Knowledge of germplasm diversity and relationships among elite breeding materials is fundamentally important in crop improvement. We genotyped 450 maize inbred lines developed and/or widely used by CIMMYT breeding programs in both Kenya and Zimbabwe using 1065 SNP markers to (i) investigate population structure and patterns of relationship of the germplasm for better exploitation in breeding programs; (ii) assess the usefulness of SNPs for identifying heterotic groups commonly used by CIMMYT breeding programs; and (iii) identify a subset of highly informative SNP markers for routine and low cost genotyping of CIMMYT germplasm in the region using uniplex assays.Genetic distance for about 94% of the pairs of lines fell between 0.300 and 0.400. Eighty four percent of the pairs of lines also showed relative kinship values ≤ 0.500. Model-based population structure analysis, principal component analysis, neighbor-joining cluster analysis and discriminant analysis revealed the presence of 3 major groups and generally agree with pedigree information. The SNP markers did not show clear separation of heterotic groups A and B that were established based on combining ability tests through diallel and line x tester analyses. Our results demonstrated large differences among the SNP markers in terms of reproducibility, ease of scoring, polymorphism, minor allele frequency and polymorphic information content. About 40% of the SNPs in the multiplexed chip-based GoldenGate assays were found to be uninformative in this study and we recommend 644 of the 1065 for low to medium density genotyping in tropical maize germplasm using uniplex assays.There were high genetic distance and low kinship coefficients among most pairs of lines, clearly indicating the uniqueness of the majority of the inbred lines in these maize breeding programs. The results from this study will be useful to breeders in selecting best parental combinations for new breeding crosses, mapping population development and marker assisted breeding. Assessment of genetic diversity, relationships, and structure within a given set of germplasm is useful in plant breeding for different reasons including: (i) assisting in the selection of parental combinations for developing progenies with maximum genetic variability for genetic mapping or further selection ; (ii) deAdvances in molecular technology, however, have produced a shift towards SNP markers ,16. BecaZea mays ssp. mays L.) is the world's third most important by acreage and is a multi-purpose crop for food, animal feed, biofuel, and raw material in the synthesis of a broad range of industrial products [Maize  from CIMMYT breeding programs in Kenya and Zimbabwe were used in this study  to select an optimal set of discriminating SNPs that tended to separate the groups obtained from population structure analysis to a maximum degree. Variables (SNPs) were chosen to enter or leave the discrimination model among groups based on the significance level of an F-to-enter and F-to-remove value of 0.05 and 0.10, respectively.An admixture model-based clustering method was used to infer population structure of the 450 lines using the software package STRUCTURE, version 2.3.3 . STRUCTUa priori heterotic groups assigned to a subset of 220 lines by CIMMYT breeders. Significance levels for variance component estimates were computed using 1000 permutations. Both AMOVA and FST were calculated using the ARLEQUIN version 3.11 http://cmpg.unibe.ch/software/arlequin3.Matrices of Roger's genetic distance  and relaOf the 1536 SNPs, 471 (30.7%) were not included in data analyses because they showed weak amplification, ambiguity or irreproducibility in allele calling, high (> 10%) missing data, or had a minor allele frequency (MAF) of less than 5%. Figure The estimated log probability of the data (LnP(D)) increased continuously with increasing K (number of groups or populations) and there was no obvious K value clearly defining the number of populations. However, LnP(D) sharply increased between K = 1 and K = 3, and much less so between K = 4 and K = 10 Figure . The ad priori group defined at K = 3. When the priori group at K = 3 were used in plotting the two axes from discriminant analyses, axis-1 separated group 2 from groups 1, group 3 and the mixed group  fell between 0.300 and 0.400 Figure . RelativIn order to assess if the SNP data show clear genetic differentiation between CIMMYT heterotic groups A and B that were established based on combining ability tests through diallel and line x tester analyses, we ran population structure analyses at both K = 2 and K = 3, and NJ-clustering and principal component analyses by selecting a subset of 220 out of the 450 lines that belong to heterotic group A (126 lines) and B (94 lines). At both K = 2 and K3, there was very little correspondence between heterotic grouping based on phenotypic data and the model-based population partition based on the SNP data  varied from 3.9% to 6.8%. When the overall SNP variance