Amomum villosum Lour., produced from Yangchun, Guangdong Province, China, is a Daodi medicinal material of Amomi Fructus in traditional Chinese medicine. This herb germplasm should be accurately identified and collected to ensure its quality and safety in medication. In the present study, single nucleotide polymorphism typing method was evaluated on the basis of DNA barcoding markers to identify the germplasm of Amomi Fructus. Genomic DNA was extracted from the leaves of 29 landraces representing three Amomum species  by using the CTAB method. Six barcoding markers  were PCR amplified and sequenced; SNP typing and phylogenetic analysis were performed to differentiate the landraces. Results showed that high-quality bidirectional sequences were acquired for five candidate regions  except trnH-psbA. Three ribosomal regions, namely, ITS, ITS2, and LSU D1–D3, contained more SNP genotypes (STs) than the plastid genes rbcL and matK. In the 29 specimens, 19 STs were detected from the combination of four regions . Phylogenetic analysis results further revealed two clades. Minimum-spanning tree demonstrated the existence of two main groups: group I was consisting of 9 STs (ST1–8 and ST11) of A. villosum Lour., and group II was composed of 3 STs (ST16–18) of A. longiligulare T.L. Wu. Our results suggested that ITS and LSU D1–D3 should be incorporated with the core barcodes rbcL and matK. The four combined regions could be used as a multiregional DNA barcode to precisely differentiate the Amomi Fructus landraces in different producing areas. Amomum villosum Lour. belonging to the monophyletic Zingiberaceae family is a valuable herbaceous plant in southern China A. villosum Lour., A. xanthioides Wall. ex Baker and A. longiligulare T.L. Wu are used as Amomi Fructus, one of the traditional Chinese medicines. Amomi Fructus is embodied in the Chinese pharmacopeia because this traditional medicine elicits significant effects, such as eliminating dampness, promoting appetite, regulating the flow of Qi , preventing miscarriage, warming the spleen, and curing diarrhea A. villosum Lour. in Yangchun City, located in Guangdong Province, is considered a Daodi (“genuine”) medicinal material A. villosum Lour. produced in Guangxi Province and Yunnan Province, as well as those of A. xanthioides Wall. ex Baker or A. longiligulare T.L. Wu, are also traded as Daodi medicinal materials of Amomi Fructus because of highly similar morphological traits. Therefore, Amomi Fructus landraces should be identified using an accurate and reliable method to ensure quality and safety in medication.rbcL and matK, as core barcodes of land plants trnH-psbA as supplementary barcoding loci to increase the discriminatory power of this method LSU D1–D2 has been successfully used to identify yeasts LSU D1–D3) has been used to identify medicinal plants Daodi medicinal materials and related substituents.DNA barcoding is a new technology in which a standard DNA region is used to identify species LSU D1–D3, matK, rbcL, and trnH-psbA) to identify three original species  of Amomi Fructus. Phylogenetic trees based on the concatenated SNP data of ITS–LSU D1–D3–rbcL–matK were also constructed.Single nucleotide polymorphism (SNP) is a single nucleotide variation in a particular and defined genetic location in at least 1% of the population A. villosum Lour. (n = 21) individuals, including seven landraces (AV01 to AV07) of Daodi medicinal materials obtained from Yangchun City in Guangdong Province, were collected from 20 culturing areas of Guangdong Province, Guangxi Province, and Yunnan Province. We selected AV03 as an authoritative specimen of A. villosum Lour. because this plant originated from Jinhuaken of Panlong Town, Yangchun City, an area where Amomi Fructus is the best, as recorded in the book of Yao Wu Chu Chan Bian by Chen-renshan in 1930. A. xanthioides Wall. ex Baker (n = 2) individuals were collected from Xishuangbanna City of Yunnan Province. A. longiligulare T.L. Wu (n = 6) individuals were gathered from Wenchang City, Danzhou City, Wanning City, and Lingshui City, Hainan Province. No specific permissions were required in sampling because these plants are not considered endangered. Our voucher specimens were verified by Prof. Ruo-Ting Zhan  and preserved at the Research Center of Chinese Herbal Resource Science and Engineering, Guangzhou University of Chinese Medicine, China.Three original plants of Amomi Fructus were sampled from July 2010 to August 2014  in Chinaet al. 2, 3 U of Ex Taq DNA polymerase , and approximately 75 ng of template DNA. PCR amplification was performed in a Peltier thermal cycler C1000  in accordance with the reaction protocol described in Genomic DNA was extracted from gel-dried leaves by using the CTAB method as previously described by Huang Sequence quality was assessed using Sequence Scanner version 1.0 software (Applied Biosystems) with two quality metrics, trace score (TS) and contiguous read length (CRL). TS, calculated as the average basecall quality value of bases in the post-trim sequences, ranged from 0 to 100 and was defined using three levels: low quality , medium quality , and high quality . CRL is the longest uninterrupted stretch of bases with a quality higher than 20 QV  in a window size of 20 bp. Sequence success rate was examined on the basis of the ratio of sequences traces with TS ≥35 and CRL ≥200 bp to the total number of PCR products.Sequence traces were trimmed, assembled, and manually edited using Sequencher 5.0 software  to obtain high-quality bidirectional sequences. Five quality control criteria were sequentially implemented: 1) the sequence trace should have a CRL ≥200 bp and a TS ≥35; 2) heterozygous sites were indicated by the second peak >40% of the first peak; 3) both 5′ and 3′ ends of the sequence were trimmed until less than three bases with quality scores <25 (or ambiguities) in a 25-base window; 4) assembled contigs should have a minimum overlap of 80% in the alignment of forward and reverse reads with a minimum match percentage of 98%; and 5) all of the heterozygous sites (mixed bases) were manually checked and edited on the basis of bidirectional reading chromatograms. Sequence files were deposited in GenBank with the accession numbers KJ151798–KJ151918 and KM411360–KM411384 .SNP sites for each region were identified using Sequencher 5.0 software in accordance with the manufacturer's instructions, and the sequences of the AV03 sample were used as references. Consensus contigs of samples were assembled, trimmed and compared with the reference sequences. The results of variable sites between the sample sequences and the reference sequence were identified as SNPs; and the SNPs of each region in each sample were considered as an SNP genotype (ST). We also concatenated the SNPs of two to four regions to compare the number of SNPs and STs revealed by different multi-region combinations by using MEGA 5 LSU D1–D3–rbcL–matK of 29 landraces were imported in BioNumerics version 7.1 , and an ST number was assigned to each distinct combination of SNPs. Phylogenetic analysis was performed using three clustering methods. Unweighted pair group method using arithmetic mean (UPGMA) was used to generate a dendrogram based on pairwise similarity. Cophenetic correlation value was calculated to determine the branch quality of the dendrogram. This value represents the correlation between the dendrogram-derived similarities and the matrix similarities, thereby providing an estimation of the reliability of a cluster analysis. For maximum parsimony analysis, character data were selected as input data, and characters were treated as categorical. Network creation algorithm was conducted using an optimized maximum parsimony tree  method, which is a huristic approach used to search for the highest parsimony by simulated annealing. All taxa with zero inter-taxon distance were identified. Root position in the tree was assigned to the deepest branch measured by maximum branch length. Bootstrap values were conducted with 1000 random addition sequence replicates. Considering that the origin of most of the landraces cultivated today can be traced back to the wide cultivation of A. villosum Lour. in China in the 1960s n-locus variants  as priority rule of the algorithm.The concatenated SNPs of ITS–LSU D1–D3 exhibited the highest sequencing efficiency of 96.6%, whereas trnH-psbA exhibited the lowest sequencing efficiency of 34.5% , and matK, indicating paralogous copies within individuals. All of the heterozygous sites were manually edited and confirmed by bidirectional sequence chromatograms in repeated amplifications and multiple samples.PCR was successfully completed in all of the barcodes; of 34.5% . High-qup<0.01). Discriminatory efficiency varied with different loci and multi-locus combinations. The highest numbers of SNPs and STs among single regions were observed in ITS (19 SNPs and 15 STs), followed by ITS2 (12 SNPs and 14 STs), and LSU D1–D3 (11 SNPs and 8 STs). The lowest numbers of SNPs and STs were observed in trnH-psbA, with . Furthermore, only a few SNPs and SNP genotypes were observed in the two remaining regions, namely, rbcL and matK (1 to 4 SNPs and 2 to 3 STs). In combined multiple loci, the highest numbers of SNPs and STs were obtained from ITS + LSU D1–D3 + rbcL+ matK (35 SNPs and 19 STs), followed by ITS + LSU D1–D3 (30 SNPs and 18 STs), ITS2 + LSU D1–D3 (23 SNPs and 18 STs), and ITS + rbcL + matK (24 SNPs and 16 STs). By contrast, the lowest numbers of SNPs and STs were detected in rbcL + matK (5 SNPs and 3 STs).Next, the discriminatory power of the 6 single regions and 9 different combinations of these regions was assessed by analyzing the number of SNPs and STs . The valLSU D1–D3 sequence (KJ151800), 4 sites at 71, 173, 230, and 542 bp in the rbcL sequence (KJ151848), and 1 site at 682 bp in the matK sequence (KJ151824) discriminated A. longiligulare T.L. Wu from A. villosum Lour. and A. xanthioides Wall. ex Baker. Additionally, 5 sites at 45, 67, 84, 512, and 549 bp in the ITS sequence identified all 3 Amomum species.Among 35 SNPs from the four combined loci, 9 sites at 45, 67, 84, 171, 205, 512, 541, 549, and 574 bp in the ITS sequence (KJ151873), 4 sites at 375, 418, 446, and 674 bp in the LSU D1–D3–rbcL–matK, all of the specimens formed 19 STs and clustered into two major clades  of A. villosum Lour. The maximum parsimony tree  T. L. Wu & S. J. Chen) belonging to A. villosum Lour.In the UPGMA phylogenetic tree constructed on the basis of 35 concatenated SNPs of ITS–r clades . Clade Iony tree  also revA. villosum Lour., including all of the samples of Guangdong Province(except AV09), Guangxi Province, and three samples of Yunnan Province . Group II was composed of 3 STs (ST16–18) of A. longiligulare T.L. Wu from Hainan Province. Seven samples, including six samples of Yunnan Province  and one sample of Guangdong Province (AV09), were not included in any group. These results clearly reflected the genetic origin of Amomi Fructus landraces of different geographic regions.To visualize genetic relationships among genotypes based on germplasms of Amomi Fructus from different geographic regions, we constructed an MST tree of the 19 STs (ST1–19) and connected these STs based on the numbers of SNP differences. MST tree  revealedet al.matK and 4 in 26S D1–D3, i.e., LSU D1–D3) in the germplasms of Amomi Fructus. In the samples collected from 26 production areas in the current study, 35 SNPs (LSU D1–D3 (gi 84095191:5378–6121), matK (gi 24634775:1223–2111), and rbcL (gi 7525012:54958–55556). Among these SNPs, 30 were novel SNPs . These