanimously reported weak population differentiation that is statistically significant only on regional scales Atlantic herring  to establish a transcriptome resource for tissue-specific gene expression profiling and microarray development. We present, to our knowledge, one of the first studies describing SNP discovery in a non-model marine fish based on transcriptome sequencing using NGS.We describe transcriptome-based SNP development in Atlantic herring using a Roche 454 GS FLX (hereafter 454) sequencing approach. Our aim was three-fold; 1) to develop a SNP assay exhibiting minimal ascertainment bias across east Atlantic populations, 2) to test the applicability of SNP development was based on muscle samples from eight fish collected from four locations from across the eastern Atlantic . These lDanio rerio) repeat library. Reads were cleaned for short sequences (<50 bp) and low quality regions using SeqClean (http://compbio.dfci.harvard.edu/tgi/software/). Sequence clustering was performed in two steps; initial clustering was performed using CLC Genomics Workbench , the resulting ace file sequences were then assembled ‘per contig’ in CAP3 in silico SNP detection.Sequences were first de-multiplexed using the barcoding tags  and sorted by sample. Mitochondrial sequences were removed from the data set by mapping the reads against the Atlantic herring mitochondrial genome  were conducted against all annotated transcripts of Gasterosteus aculeatus, Tetraodon nigroviridis, Oryzias latipes, Takifugu rubripes, Danio rerio and Homo sapiens available through the Ensembl Genome Browser, and against all unique transcripts for D. rerio, H. sapiens, O. latipes, T. rubripes, Salmo salar, and Oncorhynchus mykiss in the NCBI UniGene database. Blastx searches were conducted  against the UniProtKB/SwissProt and UniProtKB/TrEMBL databases. Lastly Blastx searches were performed against all annotated proteins from the transcriptomes of G. aculeatus, T. nigroviridis, O. latipes, T. rubripes, D. rerio and H. sapiens available through the Ensembl Genome Browser.Contigs were annotated using the Basic Local Alignment Search Tool (BLAST) against multiple sequence databases. Blastn searches  using the Blastx function . For each SNP containing contig the best match was selected and the aligned sections of the query were saved. Subsequently, two 121 bp sequences per SNP  were produced, these were used in a Blastx analysis against the file retrieved from the peptide sequences , and were then compared to determine if the SNP represented a synonymous or non-synonymous mutation.To predict the effect of the mutation underlying each SNP at the amino acid level, a pipeline was developed to predict the reading frame for each SNP-containing contig. All contigs containing SNPs were first blasted against six peptide sequence databases  utilises factors including template GC content, melting temperature, sequence uniqueness, and self-complementarity to filter the candidate SNPs prior to further inspection. The Assay Design Score (assigned between 0 and 1) is indicative of the ability to design suitable oligos within the 60 bp up/down-stream flanking region, and the expected success of the assay when genotyped with the Illumina GoldenGate chemistry. Following the Illumina guidelines, all SNPs with a score below 0.4 were discarded; SNPs with a score above 0.4 were accepted, with SNPs scoring above 0.7 being used preferentially.SNPs were validated following an G. aculeatus, T. nigroviridis, O. latipes, T. rubripes and D. rerio; see −5). Blast results were then parsed via a custom Perl script considering alignment length, start and end point of the alignment to determine the best positive match (further details of the Perl script and workflow are available from the authors on request). If the 60 bp on both sides of the SNP were present in the alignment, the candidate SNP was considered to be contained within a single exon; otherwise an intron-exon boundary was assumed to be present within the 121 bp assay design region. SNPs were then assigned to one of three categories either having, or not having an intron-exon boundary predicted within the flanking region, or as not returning a significant match against any of the five blasted fish genomes. In the other approach, the likelihood of a positive match and the reliability of intron-exon boundary predictions were increased, with SNP-containing contigs used as a query in a Blast search  against the corresponding transcriptome of the same five reference databases (see above). If the blast search produced a positive result, the matching transcript was downloaded from the Ensembl database, and blasted against its own genome sequence (see The prediction of intron-exon boundaries within the SNP flanking regions (60 bp up/down-stream of SNP position) was performed using two approaches. The first directly compared SNP-containing contigs against five high quality reference genomes for model fish species (Ensembl genome assembly for http://compbio.dfci.harvard.edu/tgi/software/) in order to rank putative SNPs within and among contigs. This was assessed by considering the overall quality of the assembly, the depth and length of alignments, and the number of mismatch sites flanking the SNP. This step was included to increase the likelihood of excluding incorrectly identified SNPs . Within each contig, one or two SNPs receiving the highest quality score were considered for further validation (see below).Finally, the remaining candidate SNP contigs were visually evaluated using clview  and a more distantly related species; anchovy (Engraulis encrasicolus) were genotyped for the full 1,536 SNP panel.To assess the utility of developed markers in related species, two species identified from a consensus phylogeny in silico SNP validation pipeline, a binomial logistic regression analysis was conducted. Two categorical variables (Conversion and Polymorphism) were evaluated which describe the outcome of the SNP assay validation; these are expected to depend on a range of candidate predictor variables (see below). Conversion was scored by assigning all 1,536 genotyped SNP assays as either failed (score  = 0) or successfully amplified and clustered (score  = 1). Polymorphism assigned all the successfully amplified SNP assays into monomorphic (0) or polymorphic (1) categories. Nine variables were then assessed for their predictive value in determining SNP assay conversion and polymorphism: i) number of ascertainment panel individuals supplying sequence reads at the SNP position, ii) number of sequences aligned under SNP position, iii) number of sequences with the minor allele, iv) frequency of sequences with minor allele, v) number of ascertainment individuals with the minor allele, vi) Illumina Assay Design Score (ADS), vii) outcome of the intron-exon boundary pipeline , viii) number of reference species supporting findings from the intron-exon pipeline, and ix) neighbourhood sequence quality . To statistically test the predictive effect of the above variables for both Conversion and Polymorphism a two-step