Recent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait.  Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies. http://www.fda.gov/ArrayTrack. SNP (single nucleotide polymorphism) and QTL (quantitative trait locus) libraries were constructed and incorporated into ArrayTrack, with user-friendly interfaces and powerful search features. Data from several public repositories were collected in the SNP and QTL libraries and connected to other domain libraries  in ArrayTrack.  Linking the data sets within ArrayTrack allows searching of SNP and QTL data as well as their relationships to other biological molecules. The SNP library includes approximately 15 million human SNPs and their annotations, while the QTL library contains publically available QTLs identified in mouse, rat, and human. The QTL library was developed for finding the overlap between the map position of a candidate or metabolic gene and QTLs from these species. Two use cases were included to demonstrate the utility of these tools. The SNP and QTL libraries are freely available to the public through ArrayTrack at These libraries developed in ArrayTrack contain comprehensive information on SNPs and QTLs and are further cross-linked to other libraries. Connecting domain specific knowledge is a cornerstone of systems biology strategies and allows for a better understanding of the genetic and biological context of the findings from genetic association studies.  Genetic variations are a major factor for inter-individual differences in disease susceptibility and response to environmental exposures such as nutrients and drugs. Recent advances in microarray-based genotyping techniques have enabled researchers to rapidly scan for known single nucleotide polymorphisms (SNPs), one of the most common genetic variations, across complete genomes.  Genome wide association studies (GWAS) have identified putative variations that contribute to common, complex diseases such as asthma, cancer, diabetes, heart disease and mental illnesses. SNPs that have been associated with complex diseases may eventually be used to develop better strategies to detect, treat and prevent these diseases. A web-based catalog of GWAS publications has been created and periodically updated at the National Human Genome Research Institute .  Such thttp://www.nugo.org/nutrialerts/39848).  In addition, many GWAS have been done in European populations and their applicability to other populations and individuals has not been adequately studied .  GWAS results must therefore be further tested to determine whether the statistical associations found offer real-world potential to predict complex phenotypes or are useful in developing testable hypotheses about the development, progression, or treatment of a disease.  However, it is uncommon for GWAS to incorporate diet or environmental exposures which are known to influence disease susceptibility  from the UCSC Genome Bioinformatics Site and the http://www.nugo.org/nutrialerts/40615. The SNP library also maps SNPs to genes in ArrayTrack’s Gene library based on the relationships downloaded from dbSNP.For additional annotations, external links are provided for each SNP to the websites of dbSNP, UCSC Genome Browser, Ensembl, and theFor the QTL library, data for mouse, rat, and human QTLs were collected from species-specific databases.  QTL data for mouse were taken from the Mouse Genome Database (MGI) at Jackson Laboratory. Only thMany databases are cumbersome and difficult to browse or search. For example, only one SNP at a time may be queried and viewed in the well-designed and cross-linked SNP database at the dbSNP. Besides collecting dispersed data in one place to facilitate data mining across multiple knowledge domains, ArrayTrack also aims to facilitate accessibility of data. The SNP library Figure  and QTL This strategy was proposed to analyze gene-nutrient interactions, aiming to discover genes that contribute to risk factors that include environmental exposures. Manual 1. Search the metabolic and regulatory pathways of a chosen nutrient to generate a list of genes regulated by or involved in the metabolism of such a nutrient.  Examples used to develop this approach included thiamine, folic acid, riboflavin, glucose, fructose, vitamin A, vitamin D, and vitamin E. The pathway for each gene or metabolite is searched individually. This step may be accomplished through GeneGo or other similar pathway search tools. 2. Using the QTL library, map each gene to QTLs contributing to a phenotype.  In this case, the metabolic genes were “mapped” to QTLs for obesity, T2DM, body weight, or other related phenotypes or to QTLs that contribute to those diseases . The chromosomal position of each gene is found with the specified species mapping information and then used to construct a chromosomal search region for QTLs with a user specified range of extension.An example of this strategy is shown in Table http://preview.ncbi.nlm.nih.gov/mapview/). This strategy is comprised of three steps: Both GWAS and QTL analyses associate a certain trait with genetic map positions. GWAS typically use unrelated populations of cases and controls.  QTL mapping studies are usually performed on inbred strains of animals or nuclear families  of humans. Combining GWAS results from human association studies with QTLs which are usually from laboratory animals increases the reliability of identifying candidate genes for further fine mapping studies, e.g., through next generation sequencing.   Primates and rodents have shared synteny, the co-localization of genes within a chromosomal region.  These shared chromosomal regions can be re-ordered within and among chromosomes between species, but their map positions have been well characterized (see NCBI MapView - 1. Obtain a list of trait-associated SNPs from published GWAS results for a chosen condition such as obesity, T2DM, or hypertension. This can be quickly accomplished through querying the GWAS Catalog[2. Using ArrayTrack’s SNP library, map each SNP to genes based on chromosomal positions. The result of this step is a list of genes.3. For each gene in the list, query ArrayTrack’s QTL library to find whether there are any nearby QTLs that may contribute to the studied condition.  As an example, a list of SNPs was obtained from the GWAS Catalog that areBesides meeting the need of SNP interpretation and exploration, the integration of the SNP library with ArrayTrack’s library collection enables users to quickly explore and compare the associated biological pathways for SNPs of interest. Along with ArrayTrack’s library collection, the SNP and QTL libraries will be maintained and periodically updated as new data become available. As the development of these libraries progresses, query based on gene names will be added to the SNP library and query based on QTL symbols will be implemented for the QTL library.The massive amount of data generated in biomedical research studies is often considered and organized as separate knowledge domains.  We are developing strategies and tools such as the SNP and QTL libraries for data mining that will allow for more targeted research studies for developing the path to personalized nutrition, medicine, and healthcare.http://www.fda.gov/ArrayTrack. The SNP and QTL libraries are freely available to the public through ArrayTrack at The authors declare that they have no competing interests.CW and JK conceived the integration of the QTL library and related data mining strategies. HF and WT conceived the integration of the SNP library and its applications. VV, CW, JX, and JK developed the case studies. BN, HH, and HF suggested functions to be implemented with the libraries and helped with testing. JX developed the databases and software. JX created the first draft manuscript. All authors helped draft the manuscript and approved the final version.


P-values across different tests for evidence of recent positive selection in common variations, as well as strategies for extracting biological information from the detected signals, were described and applied to high density single nucleotide polymorphism (SNP) data generated from dairy and beef cattle (taurine and indicine). The ancestral Bovinae allele state of over 440,000 SNP is also reported. Using this combination of methods, highly significant (P<3.17×10−7) population-specific sweeps pointing out to candidate genes and pathways that may be involved in beef and dairy production were identified. The most significant signal was found in the Cornichon homolog 3 gene (CNIH3) in Brown Swiss (P = 3.82×10−12), and may be involved in the regulation of pre-ovulatory luteinizing hormone surge. Other putative pathways under selection are the glucolysis/gluconeogenesis, transcription machinery and chemokine/cytokine activity in Angus; calpain-calpastatin system and ribosome biogenesis in Brown Swiss; and gangliosides deposition in milk fat globules in Gyr. The composite method, combined with the strategies applied to retrieve functional information, may be a useful tool for surveying genome-wide selective sweeps and providing insights in to the source of selection.As the methodologies available for the detection of positive selection from genomic data vary in terms of assumptions and execution, weak correlations are expected among them. However, if there is any given signal that is consistently supported across different methodologies, it is strong evidence that the locus has been under past selection. In this paper, a straightforward frequentist approach based on the Stouffer Method to combine  Selection changes the frequency of advantageous variants and their neighbor polymorphic sites, sweeping the genome and leaving patterns that become prevalent in a population despite chromosome recombination The available portfolio of methodologies varies in terms of the underlying selection processes assumed, the age of the sweep, and if the test is performed within-population or depends on population comparisons Table 1.If each signature provides distinct information about selective sweeps, combining the signals should have greater power for localizing the source of selection than any single test”. Driven by this thought, they developed a Bayesian method for combining P-values from different approaches, namely Composite of Multiple Signals (CMS), which was capable to discriminate causal variants from neutral markers in simulated data. Application of CMS to real data led to the discovery of evidence of recent positive selection in LARGE and IL2 in Nigeria human population, genes that were previously incriminated in resistance to Lassa Fever Recently, Grossman et al. (2010) Although