findings may allow us to precisely identify the species of Amomi Fructus and trace the origin of samples by using their SNP genotypes. Three plastid regions exhibited poor discriminatory power in Amomi Fructus landraces. Although trnH-psbA shows high discriminatory power in many medicinal plants and has been proposed as a candidate DNA barcode for plants MatK and rbcL exhibited high sequence quality, but only provided one and four SNPs, respectively, to distinguish between A. villosum Lour. and A. longiligulare T.L. Wu. Indeed, matK and rbcL are suitable markers that can be used to identify species A suitable barcoding project to authenticate the germplasms of Amomi Fructus should provide sufficient polymorphism sites to generate variable sequence types among different landraces, which often originate from the same species or from a closely related species of the same genera. Huang  35 SNPs  were fouLSU D1–D3 showed more variable sequences than the plastid regions matK and rbcL. ITS with 19 SNPs found at single locus exhibited the greatest variability; this result confirmed that ITS exhibits high levels of sequence differentiation within species LSU D1–D3, the 5′ fragment of 26S rDNA, contains three expansion segments  from a total of 12 expansion segments (D1–D12) LSU D1–D3 exhibited 11 SNPs. These results are consistent with those in a previous report demonstrating that expansion segment sequences evolve 1.2 to 3.0 times faster than rbcLThe nuclear ribosomal DNA regions ITS and LSU D1–D3, single locus may not be used to differentiate >52% of Amomi Fructus varieties; thus, multiple loci are necessary for maximal identification of these varieties. Our findings highlighted the four-locus combination of ITS + LSU D1–D3 + matK + rbcL, which provided maximal resolution of varieties with 19 STs (65.5%) among 29 samples.Despite the high discriminatory efficiency of ITS and Daodi production area of A. villosum Lour. Seven samples from different production areas, including Guangdong Province , Guangxi Province , and Yunnan Province (Jinha in Xishuangbanna City), were not successfully distinguished even using a four-locus combination. These samples notably shared an identical SNP genotype (ST3), which possibly resulted from the artificial introduction of this genotype rather than the low discriminatory effect of the four-locus combination. ST3 could be used as an ancestor genotype LSU D1–D3 incorporated in current plant DNA barcoding systems could be used to precisely identify Amomi Fructus germplasms, which comprise samples of closely related species or subspecies originating from different culturing areas. Our results may be used as basis to promote plant DNA barcoding applications. For example, this project might be used to identify medicinal plants often substituted or altered with other morphologically indistinguishable species or varieties rbcL + matK was only successful in 72% of the cases, and the remaining specimens were identified as the species group  ITS and S1 TableGeographic origins of 29 landraces of Amomi Fructus .(DOCX)Click here for additional data file.S2 TableGenBank sequence accession numbers.(DOCX)Click here for additional data file.


Accurate genotype calling for high throughput Illumina data is an important step to extract more genetic information for a large scale genome wide association studies. Many popular calling algorithms use mixture models to infer genotypes of a large number of single nucleotide polymorphisms in a fast and efficient way. In practice, mixture models are mostly restricted to infer genotypes for common SNPs where their minor allele frequencies are quite large. However, it is still challenging to accurately genotype rare variants, especially for some rare variants where the boundaries of their genotypes are not clearly defined.To further improve the call accuracy and the quality of genotypes on rare variants, a new model calling procedure, named M-D, is proposed to infer genotypes for the Illumina BeadArray data. In this calling procedure, a Gaussian Mixture Model and a Dirichlet Process Gaussian Mixture Model are integrated to infer genotypes.Applications to Illumina data illustrate that this new approach can improve calling performance compared to other popular genotyping algorithms. Genome-wide association studies (GWAS) have been designed to discover many causal genetic variants contributing to human diseases , 2. The A and B) at every SNP for each individual. Typically, a SNP with alleles A and B makes three possible genotype clusters, named AA, AB, and BB, and all possible genotypes of each SNP are called by various genotyping algorithms. One strategy is the population-based approach through which genotypes of all individuals within a SNP are inferred at one time, but its calling performances highly depend on the size of population. Thus, this method is not applicable for rare SNPs with low minor allele frequency (MAF). Another approach, GenoSNP, is designed to infer all SNP genotypes within one individual simultaneously, and is referred to as a SNP-based calling method. The applicability of this algorithm [With the application of single base extension (SBE) biochemistry technology , the Illlgorithm  relies oMost of the predominant calling algorithms employ the mixture models –15 to inIn this paper, a new model calling procedure (M-D) is an approach that is made up of two models and one SNP selection procedure, namely Gaussian Mixture Model, DP Gaussian Mixture Model, and reference SNP selection. In brief, this method partitions SNPs into three groups in terms of the SNP’s MAF and the sample size of each cluster. In this method, three models are applied in three groups individually. The performance of M-D is evaluated through comparison with other genotyping algorithms for Illumina BeadArray data.The Illumina Omni BeadArray chip collects over one million SNPs per sample, and increasingly covers the newly identified variants. In the probe design, every beadtype that is capable of assaying two SNP alleles represents a SNP . A largexis= for the ith individual at the sth SNP is the basic measurement. Within one SNP, all subjects’ intensity data may fall into three genotype clusters corresponding to three genotypes  and one null component which collects the abnormal raw intensity measurements. Model I is a Gaussian Mixture Model [xis. In principle, this model assigns each pair of raw intensities xis to one of the components with probability πks for k= 1, 2 or 3. The relevant latent genotype class is measured by an indicator variable zis generated from a multinomial distribution (Mult3) where zis= 1, 2 or 3. Then this Gaussian Mixture Model can be expressed as: The pair of raw intensity re Model  that is ns is the total number of individuals observed at the sth SNP, and S is the total number of SNPs. Ψ denotes a normal density with mean μks and variance-covariance matrix Σks in the kth component at the sth SNP; all pairs of raw intensity within the sth SNP are measured by xs= where πs=, μs=, and Σs=.where zis=k, the (t+1)th iteration is estimated by The maximum likelihood estimates of the parameters are inferred . For theμks and variance-covariance matrix Σks are expressed as, The iterative estimates of mean ps) for the sth SNP are adopted to assess the quality of SNP calling [sth SNP [Two measurements Posterior Rate  is a conditional probability of the ith individual given that this subject is assigned to the kth cluster, and nks is the sample size of the kth cluster at the sth SNP.Note that Model I is a fast and efficient genotyping model for SNPs having large values of MAF. In real experiments, many SNPs with low MAF may result in the disappearance of one or two genotype clusters. Also even though some SNPs with low MAF display three genotype groups, some clusters may lack sufficient data to support and recognize. In this case, Model II, DP Gaussian Mixture Model, is motivated by the need to carry out the model selection for SNPs with an uncertain number of genotype clusters . Generalxis into K-component Gaussian Mixture Model with K approaching a large number. The model is expressed as, A DP Gaussian Mixture Model  fits theK is the total number of clusters. Θs= denotes the unknown parameters at the sth SNP where πs=, μs=, and Σs=. Generally, the number of observations within the sth SNP (ns) are partitioned into K components  with relevant mixing proportions . The distribution of ns1, ns2,..., nKs follows a multinomial distribution and its probability mass function is written by, where ns = sth SNP. Then each pair of raw intensity for the sth SNP xis has its own indicator zis , and the distribution of indicator variables is expressed as, where The model can then be expressed as: α is the DP concentration parameter and can be thought as the inverse variance of DP. The distribution of the reciprocal of α follows a Gamma distribution with 1 degree freedom and mean 1. K is the maximum number of clusters, then πs is distributed with a symmetric Dirichlet distribution with parameter μks, and the hyperparameters ν and S−1 are degrees of freedom and inverse mean of Rks where Rks follows a Wishart distribution with parameters ν and S−1, respectively.where zis conditional on all other variables are expressed as: The inference on Model II relies on the posterior distribution of each parameter conditional on all other parameters, then the parameters, hyperparameters and indicator variables are repeatedly sampled from their posterior distributions. In particular, the conditional posterior probabilities are proportional to the likelihood function multiplying priors. Then the posterior probabilities of the cluster indicator variable p and p are the likelihood function and the joint function of parameters (μks and Rks), respectively. Once the optimal genotype clusters and their relevant component parameters are obtained, two measurements Posterior Rate (PR) and the Average Posterior Rate (APR) measuring the quality of the sth SNP can be calculated in the similar way.Note that A DP Gaussian Mixture Model with reference SNP selection step (DP-Ref) combines the benefits of the population-based method with the SNP-based approach. In this context, the reference SNP selection plays an important role in determining the effectiveness of Model III. A reference SNP is referred to as a good quality SNP providing sufficient information about three genotypes clusters, thus each SNP in the third group will be called with assistants of the carefully selected reference SNP. Practically, the final reference SNP is selected by a three-step procedure . ThroughStep I. High MAF SNPs are selected as candidate reference SNPs. In fact, SNPs with large MAF (> 0.15) before the T-SNP are selected as R1-SNPs.Step II. Good clustering property SNPs from R1-SNPs are further selected (denoted as R2-SNPs). This step requires three genotype clusters of R1-SNPs to contain at least 10 % of entire observations individually.Dt [xis is projected to an univariate variable yis [Step III. A SNP from R2-SNPs being remarkably similar to the T-SNP is selected (denoted as R-SNP). The resemblance between the T-SNP and each R2-SNP is measured by the cluster distance Dt . For simable yis , and theys=  is to find the minimum distance between the T-SNP and R2-SNPs [We select one SNP from the third group as the T-SNP, then the cluster measure  will be applied to the selected SNPs with small MAFs. According to this calling procedure, any SNP will be classified by, nks is the sample size of the kth cluster at the sth SNP. The first group (g1) collects