binomial logistic regression analysis was used as implemented in SPSS v12.0. All variables were included in the initial model, and a backward stepwise deletion approach was used for optimisation, in which the least informative variable is removed sequentially until only significantly contributing variables remain. A Wald χ2 statistic was used to estimate the relative contribution from each remaining parameter.To assess the predictive value and utility of the different parameters used in the O) and expected (HE) heterozygosity were estimated for 20 individuals from each of the four ascertainment populations  correction following Benjamini & Yekutieli For the successful polymorphic assays global values of observed  from the marker panel, may occur due to the small size (n = 8) of the ascertainment panel (compared to the whole population), and the limited geographical coverage (compared to the whole species range). When markers are then genotyped on a much larger sample of individuals the resulting ascertainment bias http://www.ebi.ac.uk/ena/data/view/ERP001233).Results for the sequencing and SNP discovery pipeline are illustrated in Sequence cleaning and processing identified 5.8% of the assigned reads as having a match of at least 94% identity over 60 base pairs to Atlantic herring mitochondrial sequences and these were removed from the data set. RepeatMasker masked 1.9% of the dataset using the zebrafish repeat library. The SeqClean program removed a further 3.5% of the assigned reads due to low-complexity , low quality (n = 169) or being below the minimum read length of 50 bases . Lastly, some reads were trimmed, yielding a total of 571,731 reads for sequence clustering and assembly. Initially reads were clustered with CLC Genomics Workbench , resulting in 16,456 clusters ranging from 200–400 bp. These were then individually re-assembled with CAP3 resulting in 19,246 contigs (some clusters produced by CLC were split into two or more contigs) and 30,344 singletons of which more than 50% could be annotated . The majSNP discovery with GigaBayes detected 6,331 putative SNPs in 1,991 separate contigs. The primary annotation of contig sequences is summarized in From the 6,331 predicted SNPs, 993 (15.6%) were located in the terminal region of the contigs and did not have the required minimum of a 60 bp flanking region to design oligos for the GoldenGate array . Of thosFrom the full 1,536 panel of SNPs that were genotyped, 290 (19%) assays failed to amplify. Of the remaining 1,246 assays, 201 were monomorphic  467 produced ambiguous clustering (30%) and 578 were polymorphic, equivalent to a conversion rate of 38%. From these 578 SNPs an open reading frame was obtained for 270 of the respective 121 bp sequences (SNP and 60 bp up/down stream), of which 66 were suggested be non-synonymous, and 204 to be synonymous, equivalent to a ratio (non-synonymous/synonymous) of 0.32 .2 = 18.520, d.f. = 9, p<0.030). When using backward stepwise deletion of predictor variables, the Assay Design Score and number of ascertainment individuals with the minor allele were identified as the only significant predictors of assay conversion, but only the Assay Design Score showed the expected positive correlation with conversion rate . However, neighbourhood sequence quality had a significant negative correlation with polymorphism. As before a backward stepwise deletion approach was used and this reduced the significantly contributing predictors to the number individuals in the ascertainment panel with the minor allele and the neighbourhood sequence quality which, as expected, respectively showed positive and negative correlation with SNP polymorphism  show that inclusion of all of the predictor variables see  marginalion rate . The binmorphism .OH and EH across the four ascertainment samples ranged from 0.00–0.63 (mean 0.18) and 0.00–0.50 (mean 0.18), respectively (Estimates of ectively . Observeectively .The majority (99%) of the 578 markers identified as polymorphic in Atlantic herring also amplified in Pacific herring, but only 12% exhibited more than one allele. Only about 10% of the 578 SNPs amplified in anchovy, and of these, only ten loci exhibited polymorphism.MsatCommander detected 6,501 microsatellites with a repeat length of between two and seven bases with four or more repeat units in 3,741 contigs . 27% of de novo discovery of 6,331 putative SNPs based on 454 transcriptome sequencing of eight individuals covering the Northeast Atlantic distribution of the Atlantic herring. Of particular interest in the approach is the single validation and genotyping step, disposing with the traditional step of testing each SNP for amplification prior to large scale genotyping  as SNPs. Additionally, as no genome reference is available for Atlantic herring, the occurrence of PSVs cannot be assessed, this was probably the cause for the majority of ambiguous clustering that was subsequently seen in the SNPs.For next generation sequencing to be successfully applied to the development of genetic resources in non-model organisms, methodological issues must be addressed to optimise the procedures for each project. SNPs can be genome- or transcriptome derived and, in the latter case, selected from more abundant or rarer expressed transcripts; in addition, marker development is influenced by sequence depth and contig length due to the sequencing platform chosen and the complexity of the hypothesis to be investigated . The choice of sequencing platform should reflect the objective of a given study. While longer reads (e.g. 454 sequencing) are expected to improve contig assembly, more, but shorter, reads (e.g. Illumina sequencing) may be preferable in order to reduce detection of false positive SNPs from higher alignment depth, especially when an existing reference sequence is available. This study took advantage of the longer read lengths obtained with 454 sequencing in a For the SNP detection, the low sequence depth of the majority of contigs  requiredThe use of transcriptome sequencing in this study has resulted in only a few per cent of the total genome being covered, but at a relatively high sequencing depth, thus limiting sequencing costs while achieving the number of SNPs required for custom-designed SNP assays. Additionally, transcriptome sequencing provides information about tissue-specific genes and their expression profile, which can be used to develop further tools for gene expression studies such as oligonucleotide microarray or RNA-seq approaches.in silico SNP detection method presented in this study may have a relatively low conversion rate to validated SNPs when compared to other methods. However, this method is still extremely competitive given a limited resource for marker development, once the time and cost associated with designing and ordering hundreds of primers, running validation PCRs, and additional Sanger sequencing for validation are considered . However, almost 49.3% of the candidate SNPs failed to work; due to either non-amplification (18.9%), false positives (monomorphic loci) (13.1%) or ambiguous