suitable for analysis of human populations, CMS is still challenging to be applied to cattle genomic data, as the computation of likelihood tables requires coalescent simulations using calibrated demographic models in an attempt to mimic the empirical data. Despite availability of good models for cattle history P-values across different tests for footprints of recent positive selection in genome-wide single nucleotide polymorphism (SNP) data, targeting common, moderate frequency variants. Two between and two within population tests for selection sweeps are covered, divided into three different categories: extended haplotype homozygosity (EHH), change in the allele frequency spectrum and local heterozygosity depression. Strategies for assigning relevant SNP to genes are also described, allowing for exploration of the biological meaning of the findings and facilitating hypothesis generation. Additionally, the ancestral Bovinae allele state of over 440,000 SNP is reported.This paper describes and applies to dairy and beef cattle data a straightforward frequentist meta-analysis approach for combining P-value for Hardy-Weinberg Equilibrium (HWE) greater than or equal to 1×10−6 or 3) Call rate (CRSNP) greater than or equal to 90%. After the SNP quality control (QC), individuals exhibiting call rate (CRIND) below 90% were also removed. This procedure was performed for each breed genotype’s dataset in parallel using PLINKPLINK. Potential parent-offspring, half-siblings and duplicate pairs were conservatively removed (see File S1 for details). SNP commonly passing QC in all four breeds were then overlapped. As the final SNP set consisted of markers passing QC with relatively small amount of missing data, and most of the methods for the detection of selection sweeps do not accommodate missing values, an imputation procedure was adopted to fill the existing missing genotypes. For this purpose, fastPHASE software was used -H-4 -K10 -T10 -C25.Genotypes for Illumina® BovineHD Genotyping BeadChip assay of Angus (ANG), Brown Swiss (BSW), Gyr (GYR) and Nellore (NEL) individuals were available for prospection of selection sweeps. Details on sample size and data source for each breed can be found in Bovinae species that included 2 Gaur (Bos gaurus), 6 Water Buffalo  and 2 Yak (Bos grunniens) with genotypes derived from the same assay. Genotypes for the three outgroup Bovinae species were pooled into a single dataset. Markers with a CRSNP of 100%  and MAF = 0  were sought. For each case, the major allele (frequency = 100%) was determined as ancestral. The final SNP set was then defined and included markers passing QC with ancestral allele information available.Since some methodologies for detecting positive selection rely on the comparison of the recombination breakdown between haplotypes carrying the ancestral and the derived allele EHH) rehh package in REHH for the ancestral allele (AiHH), derived allele (DiHH) and SNP site (iES) was calculated for each marker. EHH Method 1: Voight et al. (2006) AiHH and DiHH, called Integrated Haplotype Score (iHS):The two methodologies described here are based on the concept of Extended Haplotype Homozygosity  and derived  allele are, relative to the entire genome. As both tails from the distribution were of interest, two-sided P-values were derived as 1–2|φ(iHS)−0.5| from the Gaussian cumulative density function. EHH Method 2: Tang et al. (2007) Rsb, a between populations test, as:As iHS, the standardization procedure recommended by Rsb scores were calculated using the standardization procedure recommended by Tang et al. (2007) P-values were derived from the normal cumulative density function.The outcome also resembles a normal distribution. Unlike et al. (2010) DAF). Values range from −1 to 1 and are normally distributed. ΔDAF scores were standardized using the distribution’s mean and standard deviation, and one-sided upper tail P-values were obtained.Grossman et al. (2010) Z-score test for local heterozygosity depression (ZHp) on whole genome sequence data of domestic chicken, which basically expresses how much the expected heterozygosity in chromosome windows deviate from the average genome heterozygosity. The approach was adapted to each SNP site and computed using the observed instead of the expected heterozygosity values. The values were standardized to produce mean 0 and variance 1. For this method, negative values were of interest and the resulting site heterozygosity scores were multiplied by −1 in order to switch their direction, yielding a new statistic called SHp (i.e. site ZHp). One-sided upper tail P-values were obtained for each score.Rubin P-values retrieved from normal distributions with same parameters (mean 0 and variance 1), the weighted version of Stouffer method was adapted for the combination of Z-transformed P-values, as reviewed by i, the respective P-value was transformed into a Z-score by Zi = −φ−1(1−pi). Within population tests were performed only once per breed, hence their respective weight iω was set to 1. For each comparison of between population tests, the Z-score was weighted to 1/n, where n is the number of comparisons. Then, the combined statistic of k tests, for each SNP in each breed, was defined as:As all applied methodologies had meta-SS  scores were referred back to the standard normal distribution in order to obtain combined significance values, which were intended to address either the combination of information among different, independent tests can reject the shared null hypothesis . Significance level for genome-wide meta-SS P-values was based on a Bonferroni threshold (α = 0.05/nSNP).The Strategy 1: Since any given gene harboring signals is a direct candidate, the presence of significant intragenic SNP was checked in the Ensembl Variation 67 database using the BioMart tool Strategy 2: The closest gene in the vicinity of the most relevant SNP of a given peak could be responsible for the signal. Hence, the most significant SNP from each observed peak was isolated and the closest gene to it was mapped using the ClosestBed algorithm from the BedTools software Strategy 3: Since there are cases where variants from multiple genes in linkage disequilibrium (LD) with the marker contribute to the signal together, due to the fact that functionally related genes are often spatially close to each other DAVIDDAVID provides means for enrichment analysis, with significance tests for overrepresented terms, the inclusion criteria of functional terms was solely based on existence of information. Finally, the Enrichment Map Cytoscape plug-inFile S1.For every peak crossing the significance line, three different strategies for the annotation of functional features were applied, based on the genomic coordinates from the UMD3.1 assembly IND of 83.79%, 96.93%, 94.87% and 88.63% for Water Buffalo, Yak, Gaur and pooled data was observed, respectively. From the initial set of 742,910 autosome markers, considering only markers perfectly typed across the pooled outgroup samples (CRSNP = 100%), a total of 559,663 SNP probes were successfully hybridized (71.94%), and 111,376 SNP were polymorphic (MAF>0). Hence, a total of 448,287 SNP (56.75%) had their ancestral allele determined, being provided as a TSV file (File S2).By assessing the outgroup species genotypes, an average CRFile S1). Details on the final base dataset used for all further analyses can be found in Number of SNP passing QC was 579,470, 554,826, 485,655 and 461,702 for ANG, BSW, GYR and NEL, respectively. Overlapping of the four SNP lists retrieved a final set of 281,994 markers, from which 157,702 had ancestral allele information available. Even with the drastic drop in the number of SNP, the intermarker distance mean and median were 15.94 kb and 6.43 kb, respectively, superposing the median spacing of 37 kb declared for the BovineSNP50 assay Figure S1) and genome-wide Z-transformed P-values were weakly correlated , satisfying the independence condition for meta-analysis. Genome-wide distribution of meta-SS P-values and the closest genes to the top of the peaks can be found in P-value crossing the genome-wide significance (P<3.17×10−7) was: 153 for ANG, 212 for BSW, 3 for GYR and 13 for NEL. The most significant SNP was found in BSW (P = 3.82×10−12), and is an intronic variation in Cornichon homolog 3 gene (CNIH3 - ENSBTAG00000044171), located at BTA16:28478192.All performed tests for footprints of selection resembled a normal distribution  and NEL  was provided in CNIH3 intronic SNP) for both the haplotypes containing the derived and ancestral alleles, and 2) a bifurcation diagram for the haplotypes containing the derived allele, representing the breakdown of LD at increasing distances from the core allele  at a given core SNP . It can be seen from the BSW and NEL comparison that the signal of the unusual derived allele long haplotype in BSW, revealed by the meta-SS statistics, is not detectable in NEL. It is noticeable, by the shape of the SNP significances distribution in the meta-SS scatter plot, that iHS and Rsb had higher influence in the composite test, and the combination of methods penalized SNP with little statistical support.In order to illustrate the potentiality of combining signals resulting from different methodologies for the detection of positive selection, a regional plot of File S3). Two synonymous exonic SNP for ANG and BSW, one non-synonymous variation  for a gene of the olfactory receptor family (LOC524290/OR2W3 - ENSBTAG00000025293) in ANG (P = 7.65×10−9) and a 3′UTR variation (BTA2:47315215) for the KIF5C (kinesin family member 5C - ENSBTAG00000018125) gene in NEL (P = 2.68×10−7) were found. All other variants within genes were located in introns. The application of the LD-window approach (Strategy 3) retrieved SNP windows with an average size of 576.8 kb overall breeds, and the largest window spanned 1.83 Mb. Total number of genes within windows included in each breed specific list was: 309 for ANG, 177 for BSW, 4 for GYR and 14 for NEL (full lists can be found in File S4).The number of genes directly harboring significant SNP was 20 for ANG, 27 for BSW, 1 for GYR and 3 for NEL (the full list can be viewed in P = 2.61×10−7) was detected in the Calpain 2  catalytic subunit, which may be capturing the signal of a causal untyped variant under selection. Due to a low number of genes mapped, it was not possible to build a network of functional terms for GYR and NEL. Across all lists, a total of 69 genes (13.69%) had no functional term associated to them, being either uncharacterized proteins or novel RNAs with no functional record available. All DAVID annotation chart reports are