SNPs with high MAF (≥ 0.05), and a large proportion of SNPs is in this group. The second group (g2) includes SNPs with low MAF (< 0.05) and a certain number of subjects in either existing genotype clusters. In this study, b1 and b2 are fixed as 3 and 10 to determine the number of SNPs recruited in g2. The last group (g3) collects the rest SNPs with low MAF and a small number of observations in one or two genotype clusters. In fact, SNPs in g1 can display three genotype clusters  with a large number of subjects in each cluster. The rest poor SNPs with low MAF are contained in g2 and g3 where some SNPs may not display three genotype clusters, either one or two clusters disappear and the existing cluster may contain very few observations. In particular, the classification between g2 and g3 is not fixed, and scientists can easily manage the allocation of SNPs between g2 and g3 through adjusting the values of b1 and b2.Note that The proposed new calling procedure is based on the partitions of SNPs. b1 and b2 in Eq. b1 takes a large value, a smaller proportion of rare SNPs may enter g2 and more rare SNPs are allocated to g3, thus GMM and DP-Ref will become major methods. If b2 takes a large value, a larger proportion of rare SNPs may be assigned to g2, then GMM and DP-GMM will become main methods. This flexible option provides more solutions for scientist who are interested in this genotyping method.In the first group, Model I (GMM) is applied to genotype SNPs. A sufficient number of observations are observed in three genotype clusters, which will greatly help the genotyping procedure identify the boundary of each cluster. In the second group, SNPs with low MAF, Model II (DP-GMM) can implement the model selection to search the appropriate number of clusters for each SNP, and DP’s clustering and discreteness properties assures the optimum partition of observations, even for a small number of observations in a genotype cluster. In the third group, the number of genotype clusters for each SNP is uncertain and an extremely small number of observations are observed in either one or two clusters. In this case, applying a DP-GMM alone for clustering is not enough due to a rich-gets-richer phenomenon  where thb1 and b2 are fixed as 3 and 10, then 88.6 % of SNPs are in g1, 4.03 % and 7.37 % of SNPs will be assigned to g2 and g3, respectively. More importantly, DP Gaussian Mixture Model is powerful to infer the cluster containing a certain number of observations, thus Fig. g3, DP-Ref is used to infer genotypes , and the calling results are summarized in Fig. g3, Fig. In this study, M-D is compared to those of GenCall representing a population-based method and GenoSNP standing for a SNP-based approach. The compatible cutoffs of all three calling algorithms are carefully selected, such as: GenCall score (GC score ≥ 0.15) is used to filter good quality SNPs; GenoSNP and M-D collect good quality SNPs and samples through the posterior probability (≥ 85 %).The proposed method M-D is applied to an Illumina data consisting of 1 million SNPs and 3258 samples. Specifically, there are 38 different HapMap samples [The performances of three calling algorithms are compared in terms of the call rate mentclass2pt{minimmentclass2pt{minimBecause most samples in this Illumina data are collected from the hospital, true genotypes of these sample are not known totally, so the high agreement among 3 algorithms can not tell us which method performs best. Fortunately, 141 HapMap samples are contained in this data, and the true genotypes of these HapMap samples are explored by the HapMap project as a gold standard. Table g2 and g3 collects whole rare SNPs, again, M-D still outperforms GenoSNP and GenCall on call accuracy and call rate.Compared to the population-based methods  and the SNP-based approaches (GenoSNP) , 10, theP-value < 0.0001) is applied to four populations separately. The total number of SNPs failing the HWE test are summarized in Table Hardy-Weinberg Equilibrium (HWE) test is another important criteria to examine the quality of SNPs. In this Illumina data, most samples are from four populations: Hispanic African-American, non-Hispanic African-American, Hispanic European-American, and non-Hispanic European-American. The HWE test  is proposed to take benefits of a model selection step of a DP and the advantage of GenCall and GenoSNP to improve the quality of rare SNP calls. In brief, the new model calling procedure partitions SNPs into three classes in terms of MAF and the sample size of each cluster, and a DP Gaussian Mixture Model with or without reference SNP selection are applied to rare SNPs with low MAF. The finest performance of M-D is evaluated by comparing genotypes inferred by each discussed calling method to those from the HapMap project. Compared to GenCall and GenoSNP, M-D performs better on genotyping rare SNPs, and it also infers better quality of SNP calls than that from GenoSNP.t, cluster measure; DP, Dirichlet Process; DP-GMM, Dirichlet Process Gaussian mixture model; DP-Ref, Dirichlet Process Gaussian mixture model with reference SNP selection; GMM, Gaussian mixture model; GWAS, genome-wide association studies; HW, Hardy Weinberg; MAF, minor allele frequency; Multi3, a multinomial distribution; M-D, a new model calling procedure; PR, posterior rate; R-SNP, final reference SNP; SBE, single base extension; SNPs, single nucleotide polymorphisms; T-SNP, a SNP in the third groupAPR, average posterior rate; D


P < 0.01. Intriguingly 4q22.3 harbours an ROH to which the nuclear factor NF-kappa-B p105 subunit (NFKB1) maps (P = 0.002). The ROH at 19q13.12-2 has previously been implicated in B-cell precursor acute lymphoblastic leukaemia. Aside from these observations which require validation, it is unlikely that levels of measured homozygosity caused by autozygosity, uniparental isodisomy or hemizygosity play a major role in defining HL risk in predominantly outbred populations.Recent studies have reported that regions of homozygosity (ROH) in the genome are detectable in outbred populations and can be associated with an increased risk of malignancy. To examine whether homozygosity is associated with an increased risk of developing Hodgkin lymphoma (HL) we analysed 589 HL cases and 5,199 controls genotyped for 484,072 tag single nucleotide polymorphisms (SNPs). Across the genome the cumulative distribution of ROH was not significantly different between cases and controls. Seven ROH at 4q22.3, 4q32.2, 7p12.3–14.1, 7p22.2, 10p11.22–23, 19q13.12-2 and 19p13.2 were associated with HL risk at  Hodgkin lymphoma (HL) is a B-cell malignancy which affects around 3 per 100,000 of the population each year in most western countriesREL), 3p24.1 (EOMES), 6q23.3 , 8q24.21 (PVT1), 10p14 (GATA3), 19p13.3 (TCF3), as well as multiple associations within 6p21 (HLA region)45678Evidence for inherited genetic predisposition to HL is provided by the three to nine-fold increased risk of HL in first-degree relatives of HL patientse.g. backcrossing mice) increasing tumour incidenceThe majority of cancer predisposition SNPs that have been identified to date through GWAS act in a co-dominant fashion and studies have found no good evidence for recessively acting alleles1112131415i.e. the occurrence of two alleles at the same locus originating from a common ancestor by way of non-random mating), have been shown to occur at high frequency in outbred populations as a result of selectionet al. studied patients with rheumatoid arthritis (RA) of Northern/Western European ancestry by whole-genome loss of heterozygosity analysis using Affymetrix SNP arrayset al. showed that cases of early onset Parkinson’s disease harboured significantly more homozygous regions than healthy individualsCommon regions of homozygosity (ROH), the result of autozygosity . Hence to examine whether homozygosity is associated with an increased risk of developing HL and to search for novel recessively-acting disease loci, we conducted a WGHA of 589 HL cases and 5,199 controls genotyped for 484,072 tagging SNPsWe analysed the constitutional DNA of 622 patients with HL ascertained from the UK. Details of the study are provided in previously published materialWe used data from two publicly accessible data series for population SNP genotype frequencies: individuals from the UK Welcome Trust Case-Control Consortium 2 (WTCCC2) study of 2,930 individuals from the 1958 British Birth Cohort Collection of blood samples and clinico-pathological information from subjects was undertaken with informed consent and relevant ethical review board approval  in accordance with the tenets of the Declaration of Helsinki.As previously describedP < 1 × 10−5 in either the cases or controls, which resulted in only 139 SNPs being removed from the analysis. We also removed SNPs with MAF <0.05. To identify and exclude individuals with non-Western European ancestry, case and control data were merged with data obtained from individuals of different ethnicities from the International HapMap Project, from genome-wide IBS distances for markers shared between HapMap and our SNP panel, and from dissimilarity measures used to perform principal component analysis. After imposing these stringent quality control measures, 484,072 SNP genotypes were available on 589 HL cases and 5,199 controls which formed the basis of our analysis.To identify samples showing relatedness, identity by state values (IBS) were calculated for pairs of individuals. For any pair with >80% identical SNP genotypes, we removed the sample with the lower call rate from the analysis. We excluded SNPs on the basis of deviation from Hardy-Weinberg Equilibrium (HWE) using a threshold of 2 test. To compare the difference in average number of ROH between cases and controls we used the Student t-test. Naïve adjustment for multiple testing was based on the Bonferroni correction. In addition to conducting a PLINK-based analysis of ROH we also subsequently analysed our data using SNP & Variation Suite 8 software  which is based on implementation of a fixed window.We detected ROH using PLINK v1.90st) measures the degree of population differentiation at a locus, taking values from 0 to 1.0st metrics were obtained from dbPSHPWe used three metrics to investigate the selection pressure on each ROH. Integrated Haplotype Score (iHS) is based on LD surrounding a positively selected allele compared to background, providing evidence of recent positive selection at a locus60 × 484,072 × 5788 = 0.043). A consequence of LD is that the SNP genotypes are not always independent, thereby inflating the probability of chance occurrences of biologically meaningless ROH. Analysis based on PLINK’s pairwise LD SNP pruning function showed 363,152 separable tag groups, representing a 25% reduction of information compared to the original number of SNPs. Thus ROH of length 84 were used to approximate the degrees of freedom of 60 independent SNP calls.In order to focus on commonly occurring ROH and to empower our analysis to identify meaningful associations, only ROH in which 10 or more individuals shared the same ROH were retained for analysis. The initial search for ROH was performed using PLINKOnce all ROH of at least 84 SNPs in length were identified, these were pruned to only those ROH that occurred in more than 10 individuals. To ensure that a minimum length and minimum number of SNPs in each ROH was maintained, each individual’s SNP data were recoded as one if the SNP was in an ROH for that individual and zero otherwise. Then, for each SNP, those SNPs with fewer than 10 individuals coded as one were recoded