clustering (17.3%). Despite our attempt to screen for potential intron/exon splicing sites within flanking regions of all candidate SNPs using available reference genomes, only 41.9% of all queries matched equivalent sequences in at least one of the reference species. Thus, the presence of undetected introns may have constituted a major cause for genotyping failure red e.g. . All of in silico detected SNP being a truly polymorphic site, further sequencing would lead to greater sequence depth of the contigs, allowing more stringent selection of SNP candidates. It has been shown for multiplexed re-sequencing that more than 90% of the variants can be detected correctly using next generation sequencing technologies when an average depth of at least 20 reads per base is achieved et al.de novo assembly. Additionally, the use of next generation sequencing for analysing a restriction enzyme-generated DNA library , as expected, a positive correlation was found with the Assay Design Score, i.e. the likelihood for designing successful primers around the SNP position. Unexpectedly, a negative correlation was found with number of ascertainment individuals for which the rare allele was observed, although the reasons behind this correlation are unclear. Overall, only very weak predictive variables for Polymorphism were identified, with only the neighbourhood sequence quality significantly explaining the negative correlation; as the number of mismatches in flanking regions increases, a predicted SNP is more likely to be a false positive. This increase in mismatches of an aligned region could be indicative of erroneous clustering, for example, PSVs or other sequences with differing genomic origin .When determining the predictive value of the SNP selection parameters for successful amplification of the a priori candidates . This also allowed controlling for a potential bias from non-independent variables such as the two intron-exon and three minor allele related parameters. Results were largely congruent confirming Assay Design Score and neighbourhood sequence quality to be the most significant predictors of Conversion and Polymorphism, respectively.The two binomial logistic regression analyses were repeated with a reduced set of variables representing the strongest The range of allele frequencies within the SNP panel suggests that the strategy of carefully selecting individuals to maximise the geographical, phenotypic and genetic diversity covered by the SNP development samples has been successful in minimising ascertainment bias.A high proportion of detected SNPs also amplified single PCR products in Pacific herring albeit with a low polymorphism rate, which is as expected due to their development from conserved genomic regions. However, due to the small sample size (n = 4), this number is likely to be downwardly biased and a much higher proportion of SNPs may in fact be polymorphic and therefore prove useful in this species. As expected from the phylogenetics of these species, the proportions of SNP amplification and polymorphism were lower in the anchovy. Additionally, our sequencing effort has led to the discovery of a large resource of microsatellite markers, 36% of which have primers successfully designed . These iin silico mining of transcriptome sequences seems to be a very appropriate strategy to develop new SNP markers in non-model species as it does not require costly and time-intensive re-sequencing of target amplicons necessitating prior knowledge and availability of genome sequence information. However, the purely in silico based SNP detection comes with a trade off in the form of an expectedly lower conversion rate in the final genotyping assay Our approach of applying barcoding and multiplexing individuals for large-scale Figure S1Analysis pipeline. The path on the left of the figure illustrates the pipeline for the genomic approach, where herring transcripts are directly compared with five reference genomes. The path on the right of the figure shows the pipeline for the transcriptomic approach, where herring transcripts are first compared to the transcriptome of the five reference species. Hits were then subsequently matched to the corresponding genomes of the same species (see text for more details).(TIF)Click here for additional data file.Table S1Number of contigs and singletons annotated using a range of fish and human reference resources and databases.(XLSX)Click here for additional data file.Table S2List of the 578 validated polymorphic SNPs found in this study, including the 120 bp flanking region, with the two SNP alleles in brackets. Also global estimates of observed (Ho) and expected heterozygosity (He) in the four ascertainment populations for each SNP. The S/NS column denotes whether a SNP was either synonymous (S) or non-synonymous (NS) with NA designating SNPs with no contig match in the BLAST search (see text for more details).(XLSX)Click here for additional data file.Table S3List of the microsatellites for which primers were successfully designed, along with up to 200 bases flanking sequence.(XLSX)Click here for additional data file.


The computational analysis of regulatory SNPs (rSNPs) is an essential step in the elucidation of the structure and function of regulatory networks at the cellular level. In this work we focus in particular on SNPs that potentially affect a Transcription Factor Binding Site (TFBS) to a significant extent, possibly resulting in changes to gene expression patterns or alternative splicing. The application described here is based on the MAPPER platform, a previously developed web-based system for the computational detection of TFBSs in DNA sequences.rSNP-MAPPER is a computational tool that analyzes SNPs lying within predicted TFBSs and determines whether the allele substitution results in a significant change in the TFBS predictive score. The application's simple and intuitive interface supports several usage modes. For example, the user may search for potential rSNPs in the promoters of one or more genes, specified as a list of identifiers or chosen among the members of a pathway. Alternatively, the user may specify a set of SNPs to be analyzed by uploading a list of SNP identifiers or providing the coordinates of a genomic region. Finally, the user can provide two alternative sequences (wildtype and mutant), and the system will determine the location of variants to be analyzed by comparing them.In this paper we outline the architecture of rSNP-MAPPER, describing its intuitive and powerful user interface in detail. We then present several examples of the use of rSNP-MAPPER to reproduce and confirm experimental studies aimed at identifying regulatory SNPs in human genes, that show how rSNP-MAPPER is able to detect and characterize rSNPs with high accuracy. Results are richly annotated and can be displayed online or downloaded in a number of different formats.http://genome.ufl.edu/mapper/.rSNP-MAPPER is optimized for large scale work, allowing for the efficient annotation of thousands of SNPs, and is designed to assist in the genome-wide