was partitioned into hierarchical levels using the groups predefined from the model-based population partition at K = 2, K = 3, K = 4, K = 5 and K = 6 as categorical variables, FST accounted for 11.7%, 9.8%, 11.1%, 12.6% and 14.0%, respectively. A random permutation test indicated that the proportion of variances attributable at all groups were highly significant (p < 0.0001). We also computed FST values using the two heterotic groups created based on a priori knowledge of combining ability from field experiments and the groups predefined based on population structure analyses both at K = 2 and K = 3. The FST was the lowest (1.2%) for heterotic groups created based on field experiments and higher for groups defined based on population structure analyses of the SNP data (FST = 11.3% at K = 2 and 11.8% at K = 3).Table The main challenges in analyzing any genetic dataset are to (a) explore whether a given population is homogeneous or contains genetically distinct subgroups, and (b) identify quantitative evidence that supports the presence of these groups . Using Spriori predefined groups based on population structure so the agreement between these two methods is not unexpected. In cluster analysis, different combinations of genetic distance/similarity matrix and clustering algorithm can give rise to somewhat different groups. A single distance matrix and a single clustering algorithm even may produce several alternative clusters that often create ambiguity in selecting the best one. Because PCA produces a 2- or 3-dimensional scatter plot of the samples in which geometrical distances among samples in the plot reflect the genetic distances among them with a minimum of distortion and ambiguity compared to cluster analysis [Comparisons of the different multivariate analyses revealed high concordance among the PCA, model-based population partition and discriminant analyses in terms of the number of groups and members of each group. However, cluster analysis showed low concordance with the other methods in terms of assigning genotypes into their respective groups  indicated a moderate level of genetic differentiation. FST values were the lowest (0.039-0.068) when the prior groups from cluster analyses were used as categorical variables, suggesting little to moderate levels of differentiation. Similar FST values have also been reported elsewhere [Among the 101,025 pairwise comparisons of the 450 lines, only 0.1% fell within a genetic distance less than 0.05, indicating a lack of redundant lines among the germplasm studied. Kinship analyses agree, since kinship coefficients for 84% of the pairs of lines fell below 0.50. This suggests that each line in the study is potentially contributing new alleles to a breeding program. Our results on kinship are higher than that of Wen et al.  who repolsewhere .Maize breeders generally develop new parental inbred lines by selecting the progeny of intercrossed lines from within the same heterotic group. As heterotic group assignment is made based on combining ability from diallel experiments, several authors suggested the use of molecular markers in heterotic grouping -7. Howevhttp://www.kbioscience.co.uk. For other applications that require high density and lower cost genotyping per data point, genotyping-by-sequencing [http://www.maizegenetics.net/gbs-overview. CIMMYT is collaborating with Cornell University and the USDA Agricultural Research Service in implementing the genotyping-by-sequencing pipeline for genomic selection to reduce the genotyping costs below that of field phenotyping.Reproducibility, polymorphism frequencies, and ease of scoring are important criteria to evaluate the value of markers for germplasm characterization. Just over 30% of the 1536 SNPs scored in this study did not meet these criteria for this set of germplasm, and none of the 1065 SNPs were found to be highly informative based on PIC; this was not unexpected given the biallelic nature of SNPs. In order to recommend a useful subset of SNPs for routine genetic diversity and mapping studies in tropical and sub-tropical maize germplasm  using uniplex assays, we chose 644 of the most informative and repeatable SNPs (listed in Additional file quencing  is likelThere were high genetic distance and low kinship coefficients among most pairs of lines, clearly indicating the uniqueness of the majority of the inbred lines in these maize breeding programs. In the different multivariate analyses, several lines with similar pedigree often clustered into the same group, but the groups did not correspond to breeding programs, maturity groups or adaptation. There was no correlation between heterotic grouping based on phenotypic and SNP data. About 40% of the SNPs in the multiplexed chip-based GoldenGate assays were uninformative in this study and we recommend 644 of the 1065 for low to medium density genotyping in tropical maize germplasm using uniplex assays. The results from this study will be useful to breeders in selecting best parental combinations for starting new pedigree populations, mapping population development and marker assisted breeding.The authors declare that they have no competing interests.KS was responsible for DNA preparation, data scoring and analyses, and writing the manuscript. CM, BSV, DM, YB and SM selected the inbred lines for the study and assisted in germplasm characterization during manuscript preparation. BMP and MLW contributed to and edited the manuscript. All authors have read and approved the final manuscript.Table S1 Germplasm summary.Click here for fileTable S2 SNP summary.Click here for fileTable S3 Population structure summary for heterotic groups.Click here for file