provided in File S3.Networking of functional terms from ANG gene list . Although this comprises most of cattle domestication history, the majority of the signals detected are more likely to have arisen during breed formation, which goes up to some hundreds of years ago Concordances among et al. (2010) Gallus gallus). The strategy allowed for the detection of genome regions that were nearly fixed in the domesticated lines and exhibited low identity to the wild-ancestor haplotypes, suggesting selection sweeps during domestication.One factor that contributed to the underrepresentation of fixed loci in the dataset used is related to the SNP assay. As the SNP ascertainment strategies for the design of bovine arrays were focused on developing marker panels of common variations to support genome-wide association applications, and relied on sequence data of most major breeds for variation detection Another important factor contributing to the low representation of rare variants in the present study is the filtering of SNP with moderate allele frequencies in all breeds (MAF ≥0.03), which may have made the detection of selection sweeps dating back to early cattle domestication unlikely. Nevertheless, the strategies adopted seemed to be capable of detecting footprints of recent positive selection, which may be anthropogenically ascertained by breeding and related to milk and meat production. The functional findings discussed later support this hypothesis.CNIH3 gene in BSW (P = 3.82×10−12). CNIH3 with other proteins assessed by data integration in the STRING 9.0 database CNIH3 with multiple α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) selective glutamate receptors . CNIH3 regulates the trafficking and gating properties of AMPA receptors in the central nervous system et al. (2010) GnRH) and slower pre-ovulatory LH surge, making carrier cows less responsive to superovulation hormone treatment. Sugimoto et al. (2010) GRIA1 in Japanese Black and Holstein commercial sires and found no departures from HWE in the locus, meaning that there is no evidence of selection pressure on the reported variants in either breeds. The signal on CNIH3 found in the present study suggests that at least the underlying pathway has suffered recent selection pressure in BSW, although the selection force is unknown.The most significant signal found comes from the chemokine C-C ligand 8 present in our gene list , were deregulated in animals submitted to a surgical procedure, which in turn showed higher Warner-Bratzler shear force in beef samples after slaughter compared to the control group, suggesting that they play important role in muscle metabolism, either in vivo or in postmortem proteolysis regulation. These findings support that genes participating in chemokine/cytokine activity are under selection in ANG cattle.ANG exhibited three groups in the network map, one addressing chemokine/cytokine activity, a second with components of the transcription machinery and another related to glucolysis/gluconeogenesis. Both transcription activity and glucose metabolism are broad themes to be hypothesized, but it is possible that they have faced recent selection for high metabolic efficiency relative to increased meat yield and fat deposition. Regarding cytokines and chemokines, it has been found that they modulate different stages of muscle cell development postmortem meat tenderization in beef cattle et al. (2011) et al. (2012) The calpain-calpastatin system is a proteolytic complex that has also been largely incriminated in Another intriguing candidate pathway pointed out by the present study is the ribosome biogenesis in BSW, more particularly the step involving methylation of rRNA. The addition of a methyl group to the 2′-hydroxyl group of the backbone ribose is a conserved type of post-transcriptional RNA modification ST6GALNAC5 (ENSBTAG00000007309) in GYR (P = 1.24×10−9). ST6GALNAC5 is involved in the synthesis of gangliosides, more particularly the GD1α in the brain ST6GALNAC5 during lactation, and may be an important source of GD1α for the developing neonate brain ST6GALNAC5 has been indirectly selected in GYR via percentage of fat in the milk.It was found an intronic signal for meta-SS statistics, GYR and NEL exhibited considerable numbers of selective sweeps, but still less than the taurine breeds analyzed . However, when only between populations tests were combined, the indicine breeds showed a severe drop in signals . As tests based on LD persistency and unusual long-range haplotypes were an important part of the composite statistics, the decreased number of sweeps found in the indicine breeds could be explained by differences in haplotype block structure and extent of LD across taurine and indicine breeds. In fact, ANG and BSW were shown to have greater mean haplotype block size and average LD than GYR and NEL The present study found substantially fewer evidences of recent selection in GYR and NEL, relative to BSW and ANG. When only within population tests were combined in the melanocortin 1 receptor gene (MC1R - ENSBTAG00000023731), responsible for the black/red coat color in ANG Mast/stem cell growth factor receptor gene (KIT - ENSBTAG00000002699), incriminated in the ‘piebald’ spotted coat-color in Hereford and BSW MC1R is located at BTA18: 14757332-14759082, and KIT is located at BTA6:71796318-71917431 in the UMD v3.1 assembly. Both KIT and MC1R regions were underrepresented in SNP coverage in the present study due to QC effects and ancestral allele information availability, and gaps spanning BTA6 71.7–72.4 Mb and BTA18 14.0–15.0 Mb were observed. These observations could justify the absence of significant signals for KIT or MC1R. However, other studies searching for selective sweeps in these breeds also did not report signals in KIT and MC1R regions in BSW and ANG Many studies on signatures of selection in cattle have been published in recent years, and known genomic regions under selection are often used in literature as ‘confirmatory’ loci in order to validate new findings. Examples of such loci are the P-values) across BTA 4, 5, 16 and 19 reported by meta-SS reported herein, and BTA 6 exhibited similarities with iHS reported by et al. (2008) ST and iHS, and were able to detect signals in Holstein BTA 6 that resembled the meta-SS pattern found in the BSW dataset used in the present work. Moreover, Flori et al. (2009) ST within and across three French dairy cattle breeds, finding putative regions under selection that overlap the findings on BSW chromosomes 5 and 6 in the present study.Some of the putative loci under selection detected herein were compared to previous studies, more particularly the signals found in BSW, since information on signatures of selection is more abundant in taurine dairy cattle. The topology of iHS -log10Click here for additional data file.Figure S2Pearson correlation between each individual test Z-transformed P-values.(TIF)Click here for additional data file.Figure S3Manhattan plots of genome-wide meta-SS –log10 combining within breeds tests only.(TIFF)Click here for additional data file.Figure S4Manhattan plots of genome-wide meta-SS –log10 combining between breeds tests only.(TIFF)Click here for additional data file.File S1Supporting methods: Cryptic relatedness control and functional annotation.(PDF)Click here for additional data file.File S2BovineHD ancestral allele information.(ZIP)Click here for additional data file.File S3List of significant intragenic SNP and annotation chart reports.(XLSX)Click here for additional data file.File S4Lists of genes potentially in linkage disequilibrium with putative SNP under selection in Angus, Brown Swiss, Gyr and Nellore.(ZIP)Click here for additional data file.


This study was carried out to investigate the modulatory roles of dietary n-3 PUFA ascending levels as the treatment groups  using combinations of tuna oil and sunflower oil. All diets were isocaloric and isonitrogenous. On day 28, all birds were challenged with IBD virus. Antibody titer, cytokine production, bursa lesion pre and post-challenge and lymphoid organ weight were recorded.A total of 300 day old male broiler chicks were assigned to four dietary On d 42 the highest body weight was observed in the T2 and T3 and the lowest in T4 chickens. Feed conversion ratio of the T2 broilers was significantly better than the other groups. Although productive parameters were not responded to the dietary n-3 PUFA in a dose-dependent manner, spleen weight, IBD and Newcastle disease antibody titers and IL-2 and IFN-γ concentrations were constantly elevated by n-3 PUFA enrichment.Dietary n-3 PUFA enrichment may improve the immune response and IBD resistance, but the optimum performance does not coincide with the optimum immune response. It seems that dietary n-3 PUFA modulates the broiler chicken performance and immune response in a dose-dependent manner. Thus, a moderate level of dietary n-3 PUFA enrichment may help to put together the efficiency of performance and relative immune response enhancement in broiler chickens. Consumption of n-3 PUFA, resulted in incorporation of these fatty acids into lipid membrane of all tissues, including cells of the immune system [n-3 PUFA showed to improve the immune response and reduce inflammation in different species such as chicken, mice and fish [There has been fast growth in the global interest to the effects of diet on various aspects of immune function. Typically, different research groups have assessed different nutrients, each using different animal models. Most of these models focus on selected aspects of immune function without assessing the other functions. It is therefore always difficult to understand the broader modulating influence for instance on the productive parameters. In modern poultry industry, however, within commonly used feed there is an oversupply of e system ,2. Long and fish .