investigation of transcriptional regulatory networks, prioritizing potential rSNPs for subsequent experimental validation. rSNP-MAPPER is freely available at  Single nucleotide polymorphisms (SNPs) have become the genetic marker of choice for studies aimed at linking genotype and phenotype, thanks to the availability of a very large number of well-characterized SNPs in public databases and of high-throughput technologies able to reliably genotype a large number of SNPs in parallel at a low cost. The most recent release of the dbSNP database, the largest public SNP repository , includeAs the number of SNPs that can be sampled in Genome-Wide Association Studies (GWAS) increases, there is a growing opportunity for using SNPs not simply as genetic markers, but also as causal candidates for diseases. Although the basic idea of GWAS is to look for a significant statistical association between a phenotype and one or more genetic markers in an unbiased way, knowledge of the potential functional roles of SNPs can greatly help in the design and interpretation of these studies. A drawback of the ability to genotype a very large number of SNPs offered by current high-throughput technologies is the high rate of false positives in the results . DetermiIn this work we focus on the large-scale computational identification of putative regulatory SNPs (rSNPs). We define rSNPs as those SNPs that potentially affect a Transcription Factor Binding Site (TFBS). By modifying the site's DNA sequence, a regulatory SNP can change the its binding affinity towards the corresponding TF, which in turn can have an effect on the expression level of the downstream gene. While coding SNPs amount to about 1% of currently known SNPs, out of which only a fraction cause an amino acid change, regulatory SNPs are expected to be much more numerous and to cause a wide range of biological and pathological consequences, since they can potentially affect the regulation, expression level and structure or function of the gene product . If a meSeveral online tools for the computational identification of putative regulatory SNPs exist. For example, the RAVEN application  determinDatabase of pre-computed putative binding sites in the promoter regions of three genomes, a Search Engine able to analyze a novel DNA sequence in real time, and rSNP-MAPPER, the tool described in this paper, designed to identify putative regulatory SNPs with high sensitivity in a high-throughput setting.The tool presented here is a component of MAPPER, a modular online application for the computational identification of TFBSs in DNA sequences ,8. MAPPEThe MAPPER model library is divided into three groups of TFBS models. The first one (TRANSFAC models) was built using the same optimal alignments used to build the PWMs in the TRANSFAC database . The sechmmpfam program, from the HMMER package ))6]), whiet al. [We tested MAPPER on the 77 rSNPs from Dataset S1 in Andersen et al.  for whicet al. as Dataset S2 [We performed the same test on the set of 20 rSNPs for which the affected TF is known provided by Andersen taset S2 . rSNP-MAet al. as part of the description of their is-rSNP system (see Table 2 in [We tested rSNP-MAPPER on a list of 11 known disease-associated regulatory SNPs provided by Macintyre ble 2 in ). rSNP-MMSH6 gene, and were shown to be required for its transcription. This region contains three known SNPs, two of which are contained in Sp1/Sp3 sites and may therefore affect them by abolishing or decreasing Sp-1 binding. The effect of these two polymorphisms on Sp-1 binding was experimentally demonstrated by gel-shift assays and luciferase expression following co-transfection of the transcription factor gene with reporter constructs containing the substitutions in CHO and human BJ skin fibroblasts [Seven functional Sp1/Sp3 binding sites were detected computationally and validated experimentally within the first 500 bps upstream of the ATG of the roblasts .rSNPs-MAPPER analysis indicates that SNP rs3136229 (A-448G) causes the deletion of a predicted Sp-1 site at -449 to -439 . The same allele change also appears to highly increase the strength of a putative PacC site at the same position, with a positive score change of 2.6. The second SNP, rs41540312 (C-159T), causes the deletion of a putative Sp-1 site at -168 to -158. This is confirmed by two different models for Sp-1, with score changes of 2.3 and 2.2 respectively. The same SNP causes a putative NRF-1 binding site to appear at the same position, again confirmed by two different models (score changes of 3.1 and 2.9 respectively).MSH6 gene.This example demonstrates the program's ability to correctly detect experimentally validated rSNPs, and to complement them with additional predictions about the consequences of the allele substitutions they produce. While the effects on the the Sp-1 sites had been observed experimentally, rSNP-MAPPER was able to predict the binding of other transcription factors to the altered sites, potentially leading to further disruptions in the regulation of the F9) expresses a liver-produced protein that is an essential component of the coagulation cascade. Its promoter contains experimentally characterized binding sites for three TFs: CCAAT / enhancer binding protein (C/EBP), hepatocyte nuclear factor 4 (HNF-4) and androgen receptor (AR). Point mutations in these TFBSs are known to lead to various forms of hemophilia B: mutations in the C/EBP and HNF-4 sites lead to hemophilia B Leyden; mutations in the AR site lead to hemophilia B Brandenburg, characterized by a life-long deficiency in factor IX [The coagulation factor IX gene . As shown in Table 6G. As shCOL18A1) is a homotrimeric basement membrane molecule of unknown function, whose COOH-terminal NC1 domain contains endostatin (ES), a potent antiangiogenic agent [COL18A1 expression have been associated with several pathological processes such as cancer, liver fibrosis, diabetic retinopathy and Alzheimer’s disease. Studies focused on the characterization of cis-regulatory elements interacting with human COL18A1 promoter 2 and the identification of SNPs in this region in different ethnic groups have reported that SNP rs755548 (T-700G) is associated with an increase in transcription and binding for an unknown transcription factor for the -700G allele in hepatocytes [Type XVIII collagen . Its simple and intuitive interface supports multiple modes of operation: it can be used to analyze SNPs located within genes or close to them, SNPs in a specified genomic region, or a user-specified set of SNPs. The output of the program is fully annotated and can be exported or displayed in several different formats, in order to facilitate subsequent analysis and validation of the results.rSNP-MAPPER implements a highly accurate rSNP analysis method, thanks to the use of a powerful TFBS-detection method based on Hidden Markov Models rather than Probability Weight Matrices. In addition to the the examples presented in the Results section, we have tested it on the experimentally-validated regulatory SNPs available in the HGMD database with