Congenital Cytomegalovirus (CMV) infection is an important medical problem that has yet no current solution. A clinical trial of CMV glycoprotein B (gB) vaccine in young women showed promising efficacy. Improved understanding of the basis for prevention of CMV infection is essential for developing improved vaccines.We genotyped 142 women previously vaccinated with three doses of CMV gB for single nucleotide polymorphisms (SNPs) in TLR 1-4, 6, 7, 9, and 10, and their associated intracellular signaling genes. SNPs in the platelet-derived growth factor receptor (PDGFRA) and integrins were also selected based on their role in binding gB. Specific SNPs in TLR7 and IKBKE (inhibitor of nuclear factor kappa-B kinase subunit epsilon) were associated with antibody responses to gB vaccine. Homozygous carriers of the minor allele at four SNPs in TLR7 showed higher vaccination-induced antibody responses to gB compared to heterozygotes or homozygotes for the common allele. SNP rs1953090 in IKBKE was associated with changes in antibody level from second to third dose of vaccine; homozygotes for the minor allele exhibited lower antibody responses while homozygotes for the major allele showed increased responses over time.These data contribute to our understanding of the immunogenetic mechanisms underlying variations in the immune response to CMV vaccine. Infection with CMV is common in humans, causing severe morbidity and mortality in congenitally-infected newborns and in immunocompromised patients -3. The iData from human studies suggest that single nucleotide polymorphisms (SNPs) in immune response genes may influence severity of infections and response to vaccinations such as rubella, measles and hepatitis B -16. TollThe study cohort included healthy women who were enrolled in the CMV vaccine after obtaining written informed consent . Women wAntibody to CMV gB was measured using an Enzyme-linked immunoabsorbent assay (ELISA) . The vacUsing a candidate gene approach the following genes were selected: TLRs and associated intracellular signaling molecules: TLR1-4, TLR6, TLR7, TLR9, TLR10, JUN, MYD88, IKBKE, CHUK (IKKα), NF-KB1, CD14, MXD3 (MAD3), MAPK8 (JNK1), MAPK14, MAP3K7 (TAK1), LY96 (MD2), TRAF6, IRAK1, IRAK4, TBK1, TICAM1 (TRIF) and IRF3. In addition, PDGFRA, PDGFRB and integrin alpha V and integrin B1 were selected based on data showing their role in binding gB ,23.2 > 0.8. SNPs with a high r2 are correlated and can give information about each other, so they are grouped together and one SNP is selected to represent them all. This is known as a "tagging" SNP. Selection was restricted to those SNPs with a minor allele frequency (MAF) > 1% in the African population. Additionally, all SNPs were evaluated using a proprietary algorithm from Illumina, Inc.  which assesses the likelihood that an assay will succeed on the Illumina genotyping platform. Only those with a design score > 0.8 were selected for use. For non-synonymous SNPs we used a more liberal cut off score > 0.4 to ensure inclusion of functional variants. Forty-one additional SNPs with previously reported associations in a variety of human diseases, 157 non-synoymous SNPs, and 28 ancestry informative markers (AIMs) were also typed for a total of 702 SNPs. African Americans are typically individuals with a mixed ethnic background. Allele frequencies of SNPs can vary by race and ethnicity. Therefore, it is important to assess the degree of mixture in an admixed individual when analyzing SNP data. If the cases and controls have differing amounts of admixture, variation in allele frequency by race can cause the appearance of an association with the phenotype of interest, when in reality the association is with race. Including AIMs in the SNP panel allows an assessment of admixture and means that differing amounts of admixture can be controlled for in the data analysis.We identified tagging SNPs within the genes and in the 10 kb flanking each side of the genes, using the LDselect algorithm in individuals from the publicly available HapMap Yoruba, Nigeria population (YRI) . For tagGenomic DNA  and 200-400 ng/μl (whole genome amplified DNA each) were obtained from frozen EDTA-blood samples using Gentra Puregene extraction (Qiagen). The study samples and duplicates were plated and genotyped together with 