to zero before removing any ROH that due to this recoding were now less than the required number of SNPs in length. This process therefore resulted in a list of “common” ROH having a minimum of 75 consecutive ROH calls across 10 or more samples and with each ROH having the same start and end locations across all individuals where that ROH is observed.P < 1 × 10−5. We evaluated datasets for ancestral differences by principal component analysis and removed all outliers. We have previously subjected cases and controls to rigorous quality control in terms of excluding samples and SNPs with poor call rates. Furthermore, we excluded SNPs showing significant departure from HWE. The results in Using PLINK a total of 398 common ROH were identified in samples , encompaThe 37 longest ROH exceeded 12 Mb in length each and included ROH encompassing the centromeric regions of chromosomes 1, 2, 3, 4, 5, 6, 7, 8, 11, 12, 16, and 20. The lengths of these ROH are partly a consequence of long regions for which there are no annotating SNPs. This is however unlikely to be the sole explanation, as in each case these centromeric regions were flanked by large homozygous regions containing numerous SNPs. One of these centromeric regions (chromosome 8) has been previously highlighted in several genome-wide studies of selective sweeps, thus providing validation of our methodology242829The ROH covering the largest genomic region (40 Mb) and the largest number of SNPs 2885) was found to be ROH83 spanning the centromere of chromosome 3, a region previously shown to be characterised by a high frequency of ROH in the European population85 was foet al.242829st metrics for each region are compatible with positive selection in Caucasian populations (There are twelve ROH that were very common (>25% frequency) in the control series . Five ofulations .5788 = 0.2851, P = 0.76). To also examine whether there were differences in the distributions of ROH in the genomes of cases and controls we computed the cumulative distributions of both series  and controls  showed no significant difference in the average number of ROH (th series . This anP < 0.01) between cases and controls . Over 50 genes or predicted transcripts map to the region encompassed by this ROH including the gene encoding NF-kappa-B p105 subunit . Although speculative, it is intriguing to note that NF-κB pathways are constitutively overactive in a variety of lymphomas including HL32NFKB1 have been identified in tumour samples from patients with HLREL (a known causative locus in HL) interacts with NF-kappa-B p105 subunit as a mediator of immune and inflammatory responses4NFKB1 genotype and HL risk through single point analysis based on SNPs that map within 25 kb of the gene. The strongest association was provided by an imputed SNP located in an intron of NFKB1, rs4647965 .ROH111 was identified in 17.1% of cases (n = 101) compared with 12.7% of controls (n = 660) (Recent studies have provided evidence that signatures of autozygosity correlate to cancer incidence and that these regions showing identity by descent may be the locations of genes contributing to tumour heritability36201113NF-κB1), and ROH375 (previously implicated in susceptibility to childhood B-cell precursor acute lymphoblastic leukaemia [BCP-ALL])Here we have used a high-density genomic scan to compare the structure of genetic variation in patients with HL with healthy controls. This same sample series has recently been used to identify six predisposition loci for HL4383940The assertion that increased autozygosity correlates with cancer incidence provides an attractive explanation for reported increased cancer risk in inbred populations. However, as recently articulated, several criticisms can be levelled at such an idea364344In conclusion our findings make it unlikely that levels of measured common homozygosity, from autozygosity, uniparental isodisomy or hemizygosity, play a significant role in defining the risk of developing HL in a predominantly outbred population. Moreover it is unlikely that there exist large numbers of recessive alleles in most European populations, that predispose to HL and are unmasked by autozygosity. This analysis does not however exclude the possibility that recessively acting disease alleles exist for HL.How to cite this article: Sud, A. et al. Genome-wide homozygosity signature and risk of Hodgkin lymphoma. Sci. Rep. 5, 14315; doi: 10.1038/srep14315 (2015).


Brassica napus based on genome-wide markers and the detection of underlying genetic factors is important not only for oilseed producers around the world but also for the other crop industry in the rotation system in China. In previous studies the low density and mixture of biomarkers used obstructed genomic selection in B. napus and comprehensive mapping of FT related loci. In this study, a high-density genome-wide SNP set was genotyped from a double-haploid population of B. napus. We first performed genomic prediction of FT traits in B. napus using SNPs across the genome under ten environments of three geographic regions via eight existing genomic predictive models. The results showed that all the models achieved comparably high accuracies, verifying the feasibility of genomic prediction in B. napus. Next, we performed a large-scale mapping of FT related loci among three regions, and found 437 associated SNPs, some of which represented known FT genes, such as AP1 and PHYE. The genes tagged by the associated SNPs were enriched in biological processes involved in the formation of flowers. Epistasis analysis showed that significant interactions were found between detected loci, even among some known FT related genes. All the results showed that our large scale and high-density genotype data are of great practical and scientific values for B. napus. To our best knowledge, this is the first evaluation of genomic selection models in B. napus based on a high-density SNP dataset and large-scale mapping of FT loci.The prediction of the flowering time (FT) trait in  Brassica napus), as one of the leading sources of livestock feed, vegetable oil and biofuel, is the second most prominent oil seed crop in the world, supplying approximately 62.4 million tonnes of oilseed production per year. China is the top rapeseed oil producer in the world, yielding about 4.8 milion tonnes of oil each year . Rapeseed was planted mainly as a rotational crop with rice, maize, cotton or some vegetables in China [Rapeseed (B. napus [Recent efforts have been made in mapping genomic locations related with agronomic traits (including FT) in B. napus –8, whichB. napus . FurtherB. napus , 5, whicArabidopsis thaliana [Single nucleotide polymorphisms (SNPs) are the simplest and most prevalent type of markers across the genome. To date, with the availability of the abundance of SNPs, high-throughput technologies of simultaneously genotyping high-density have been applied to plants to unravel the genetic effects of agronomical traits and significant findings were observed, such as in thaliana , 10, ricthaliana , maize [thaliana  and barlthaliana . Howeverthaliana . MoreoveB. napus.To remedy the drawbacks of the conventional MAS, genomic selection (GS) was proposed by Meuwissen et al. to predict phenotypic values based on all available markers across the entire genome , which aB. napus, and assessment of their predictive ability. RF and Multivariate Adaptive Regression Spline Models (MARS)[B. napus. These findings would facilitate the development of breeding lines with superior flowering time in Chinese ecological conditions.In this study, we employed an unbiased and high-density genotyping platform on a relatively large dataset (including 1674 SNPs genotyped from 190 DH lines of cross of Tapidor×Ningyou7). Various types of GS models  were applied to our data to evaluate the practicability of genome prediction of FT in ls (MARS) were subls (MARS). To demo2) between SNPs was calculated and visualized via the R package ‘LDheatmap’.The TN DH mapping population generated from a cross of Tapidor×Ningyou7  was usedB. napus, including two frequentist based methods: ridge regression best linear unbiased prediction (RR-BLUP)[Eight genomic prediction models of diverse types were used and compared in (RR-BLUP) and repr(RR-BLUP); three B(RR-BLUP), BayesA (RR-BLUP); and two(RR-BLUP) and supp(RR-BLUP). The det(RR-BLUP). Brieflyith line of the population in a single environment is the regression of markers across the genome below:i is the phenotype, μ is the intercept, xij is the genotype of the jth maker of the ith line (coded as −1 and 1 for genotype inherit from Tapidor or Ningyou7 respectively), βj is the regression coefficient of marker j and εi is the error term. To estimate the parameters, the most popular way is to minimize the residual sum of squares:For linear models, the phenotypic value of the For GS, the markers (p) usually largely outnumber the lines (n), which would bring in the curse of dimension. In RR-BLUP, an L2-norm regularization term is introduced as a tradeoff between the complexity of the model and the fitness to the training data. The loss function is denoted as:ian LASSO (BL) assij is the Euclidean distance between line i and j, and θ controls the decay rates.Unlike multiple linear regression models, semi-parametric and non-parametric models are capable of accommodating non-additive genetic effects on phenotypes, such as epistasis interactions. RKHS models nSVR can be viewed as a specific learning process of RKHS. In this study, the ‘ε-insensitive’ SVR was used, which only considers absolute values of residuals larger than ε.RF is an enAll the analysis was performed in R statistical computing environment. RR-BLUP and RKHS were implemented in the ‘rrBLUP’ package . ‘BGLR’ B. napus, we first applied the two most frequently used methods RR-BLUP and RKHS to the FT trait in B. napus, collected from ten environments. For both models, genetic factors and errors were taken as random effects, and year-site combination as covariates. After that, Genome Breeding values under each environment were predicted and compared via all the statistical and machine-learning models mentioned above.To verify the feasibility of our chip data in genomic selection of candidates in The performances of predictive models were evaluated using a 10-fold cross-validation (CV) scheme. Namely, the lines were divided into 10 disjoint subsets of equal sizes, and each subset was sequentially taken as a testing-set while the remaining ones were used to train the predictive model using different methods. This CV process was repeated ten times and the mean Pearson correlation between the observed and predicted trait value were calculated as the accuracy. For each run of CV, the same training and test set were used for all the models to guarantee a fair comparison.The overall genomic estimated breeding values (GEBVs) were predicted using RKHS with the genetic effect and error as random effects and site-year combination as a covariate implemented in the R package ‘rrBLUP’. The site-specific  breeding values were also measured respectively, with year as a covariate included.2 < 0.7) was investigated.Unlike conventional QTL approaches, RF scores the importance of SNPs, considering multi-loci and the interactions among them, so it’s capable of discovering SNPs with small effects and with strong epistasis effects. For each RF model, at each split one third of the SNPs were tried and 1000 trees were grown. The SNPs were ranked in descending order, based on average importance scores obtained from 20 runs. The SNPs highly related