excellent results. Further strengths of rSNP-MAPPER include its very extensive library of binding site models, and a powerful and flexible user interface that cover most analysis needs, ranging from single-gene searches to high-throughput analysis of thousands of SNPs.GWAS: Genome-Wide Association Study; PWM: Probability Weight Matrix; SNP: Single-Nucleotide Polymorphism; rSNP: Regulatory SNP; TFBS: Transcription Factor Binding Site.The author declares no competing interests.et al.rSNP-MAPPER results on the data provided by Andersen  Excel spreadsheet showing the results of rSNP-MAPPER analysis on 64 putative regulatory SNPs from Andersen et al [en et al .Click here for fileet al.rSNP-MAPPER results on the data provided by Andersen  Excel spreadsheet showing the results of rSNP-MAPPER analysis on 20 known regulatory SNPs from Andersen et al [en et al .Click here for fileet al.rSNP-MAPPER results on the data provided by Macintyre  Excel spreadsheet showing the results of rSNP-MAPPER analysis on 11 disease-associated regulatory SNPs from Macintyre et al [re et al .Click here for file


Genome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared.Regardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case–control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively.Correction of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ≤1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0. Genome-wide association studies (GWAS) using array-based genome-wide individual single nucleotide polymorphism (SNP) typing technologies are performed to identify genetic loci associated with complex diseases. The costs of projects based on individual genotyping using microarrays are still high and increase directly with the number of samples to be analyzed. GWAS using pooled DNA samples, the so called allelotyping approach, offers an alternative at significantly lower costs .Several earlier studies have described the identification of new or validation of known phenotype-associated genetic loci by allelotyping using older SNP genotyping array versions -4. RecenDespite the great advantage of cost reduction, allelotyping has also several drawbacks. Most importantly, allelotyping allows only for extraction of the mean allele frequencies of the pooled DNA samples instead of determining the individual genotypes. In addition, adjusting for covariates is almost impossible after DNA pooling. This limitation includes the use of principal components as covariates in the association analysis to adjust for population stratification as commonly done in GWAS. If researchers wish to use the allelotyping approach they need to be certain that little to no stratification exists, or that it will be adequately controlled by e.g. applying genomic control. Furthermore, the error rate is usually higher in pooled samples compared to individual samples. Notwithstanding these weaknesses, allelotyping represents a well-priced alternative, especially when applied to case–control studies with larger cohort sizes, because in these cases, stronger effects of individual gene variants compared to general-population based cohorts can be expected.In the present study, we estimated the pooling-related error by comparing the allele frequencies determined from pooled DNA samples with those obtained from individually genotyped samples. In both cases the Affymetrix Genome-Wide Human SNP Array 6.0 was used as genotyping platform. Furthermore, we compared differences in allele frequencies between several DNA pools used for allelotyping. Finally, we evaluated some known and newly developed methods for correcting the pooling-specific error and compared these results to the uncorrected allele frequencies. Our results may help researchers set up an appropriate experimental design for case–control allelotyping studies.n = 0.5,1,2 (see Methods section).The study population included two groups consisting of 50 individuals each. The DNA samples of each group were either pooled and analyzed using one array, or individually genotyped resulting in 50 arrays per group. In each case, the Affymetrix Genome-Wide Human SNP Array 6.0 was used. We determined the allele frequencies using relative allele signals (RAS)  . The corresponding error value of the k-correction method amounted to 0.18 (0.1). The comparison of the pooling-specific errors using different correction methods applied on this SNP set is shown in Figure Comparison of 10,000 uncorrected RAS values calculated from single DNA pools of 50 samples with those derived from individual genotyping calls and a SNP minor allele frequency (MAF) >1% resulted in the 90% quantile (median) of the absolute errors of 0.202 (0.108). Applying the PPC correction method reduced this error to 0.064 (0.026). Our newly developed tan-correction with parameter n = 0.5 performed best with a 90% quantile (median) error of 0.122 (0.042) compared to 0.25 (0.138) using uncorrected RAS values obtained from the Birdseed2 calling algorithm. The k-correction method slightly increased the accuracy after removing the low-MAF SNPs but did not outperform the accuracy of uncorrected RAS values of the complete SNP set.Using 10,000 RAS values with a MAF >5% increased the allelotyping accuracy slightly, whereas inclusion of all 772,731 SNPs with a genotype call rate of 100% in the analysis strongly increased the overall pooling-specific error. Since the computationally intensive PPC correction algorithm was originally developed for the Affymetrix GeneChip 10 K Mapping array representing approximately 10,000 SNPs, we could not apply this correction method on the complete set of SNPs. For the complete set of SNPs, the tan-correction method with parameter The differences of the error distribution between both single DNA-pools varied only marginally. Detailed results are given in Table The advantage of the RAS correction was diminished when comparing the SNP-specific differences of the RAS values between the two groups Figure . HoweverThe pooling-specific error of the Affymetrix Genome-Wide Human SNP Array 6.0 in the context of allelotyping studies and the optimal method for correction strongly depend on the study design. When comparing allele frequencies determined using pooled DNA samples with those obtained by individual genotyping, an appropriate correction for RAS values may dramatically increase the overall accuracy. Uncorrected RAS values are, however, almost as reliable as (if not even better than) corrected values when comparing the differences in allele frequencies between two pooled DNA sample groups. This finding is of great relevance, since the correction algorithms require prior knowledge of correction values for each SNP, which are array specific.In detail, the k-correction method requires intensity values of heterozygote individuals, the PPC and tan-correction require intensity values from all three genotypes of each SNP, which are difficult to obtain, especially for rare SNPs. For both the k-correction and the tan-correction algorithms, correction values are calculated only once for each SNP as the mean of the