33 HapMap controls . Genotyping was performed using the Illumina GoldenGate chemistry. Allele cluster definitions for each SNP were initially determined by Illumina's BeadStudio Genotyping Module (version 3.3.7) clustering algorithm using all samples in the project, applying a quality threshold  of 0.25. This was followed by manual review of the cluster definition for each SNP and adjustment of cluster boundaries to drop questionable genotype calls.http://pngu.mgh.harvard.edu.ezproxy.welch.jhmi.edu/~purcell/plink/gplink.shtml using linear regression and an additive model. A Hardy-Weinberg p-value threshold of 10-3 and a minor allele frequency (MAF) greater than 0.01 were used. A cut off at p value ≤ 0.03 was selected.28 ancestry informative markers were genotyped for evaluation of population stratification using principal components (PCA) in the statistical program Eigenstrat . Six HapThe study cohort was primarily African American (75%) with a median age of 19.6 years (range 14-40 years).rd vaccine ranged from 370-86,732 (median- 9847.7).SNP data were available from 152 women who received gB vaccine (99% of attempted samples). Of 152 vaccine recipients, 142 received 3 doses of vaccine and had data available on both genotypes and ELISA titers. The titer of gB antibodies six month after administration of 3The first goal was to determine whether genetic variation within TLR genes contributed to the observed antibody responses to CMV gB vaccine. Linear regression was performed of individuals' log transformed ELISA levels measured 6 months after the third dose of vaccine. SNPs yielding p-value < 0.03 are summarized in Table Homozygous carriers of the minor allele at four SNPs in the TLR7 gene showed higher vaccination-induced antibody responses to gB compared to heterozygotes or homozygotes for the common TLR7 allele Table . Three oNext, a possible association of SNPs in TLRs with changes in antibody responses from one month after the second dose of vaccine to six months following the third (and last) dose was investigated. For this analysis, we used data from 69 women who initially were at the top 50% of responses , and then the change in antibody levels following the last dose of vaccine. Data were compared between women who had no change or an increase in antibody levels, versus those who had a decrease in antibody level. The OR was calculated as the odds of being a weak responder (i.e. having a negative change between visits) versus a strong responder (i.e. having no or positive change) in those who carried the risk allele versus not Table . Two othOur analysis suggests that polymorphism in TLR genes may influence antibody responses to CMV gB vaccine. Specifically, SNPs in TLR7 and IKBKE may influence these responses. Viral nucleic acids are a major class of pathogen-associated molecular patterns recognized by TLRs. TLR3 (dsRNA), TLR7 (ssRNA), TLR8 (ssRNA), and TLR9 (CpG DNA) signal from the endosome where degradation of virus particles exposes the viral genome. Several adapter proteins interact with the TLRs: MyD88 , Mal (or Tirap) (TLR2 and 4) and TRIF (TLR3). These adapters link TLR proteins to a downstream signaling pathway including IRAK-1, IRAK-2, TRAF6, and IKK- αβγ. The primary consequences of TLR activation include NF-κB activation, inflammatory cytokine secretion, up-regulation of immune co-stimulatory molecules and, for a subset of TLRs, the production of type I interferon.Herpesviruses activate signaling through TLR2 and TLR9 -28. TLRsSurprisingly, we found that specific SNPs in TLR7 were associated with variability in gB antibodies. SNP rs179008 is associated with amino acid change in position 11 of the TLR7 protein from glutamine to leucine. Several recent studies provide support for a functional effect of this SNP ,35. TLR7The best known TLR member expressed in human B cells is TLR9 , but othIKBKE, a noncanonical I-kappa-B kinase (IKK), is essential for regulating antiviral signaling pathways. It phosphorylates inhibitors of NF-kappa-B, leading to dissociation of the inhibitor/NF-kappa-B complex and ultimately degradation of the inhibitor. IKBKE is expressed predominantly in immune cells and tissues, including peripheral blood leukocytes, spleen, and thymus . IKBKE pThis study shows for the first time that host genetics plays a role in the development of gB antibodies following CMV immunization. It is unclear whether these polymorphisms contribute to protective immunity. Future studies will provide additional understanding of the roles of immune response genes in CMV immunization and will evaluate the role of gB in activation of TLR7-related pathways.The data set supporting the results of this article is included within the articleCMV: Cytomegalovirus; gB: glycoprotein B; TLR: Toll-like receptors; PDGFR: Platelet derived growth factor receptor; IKBKE: Inhibitor of nuclear factor kappa-B kinase subunit epsilon; SNP: Single nucleotide polymorphismRAB conceived of the study, its design and wrote the