with FT genetic effects were selected in a recursive inclusion process. First, the top 5% of important SNPs were used, and another 5% SNPs were added in the model iteratively. For each model, mean square error (MSE) was recorded. This process was repeated 20 times, and the set of SNPs with minimal MSE was selected . As seenThe directions of SNPs on flowering time were then examined. The average flowering time (in days) of lines with either genotype (A or B) at every single identified SNP were calculated respectively and compared in each environment. The allelic direction of a SNP is considered consistent when the genotype representing early blossom is the same in the environments that it is significant.Similar to the procedure used in , the SNPB. napus genome, we adopted the function information of the homologues from well-annotated genomes like Arabidopsis thaliana to better understand the associated SNPs. First of all, BLASTX analysis of the probes of the SNPs against RefSeq proteins of Arabidopsis thaliana was performed, and only the hits with expected values < 1×10−6 were retained. Among 1, 248 SNPs used in this study, 566 SNPs could be annotated in this manner. A large number of SNPs are located in the intergenic regions, so their functional information would not be identified by BLASTX against coding genes. Due to linkage disequilibrium, one SNP tags multiple genes in a region and therefore the flanking genes of a significant SNP may also be the candidates associated with FT. To further pinpoint the candidate genes, we mapped the probes to the Brassica rapa genome using bowtie2 [B. rapa genome sequence was downloaded from Brassica database . The results showed that 70.97% of the 682 probes left map at least once on the B. rapa genome and 279 SNPs have genes in vicinity (1 kb). The corresponding orthologs were searched among RefSeq proteins Arabidopsis thaliana via BLASTP . Finally, the genes represented by the rest of the 403 SNPs were searched by BLASTX to the NCBI non-redundant protein sequences (nr), and hits with expected values < 1×10−6 were retained. In this stage 40 SNPs were annotated.Due to the lack of available annotations for the  bowtie2 , which iB. rapa was downloaded at http://brassicadb.org/brad/flowerGene.php. Genes of ‘Flowering time pathway’ were downloaded from the wikiPathways website at www.wikipathways.org (Pathway:WP2312).The list of curated flowering time related proteins was gathered from multiple sources. We first searched the NCBI protein database with ‘flowering time’ as query. The flower gene in http://david.abcc.ncifcrf.gov/home.jsp) was used for functional enrichment analysis. The functional clusters of significant Gene Ontology terms among candidate genes were surveyed via DAVID Functional Annotation Clustering Tools[DAVID  and two powerful machine-learning methods. To simplify the comparison, all the GS models were tested in each of the environments via 10-fold cross-validation, and the mean Pearson correlation coefficients of predicted and observed FT were calculated as prediction accuracy. All of the methods achieved relatively high and comparable accuracies (0.297~0.751) . The twoB. napus were detected by random forest, which is capable of capturing interactions and scoring the importance of the SNPs and has been used in feature selections. To reduce spurious associations due to accidental factors, the associations of SNPs with estimated breeding values (EBVs) derived from each geographic site trait values were tested respectively, instead of FT trait values of each site-year combination. The observed strong correlations among SNPs  and UQnapus0104 represented by UQnapus0669 resided in a homologue of another known FT gene, AP1 ‘Floral homeotic protein’. Some other FT genes were also found tagged by the detected SNPs (PHYE ‘phytochrome E’ (a homolog of ‘phytochrome B’ in Aquilegia formosa), AT1G68920 ‘transcription factor bHLH49’ (a homolog of established FT gene CIB5) and GRF8 ‘14-3-3-like protein GF14 kappa’ (participating in flowering time pathway) were tagged by three site sharing SNPs . It’s interesting to see that two known FT genes, AGL24 ‘MADS-box protein’ and AT2G01820 ‘receptor-like kinase TMK3’ (a homolog of flowering time protein CAM31941 in Lolium perenne) were only tagged by two eastern site-specific SNPs.A large number of SNPs detected in one site were found to be significant in another site. As shown in ted SNPs . For insBesides association analysis, the 1674 polymorphic SNP markers were combined with some common SSR markers to construct a linkage map , and FT We then examined whether the genotypes of the associated SNPs show the same allelic direction across environments, i.e. lines that flower earlier have the same allele at a specific SNP under all the environmental conditions. Among 437 identified SNPs, 72.5% (317 SNPs) have the consistent early blossom genotypes. For instance, samples that had inherited the allele from ‘Tapidor’ at UQnapus0052 tend to blossom earlier than the ones with allele from ‘Ningyou7’ in all ten environments , which iB. rapa genome as candidate genes (see −2); tissue developments, especially flower development ; and regulation of expression ; and these three functional clusters comprise about 5.6%, 3.5% and 21.8% of the candidate genes respectively. To further characterize the functional differences between the SNPs detected common to all geographic sites and specific to only one or two regions, we further checked the function annotation of genes tagged by SNPs detected in all three regions and the ones only reproducible in one or two regions respectively by using DAVID Functional Annotation Table Tool, and again performed function enrichment analysis on these two sets of genes  were annotated with GO term GO:0009908~flower development. The top two groups, involved in mRNA processing and regulation of transcriptions, however neither of these functional clusters were found significant (enrichment score > 1.3). As for region-specific candidate genes, genes of leaf morphogenesis and phyllome development are enriched (with enrichment score of 1.45).To further dissect the function of the significant SNPs, we obtained the genes bearing or near the associated SNPs by searching for the probes’ homolog or the genes within a 1 Kb window around the mapped probe on enes see . In totaenes see . The resof genes . For regSKIP1 ‘F-box protein SKIP1’ and AT1G53100 ‘core-2/I-branching beta-1,6-N-acetylglucosaminyltransferase-like protein’. UQnapus0669 was revealed to interact with SNPs that were also in/near known FT genes. For instance, the interaction of UQnapus0669 and UQnapus3878 was found in the north, the latter of which represented Dof zinc finger protein DOF5.2; UQnapus0669 also interacted with UQnapus5033, a proxy for PHYE ‘phytochrome E’; the epistasis effects of UQnapus0669 and UQnapus4810 was identified in the east, representing AGL24 ‘MADS-box protein’. Two other pairs of known FT genes showed epistasis effects in east, such as AT1G68920 ‘transcription factor bHLH49’ and PHYE ‘phytochrome E’, tagged by UQnapus1450 and UQnapus5033, and LFY and GRF8 ‘14-3-3-like protein GF14 kappa’, represented by UQnapus0238 and UQnapus1789.The epistasis interactions between SNPs associated with flowering time were detected for each geographic site by MARS. To faciB. napus genome. Semi-parametric model RKHS were presumed to perform better by taking cryptic non-additive genetic effects in consideration; however, in our results RKHS did not outperform linear models. The possible reason is that the marker density is relatively low, despite of high accuracies achieved. With a higher density panel, the semi-parametric methods are more accurate [We assessed the predictability of various types of GS models, including statistical models , semi-parametric model and machine learning methods. Our results showed that no apparent divergence of accuracies was observed among these GS models, which agreed with previous studies , 35, 46;accurate . SVR witaccurate .Previous studies showed genetics and environment interaction took up a large proportion of phenotypic variation . And theTo enhance the GS accuracies, a higher marker density would be used. Although it would bring troubles to linear models, due to the colinearity among markers, it would be beneficial for non-linear models . AnotherB. napus, we selected the FT candidate genes by mapping the probes to well-annotated genomes such as A. thaliana and B. rapa. And among these candidate genes, some genes are established FT genes  and the others are the homologs of reported FT genes in other species , and the results implicated these genes’ involvement in flowering process. It’s intriguing to see that now all the curated FT genes (or homologs) are reproducible under all environmental conditions, such as LFY and CDF2. Especially, AGL24 and AT2G01820 are solely detected in east site, indicating flowering is susceptible to geographic and climate conditions. And according to functional analysis, three functional clusters were found significantly enriched among candidate genes. Although, a number of candidate genes would be missed since only small surrounding regions of SNPs were considered, the well-established FT related biological processes (such as flower development and meristem development) were reproduced, indicating our method works for the unannotated genome. And among these functional groups, transcription regulation took up a largest amount of detected candidate genes (28.1%), showing the association of essential function for maintenance with flowering. Moreover, the overrepresented signaling transduction annotation among candidate genes implies flowering is influenced by multiple factors. We tried to address different characteristics between region-common and region-specific SNPs. Although no significant functional clusters were found among region-common genes, the top two groups, involved in mRNA processing and regulation of transcriptions, somewhat suggest that the common genes mainly exert essential function for maintenance. On the contrary, region-specific candidate genes are enriched for genes of phyllome development significantly and flower development marginally, implying that the blossom process is more likely affected by environment. Almost no overlap was found among epistasis interactions among three regions, indicating epistasis effects are more prone to be impacted by environments.QTL mapping based on the EBVs derived from the replicates would remove some spurious signals due to accidental factors. As showed in the results, there were a large proportion of overlaps among three sites, while among the SNPs detected in each environment by univariate method only ten were common for all ten environments (data not shown). Compared with 46 SNP loci detected by our method, the efficiency of QTLs mapping was lower. Some knS1 Fig(TIF)Click here for additional data file.S2 FigThe arrows showed that the three re-mapped SNP markers in the linkage groups.