RAS values obtained from the individually genotyped samples. In contrast, for the PPC method the RAS values of each individually genotyped A and B probe pair have to be included in a regression model, and, in a second step, the median of all corrected probe-specific RAS values needs to be calculated per SNP. This results in a large computational effort and increased data handling. The PPC algorithm was originally developed using approximately 8000 SNPs of the former Affymetrix GeneChip 10 K Mapping array, which included 10 probes per SNP distributed over both target DNA strands. This probe-based correction was implemented to compensate for the specific hybridization efficiencies of the probe pairs . Since tAt least for comparing differences between allele frequencies of DNA pools, the use of uncorrected RAS values, obtained from the intensity values of the Birdseed2 calling algorithm, may represent a reliable and easy-to-implement approach.There are several limitations of the present study including the lack of a more detailed examination of the SNP-specific pooling error and an analysis of the impact of different numbers of pool replicates. Since the number of individual DNA samples combined in the pools should not significantly affect the pooling-specific error , we deciThe comparison of the allele frequency differences between two DNA pools were more accurate than the comparison of the allele frequencies of a DNA pool with those obtained from individual genotyping. Removing SNPs with a MAF <1% notably increased the accuracy especially in the latter scenario. Compared to the MAF <1% filtered SNP set, additional removal of all SNPs with a MAF <5% resulted, if at all, in an only marginally increased accuracy.Other studies already estimated the pooling-specific error using earlier Affymetrix genotyping array models -12. Heren = 0.5. In case an association analysis using DNA pools in both groups is to be conducted, the k-correction seems to be a slightly more precise method  than using uncorrected RAS values obtained from the Birdseed2 calling algorithm being the second best option. In any case, we suggest to focus on SNPs with a MAF >1%.Based on the results of this study, we suggest the following approach for analyzing pooled data in the given scenarios. When comparing pooled DNA samples of one group with individual genotypes from another group, and if correction values of all three genotypes per SNP are available, we recommend to use the tan-correction with parameter Array data from 4096 individuals of the Study of Health in Pomerania (SHIP) , a populChromosomal DNA was purified from whole blood samples using the QIAamp DNA blood mini kit  following the manufacturer’s instruction. To remove contaminating RNA, the optional RNase A treatment step (30 min at RT with 20 μl Ribonuclease A (20 mg/ml)) was routinely performed. The DNA quantity was measured with a NanoDrop® ND-1000 UV–vis Spectrophotometer (NanoDrop Technologies) and the integrity of the DNA preparations was analyzed by electrophoresis using 0.8% Agarose-1x TBE gels stained with ethidium bromide. Only DNA preparations showing no signs of degradation at all were used for allelotyping.The 4096 DNA samples used to determine the correction values which included the 100 samples used for estimation of the pooling-specific error were individually genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Processing of genomic DNA and array hybridization were performed in accordance with the manufacturer’s standard recommendations. Genotypes were determined using the Birdseed2 calling algorithm . For quaHWE > 0.001).Only SNPs with a call rate of 100% where used for estimating the pooling-specific error. Of these 772,731 SNPs, two subsets of 10,000 SNPs with MAFs >1% and >5%, respectively, were selected and used in corresponding subset analyses. All but 147 SNPs included only in the overall SNP set were in Hardy-Weinberg equilibrium , each consisting of equimolar DNA samples from 50 individuals , were prepared. As pilot experiments had revealed that it is extremely important that the individual DNA samples in the pools are indeed present in equimolar amounts, the pooling of the individual DNA samples was carried out with maximal pipetting accuracy. Subsequently, the pools were purified by an additional phenol-chloroform extraction procedure including a final ethanol precipitation, because the pilot studies had also demonstrated that this treatment strongly improves the results of the array analyses. This purification was performed as follows: The total volume of the respective DNA pool was adjusted to 200 μl using 1xTE buffer in a reaction tube. To this DNA solution, 200 μl TE-equilibrated (pH 7.5 – 8.0) Phenol solution  was added and the resulting mixture was manually mixed for 1 min. Subsequent phase separation by centrifugation for 15 minutes at room temperature and 8000 rpm was accomplished using a bench-top centrifuge. Upper and middle phase were completely transferred to a new reaction tube, and again 100 μl TE-equilibrated Phenol and a 100 μl Chloroform/isoamyl alcohol mix (24:1 ratio) were added. The resulting mixture was manually shaken for 1 min. The centrifugation step was repeated and upper and middle phase were again transferred to a new reaction tube. Next, 200 μl of chloroform/isoamyl alcohol mix (24:1) were added, and the resulting mixture was manually shaken for 1 min. The subsequent centrifugation step was carried out at room temperature for 5 min at 13000 rpm. The upper and middle phases were transferred to a new reaction tube, and the complete chloroform/isoamyl alcohol extraction step was repeated. Subsequently, only the upper phase was transferred to a new reaction tube, leaving the middle phase behind. After adding 40 μl of 3 M sodium acetate (pH 5.2) and thoroughly mixing, 1 ml of ethanol was added. The mixture was left overnight at – 20°C for precipitation of the sodium-DNA salts. The precipitate was harvested by centrifugation for 15 min at 4°C and 13000 rpm. After removing the supernatant, 1 ml of 75% ethanol was added and the DNA-sodium pellet was washed by repeatedly inverting the reaction tube to remove excess sodium-acetate salts. The preceding centrifugation step was repeated and the supernatant carefully removed. The pellet was dried in a vacuum centrifuge for 20 min, and resuspended in 30 μl 1x TE buffer for 1 h at 37°C under agitation using a thermomixer (Eppendorf) at 500 rpm. The DNA concentration was again determined using a NanoDrop Spectrophotometer, and the integrity of the DNA preparations was analyzed by electrophoresis using 0.8% Agarose-1xTBE gels stained with ethidium bromide.Two technical replicates were performed with each DNA pool. To this purpose, both pools were independently processed two times, starting with enzymatic DNA restriction and ending up with array hybridization, detection, and scanning.As quality control, a principal component analysis (PCA) was performed on all arrays that were used as input for the Birdseed2 calling algorithm for obtaining the RAS values. Arrays that were