manuscript; GLW, PD and TB performed and reviewed the statistical analysis, RGI helped in SNP selection, RFP provided essential samples and edited the manuscript, RHY provided input into experimental design. All authors read and approved the final manuscript.Ravit Arav-Boger received research funding from Sanofi-Pasteur. Robert F Pass receives research funding from Sanofi-Pasteur. In addition, Dr. Pass has served as a consultant to Merck, Vical, AlphaVax and MedImmune. Other authors: no conflict.Figure S1. SNPs in ancestry informative markers and antibody levels in the study population. A principal component analysis was performed to correct for potential confounding due to ancestry. The distribution of antibody levels is depicted for African and European women.Click here for fileFigure S2. Quantile-Quantile (QQ) plots of the log antibody levels QQ plots of log antibody levels and lambda values are depicted. In the right panel, nothing is done to account for the structure. On the left panel, the results are adjusted for principal components, leaving about the same amount of inflation as the case with no population stratification.Click here for file


Although GWASs are primarily focused on single-locus effects, gene-gene interactions  are also assumed to contribute to the genetic risks for complex diseases including breast cancer. While it has been hypothesized that moderately ranked  weak single-locus effects in GWASs could potentially harbor valuable information for evaluating epistasis, we lack systematic efforts to investigate SNPs showing consistent associations with weak statistical significance across independent discovery and replication stages. The objectives of this study were i) to select SNPs showing single-locus effects with weak statistical significance for breast cancer in a GWAS and/or candidate-gene studies; ii) to replicate these SNPs in an independent set of breast cancer cases and controls; and iii) to explore their potential SNP-SNP interactions contributing to breast cancer susceptibility. A total of 17 SNPs related to DNA repair, modification and metabolism pathway genes were selected since these pathways offer a priori knowledge for potential epistatic interactions and an overall role in breast carcinogenesis. The study design included predominantly Caucasian women  from Alberta, Canada. We observed two two-way SNP-SNP interactions  in logistic regression that conferred elevated risks for breast cancer (Pinteraction<7.3×10−3). Logic regression identified an interaction involving four SNPs  (Ppermutation = 2.4×10−3). SNPs involved in SNP-SNP interactions also showed single-locus effects with weak statistical significance, while BRCA2-rs1799943 showed stronger statistical significance (Pcorrelation/trend = 3.2×10−4) than the others. These single-locus effects were independent of body mass index. Our results provide a framework for evaluating SNPs showing statistically weak but reproducible single-locus effects for epistatic effects contributing to disease susceptibility.Genome-wide association studies (GWASs) have identified low-penetrance common variants ( BRCA1BRCA2ATMPTENTP53BRIP1PALB2CHEK2BRCA breast cancers Breast cancer is a multifactorial disease, which results from combined effect of genetic, reproductive, environmental, and lifestyle risk factors. Linkage and twin studies revealed familial clustering of breast cancer, giving an approximately two-fold higher risk for first-degree relatives with family history e.g., BRCA1 and BRCA2), explain only a small proportion of the total genetic risk of breast cancer i.e., epistatic effects) Several genome-wide association studies (GWASs) identified multiple single nucleotide polymorphisms (SNPs) statistically significantly associated with breast cancer susceptibility Recently, we conducted a two-stage association study using SNPs selected from GWAS for sporadic breast cancer To enable a more comprehensive evaluation of epistatic interactions among SNPs, we also considered additional SNPs from cancer related DNA repair genes, with prior evidence of their weak single-locus effects for breast cancer http://www.abtumorbank.com/), and the description of these has been described in detail elsewhere i.e., median age and range at diagnosis = 54 and 21–92 years, respectively, with >92% of the cases aged 40+ years at the time of diagnosis). The median BMI of breast cancer cases at the time of diagnosis was 27.4 and range 15.6–62.3. Apparently healthy controls  were accessed from the Tomorrow Project (http://in4tomorrow.ca/) Breast cancer cases  used in this study were accessed from the provincial tumor bank located at the Cross Cancer Institute, Edmonton, Alberta, Canada  were considered (S2). Of these, six SNPs  were selected from GWAS and previously replicated in an independent set of breast cancer cases and healthy controls Text S1 and Table S1). Overall, we present our findings from a combined sample size of 2,795 breast cancer cases and 4,496 controls from all