+ cells [n-3 PUFA from fish oil have an antiviral effect in mice [n-3 PUFA in fish oil can affect immune cell activation in both the innate and specific immune systems in different species [On the other hand, infectious bursal disease (IBD) is one of the most important chicken diseases that have emerged in the last 30 years. IBD viral infection suppresses the immune system in young chickens, making birds sensitive to other diseases -8. In po+ cells . Immunog+ cells . Another+ cells . It has  in mice ,15. The  species ,16-18. T species ,20.As for the chicken nutrition and immune response, to date, there is no report on immunomodulatory effects of dietary n-3 PUFA under IBD challenge in broiler chickens. Therefore, this study aimed to investigate the influence of dietary n-3 PUFA enrichment on immune response and performance of IBD challenged broiler chickens.n-6) was the main n-6 PUFA in the diets and it was decreased as the fish oil increased. An opposite trend was observed for EPA and DHA contents while α-linolenic acid (C18:3n-3) remained unchanged. The resulting DHA content of the diets were 0, 5.6, 8.17 and 11.93 for T1 to T4, respectively. Influence of treatment diets on plasma fatty acid composition on d 28 and 42 are shown in Table n-3 PUFA in plasma. On d 28 and 42 the highest plasma n-3 PUFA was observed in the T4 birds diet while the highest plasma n-6 PUFA was measured in T2 birds. The same results were observed in breast muscle on d 42  Table . Spleen n-3 PUFA enrichment on lesion score of bursa in IBD challenged broiler chickens is presented in Table The Effect of dietary n-3 PUFA enrichment on serum antibody titer (log10) in broilers vaccinated against ND and challenged with IBD virus is shown in Figure The effect of dietary titer log0 in broin-3 PUFA enrichment pre and 2 days post-challenge from T2 group to T4 , docosapentaenioc (DPA 22:5 n-3) and DHA (22:6 n-3) [n-6: n-3 PUFA ratio alters the fatty acid profile of meat toward long chain PUFA [n-3 PUFA, EPA and DHA of plasma and breast muscle significantly increased along with ascending n-3 PUFA inclusion from T1 to T4. There was a relatively high level of arachidonic acid  to linoleic acid  in plasma of T1 on day 28 and 42. This phenomenon can be explained by the absence of linolenic acid  in the diet of this group, while the other groups have LNA of 0.5, 0.6 and 0.6%  . On the nioc DPA :5 n-3 anain PUFA . In agren-3 and n-6 PUFA FCR in avian metabolism through the modulation of lipid deposition and oxidation. However, the enhancement was not observed in highest n-3 PUFA (T4) group, leading to the postulation that effect of n-3 PUFA enrichment is a dose-dependent response. Hulan [Dietary n-3 PUFA enrichment also increased body weight and improved FCR as measured in T2 and T3 at 42 days of age. This may be due to the superior digestibility of unsaturated fatty acid compared to saturated type -27. Newme. Hulan  reportede. Hulan  demonstrn-3 PUFA from fish oil (5%) significantly increased serum IgG concentration and IgM+ B cells in chicken [Immune tissue development can in some cases reflect immune system response and functionality. In the current study, there was no affect of dietary treatment on the bursa of Fabricius weight and lesion score, but spleen weight increased as the level of n-3 PUFA in diet increased 7 days post-challenge. Some other studies reported that feeding PUFA to chickens  and mice chicken . Immunog chicken ,10,11. Tn-3 PUFA enrichment from T2 to T4. Cytokine production plays an important role in mounting a complete and full immune response of both the innate and specific systems. These cytokines are produced by T helper lymphocytes (Th1) and activate macrophages, NK cells, and cytotoxic T lymphocytes and are the principal effectors of cell-mediated immunity. Interactions with bacteria, viruses, and fungi tend to induce Th1 activity. Because Th1 cytokines activate monocytes and macrophages, these cytokines may be regarded as pro-inflammatory. Interlukin-2 can enhance interferon production [n-3 fatty acids. It is possible that eicosanoid production derived from n-3 PUFA might stimulate the production of IL-2 and IFN-γ [n-3 PUFA modulate cytokine production is not clarified. However, the possible mechanism might be the decreased production of metabolites of n-6 PUFA, such as PGE2 and changes in phospholipids composition of immune cell membranes. Some studies suggested the PGE2 which is derived from arachidonic acid and n-6 PUFA as the possible candidate for inhibition of T cell proliferation, Th1 cell, IL-2 and IFN-γ production [2 primes human naive T cells in a dose-dependent fashion for production of high levels of IL-4, IL-10, and IL-13 and very low levels of IL-2 and IFN-γ [n-3 PUFA decreases production of PGE2 [n-3 PUFA and n-6 PUFA for incorporation into the cell membrane phospholipids. This finding is in contrast with the result of dietary fish oil supplementation in human [One of the interesting results of the current study is the ascending elevation of IL-2 and IFN-γ concentrations along with the oduction ,35. Intend IFN-γ ,37. At poduction -40. The nd IFN-γ . Furthernd IFN-γ . It is r of PGE2  due to ain human  and rat in human . Howeverin human  in humanin human ,46 and min human . A possiin human ,42 or loin human ,49, suggIn conclusion, dietary n-3 PUFA enrichment may improve the immune response and IBD resistance, but the optimum performance does not coincide with the optimum immune response. It seems that dietary n-3 PUFA modulates the broiler chicken performance and immune response to IBD challenge in a dose-dependent manner. Thus, a moderate level of dietary n-3 PUFA enrichment may help to put together the efficiency of performance and relative immune response enhancement in broiler chickens.2. Feed and water were provided ad libitium and lighting was continuous. The chicks were vaccinated against Newcastle disease  on day 7 (intraocular) and on day 21.A total of three hundred day-old male broiler chicks (Cobb 500) were obtained from a local hatchery. The chicks were wing-banded, individually weighed, and housed. The birds were raised on conventional deep litter system in an open sided house with cyclic temperatures . The relative humidity was between 60 to 80%. The area of each pen was 2 mn-3 PUFA ascending levels as the treatment groups  achieved using a combinations of tuna oil, sunflower oil and palm oil in feed formulation  were calculated. Mortality was recorded daily in each subgroup.4.0 EID50 IBD virus into the lumen of the crop by oral gavage [On day 28 of age, all birds were challenged by an oral route with a commercial live IBD vaccine . The strain was characterized as an intermediate classical strain. Each bird was inoculated with a dose of 10l gavage .Prior to IBD challenge (d 28), 7 and 14 d post-challenge, eight birds from each group were chosen randomly and killed by cervical dislocation, the bursas of Fabricius and spleen were removed and their weight recorded and presented as relative to body weight . The bur3)  according to methods in AOAC [The total fatty acids were extracted from diets, oils and breast muscle samples using chloroform: methanol 2:1 (v/v) based on the Folch  modified in AOAC . The metPrior to IBD challenge (d 28), 7 and 14 d post-challenge, ten birds from each group were chosen at random, and blood samples were collected from the brachial vein. Serum was separated by centrifugation  and antibody titre against ND and IBD were measured using commercially available ELISA kits  according to manufacturer's instructions. The absorbance of controls and samples were read at 405 nm using an ELISA reader .The serum levels of IL-2 and IFN-γ were determined using a chicken specific ELISA kits . The procedure was following the procedure recommended by the manufacturer.10. The IL-2 and IFN-γ data were normalized using square root transformation prior to analysis. Mortality data were subjected to chi-square analysis. The results were expressed as mean ± SEM. Statistical significance was considered at P < 0.05.A completely randomized design (CRD) with five replicates was employed for this study. Statistical analyses were conducted using one-way ANOVA general linear models. Means were separated by Duncan's multiple range tests. Antibody titers of IBD and ND were logarithmically transformed prior to analysis to achieve homogeneity of variance and were expressed as logIBD: infectious bursal disease; ND: Newcastle disease; IL: interleukin; IFN: Interferon; ELIZA: Enzyme-linked immunosorbent assay; SRBC: sheep red blood cell; PGE2: prostaglandin E2; PUFA: polyunsaturated fatty acid.The authors declare that they have no competing interests.EM, AFS and ME conceived and designed the study, participated in data collection and analyses and drafted the manuscript; AK, TCL, FH, MHB participated in the design of the study and drafted the manuscript. YMG participated in the data analyses and fatty acid analysis; TZ helped for the interpretation of the histopathological examination. All authors read and approved the final manuscript.


The presence of homoeologous sequences and absence of a reference genome sequence make discovery and genotyping of single nucleotide polymorphisms (SNPs) more challenging in polyploid crops.Brassica napus inbred lines and their 91 doubled haploid (DH) progenies using a modified ddRADseq technique. A bioinformatics pipeline termed RFAPtools was developed to discover and genotype SNPs and presence/absence variations (PAVs). Using this pipeline, a pseudo-reference sequence (PRF) containing 180,991 sequence tags was constructed. By aligning sequence reads to the pseudo-reference sequence, allelic SNPs as well as PAVs were identified and genotyped with RFAPtools. Two parallel linkage maps, one SNP bin map containing 8,780 SNP loci and one PAV linkage map containing 12,423 dominant loci, were constructed. By aligning marker sequences to B. rapa sequence scaffolds, whose genome is available, we assigned 44 unassembled sequence scaffolds comprising 8.15 Mb onto the B. rapa chromosomes, and also identified 14 instances of misassembly and eight instances of mis-ordering sequence scaffolds.To address this challenge, we constructed reduced representation libraries (RRLs) for two These results indicate that the modified ddRADseq approach is a cost-effective and simple method to genotype tens of thousands SNPs and PAV markers in a polyploidy plant species. The results also demonstrated that RFAPtools developed in this study are powerful to mine allelic SNPs from homoeologous sequences in polyploids, therefore they are generally applicable in either diploid or polyploid species with or without a reference genome sequence. When coupled with the barcoded multiplexed sequencing strategies [Brassica napus L., the presence of two sub-genomes (A and C) makes SNP discovery and genotyping more challenging because homologous and homoeologous DNA sequences from the two sub-genomes have to be analyzed simultaneously. Two classes of DNA polymorphisms, inter-variety polymorphisms  and inter-homoeologue polymorphisms (IHPs), are usually mixed together. In addition, the original A and C genomes are already duplicated or triplicated as a result of ancestral polyploidization events [B. napus[Single nucleotide polymorphisms (SNPs) are the most frequent polymorphism in the genomes of animals and plants. Next-generation sequencing (NGS) technologies are able to produce millions