identified as outliers were discarded, and array analysis was repeated for these samples. Finally, for all arrays, both first two principal components were within the range of ±4 standard deviations of their mean. The PCA results are shown in Figure 2 value between two technical pool replicates based on the RAS values obtained by Birdseed2 of 10,000 SNPs with a MAF >1% was 0.97 and 0.96 for pool 1 and pool 2, respectively . To accomplish the minimal number of genotyping arrays necessary to apply this algorithm, we included 40 additional DNA pools, also originating from individuals of European ancestry. The DNA pools were prepared in the course of a different allelotyping project (genetic determinants for breast cancer susceptibility) and were processed using the same protocol and array system (Affymetrix Genome-Wide Human SNP Array 6.0). The CEL file intensity values, the output from the Birdseed2 algorithm (CHP files) as well as the individual genotype calls were imported into a database build on Caché  for further analyses.The RAS values based on the Birdseed2 intensity values were calculated for each SNP as RAS = SignalA/, where the SignalA- and SignalB-columns of the corresponding CHP files were used. For the CEL file intensity based RAS-values, we obtained the signal intensities for alleles A and B by calculating the median probe intensities from all probes of a SNP for alleles A and B, respectively, and used those values as input in the RAS computation formula.c is specific for each probe set and has to be determined empirically by individual array-based genotyping of heterozygous individuals. The k-corrected RAS value K for allele A of a given SNP was calculated as follows:Ideally, the RAS values for a given individually genotyped AB SNP will amount to 0.5 , 1  and 0 , respectively. In practice, the values obtained for each probe set deviate from this ideal due to differential hybridization efficiencies. Therefore, if RAS values generated using pooled DNA samples from one group (e. g. patients) shall be compared to averaged SNP-specific call values generated by individual genotyping of a different group , a correction step has to be included to compensate for this hybridization-specific bias . One optc < 1, where the correction value c is the mean RAS value of the individual heterozygous calls, RAS is the observed relative allele frequency and K is the k-corrected RAS value.with 0 ≤ RAS ≤ 1 and 0 < In contrast to the k-correction, the PPC correction algorithm uses the intensity values of all individually genotyped homozygous AA and BB as well as all heterozygous AB samples as input and fits for each SNP a second-degree polynomial using the relative intensity value of a probe as independent variable. Our calculation was done according to the R-script provided in  using thWhen comparing uncorrected RAS values from pools with the corresponding allele frequencies obtained from individual genotyping, values close to 0 or 1 seem to deviate more from the expected frequencies than values closer to 0.5 : one with parameters for observed RAS values below the correction value of the AB genotype (c1) and the other for RASobs values above this correction value /arctan(m) for RASobs ≤ c1 and b = (c2-c1)/arctan(n + m) for RASobs ≥ c1.with If any SNP correction values were missing, e.g. due to lack of genotyped individuals of the respective genotype, the uncorrected RAS value was used. This was the case for 1.87% of the 10,000 SNPs of the 1% MAF-filtered SNP set.http://www.perl.org). A Perl script for calculating and testing allele-specific differences between pools using uncorrected RAS values, k-correction or tan-correction was developed and successfully implemented in the allelotyping workflow used in [The intensity values, RAS values and genotype calls were stored in a database build on Caché . Computations and data management were performed in Caché, InforSense , R  and the  used in  and is aThe array data of each technical replicate of Pool 1 and Pool 2 as well as the correction values used for the k-correction and tan-correction are available at the GEO (Gene Expression Omnibus) public repository under accession number GSE48190. Additional file The authors declare that they have no competing interests.in silico analyses and drafted the manuscript. AT, UV and GH designed the study. FDE, AW, KU, and AP performed isolation of DNA samples, preparation of DNA pools, and subsequent sample processing including array hybridization. MN and HV participated in design and coordination of the SHIP study. FDE, UV, HV and GH revised the manuscript. All authors read and approved the final manuscript.AT performed all Expected RAS values of Pool 1. Table of expected RAS values calculated from calls  and average RAS values using the Birdseed2 calling algorithm (MEANindRASPool) obtained by individual genotyping of all DNA samples included in Pool 1. The column numElementsPool denotes the number of successfully genotyped samples per SNP (probeset).Click here for fileExpected RAS values of Pool 2. Table of expected RAS values calculated from calls  and average RAS values using the Birdseed2 calling algorithm (MEANindRASPool) obtained by individual genotyping of all DNA samples included in Pool 2. The column numElementsPool denotes the number of successfully genotyped samples per SNP (probeset).Click here for filePPC correction values. Table of the correction values used for the PPC correction of the subset of 10,000 SNPs with MAFs > 1%. For each SNP (Probeset) the columns b0, b1, b2 and Rsq denote the intercept, the effect of the RAS value, the effect of the squared RAS value and the R2 value of the PPC regression model, respectively, whereas n number of samples were included in the regression.Click here for fileList of 10,000 SNPs included in the MAFs >5% subset.Click here for file


HASTY, a previously characterized gene involved in microRNA biogenesis. We have evaluated the feasibility of this backcross approach using three additional SNP mapping pipelines; SHOREmap, the GATK pipeline, and the samtools pipeline. Although there was variance in the identification of EMS SNPs, all returned the same outcome in clearly identifying the causal mutation in HASTY. The simplicity of performing a single parental backcross and genome sequencing a small pool of segregating mutants has great promise for identifying mutations that may be difficult to map using conventional approaches.Forward genetic screens have identified numerous genes involved in development and metabolism, and remain a cornerstone of biological research. However, to locate a causal mutation, the practice of crossing to a polymorphic background to generate a mapping population can be problematic if the mutant phenotype is difficult to recognize in the hybrid F2 progeny, or dependent on parental specific traits. Here in a screen for leaf hyponasty mutants, we have performed a single backcross of an Ethane Methyl Sulphonate (EMS) generated hyponastic mutant to its parent. Whole genome deep sequencing of a bulked homozygous F2 population and analysis