three stages to meet the statistical rigor. The remaining 11 candidate DNA repair SNPs  were selected based on published DNA repair gene polymorphisms and their associations with breast cancer susceptibility A total of 17 candidate SNPs located in or close to 14 DNA repair, modification and metabolism pathway related genes  using services from the McGill University and Genome Quebec Innovation Center, Montreal, Canada. Genotype concordance among SNPs was assessed using 66 duplicate samples (8 cases and 58 controls). Thresholds for SNP call rates of >99% and HWE epistasis’ in PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/) P values associated with the b3 coefficient of the following model:We evaluated potential interactions among the select 17 candidate SNPs at two loci using logistic regression and multiple loci using logic regression. Logistic regression models using the command ‘-LogicReg package LogicReg do not allow missing data. Since SNPs can have three possible genotypes , we first recoded the 17 SNPs into two sets of binary covariates by using both dominant  and recessive  and fitted the logic regression of the following form:i is a Boolean combination of the binary SNP covariates such as [(SNP A = AA OR SNP B = AA) AND SNP C = AB or BB], also known as a logic tree. A score function (deviance of the model) was then used to evaluate models with the number of trees, n, in the range of Logic regression is a method to assess SNP-SNP interaction among multiple loci, and it has been successfully applied to a GWAS SNP data recently P>10−6) in controls (S2). Average genotype concordance was 100% for the 17 SNPs. Single-locus association tests in independent stages or in combined stage, adjusted for BMI were also profiled. Overall, SNPs considered in this study conferred weak single-locus effects for breast cancer, as we expected. We also analyzed the SNP-breast cancer associations by removing subjects from cases and controls with extreme ages (<35 yrs. and >80 yrs.) and BMI (<18.5 and >40). The associations did not change materially, suggesting that the small fraction (∼6.4% or 468 subjects) of extreme subjects may not have modified the observed overall SNP-breast cancer associations, data not shown.Genotyping assays for each of the 17 SNPs were successful with a SNP call rate of >99% and the SNPs also passed HWE (APEX1-rs1130409 *RPAP1-rs2297381 and MLH1-rs1799977 *MDM2-rs769412) showed the strongest statistical association with breast cancer (P<7.3×10−3), with modest FDR values of 0.30 and 0.49, respectively   and two low risk groups, with ORs 0.79 and 0.90, respectively and a high risk group with OR 1.18. The observed logic structure was tested in subgroups of tumors. It was statistically significant for the subgroup of cases with luminal A tumors (P = 3.3×10−3), while it was not in other subgroups .Logic regression including the 17 SNPs identified a logic structure representing a SNP-SNP interaction involving four SNPs and was statistically significant (.4×10−3) Table 2.APEX1-rs1130409, the SNPs participating in SNP-SNP interactions also showed weak single-locus effects  for breast cancer, independent of BMI (S2). Of these, BRCA2-rs1799943 showed the strongest single-locus effects. Overall, our findings support the notion that SNPs with reproducible weak single-locus effects are useful candidates for studying their potential epistatic effects contributing to breast cancer susceptibility.In this study of more than seven thousand women, we evaluated the contribution of epistasis to breast cancer susceptibility among 17 SNPs located in or close proximity to 14 DNA repair, modification and metabolism pathway related genes. We identified two SNP pairs and interactions involving four SNPs among seven candidate SNPs located in seven genes. Except for MBD2 SNP we reported earlier and the other three were from the candidate DNA repair SNPs considered in this study. The first pair consisted of APEX1-rs1130409 and RPAP1-rs2297381, with an OR of their interaction as 1.16, which was greater than their individual single-locus effects of 1.01 and 1.07, respectively. Similarly, another pair included MLH1-rs1799977 and MDM2-rs769412, with an OR of their interaction as 1.33 conferring risk. Interestingly, their individual single-locus effects were in opposite direction with ORs of 0.94 and 0.86, respectively and deserve further independent replication of findings.We identified two SNP pairs that demonstrated significant interactive effects on breast cancer risk and carried modest FDR values. Of these, one was MBD2-rs4041245, MLH1-rs1799977, MDM2-rs769412 and BRCA2-rs1799943). Interestingly, one of the SNPs we entered in this analysis and was predicted to participate in epistatic effects from a previous study MLH1-rs1799977 and MDM2-rs769412, this model captured distinct set of SNPs from the ones profiled in the two-way epistatic interactions, suggesting a possible convergence of multiple DNA repair pathways while conferring breast