of short sequence reads in a high-throughput, cost-effective fashion and thus can be used for rategies , these trategies -6. All tn events -9, which[B. napus,11.de novo assembled into contigs, which subsequently served as a reference sequence to which all short reads derived from multiple individuals were mapped accurately [To overcome the complexity of genomes and improve genotyping through-put, approaches such as complexity reduction of polymorphic sequences (CRoPS), restriction-site-associated DNA sequencing (RADseq) and genotyping by sequencing (GBS) that coupled reduced representation libraries (RRLs) with NGS technologies have been developed -17 and ecurately . Stacks curately ,23. But In this study, we employed a modified ddRADseq approach to sequence the reduced representation genomes of the two parents, ZY821 and No2127, and the BnaNZDH population derived from these two parents in deep coverage for SNP discovery and genotyping. We also developed a bioinformatics pipeline termed RFAPtools to unambiguously discriminate between SNPs versus differences of homoeologous sequences in polyploid crops such as oilseed rape without a reference genome sequence. A pseudo-reference sequence (PRF) was built using the sequence reads from the parents and their segregating DH population. By aligning sequence reads of each individual DH line to the PRF, both allelic SNP and presence/absence variations (PAVs) were discovered and genotyped.A modified ddRADseq protocol was used to construct RRLs for ZY821 and No2127, and their 91 DH progenies  as a representative with the same restriction enzymes and obtained 3,904 SacI-MseI fragments within the size range of 141–420 bp. The distance between every two adjacent in silico sequence tags on the B. rapa A03 reference sequence was calculated. The majority (91.92%) of adjacent in silico tags were located within less than 25 kb, with 44.63% tags within 5 kb (0–5 kb) and 22.33% within 5–10 kb . The total genome size of B. napus is estimated to be 1.2 Gb [in silico simulation and assuming that B. rapa and B. napus had similar GC content, genomic reduction with SacI and MseI double digestion should result in ~36 Mb of DNA or about 147,000 SacI-MseI fragments in the size range of 141–420 bp for sequencing, which is equivalent to about 3.0% of the B. napus genome. The above analyses suggested that the modified ddRADseq could significantly reduce the genome complexity of B. napus and also result in a considerably large number of target fragments for SNP discovery at the same time.The cumulative length of all e 1.2 Gb . AccordiB. napus to differentiate allelic SNPs from the abundant IHPs. To address these challenges, we developed a bioinformatics pipeline named RFAPtools that included three modules: assembly of a pseudo-reference sequence, SNP discovery and genotyping, and discrimination of allelic SNPs from homoeologous loci  and A04 (66.8 cM) respectively. All information of the PAV genetic map was listed in Additional file To construct a linkage map with the dominant PAV markers, 984 anchor SNP loci with 10% MMD in the 45 DH lines were selected from the SNP linkage map to form a skeleton map. A linkage map contained 12,423 dominant PAV loci was constructed with these anchor SNP loci  for validation of single nucleotide variations. PCR primers were designed to amplify the restriction fragments containing the SNPs. PCR products from the two parents were digested with the corresponding REs. Of the 44 SNPs, 26 (59%) could be confirmed by digestion, while the other 18 did not yield the expected restriction fragments. We further sequenced PCR products of all 44 loci amplified from the two parents using the Sanger sequencing method to further confirm these SNPs. All the 26 confirmed SNPs had the expected nucleotide variations, while the remaining 18 SNPs were a mixture of the expected allelic variations and homoeologous sequences. We also surveyed the 91 DH lines to validate the called SNP genotypes using the above confirmed 26 SNPs. A total of 2,251 genotypes were generated with an accuracy of 99.33%, indicating that the genotypes called by aligning sequence reads to the pseudo-reference sequence were reliable. These results further revealed the complexity of B. rapa reference genome sequence to validate the genetic linkage maps constructed in this study  was the largest and assigned to A06 by 17 SNP and 38 PAV loci. Scaffold000618 (3.9 kb) was the smallest and assigned to A06 by two adjacent PAV loci. In addition, we also identified 14 instances of and eight instances of misordering of sequence scaffolds . In this study, we used two strategies in the RFAPtools to solve these problems. The first strategy was to separate the homoeologous sequences that differed with even only one nucleotide into distinct loci on the pseudo-reference sequence. The second strategy was to use the segregation data of the DH population to judge which two of the mixed alleles were allelic. In a segregating DH population with only homozygous genotypes, each individual should have two identical alleles from either parent. If two different alleles appeared in one individual, they should come from different homoeologous loci. Followed the segregation rule, we were able to distinguish allelic sequences from homoeologous sequences even just having one nucleotide variation between them using the integrated RFAPtools. Of the 9,210 SNP loci, 1,742 were mined from a mixture of homoeologous sequences. In contrast, only 19.3% SNPs could be identified in B. napus by aligning RNAseq reads to about 95,000 unigenes collected from the Brassica species [In diploid species, it is relatively easy to call SNPs and genotypes because they do not have homoeologous sequences. But in polyploid species, the existence of homoeologous sequences makes it difficult to differentiate the allelic SNPs from the homoeologous and also paralogous sequence differences, because multiple sequence reads can align to the same genomic location or an individual sequence read can show significant alignment to multiple genomic locations. For these reasons, previous SNP identification methods have utilized only the subset of sequence reads that can be mapped uniquely to a reference  and disc B. napus. Therefo species ,28, indiBrassica genome [Many analyses of genetic variation using reduced representation sequencing only focused on discovery and genotyping SNPs ,36. Howea genome . In thisa genome ,20.De novo sequencing of the B. napus genome is undergoing with the NGS technologies. Assembly of short reads is a great challenge in such a complex genome and high-density sequence-based genetic maps can be a great help to assemble the sequence scaffolds to form integrated chromosomes. However, such a high-density linkage map that may aid in the ordering and anchoring of the sequence scaffolds or contigs is still lacking in the Brassica species. In this study, the two high-density linkage maps were constructed, which allowed us to assign 44 previously unassigned scaffolds to the B. rapa chromosomes and identify 14 possible misassembly and 8 possible misordering sequence scaffolds. Bancroft et al. identified 32 instances of misassembly of sequence scaffolds and a segmental rearrangement using a similar SNP bin map [de novo sequencing of the A, C and AC genomes. Furthermore, these maps also provide a platform for maker-assisted selection breeding, map-based gene cloning and comparative genomic research in Brassica species. bin map . TherefoB. rapa reference genome sequence. Using these linkage maps, we assigned 44 unassembled sequence scaffolds to the B. rapa chromosomes, and also identified 14 instances of possible misassembly and 8 possible instances of mis-ordered sequence scaffolds. These results suggested that modified ddRADseq procedure and the bioinformatics pipeline RFAPtools developed here is a powerful tool to mine allelic SNPs from homoeologous sequences, therefore they are generally applicable in either diploid or polyploid species with or without a reference genome sequence.In this study, we constructed reduced representation libraries (RRLs) for two parents and their 91 DH progenies using a modified ddRADseq technique. A bioinformatics pipeline termed RFAPtools was developed for SNPs/PAVs discovering and genotyping in polyploid species. Using these tools, we constructed a SNP bin map containing 8,780 SNP loci and one PAV linkage map containing 12,423 dominant loci. Both linkage maps showed good collinearity with the Samples comprised of 91 DH lines from the BnaNZDH population along with its two parents, No2127 and ZY821. This BnaNZDH population has already been used to construct genetic linkage maps with microsatellite markers ,39-41. TTwo types of adaptors, MseAD and SacAD, compatible with the MseI and SacI cohesive ends, were designed for RRL construction. Each adaptor contains three parts, the common Illumina PE adapter sequence including sequencing primer, a 5-base barcode and complimentary sequence to the overhangs produced by the restriction enzymes. The top sequence of the MseAD and the bottom sequence of SacAD were 5′-phoshorylated. All adapter sequences including barcodes are listed in Additional file 2O. Then each library was amplified in a 50 μl of volume with 50–100 ng of adaptor-ligated DNA fragments as template, 1× HF buffer, 3.5 mM MgCl2, 0.4 mM dNTPs, 1 U iProof polymerase (Bio-Rad), 5 pmol of two overhang primers . PCR amplification was performed as follows: 98°C for 2 min, followed by 11–13 cycles at 98°C for 30 s, 65°C for 30 s and 72°C for 15 s, and a final extension at 72°C for 5 min. PCR products were separated on 2% agarose gel and fragments in the size range of 270–550 bp were purified with the Qiagen gel purification kit and eluted with 25 μl H2O. The libraries were quantified using Qubit flourimeter (Invitrogen), Agilent 2100 (Agilent Technologies) and real-time quantitative PCR, then submitted for sequencing on Illumina GAIIx or HiSeq2000 platform. The clean data were then parsed into different individuals with exact match to the barcodes and remnant restriction sites at both ends. After trimming the barcode sequence, the first 70 nucleotides of each PE read were kept for further analysis.About 200 ng of genomic DNA from each sample was double digested separately with 5 U of the restriction enzymes SacI and MseI (Fermentas) with 1 × restriction buffer in a reaction volume of 25 μl. The reaction mixture was first incubated at 37°C for 6 hr, then at 65°C for 90 min. The restriction fragments of each individual were then ligated to the SacAD and MseAD adaptors with unique barcode combinations to differentiate the samples. Ligation