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Spartina maritima from next-generation sequencing (Roche-454) reads. The heterogeneity of the different domains of the highly repeated 45S unit was explored by identifying single nucleotide polymorphisms (SNPs) and assembling reads based on shared polymorphisms. SNPs were validated using comparisons with Illumina sequence data sets and by cloning and Sanger (re)sequencing. Using this approach, 29 validated polymorphisms and 11 validated haplotypes were reported . The rDNA domains of S. maritima have similar lengths as those found in other Poaceae, apart from the 5′-ETS, which is approximately two-times longer in S. maritima. Sequence homogeneity was encountered in coding regions and both internal transcribed spacers (ITS), whereas high intragenomic variability was detected in the intergenic spacer (IGS) and the external transcribed spacer (ETS). Molecular cytogenetic analysis by fluorescent in situ hybridization (FISH) revealed the presence of one pair of 45S rDNA signals on the chromosomes of S. maritima instead of three expected pairs for a hexaploid genome, indicating loss of duplicated homeologous loci through the diploidization process. The procedure developed here may be used at any ploidy level and using different sequencing technologies.Gene and whole-genome duplications are widespread in plant nuclear genomes, resulting in sequence heterogeneity. Identification of duplicated genes may be particularly challenging in highly redundant genomes, especially when there are no diploid parents as a reference. Here, we developed a pipeline to detect the different copies in the ribosomal RNA gene family in the hexaploid grass  Spartina maritima (Curtis) Fern. .Gene and genome duplications play an important role in the diversification of eukaryotic functions and the formation of new species . These cPolyploidy or whole genome duplication is a process that has played a major role in the evolution and the adaptation of many eukaryotes in both animals . AutopolSpartina Schreb. belongs to the subfamily Chloridoideae (a poorly investigated group in genomics), where it forms a monophyletic lineage embedded in the paraphyletic Sporobolus genus, which recently led some authors to consider taxonomical inclusion of Spartina in genus Sporobolus  (Spartina x neyrautii in the Basque region (France) (Foucaud 1897). Genome duplication of S. x townsendii gave rise to the highly fertile and invasive allopolyploid Spartina anglica C. E. Hubbard  . This expansion has many ecological effects, with this species playing an important role in the salt marsh sediment dynamics. S. anglica is able to colonize mudflats and to accelerate sediment accretion, thus altering the characteristics of the colonized habitats (referred to as an “ecosystem engineer” species)  offers a special opportunity to understand the genomic and adaptive mechanisms associated with the formation of a new species in the wild (The grass genus n events . It contEngland)  and Spar122,124) . S. anglS. maritima (2n = 60), which has a genome size that is estimated to be 2C = 3.8 pg or ∼3700 MB  has been used to elucidate phylogenetic relationships among the different species (Spartina hybrid (S. x townsendii) and its allododecaploid derivative (S. anglica) exhibit parental additivity of the ITS regions inherited from their hexaploid parents (The 45S rDNA unit is composed of a high number of transcription units (TU) per genome (>500 in plants) arranged in tandem repeats on one or several loci  Schaal . The 5′-e.g., pyrosequencing or sequencing by synthesis) offer powerful tools to generate suitable sequence read depth especially in nonmodel species where genomic resources were previously lacking , or Triticum  identify single nucleotide polymorphisms (SNPs) and indels among reads from the hexaploid genome and (ii) assemble reads based on shared polymorphisms (after removing putative sequencing errors) to distinguish the different copies from reads. Polymorphisms were validated using comparisons with Illumina sequence data sets and by cloning and Sanger (re)-sequencing. The method presented here can be used to identify duplicated copies from any sequenced regions of polyploid or diploid genomes.Spartina maritima were collected at the Etel river estuary . Plants were transplanted and maintained in controlled conditions in the greenhouse . Total genomic DNA was isolated from fresh leaf tissue with the Nucleospin Plant II (Macherey-Nagel) extraction kit following the manufacturer’s instructions. One run of S. maritima DNA was sequenced at the Functional and Environmental Genomics platform  using a 454 GS FLX Titanium pyrosequencer  that generated 999,229 reads with an average length of 377.0 bp. Illumina genomic reads  from S. maritima (from the same population) were also used for mapping and validation or correction of the detected SNPs. Genome coverage and 45S rDNA copy number estimates are presented in Supporting Information, Table S1 for both Roche-454 and Illumina Whole Genome Shotgun sequencing.Samples from S. maritima  using BLASTn with default parameters for the alignment algorithm Megablast (Oryza sativa genome was downloaded from www.phytozome.net (v. 204) to locate the 5′-ETS region. To identify this region, we followed the approach of We first analyzed Roche-454 genomic reads of maritima  using throcedure  was perfegablast  (Altschuhttp://biopython.org/) and custom python script for editing homopolymer-driven false-positive SNPs. This step produced three files containing original reads, the original contig, and the list of the original reads  the read trimming length corresponding to the number of nucleotides at the beginning and the end of each read not to consider in the SNP detection step; (ii) the minimum read depth corresponding to the number of reads required to detect SNP from; (iii) the SNP detection threshold corresponding to the minimal proportion of reads displaying a putative polymorphism; and (iv) the minimal number of shared SNPs to assemble two reads into a haplotype. The minimum depth of the analyzed rDNA contig was 61. Consequently, the minimum read depth parameter was fixed to 60. The other default parameters used in our pipeline were as follows: read trimming length = 5; SNP detection threshold = 20; number of shared SNPs = 1.2), 2 µl of water, and 1 µl of plasmid vector pCR4Blunt-TOPO (Invitrogen). The reactions were incubated for 5 min at room temperature. Transformations were realized using electro-competent Escherichia coli DH5α and Gene Pulser Xcell Electroporation System (BioRad); 18 µl of water was added to 6 µl of TOPO cloning reaction. Transformations were done in 0.2 cm cuvettes with 2 µl of DNA added to 40 µl of ElectroMax DH5α Competent Cells (Invitrogen) incubated 1 min on ice and pulsed at 2.5 kV, 25 µF, and 200 Ω. After the addition of SOC medium (1 ml), the cell suspension were incubated at 37° for 1 hr; 25–100 µL of each transformation culture was plated onto LB Agar with ampicillin (100 mg/ml) and incubated overnight in liquid medium at 37° (LB Broth and 100 mg/ml of ampicillin). Plasmids were purified with the Pure Yield Plasmid Miniprep System (Promega) and sequenced using T7 and T3 primers to sequence the samples (16–24 clones for each region) with 3730XL DNA sequencer technology (Sanger method) from both ends. For each region, the different sequences were aligned with MAFFT (v 6.864b) -sequencing (Sanger method). We have designed four pairs of primers using Primer 3, v. 1.1.4  Table 2Table 2 f 6.864b) . All seqS. maritima using Bowtie2  (f(x)= 52 + 8 * ln(x), where x corresponds to the read length. This function corresponds to a minimum of 87.06–90.30% of identity between the mapped reads and the reference sequence for reads with a length of 80–120 bp. The “.SAM” file created by Bowtie2 was converted in a “.PILEUP” format using the Samtools software suite (f(x)= 100 + 0 * x where x = length of read). Results obtained were filtered using custom python scripts to identify the number of reads mapped with 100% of identity.To compare pyrosequencing and cloning SNPs to Illumina SNPs, we mapped 1,263,153 Illumina paired reads with an average read depth of 7886 (SE = 273.84) on the rDNA sequence consensus of , 52, 8) . These “In situ hybridization was performed on mitotic chromosomes from S. maritima roots. Root tips with a length of 0.5–1.5 cm were treated in the dark with 0.04% 8-hydroxiquinoline for 2 hr at 4°, followed by 2 hr at room temperature to accumulate metaphases, then fixed in ethanol–acetic acid  for 48 hr at 4°, and stored in ethanol 70% at −20° until required. After washing in 0.01 M enzyme buffer  for 15 min, the prepared roots were digested in a solution of 5% Onozuka R-10 cellulase (Sigma) and 1% Y23 pectolyase (Sigma) at 37° for 30 min. The root tips were then washed with distilled water for 30 min. A root tip was transferred to a slide and macerated with a drop of 3:1 fixation solution. After air-drying, slides with good metaphase chromosome spreads were stored at −20°. Fluorescence in situ hybridization was carried out using the ribosomal probe pTa 71 (EcoRI fragment of rDNA repeat unit (18S-5.8S-25S genes and spacers) isolated from Triticum aestivum. The pTa 71 probe was labeled by random priming with biotin-14-dUTP . Chromosome preparations were incubated in RNase A (100 ng/µl) and pepsin (100 mg/ml) in 0.01 M HCl, and fixed with paraformaldehyde (4%). Chromosomes were denatured in a solution of 70% formamide in 2X SSC at 70° for 2 min, dehydrated in an ethanol series , and air-dried. The hybridization mixture, consisting of 50% deionized formamide, 10% dextran sulfate, 2X SSC, 1% SDS, and labeled probe (200 ng per slide), was denatured at 92° for 6 min and transferred to ice. The denatured probe was placed on the slide and in situ hybridization was carried out overnight in a moist chamber at 37°. After hybridization, slides were washed for 5 min in 50% formamide in 2X SSC at 42°, followed by several washes in 4X SSC-Tween. The biotinylated probe was immunodetected by Texas Red avidin DCS (Vector Laboratories). The chromosomes were mounted and conterstained in Vectashield (Vector Laboratories) containing 2.5 µg/ml 4’,6-diamidino-2-phenylindole (DAPI). Fluorescence images were captured using a CoolSnap HQ camera  on an Axioplan 2 microscope  and analyzed using MetaVue .e pTa 71 , which cmaritima rDNA consensus sequence and haplotype sequences are available at NCBI under the KT874468 to KT874488 accession numbers.The S. S. maritima were then mapped to the contig with GSMapper   available in GenBank (NCBI) and by motif searches for the 5′-ETS region , which is approximately two-times larger than the 5′-ETS of Z. mays  and O. sativa (966 bp). The S. maritima rDNA contig covers the whole coding region, spanning the 18S, 5.8S, and 25S, the ITS-1, ITS-2, 5′-ETS  were identified. These polymorphisms were localized both in coding  and noncoding regions . Cloning and resequencing were performed and confirmed 10 of the 34 polymorphic sites  that had been detected using the mapping procedure. Twenty-nine SNPs and indels (including nine polymorphic sites found by cloning and (re)-sequencing) were subsequently validated using Illumina data . Within 95% = 786.47–1608.93) were constructed using our program. These haplotypes were constructed from 69.35 reads on average . The 20 different haplotypes were localized on the different rDNA domains as shown in After rDNA domain annotations and SNP detection and validation, the number of haplotypes for each region of the 45S unit was determined. In this study we retained only the Roche-454 haplotypes constructed with a minimum of 10× read depth. The five indels not validated by cloning and Illumina data were not considered. From the 29 polymorphic sites retained, 20 haplotypes with a mean length of 1197.70 bp . Four regions were selected for these comparisons . In the 5′-IGS/ETS noncoding region, seven polymorphisms were validated by cloning, including the indel block of 2 bp, one substitution, two deletions, and two substitutions/insertions/deletions. Using these polymorphisms, it was possible to validate three of nine