cancer risk. Future independent studies through large international consortia are warranted to further evaluate the contributions of the observed SNP-SNP interactions to breast cancer predisposition. We believe these findings reflect important biology, rather than simply statistical artifacts because of the unprecedented amount of literature indicating DNA-protein and protein interactions involved in DNA repair process.Using a logic regression model, we also detected SNP-SNP interactions involving four SNPs . We observed that the SNP-SNP interactions we identified were also complimented by observed/predicted interactions among the proteins encoded by participating genes. Proteins encoded by APEX1 and RPAP1 genes were not in direct cross talk but were mediated by a third protein, cyclin O protein (CCNO). Similarly, protein-protein interactions between proteins encoded by MLH1 and MDM2 genes were predicted to be mediated by cyclin G1 protein (CCNG1). It was noteworthy that CCNG1 was acting as a central molecule interacting with proteins encoded by the four genes involved in our two-way SNP-SNP interactions and the mediating CCNO gene. Further, protein-protein interactions facilitated by CCNG1 and GATA zinc finger domain containing 2A (GATAD2A) proteins were also predicted to mediate interactions among proteins encoded by MDM2, MLH1, MBD2 and BRCA2 genes. We were limited in our ability to draw any finer conclusions since the number of genes considered for the study does not represent a comprehensive view of all DNA repair/metabolism genes on the human genome. To-date, the total number of human DNA repair genes annotated is around 130 We further investigated for possible biological insights in to the observed SNP-SNP interactions using a Cytoscape plugin, GENEMANIA In summary, we demonstrated both two-way and multi-way SNP-SNP interactions contributing to breast cancer risk, among candidate SNPs related to DNA repair, modification and metabolism pathway genes. The interactions were not previously reported and were mostly among the SNPs with weak but reproducible single-locus effects. Our results suggest SNP-SNP interactions among SNPs with weak but reproducible single-locus effects in a typical multi-stage GWAS or candidate-gene studies may identify cross talk among members of multiple cancer-related pathways, and help account for the heritability for complex diseases.Table S1Associations of the six putative breast cancer susceptibility loci in stage 3 and in combined stages.(XLSX)Click here for additional data file.Table S2Eleven candidate DNA repair SNPs and their associations with breast cancer susceptibility in 2,720 breast cancer cases and 4,505 apparently healthy controls.(XLSX)Click here for additional data file.Text S1Methodology and pertinent discussion for single-locus association analyses of the 17 SNPs considered in the current study.(DOCX)Click here for additional data file.


Genetic variations may influence plasma lipid responsiveness. The aim of the present study was to examine the effects of a supplementation with n-3 PUFA on the plasma lipid profile in relation to the presence of single-nucleotide polymorphisms (SNPs) in the fatty acid desaturase (FADS) gene cluster. A total of 208 subjects from Quebec City area were supplemented with 3 g/day of n-3 PUFA, during six weeks. In a statistical model including the effect of the genotype, the supplementation and the genotype by supplementation interaction, SNP rs174546 was significantly associated (p = 0.02) with plasma triglyceride (TG) levels, pre- and post-supplementation. The n-3 supplementation had an independent effect on plasma TG levels and no significant genotype by supplementation interaction effects were observed. In summary, our data support the notion that the FADS gene cluster is a major determinant of plasma TG levels. SNP rs174546 may be an important SNP associated with plasma TG levels and FADS1 gene expression independently of a nutritional intervention with n-3 PUFA.Eicosapentaenoic and docosahexaenoic acids have been reported to have a variety of beneficial effects on cardiovascular disease risk factors. However, a large inter-individual variability in the plasma lipid response to an omega-3 ( High triacylglycerol (TG) levels are associated with cardiovascular disease (CVD) . Populatn-3) polyunsaturated fatty acids (PUFA), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on reducing CVD risk factors [n-3 PUFA per day decreased plasma TG concentrations by 25% to 30% [n-3 PUFA appears to be secondary to the increased hepatic β-oxidation and decreased lipogenesis [Numerous studies have demonstrated the beneficial effects of omega-3  study showed that TG levels of 31% of the volunteers did not show any reduction after an 8-week supplementation with 1.8 g/day of EPA + DHA . Innate -3 PUFAs . Therefo-3 PUFAs . n-3 PUFA may partly result from genetic variations. Recent data suggest that single-nucleotide polymorphisms (SNPs) found in genes