reaction was done in a reaction volume of 50 μl at 16°C for overnight with 25 pmol of SacAD and MseAD adaptors, and 50,000 Units of T4 DNA ligase. The restriction sites of both MseI and SacI were disrupted upon ligation to adaptors. In order to remove all residual recognition sites due to incomplete digestion or chimeric fragments generated by re-ligation, an additional double digestion with the same enzymes was performed. The final ligates of 12 individuals were pooled together to form a library and were separated on 2% agarose gel. Fragments in the size range between 220 and 500 bp, which corresponded to restriction fragments in the size range of 141–420 bp before adaptor ligation, were recovered from the gel and purified with the Qiagen gel purification kit. Each library was eluted with 25 μl HThe RFAPtools include three modules: assembly of a pseudo-reference sequence, SNP discovery and genotyping and discrimination of allelic SNPs from homoeologous loci.Figure SNP and genotype calling were done by aligning all reads of the parents and each DH line to the pseudo-reference sequence employing the SOAP software. Two mismatches were allowed on each strand of the PE read. SNPs of each DH individual were identified and sorted afterward to obtain the following information: the start and end sites of each PE read on the PRF, the number of SNPs on each read, the relative position of each SNP on the 200 bp unique tag, nucleotide variation and quality score for each SNP. The SNPs of a PE read were expressed as: 1201_1400 2 15@T- > C 30 196@T- > C 38, where 1201_1400 standed for the start and end sites of the PE read on the PRF, 2 standed for two SNPs existed on this read, 15 and 196 for the relative positions of the SNPs on the 200 bp unique tag, and 30 and 38 for the quality score of the two SNPs respectively. Next, all SNPs from the parents and all DH individuals were sorted according to the position of the sequence read on the PRF and the relative positions of SNPs on the 200 bp unique tag. The coverage depth and average quality of each SNP variation was counted in individual parents and DHs. All these processes in this section were performed by the prf_snpcalling.sh script and depicted in Figure In oilseed rape, several types of mixed sequence reads from homoeologous loci are usually aligned to the same position on the pseudo-reference sequence. To mine allelic variants from mixed sequence reads  at the same position, we first designated the alleles as A1, A2, A3 etc. The occurrence, coverage depth of each allele in each individual and the average quality score of each nucleotide variation were analyzed. The presence of an allele in a DH line was scored as “+”, and the absence of an allele in a DH line was scored as “-”. All alleles at a locus in the population were scored and tabulated in a matrix for genotype calling. The next step was to make a co-occurrence matrix and determined which two variants were allelic variations of a locus in the segregating DH population. Theoretically, all loci in DH lines are homozygous. If two alleles belong to a locus, only one allele rather than two alleles occur in a DH line simultaneously. If two alleles occur in the same DH individual simultaneously, they are definitely from two homoeologous loci. Based on this principle, allelic variants were discriminated for each locus using the script of prf_allele.sh . The reactions were carried out at 94°C for 3 min, 35 cycles of 94°C for 30 s, 60°C for 1 min, 72°C for 45 s, one final extension step of 72°C for 5 min, and kept at 4°C. After amplification, 5 μl PCR products were digested using 2 U corresponding RE and reaction buffer in a volume of 8 μl. The products were separated on 6% denaturing polyacrylamide gels and visualized using silver staining. The SNPs that were validated between the two parents were subjected to genotypes analysis in the DH population. Meanwhile, the PCR products of two parents were also directly sequenced to validate the variations.A suit of SNPs that create or destroy a restriction site was randomly selected for validation by CAPS markers. Primers were designed to amplify the target fragment including the SNP variations. PCR amplifications were performed in a volume of 20 μl containing about 100 ng DNA templates, 1× Taq buffer, 4 mM MgClhttp://211.69.128.139/~xunchen/RFAP/index.php.The Illumina sequencing data from this study have been deposited in the NCBI Sequence Read Archive under accession number SRA056328. RFAPtools_v1.0 and pseudo-reference sequence constructed in this study “ZY821_prf.fasta” are available at AFLP: Amplified fragment length polymorphism; CRoPS: Complexity reduction of polymorphic sequences; ddRADseq: Double digest RADseq; ddGBS: Double digestion GBS; DH: Double haploid; GBS: Genotyping by sequencing; IHP: Inter-homoeolog polymorphism; LG: Linkage group; MMD: Maximum missing data; PAV: Presence/absence variation; PRF: Pseudo-reference sequence; RADseq: Restriction-site-associated DNA sequencing; RE: Restriction enzyme; RRL: Reduced representation library; SNP: Single nucleotide polymorphism; SSR: Simple sequence repeatThe authors declare that they have no competing interests.XC, XL, JX, ZW, BZ and HL performed the experiments. XC, YL and BW analyzed the data and performed the bioinformatics analyses. JW provided the plant materials. KL, XC and MY wrote the paper. KL, XC and SH conceived the study. The manuscript was read and approved by all authors.Figure S2. Shows the number of sequence reads of the 91 DH lines. Figure S3. Shows the distribution of SNPs along pair-end sequence reads. Figure S4. Shows the PAV genetic map constructed with 45 DH individuals with about 2.0 million of sequence reads. Figure S5. Shows collinearity between the B. rapa and the A sub-genome in B. napus revealed by the PAV linkage groups. Figure S6. Shows four instances of mis-ordered sequence scaffolds on the B. rapa A10 chromosome.Shows the modified ddRADseq protocol. Click here for fileTable S2. Shows the information of possible misassembled sequence scaffolds detected by the SNP and PAV genetic linkage maps. Table S3. Shows the information of assignments of unassembled sequence scaffolds using the SNP and PAV genetic linkage maps. Table S4. Shows the sequences of adapters used in this study.Shows the summary information of the PAV genetic linkage map. Click here for file


Gallus gallus) chromosome (GGA) 1 to be strongly associated with chicken growth using 47,678 SNPs and 489 F2 chickens. The growth traits included aggregate body weight (BW) at 0–90 d of age measured weekly, biweekly average daily gains (ADG) derived from weekly body weight, and breast muscle weight (BMW), leg muscle weight (LMW) and wing weight (WW) at 90 d of age. Five SNPs in the 1.5 Mb KPNA3-FOXO1A region at GGA1 had the highest significant effects for all growth traits in this study, including a SNP at 8.9 Kb upstream of FOXO1A for BW at 22–48 d and 70 d, a SNP at 1.9 Kb downstream of FOXO1A for WW, a SNP at 20.9 Kb downstream of ENSGALG00000022732 for ADG at 29–42 d, a SNP in INTS6 for BW at 90 d, and a SNP in KPNA3 for BMW and LMW. The 1.5 Mb KPNA3-FOXO1A region contained two microRNA genes that could bind to messenger ribonucleic acid (mRNA) of IGF1, FOXO1A and KPNA3. It was further indicated that the 1.5 Mb GGA1 region had the strongest effects on chicken growth during 22–42 d.Chicken growth traits are important economic traits in broilers. A large number of studies are available on finding genetic factors affecting chicken growth. However, most of these studies identified chromosome regions containing putative quantitative trait loci and finding causal mutations is still a challenge. In this genome-wide association study (GWAS), we identified a narrow 1.5 Mb region (173.5–175 Mb) of chicken ( Gallus gallus) chromosome (GGA) 1, 2, 3, 4, and Z. Although great advances have been achieved, most of the reported QTLs were mapped with low-density microsatellite markers that were inadequate for further fine mapping analysis Growth traits play important roles in studying animal developments. During the last two decades, many quantitative trait loci (QTL) underlying growth were identified P<2.04×10−6)  . All exc04×10−6) . Other t04×10−6) . However04×10−6) . No SNP forkhead box O1A gene (FOXO1A) for ADG28 and a SNP at 20.89 Kb downstream of ENSGALG00000022732 for ADG42  and that for WW was from a SNP at 8.9 Kb upstream of FOXO1A for several BW and ADG traits  aimed at identifying SNP effects associated with net body growth for a given time period. The results showed that the 3 Mb region of 172–175 Mb had the largest numbers of significant SNP effects (10 out of 13) and had the most significant effects for ADG28 and ADG42. The most significant effect was from a SNP at 8.9 Kb upstream of the or ADG42 . For earor ADG42 . The resG traits .integrator complex subunit 6 gene (INTS6)   and was As secondary evidence of SNP effects on growth traits, frequencies of favorable and unfavorable alleles of the 15 significant SNPs in the 1.5 Mb region were compared in four chicken populations with divergent body growth rates: two populations of White Recessive Rock (WRR and WRR1), and two Chinese breeds, XH and Bai Er Huang (BEH). The WRR and WRR1 chickens were fast-growing broilers while XH and BEH are slow-growing chickens. We hypothesized that SNPs relevant to growth had higher frequencies in the fast-growing group than in the slow-growing group.P<0.0033, P = 0.0127, KPNA3 that was most significant for BMW and LMW  had higher frequencies of the favorable alleles in the fast-growing group than in the slow-growing group and the between-group frequency differences were significant , and two Ensembl genes (ENSGALG00000017013 and ENSGALG00000022732). No biological information was available for these three genes. Allele frequency estimates showed that the SNP was fixed for the favorable allele in fast-growing WRR and WRR1 and had relative low frequencies of 0.38–0.44 in slow-growing XH and BEH. These frequency results favored the hypothesis that a causal mutation for chicken growth existed near the SNP, although none of the three genes nearest to this SNP had known biological functions. The next closest gene to the SNP was the ribonuclease H2 subunit B gene , which was known to specifically downgrade RNA The most significant SNP for ADG42 was in a region lacking gene information. This region had one NCBI gene  in animals and human gga-miR-15a and gga-miR-16-1, were known to target some key genes such as B-cell leukemia/lymphoma 2 to regulate tumor growth Insulin-like growth factor 1 (IGF1) gene, which involved in mediating growth and development, had