Roche-454 constructed haplotypes. Cloning depth could explain the low number of haplotypes validated. In the 5′-ETS noncoding region, only one polymorphism was validated by the cloning approach, which confirms one haplotype (two haplotypes were detected with this variant using Roche-454 data) , Block IS. maritima genome.Using the number of Illumina reads mapped on the different haplotypes, it was possible to estimate the relative proportion of each haplotype. For each rDNA region, the number of reads per haplotype was estimated . In the in situ hybridization (FISH) was performed from S. maritima somatic metaphase chromosomes (counterstained with DAPI) to identify the physical position of 45S rDNA arrays. The chromosomal locations of 45S rDNA arrays in S. maritima are shown in Fluorescence S. maritima. The objective was to detect the different rDNA sequences (designated as haplotypes) in this hexaploid species where both paralogous and homeologous copies are expected for this gene family. Results were validated by cloning and (re)-sequencing and also from Illumina sequencing datasets; the results allowed the estimation of the relative proportion of haplotype variation. To date, very few programs or pipelines have been developed to distinguish duplicated copies from next-generation sequencing data without diploid parents as a reference. SNiPloid  and one in the diploid Aegilops tauschii (DD)]  ] . Similar species . In contrupensis , whereasSpartina are reported here. Analysis of the contig corresponding to the 45S ribosomal DNA unit of S. maritima enabled us to annotate the different regions of the transcriptional unit and to detect the different copies for each region  using an approach relying on a low-stringency mapping of NGS reads.The intraindividual homogeneity of ribosomal genes was so far mainly examined across plant species by cloning and sequencing, a relatively time-consuming procedure that limits the number of sequences that can be sampled in the genome. Massively parallel sequencing now provides the opportunity to assess more accurately the intraindividual sequence polymorphism  or the 1S. maritima are similar to those found in two Poaceae. However, the percentage of GC of the two ITS and 5.8S coding regions is very low in comparison with other Poaceae . In more distantly related Chloridoideae tribes such as Triraphideae and Centropodieae (Table S2). Other monocot lineages  also exhibit higher GC content. Although information about more monocot representatives is needed, these results examined in the light of recent grass phylogenies . Lengths of the rDNA coding regions and ITS of  Poaceae . When exS. maritima with Zea mays and Oryza sativa (Oryza sativa (966 bp) has a similar length compared to the 5′-ETS of Zea mays (825 bp) identified by S. maritima (1754 bp), with lower GC content (50.9%) than in other Poaceae . These results are consistent with the literature showing that the size of 5′-ETS is variable across species, even within genera. Nicotiana sylvestris and Nicotiana tomentosiformis, which have, respectively, an ETS size of 1444 bp and 2172 bp. The size difference between these two ETS is explained by the number of subrepeats present in region 2 of the ETS, where five and 10 subrepeats are present in N. sylvestris and N. tomentosiformis, respectively  was encountered in the 18S coding region, highlighting their high sequence homogeneity. The 25S unit also presents little variation, with two SNPs (variants presenting substitution and indel at the same position) detected using pyrosequencing data and validated by the other methods. Although most coding regions are highly conserved, some variation may be observed in the 18S region. Such results are in accordance with those of Arabidopsis thaliana 18S, or those obtained in animals   . SimilarS. maritima exhibit less heterogeneity than the IGS/ETS regions, without any polymorphism in ITS-2 and only one substitution/deletion in ITS-1. The high polymorphism encountered in the IGS/ETS region (25 variants in the 5′-IGS/ETS) is consistent with the literature, which indicates that intergenic regions evolve more rapidly than the ITS region, which is most particularly prone to homogenization  than the other regions. Interspecies variation is then most likely more important than intraindividual (intragenomic variation), which explains why this region is useful in phylogenetic studies aiming at reconstructing organismal  history (Spartina). In the 5′-IGS/ETS, three haplotypes are present in high proportion and two haplotypes are less represented, which could indicate that the homogenization process is underway in these regions.Like coding regions, the ITS region in i.e., 454 read depth) and the parameters used to avoid false positives resulting from sequencing errors. Thus, the number of actual rDNA variants and their relative proportion (or copy number) in the S. maritima genome is most likely underestimated. In our developed program, several parameters can be tuned for relaxing or constraining read mapping, SNP detection, and haplotype construction. For example, the choice to not consider nucleotides present in less with a frequency lower than 20% at a specific position is in line with the procedure usually used when analyzing polyploid genomes. In their analysis of allotetraploid cotton EST (Expressed Sequence Tag) assemblies, Fragaria species. Our parameter is less stringent than the parameter used for homeo-SNP detection (fixed to 40%) in allotetraploid cotton . New and emerging single molecule sequencing methods that are producing longer reads should provide precious information regarding the assembly of longer (or even whole-length) 45S rDNA haplotypes.The number of SNPs and haplotypes that may be detected in our study is indeed limited by the employed technology . In the polyploid Spartina genus, which contains various ploidy levels , such a program will be useful to detect duplicated homeologous genes and to increase our understanding of their origin and evolutionary fate.In summary, our program enables detection of SNPs and haplotypes within NGS read datasets. Haplotype construction and validation are based on three methods/technologies  in the context of polyploidy in a nonmodel species where diploid reference parental genomes are not available. The program developed in this study could then be used for any polyploid species (including high ploidy levels) with or without reference genome sequences but also on diploid species (


Docosahexaenoic acid (DHA) is a structural constituent of membranes specifically in the central nervous system. Its accumulation in the fetal brain takes place mainly during the last trimester of pregnancy and continues at very high rates up to the end of the second year of life. Since the endogenous formation of DHA seems to be relatively low, DHA intake may contribute to optimal conditions for brain development. We performed a narrative review on research on the associations between DHA levels and brain development and function throughout the lifespan. Data from cell and animal studies justify the indication of DHA in relation to brain function for neuronal cell growth and differentiation as well as in relation to neuronal signaling. Most data from human studies concern the contribution of DHA to optimal visual acuity development. Accumulating data indicate that DHA may have effects on the brain in infancy, and recent studies indicate that the effect of DHA may depend on gender and genotype of genes involved in the endogenous synthesis of DHA. While DHA levels may affect early development, potential effects are also increasingly recognized during childhood and adult life, suggesting a role of DHA in cognitive decline and in relation to major psychiatric disorders. Long chain polyunsaturated fatty acid (LC-PUFA), including docosahexaenoic acid (DHA) and arachidonic acid (AA), are incorporated into membrane phospholipids and, apart from their structural role in these membranes, they also act as precursors of autocoid signaling molecules  and as potent activators of a number of gene transcription factors . The essentiality of n-3 LC-PUFA is generally mainly contributed to the incorporation of DHA in uniquely high levels in the central nervous system—although DHA is incorporated in most other tissues where it may also have important functional effects.FADS) gene cluster and to vary depending on age and circulating levels of sex hormones.Overall, membrane PUFA composition , AA and DHA) seems to be more responsive to DHA in the diet than to intake of LA and AA . Animal The present paper will give an update of the current literature and try to answer the following questions: (1) Does the high rate of DHA accumulation in the brain have any functional importance? (2) If yes, is the endogenous synthesis of DHA high enough to support optimal functional levels of DHA in the brain? Finally, we will also address whether DHA may contribute to normal brain functioning later in life.de novo PUFA synthesis, the rate of membrane DHA incorporation in early life—in the brain as well as in other tissues—depends on maternal transfer, dietary supply  and endogenous LC-PUFA production. The DHA accumulation in the brain during the third trimester of pregnancy is substantially higher (in % of fatty acids (FA%)) than the overall body deposition rates, whereas brain incorporation of AA is more in line with that which occurs in other tissues [post-mortem studies it has been calculated that whole-body DHA accretion during the third trimester amounts to around 50 mg/day while the accretion of AA is approximately twice as high (100 mg/day) [The accumulation of DHA in the brain takes place during the brain growth spurt in the intrauterine and neonatal period up to two years of age and the high levels of DHA in the brain are maintained throughout life . Due to  tissues . Fetal L tissues . Fetal f tissues ,9 compar tissues ,10. Howe tissues . Based o mg/day) . It has  mg/day) .The intrauterine PUFA supply occurs via transfer of non-esterified PUFA mainly derived from the maternal circulation across the placenta ,11. The Post-natal accumulation of LC-PUFA in infant tissues is supported by maternal transfer of PUFA through breastmilk, and blood levels of LC-PUFA in breast-fed infants remain higher than maternal levels for some time postnatally ,25. In ni.e., the combined PUFA metabolism and conversion of LA and ALA to AA and DHA in both the mother and the fetus/infant. Both term and preterm infants have been shown to convert stable isotope labeled LA and ALA to AA and DHA, respectively [The phenomenon of increasing LC-PUFA in fetal and infant blood and tissues relative to that of their mother has been described as “bio-magnification” , but couectively ,36,37, aectively . The desectively . This syectively . HoweverFADS gene cluster than n-3 PUFAs, typically with an increase in LA and a decrease in AA levels in minor allele carriers [FADS SNPs have also been found to have lower blood (RBC and plasma) levels of AA and higher levels of LA and ALA during pregnancy [FADS polymorphisms have been estimated to explain as much as 29% of the variation in serum AA contents in adults, in whom serum DHA concentrations are determined primarily by the dietary supply of preformed DHA [FADS SNPs [FADS SNPs and the concentrations of DHA and eicosapentaenoic acid (EPA) as well as AA in the newborn infants, thus confirming that synthesis of DHA provides a relevant contribution to status [FADS SNPs in the mother gave rise to increased levels of n-6 PUFA before the delta-5 desaturation step (LA and di-homo-γ-linoleic acid), whereas minor allele SNPs in the child resulted in decreased levels of AA and other n-6 LC-PUFA beyond this point in the metabolic pathway [FADS polymorphisms are needed to derive a biologically plausible interpretation of their potential functional effects. Furthermore, both AA and DHA needs to be considered together since apart from the main determinants of their levels, either endogenous or exogenous, their balance may be critical for the functional outcomes in infancy and beyond.Overall, data suggest that n-6 PUFAs in breastmilk, plasma and RBC membranes across all ages are more affected by single nucleotide polymorphisms (SNPs) in the carriers ,43,44,45regnancy ,42. FADSADS SNPs , but stuADS SNPs ,47,48. Fo status . Curious pathway . More daFADS polymorphisms may substantially contribute to RBC DHA levels in late infancy (to the same extent as breastfeeding) [FADS SNPs compared to tracking in carriers of at least one minor allele [We have recently found that some feeding) . Some SNfeeding) , whereasfeeding) . Interesfeeding) , which cfeeding) . Furtherfeeding) . Insteadr allele . More loThe majority of the randomized controlled trials investigating the effect of dietary LC-PUFA supplementation in term infants have added both DHA and AA, and only few have investigated the effect of varying DHA intakes at a constant intake of AA. With respect to the functional effects of LC-PUFA supplementation in infancy, the most accepted developmental effect is an increased rate of visual acuity development . This efOverall, meta-analyses of the randomized controlled trials that have investigated the effect of LC-PUFA supplementation on neurodevelopmental outcomes throughout the first two years of life have not shown any clear benefit of LC-PUFA addition to infant formula on development of term or preterm infants ,56,57. HSo far, few studies have shown that the effect of perinatal n-3 LC-PUFA supplementation may be affected by the gender of the child. In two large investigations, the DINO and DOMInO trials ,61, an iMany of the available studies on the effects of maternal or, more commonly, infant n-3 LC-PUFA supplementations on neurodevelopmental outcome during infancy have several limitations, which become more and more evident as our knowledge on the physiology of LC-PUFA, and DHA in particular, progresses. The vast majority of the studies, whether on cognition of other functional outcomes or if they provide the supplement during pregnancy, lactation or to the infant in various types of formula, show a great heterogeneity with respect to LC-PUFA sources, doses of DHA (and AA) and durations of interventions. It should be noted that the methodologies for primary outcome assessment as well as age of effect examination differed between trials, and the effects in the first few years of life and potential long-term effects may be quite different. For outcomes such as neurological and cognitive development, there may be a necessity to use different tests at different ages to accommodate changes in age and maturity level. However, many trials have investigated effects on numerous outcome measures, which are often internally inconsistent, or show no apparent pattern over time. In addition, studies often have low power in terms of the number of participants and sometimes also high rates of dropouts as well as lack of intention-to-treat analysis and a sufficient description of allocation concealment. Although baseline demographic characteristics are constantly reported, often baseline n-3 PUFA intake or status is not included in the characterization. This omission is critical for the interpretation, since baseline n-3 PUFA status will likely affects the response to changes in n-3 PUFA intake—both with respect to acute and persistent functional effect. Finally, as mentioned above, the effects of early n-3 LC-PUFA supply may vary in boys and girls, and this is not taken into account in the older studies. The emerging knowledge indicates that it is critical to take these aspects into account and that the variation in these aspects complicate attempts to combine data in meta-analyses to achieve conclusions with respect to the functional consequences of the addition of LC-PUFA, even beyond the single, specific effects of DHA.FADS polymorphism is limited and although the most clear effect on PUFA metabolism as mentioned is a decrease in AA production, functional associations with FADS genotype cannot be interpreted as a consequence of a reduction in AA. The influence of FADS polymorphisms on LC-PUFA status—and specifically the observed variations between specific SNPs and specific LC-PUFA over time—introduces new variables to be considered in the evaluation of the effects of FADS genotype on development and health of young children.Current knowledge about the functional effects of FADS genotype, examined by use of different individual SNPs, modifies the effect of breastfeeding on IQ-like neurodevelopmental outcomes in childhood [Several studies have showed that infant hildhood ,65,66, whildhood ,68 . In the studies that found an interaction this is based on an equal “IQ” in breast-fed and formula-fed in the two cohorts in the Caspi study  and the FADS SNPs , but, as indicated by the aforementioned study from Harsløf and coworkers, they may not all down-regulate the endogenous DHA synthesis in the infants [FADS SNP studies. Interestingly, Steer et al. reported opposing effects of rs174574 and rs3834458 in the modulation of the association between breastfeeding and IQ [FADS SNP-breastfeeding interaction studies have considered whether the effect might differ between boys and girls. As in the DINO trials [FADS SNPs versus 3-year ASQ outcomes found that the effect of DHA increasing SNPs appeared to be negative in girls and positive in boys [FADS SNPs and DHA status at 3 years of age, these results might be interpreted as proof of a programming effect specifically of early DHA dietary intakes. However, the lack of effect of the FADS SNPs on DHA status later in life could be due to a blurring effect of a more diverse dietary intake of DHA from fish. Therefore, it is not possible to rule out that DHA supply contrary to the current hypothesis of an early window of vulnerability might have an effect on brain function at all stages of life.Additionally, the studies on interactions between breastfeeding and IQ used different  infants . This cog and IQ . Opposing and IQ . None ofO trials , the Men in boys . Due to FADS SNPs or n-3 LC-PUFA supplementation on cognitive development, emotions and behavior in toddlers and later in childhood or even in healthy adults.Brain DHA accretion continues into childhood, and although the accretion rate declines, the incorporation of DHA is still high at least during the preschool years. Once high levels of DHA are achieved in the brain these are maintained during later life, and this presumably also depends on an optimal dietary supply, as dietary intake of DHA from fish in adults has been shown to be the dominant determinant of DHA levels in various lipid pools . HoweverA single study pooling data from three trials that randomized to LC-PUFA formulas immediately after birth or after breastfeeding for 6 weeks or 4–6 months, respectively and continued supplementation throughout the first year of life, found significant beneficial effects on problem solving at 9 months of age only in the two studies that started intervention early . HoweverResults from studies in schoolchildren in low-income countries have shown relatively convincing cognitive effects of fish oil supplementation. The effects have been shown to be stronger in children with low socioeconomic status or malnutrition-related health problems and a low consumption of fish and very little n-3 PUFA ,75. Howei.e., a reduction in impulsivity and anti-social behavior, in 450 healthy 8–10 year-old children supplemented with fish oil versus olive oil [FADS SNPs and the development of ADHD, specifically in the context of prenatal alcohol exposure [In the last mentioned study, all the DHA supplemented children had a more relaxed mood compared to controls , which ilive oil . Similarlive oil . Comparalive oil  and in aexposure . It is dversus no supplement during the complementary feeding period [So far, the effect of gender has not been given much attention in intervention trials with n-3 LC-PUFA in preschool and school-aged children. However, as was the case in the studies on the effects of DHA in the perinatal period, a gender-treatment effect has been observed on mean arterial blood pressure after fish oil supplementation from 9 to 18 month of age in healthy Danish infants, which just as in the previously mentioned maternal fish oil supplementation study was mostly affected in boys . In thisg period . In thatg period , which cg period .Only a few studies have examined if fish oil supplementation can affect brain functions in healthy young adults, but some studies indicate that DHA may be important for cognition and behavior during late adulthood. DHA supplementation improved memory in healthy, young adults whose habitual diets were low in DHA, and the response was still modulated by sex  suggestiDHA has also been suggested to be effective in major psychiatric disorders. Most of the conducted studies have used n-3 LC-PUFA levels in RBC membranes as a measure of exposure, due to the objectivity of this measure and their high correlation with habitual dietary intake  in additAccumulating evidence also suggests that n-3 LC-PUFA supplementation may be efficacious for the treatment of positive and negative symptoms in patients with schizophrenia or at ultra-high risk for psychosis . There iFADS, gender, dietary intakes and lifestyle. For these reasons it is difficult to disentangle the effects of dietary DHA from the results of the randomized supplementation trials. In future studies an appropriate sample size should be calculated in order to adjust for the different variables. Mendelian trials provide a new tool to investigate the effects of LC-PUFA on cognitive development, but the interpretation of results from such trials requires an improved understanding of the biochemical effects of individual FADS SNPs and also needs to consider the potential differences between boys and girls.The effects of DHA on brain and cognitive development have been extensively investigated in the last years. Its functional effects have been progressively, but not entirely, separated from those of AA. Clinical trials on maternal and infant dietary intakes are not entirely clear and consistent, but seem to indicate a complex interaction between the genotype pattern of FADS polymorphisms. Most evidence indicates that the DHA accumulation is mainly affected by dietary intake, specifically of preformed DHA. However new studies indicate that the genetic make-up in the FADS gene cluster may contribute substantially to the current understanding, but that the effects may be SNP-specific and may even vary with age, or at least are most evident in the perinatal period, where the endogenous synthesis of LC-PUFA is upregulated and diet may be more easily controlled for, especially during lactation (or formula feeding).Thus, our two questions, (1) Does the high rate of DHA accumulation in the brain have any functional importance? (2) If yes, is the endogenous synthesis of DHA high enough to support optimal functional levels of DHA in the brain? Regarding the first, there is clear evidence that DHA contributes to the visual development of infants, as also concluded by EFSA , but the