via the Next Generation EMS mutation mapping pipeline (NGM) unambiguously determined the causal mutation to be a single nucleotide polymorphisim (SNP) residing in  A forward genetic approach has been the foundation of determining gene function for many decades. However, map-based cloning has historically been a labor intensive and cumbersome process, often involving outcrossing of the mutant to a polymorphic line followed by fine mapping using a pool of potentially thousands of individual F2 plants carrying the mutation  × Columbia (Col-0) bulk F2 segregants. The utility of the SHOREmap pipeline has extended to include mapping of non-EMS mutations in non-model plants  is observed, due to the deregulation of miR159 target genes MYB33 and MYB65  pathway. To do this we used a loss-of-function mir159a seed, 500 M1 plants were self-pollinated, and ~50 M2 plants from each original M1 plant were grown; thus ~25 000 M2 plants were screened for leaf hyponasty. From this initial screen, twenty hyponastic M2 lines were identified. Of these, line 26 was chosen for further analysis due to its phenotypic similarity to miRNA biogenesis mutants , and aimed to use the EMS generated SNPs rather than ecotype specific SNPs as markers to map the mutation. The principle of the method is shown in Figure 2) showing the hyponastic phenotype were selected from a segregating pool  of F2 individuals. The plant material of these individuals was pooled and the DNA was extracted from both the BCF2 pool and also from the mir159a parent for deep sequencing. As laboratory strains have been shown to contain up to several thousand SNP differences compared to the Col-0 reference genome . Pre-processing of reads by the trimmomatic software , respectively.As the phenotypes of some miRNA mutants are not always apparent in different backgrounds  segregants against the Col-0 reference genome. This identifies the “SNP desert”—a region reduced in SNPs derived from the polymorphic ecotype used in the cross. The Ler specific SNPs should be reduced in this SNP desert due to selection for the Col-0 derived mutation. Obviously this would not be the case if the mutant was back-crossed to the parent; as both the mutant and parent are of the same ecotype, only EMS SNPs should be present, where the SNPs that are linked to the mutation would have an allelic frequency affected by the rate of recombination in the region, and the strength of phenotypic selection, producing a SNP peak.The utility of NGM software in identifying causal SNPs has been demonstrated using mapping populations derived from a cross between EMS mutated Col-0 and polymorphic LD). This is a function of the “purity” of a SNP at a certain position, and is calculated by dividing the number of observations of the most-frequent non-reference base by the sum of the abundances of the two most common bases  to compile a modified “.emap”  pileup   Table . HASTY is Figure  highly sHASTY using these other pipelines, and also to assess if any offered a particular advantage for causal SNP determination using the backcross method. Therefore, using separate analyses, SNPs were called using GATK, samtools and SHOREmap algorithms for both the mir159a parent and BCF2 sequence data.In addition to NGM, there are several programs that can interrogate deep sequencing data for mapping purposes  data. SHOREmap utilises its own SNP caller and can implement a number of filters including quality scores, read depth and allele frequency, and can filter out parental SNPs. For this study we set the default parameters as used by Hartwig et al.  concept which attempts to identify false SNPs caused by nearby indels Li, . Howeverg et al.   was the same stop-gain SNP in M Figure . Thus, tAcross the alternative pipelines, there was strong concordance between SNPs identified of high mutant allelic frequency Figure . ConversHASTY that were identified by all other pipelines , in our parent and mutant, respectively, but did not find any difference in results whether we removed duplicates or not. Nonetheless, it is still good practice to remove duplicates prior to SNP calling and would recommend users to assess the effect of PCR duplicate removal from their reads data.er, was not only able to narrow down the causal candidates to a single gene, but also that so few candidates were actually identified. Previous examples in the literature failed to identify the causal mutation after the first round of deep sequencing of bulk segregant pools  for the mutant, does not appear to be greatly less than other reports , and position of the SNP along the read. This relies on a database of known SNPs, which had to be generated The other pipelines used in this study also possess different procedures and filters. The result is that while some of these can be controlled, this usually incurs flow on effects to downstream parts of a pipeline and will generate different sets of SNPs between the programs. Filters should therefore be empirically tested. However, despite the SNP calling intricacies, in practice if the correct methods and filters have been implemented, high quality SNPs should still be called, as demonstrated in this study. An important caveat is that while we could identify the causative mutation regardless of the platform used, other mutant screens that may potentially be compromised by less recombination in the causative mutation region, lower mutational loads or weaker selection may be harder to identify using a particular pipeline. Overall, we believe it may be beneficial to implement a variety of methods based on the same input data (as done in this study).HASTY had a discordant chastity/allelic frequency of 1 demonstrates the selection was specific and possibly all 110 F2s selected for the bulk segregant pool were homozygous for the causative mutation. This is important because due to the very high discordant chastises/allelic frequencies we found for linked SNPs, contamination by a single wild type segregant may have blurred the distinction between causal and non-causal but closely linked SNPs. This has recently been also demonstrated in-silico, where assessment of mis-scoring revealed drastic effects on the resolution of mapping by sequencing outcomes  was grown to identify mutants. One hundred and ten individuals showing the leaf hyponasty phenotype were selected from the BCF2 population. A small leaf from each plant was used for a pooled DNA extraction of all 110 individuals. A Qiagen Plant DNA maxi-kit was used for Arabidopsis genomic DNA extraction.A leaf curl mutant M2 line (Line 26) was identified and pollen from the MYB33 and mir159b was performed as described previously , and was trimmed and quality filtered with the Trimmomatic software  genome with BWA sampe . The output of each round of SNP calling was used as the input for the next round of base score recalibration and unified genotyper calling. For the NGM pipeline (Austin et al., SNPs were called using four software packages, on both the parent and mutant line. For samtools v0.1.18 (Li et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.