a conserved binding site with miR-15 and miR-16 family in human IGF1's conserved binding with the two microRNA genes also existed in chickens and whether the two microRNA genes could have a gene regulation role by targeting certain coding genes in the 3 Mb region with 63 coding genes by bioinformatics prediction of molecular interactions requiring minimal free energy (MFE) <−20 calculated by RNAhybrid IGF1 had a conserved binding site with gga-miR-15a and gga-miR-16-1  and a NCBI gene (LOC770337) between gga-miR-16-1 and RNASEH2B could not be excluded as potential functional units affecting chicken growth because they were close to highly significant SNP effects, although these three genes had unknown biological functions.Combining results of RNA analysis and association analysis, the 3 Mb region of 172–175 Mb with 63 coding genes and two microRNA genes likely contained more than one causal mutations affecting chicken growth and could contain a gene regulatory mechanism. The 1.5 Mb region of P = 3.20×10−07∼3.62×10−05, Results in this study identified novel candidate genes in a 3 Mb GGA1 region and provided strong confirmation of some previously reported QTL effects. A number of studies reported significant QTL effects in the 167–179 Mb region of GGA1. Uemoto et al. also detected QTL affecting BW42 and BW63 in 165–172 Mb on GGA1 All of the animal experiments were conducted in accordance with regulations for the Administration of Laboratory Animals of Guandong Province. Animal experiments were approved by the Animal Care Committee of South China Agricultural University  with approval number SCAU#0005.♂)×WRR (♀) and WRR (♂)×XH (♀) were selected on the basis of satisfactory egg and semen yields to produce the F1 generation. At 30 wk of age, 17 F1 males and 17 F1 females were selected to produce the F2 generation, resulting in a total of 489 birds in 17 full-sib families from six hatches at two-week intervals. The F2 individuals were raised in floor pens and fed commercial corn-soybean diets that met NRC requirements. Body weights were measured in grams at hatching, 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, 84, and 90 d of age . The ADG was calculated based on the difference between the current BW and the BW of two weeks ago for 1–14, 15–28, 29–42, 43–56, 57–70, and 71–84 d of age . For example, ADG28 was calculated as (BW28 – BW14)/14 and represented the net daily weight increase during the period of 15–28 d. All 489 F2 individuals  were slaughtered and measured for BMW (g), LMW (g), and WW (g) at 90 d of age An F2 design resource population was employed in the present study. The F2 resource population was derived from reciprocal crosses between WRR and XH chickens. WRR chicken is a fast-growing broiler breed and XH chicken is a slow-growing Chinese indigenous breed. Nine females and seven males from XH, eight females and nine males form WRR were selected for mating on the basis of consistent egg laying and semen production. Each male was paired with a female from the other line, except one male from XH, which paired with two females from WRR. Reciprocal mating of the XH (P>0.00001). As a result of these SNP selection criteria, 47,678 SNPs were selected for use in the GWAS. Distribution of the 47,678 SNPs by chromosome is presented in Genomic DNA was extracted from vein blood samples using saturated phenol-chloroform extraction method. Thirty-three F0, 32 F1, and 489 F2 individuals were quantified for DNA concentrations and genotyped using the 60 K SNP Illumina iSelect chicken array developed by USDA Chicken GWMAS Consortium, Cobb Vantress, and Hendrix Genetics, containing more than 57,000 SNPs Y = corrected phenotypic value, μ = common mean, S = fixed gender effect, H = fixed hatch effect, f = random family effect, SNP = the single-locus SNP genotypic effect, and e = random residual. Additive and dominance effects were tested using linear contrasts of the single-locus SNP genotypic effect Statistical tests of SNP-phenotype association were implemented using the generalized least square version of epiSNP computer package that considered sib correlation within each family −5  for suggestive significance, 2.04×10−6  for genome-wide significance and 4.08×10−8  for “highly significant”. Overview of SNP effects by Manhattan plots were produced by SNPEVG version 2.1 Genome-wide significance was defined based on the “LD adjusted” Bonferroni method <−20 calculated by RNAhybrid was required for reporting binding. Gene locations were based on Ensembl Bioinformatics prediction of molecular interactions between microRNAs and three prime untranslated regions of the coding genes used RNA22 Four random chicken populations, WRR, WRR1, XH, and BEH, were used for analyzing allelic frequency spectrum among breeds. WRR1 was a fast-growing chicken line from a commercial company in Guangdong, China. Both XH and BEH were slow-growing and from Gongdong, Jiangxi Province, China, respectively. Sample sizes of WRR, WRR1, XH and BEH were 80, 79, 79, and 59 birds, respectively. Sequenom technique platform was used for genotyping the 16 significant SNPs located in the 173.5–175 Mb region of GGA1. The SNP at 173,776,019 bp was fixed in WRR, WRR1 and BEH and had a high frequency of 0.92. We considered this SNP to have a high likelihood to be a monomorphic marker and removed this marker from frequency analysis, so that only 15 of the original 16 SNPs were used for frequency analysis. The four populations were subjected to primer extension and MALDI-TOF mass spectrometry using MassARRAY Compact System by Bioyong Technologies Incorporation . SNPs were genotyped with the use of a commercially available Complete Genotyping Reagent Kit for MassARRAY® Compact 384 and ABI GeneAmp® 9700 384 Dual , in accordance with the manufacturer's instructions. The sequence detection software, Typer 4.0, provided by Sequenom, was used for genotyping analysis.Figure S1Manhattan plots for SNP effects for 23 growth traits. Aggregate weekly body weight: BW0, BW7, BW14, BW21, BW28, BW35, BW42, BW49, BW56, BW63, BW70, BW77, BW84, BW90; Biweekly average daily gain: ADG14, ADG28, ADG42, ADG56, ADG70, ADG84; Brest muscle weigh: BMW; Leg muscle weight: LMW; and Wing weight: WW. The green solid line indicates genome-wide significance (P<2.04×10-6) with “LD adjusted” Bonferroni correction.(PDF)Click here for additional data file.Table S1SNP effects with suggestive significance (P<−5) for 23 growth traits.4.08×10(XLSX)Click here for additional data file.Table S2Chi-square tests of frequency differences between fast- and slow-growing breeds for SNPs in the 173.5–175 Mb GGA1 region.(XLSX)Click here for additional data file.Table S3Distribution of SNPs by chromosome.(XLSX)Click here for additional data file.Table S4LD blocks in the F2 population.(XLSX)Click here for additional data file.


Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal’s life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality.The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism.These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle. Bos taurus are commonly raised as livestock for beef in the South of Brazil, but in most parts of the country, beef cattle production is based on Bos indicus (zebu) breeds raised on natural pastures. A good description of Brazilian beef cattle production was recently published[Bos taurus. The Canchim (3/8 zebu + 5/8 Charolais) breed was developed in the early 1960’s in Brazil[Beef cattle production in Brazil is based on several breeds, depending on the geography and climate of a given area. Breeds based on ublished. Zebu brublished-5, but tublished-10, and ublished,12 when n Brazil with theBos taurus. One such trait is backfat thickness, which has been a concern for Canchim producers, and for the beef cattle industry in general, due to its low fat deposition in animals raised on pasture (1.90mm ± 0.77)[Although the Canchim breed has fared well when raised on natural pastures in Brazil, some carcass traits have still remained inferior when compared to  ± 0.77). Improve ± 0.77), and bec ± 0.77)-19, therBos indicus × Bos taurus crosses in Brazil[DDEF1 and LEP genes[In attempts to improve meat quality, previous studies have focused on the identification of candidate markers associated with meat quality traits, as well as backfat thickness, in Canchim and other n Brazil-24. HoweEP genes,23. TherEP genes. More spEP genes-35. By tEP genes,28,32,35A previous study using high-density SNP panel has associated 100 SNPs to backfat thickness in a Canchim population using an approach that selected animals with extreme phenotypes for genotyping. Those SAlthough these results are useful, it is well known that quantitative traits are polygenic as each SNP may account for only a small part of the phenotypic variance, therefore joint analysis of many SNPs has become a more interesting strategy,37. ThisOne solution to this problem is through the use of Random Forest, a machine learning algorithm capable of dealing with certain datasets for building model independent classification and/or regression problem predictors. SpecifiConsidering all of the above, the objectives of this study were to identify SNPs associated with backfat thickness in Canchim beef cattle using the random forest approach for genome-wide association studies, to shed insight on potential genes associated with this trait, and to discover potential SNPs for future implementation of genomic selection (GS). The set of SNPs identified by this methodology explains as much as 50% of the deregressed estimated breeding value variance associated with the observed phenotype. These results intend to provide a better understanding of the backfat deposition and regulatory pathways, and to enable the use of the identified SNPs in validation studies for genomic selection.in vivo over the 12th rib around the age of 18 months. All animals evaluated were born between 2003 and 2005 and raised on natural pastures.Animals used in this study were part of the Canchim Breeding Association from seven herds located in two Brazilian states (São Paulo and Goiás). This research is in agreement with the ethical principles of animal experimentation of Embrapa Southeast Livestock Ethical Committee of Animal Use (CEUA-CPPSE), and has been performed with the approval of CEUA-CPPSE under protocol number 02/2009. An initial sample of 987 animals  was evaluated for backfat thickness by ultrasound These 987 animals had the estimated breeding value (EBV) predicted by restricted maximum likelihood using the MTDFREML software. The aniThe selected 400 animals were genotyped using the BovineHD BeadChip . The quality control filters included call rate (< 0.90) for samples and SNPs, minor allele frequency (MAF < 0.01), and heterozygosity (< 3 standard deviations). After quality control processing, 396 animals and 708,641 SNPs with an average call rate higher than 0.99 remained in the study.Genome-wide association (GWA) analysis was performed on deregressed EBVs (dEBV), which tAssociation of SNPs to dEBVs was undertaken by a random forest package availablTaking into account the unbalanced offspring range among sires, 10 subsamples consisting of 198 animals each were also analyzed in the same two-step process as previously described. The 10 subsamples were selected as follows: i) The first animal was chosen at random from the 396 genotyped animals; ii) The next animal was selected based on the lowest relationship with the previous selected animal, but most representative from the rest of the genotyped animals; and iii) Step ii was repeated until 198 animals were selected.Two approaches were considered for further SNP investigation among the results obtained by the random forest analysis. One approach selected the SNPs in common among the analysis with the 396 animals and the 10 subsamples, called the Common SNPs strategy. Another approach selected only the top 1% (importance score) from the analysis with 396 animals, called the Highest 1% SNPs.2 values correspond to the dEBV variance explained by the model, which are reported in Table 2 obtained from the stepwise regression analysis. In the permutation test, the dEBV values were shuffled and then regressed to the same SNPs previously selected. The permutation test was repeated 1,000 times.Finally, after both sets of SNPs (Common SNPs and Highest 1% SNPs) had been selected, each set of selected SNPs were fitted into a final stepwise regression model using SAS/STAT software to estimA pathway analysis was conducted to characterize the genomic regions identified by the set of SNPs previously selected and to identify candidate genes influencing biological functions and pathways related to backfat thickness and fat-related traits.2). Considering the extent of LD based on the overall average r2 , a window of 500Kb (SNP position ± 250Kb) surrounding each SNP previously selected by the stepwise regression was considered to define the region used for candidate gene discovery and pathway annotation.The software fastPHASE version 1.4.0 was usedThe Cattle Genome Browser through the UMD 3.1 Cattle genome assembly, was useWe performed regression analysis for both strategies (Common SNP and Highest 1% SNP), and the results were very similar in the final number of SNPs selected, and the percentages of dEBV variance explained by the final set of SNPs  and are very similar, provides reliability to the random forest methodology as can be seen in the previous study.With the exception of four selected SNPs in the Highest 1% SNPs strategy , all other SNPs presented a fat-related QTL described in their chromosome region. Also, only one SNP on chr 3 (rs42021729) is not close to any described gene in the surrounding area (± 250kb)  thereby indicating THSD4, LRRC49 and the surrounding areas as strong candidates for further investigations and validation. The LRRC49 gene has been linked to breast cancer in humans, but very little is known about the biological function of the protein encoded by this gene[From the SNPs identified in this study, there were two on chr 10  associated with backfat thickness, which together accounted for almost 12% of the dEBV variation , which has a validated status in RefSeq, and to H. sapiens (93.1%), suggesting a well-conserved homology of the THSD4 gene in these species.The m RefSeq, which, m RefSeq when comTHSD4 gene encodes a protein with conserved disintegrin and metalloprotease domains, which it shares with the ADAM-TS1 protein family, and plays an import role in adipogenesis[The ogenesis. Previouogenesis.The subcutaneous fat percentage QTL reported on chr 10 Table  is from SOX14 (sex determining region Y – box 14), CLDN18 (claudin 18), and DZIP1L (DAZ interacting protein 1-like). The SOX14 gene seems to be involved in the regulation of embryonic development, whereas CLDN18 belongs to a multigene family that encodes a tetraspanning membrane protein acting on components at tight junctions, but its regulatory mechanisms, and roles in physiology and pathology are still under investigation[DZIP1L gene encodes a zinc finger protein, but how it affects either adipogenesis or lipid metabolism has not been depicted from the current literature. Nonetheless, the functions of these gene products are still being elucidated.The SNP on chr 1 rs137294146) associated with backfat thickness is responsible for approximately 9.4% of the dEBV variation  catalyzes the conversion of mevalonate 5-phosphate with ATP to form mevalonate 5-diphosphate and ADP, which is one of the initial reactions involved in the cholesterol biosynthetic pathway[ADAR , which encodes an RNA-editing enzyme by site-specific deamination of adenosines, resulting in changes in protein function or gene expression. A study in humans was conducted that found ADAR enzymes were associated with serum triglyceride and adiponectin levels, abdominal circumference, and body mass index[SHC1 (Src homology 2 domain containing – transforming protein 1) which has been reported as having a role in human obesity[IGF-1) pathway, which plays a key role in regulating cell proliferation, differentiation and apoptosis[ADAM15 , which belongs to the ADAM protein family previously discussed. These studies corroborate our findings and require further investigation to elucidate how these genes are affecting the deposition of subcutaneous fat in bovines.The 500Kb window around the SNP on chr 3 (rs109349988) reveals many annotated genes, of which some have been reported as participating in lipid metabolism. For example,  pathway. Other pss index. Interes obesity, and as poptosis. Lastly,PHOSPHO1  gene, which encodes a phosphatase enzyme that has been implicated in the mineralization of the extracellular matrix, a key process for skeletal development[PHOSPHO1 gene product has high activities toward phosphoethanolamine (PEA) and phosphocholine (PCho)[PHB gene (prohibitin), which is thought to be involved in regulating cell proliferation, gene transcription, and apoptosis. In recent studies, deficient PHB activity in the liver has been associated with non-alcoholic steatohepatitis and obesity, although the mechanism remains unknown[IGF2BP1 (insulin-like growth factor 2 mRNA binding protein 1) gene, which encodes a protein that binds to the mRNAs of certain genes and regulates their translation. Lastly, the GIP  gene has a known effect on stimulating the release of insulin from pancreatic β cells, but also has an insulin-like effect on adipocytes, suggesting that the GIP gene product enhances adipocyte glucose uptake, and that, at least in humans, it has an important role in the development of nutrition-induced obesity[GIP gene product has an effect on reducing free fatty acid release from adipose tissues, either by increasing reesterification or by inhibition of lipolysis[GIP gene in the bovine genome, which further suggests an association between this gene and free fatty acid processing.The SNP associated with backfat thickness on chr 19 (rs136717249) is responsible for approximately 4.88% of the dEBV variance. This region contains the elopment. The PHOe (PCho), which ae (PCho). These c unknown,81. Othe obesity. A recenipolysis. Indeed,ipolysis,85 in clCCDC7 gene (coiled-coil domain containing 7) seems to be associated with human cancer[ARL5B gene product (ADP-ribosylation factor-like 5B), also known as ARL8, belongs to a family of proteins that show similar structure to ADP-ribosylation factors (ARFs family). ARLs and ARFs belong to the RAS superfamily of small GTPases, which function as modulators of complex and diverse cellular processes[ARL5B enhances retrograde transport from endosomes to the TGN[MGC152301 (uncharacterized LOC783682) and the LOC524240 (Alk-like) genes do not have any available information in terms of function of their gene products, but both show the same two conserved domains: cd00112 (LDLa) and cd06263 (MAM)[The SNP rs134790147 on chr 13 also was associated with backfat thickness, and it is carrying 3.51% of the dEBV. Within this SNP region, a QTL for fat thickness over the 12th rib was found and described in an Angus population. Also, an cancer,87, and rocesses,89, of wrocesses, and als the TGN. The MGC63 (MAM). The LDL63 (MAM). The MAM63 (MAM). The rem63 (MAM),85,97-99In this study, we were able to identify a set of SNPs that correlates with approximately 50% of the deregressed estimated breeding value variance for backfat thickness in Canchim beef cattle, which introduces the possibility of including these SNPs in the development of a low density SNP panel for future implementation of genomic selection program in Canchim beef cattle. We also have applied a new methodology using the Random Forest approach to identify novel gene candidates for improving backfat thickness in Canchim beef cattle. In addition, although this study used backfat thickness as a target trait, other analyses of this type have successfully used other traits, thereby supporting the random forest approach as a means of future investigations of livestock production traits. Lastly, some regions identified are not conspicuously associated with any specific genes. This suggests that they may be involved in as of yet unidentified regulatory functions of gene expression or processing. Given the intrinsic complexity of biochemical pathways, these regions and the genes within them merit a great deal of future investigations, specifically to how they correlate with backfat thickness deposition in Canchim beef cattle and to other breeds.The authors declare that they have no competing interests.FBM: data analysis and interpretation, and primary author of the manuscript; RHH: SNP quality control analysis, SNP selection using Random Forest, and manuscript revision; MAM: data analysis, interpretation, and manuscript revision; AOL: data mining, interpretation; SLCM: data collection and analysis, DNA processing; MVGBS: experimental design, data analysis, interpretation, and manuscript revision; FFC: R script development, data analysis, and manuscript review; MMO: R script development; IU: R script development; SCMN: experimental design, preparation and handling of DNA samples for genotyping, and manuscript review; RRT: ultrasound measurements; MMA: experimental design; LCAR: experimental design, interpretation, and manuscript revision. All authors read and approved the final manuscript.