involved in metabolic pathways of n-3 PUFA contribute to the variability of PUFA levels [FADS1) and fatty acid desaturase-2 (FADS2) genes encode respectively for two desaturases: δ-5 desaturase (D5D) and δ-6 desaturase (D6D) [n-3 PUFA pathways, have been associated with differences in fatty acid (FA) composition of plasma [FADS3) [The large inter-individual variability observed in the plasma lipid response to a supplementation with A levels ,17. The f plasma , erythrof plasma  and adipf plasma . Another [FADS3) .n-3 PUFA supplementation remain. The TG lowering effects of n-3 PUFA using several ratios and doses of EPA and DHA have been reported in different studies. However, these studies do not allow setting the optimal conditions. Conflicting data exist and may arise from inter-individual genetic variations. The purpose of the present study is to test whether the plasma lipid response to a 6-week n-3 PUFA supplementation is influenced by genetic variations in the FADS gene cluster.Concerns regarding the efficiency of 2 and with no current lipid-lowering medications. Subjects were not included if they had taken n-3 PUFA supplements for at least 6 months prior, used oral hypolipidemic therapy or had been diagnosed with diabetes, hypertension, hypothyroidism or other known metabolic disorders such as severe dyslipidemia or coronary heart disease. The experimental protocol was approved by the ethics committees of Laval University Hospital Research Center and Laval University. This trial was registered at clinicaltrials.gov as NCT01343342. A total of 254 subjects from the greater Quebec City metropolitan area were recruited between September 2009 and December 2011 through advertisements in local newspapers as well as by electronic messages sent to university students/employees. However, only 208 subjects were eligible for further analyses. Missing values of blood lipid profile pre- and/or post-supplementation did not allow those 46 subjects to be included in statistical analyses. Participants were aged between 18 and 50 years. They were non-smokers, with a body mass index (BMI) between 25 and 40 kg/mn-3 PUFA as fish with white flesh. With the growing popularity of grocery products fortified with n-3 PUFA, participants were asked to avoid these products during the study period. Among these enriched products, some eggs, milk, juice, bread and yogurt have been identified. Subjects were also asked to limit their alcohol intakes to no more than two drinks per week. Subjects were not allowed to take n-3 PUFA supplements, including those of vegetable sources, and to take vitamins or natural health products during the protocol. Two hundred and eight subjects followed a run-in period of two weeks where a trained registered dietitian gave individual dietary instructions. Recommendations were drawn from the Canada’s Food Guide to Healthy Eating. All subjects were asked to apply these dietary recommendations and to maintain stable body weight throughout the protocol. Among these recommendations, some specifications have been imposed to ensure the success of this study such as not to exceed two portions of fish or seafood per week (maximum 150 g) and to choose, preferably, fish other than oily fish known to be richer in n-3 PUFA capsules  covering the following 6-week period. They had to take 5 capsules per day, which gave them a total of 3 g of n-3 PUFA (1.9 g EPA and 1.1 g DHA) per day. Compliance was measured by bottles returning and by calculating the number of remaining capsules in the bottles at the end of the supplementation. Subjects had to report any deviations that may have occurred during the protocol. They also had to write their alcohol and fish consumption on a log sheet. Before each phase of the study, subjects received written and oral dietary instructions by a registered dietitian.After the run-in period, each participant received a bottle containing A dietitian administrated a validated food-frequency questionnaire (FFQ) before the run-in period to each participant . This FFn-3 PUFA supplementation. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2).Body weight, height, and waist girth were measured according to the procedures recommended by the Airlie Conference  and weren-3 PUFA supplementation period. Plasma was separated by centrifugation (2500× g for 10 min at 4 °C) and samples were aliquoted and frozen for subsequent analyses. Plasma total cholesterol (TC) and TG concentrations were measured using enzymatic assays [Blood samples were collected from an antecubital vein into vacutainer tubes containing EDTA after 12 h overnight fast and 48 h alcohol abstinence. Blood samples were taken to identify and exclude individuals with any metabolic disorders. Afterwards, participants had blood samples taken prior to and after the c assays . The higc assays . Low-denc assays . Plasma c assays . FADS1, FADS2 and FADS3 were identified using the International HapMap Project SNP database, based on the National Center for Biotechnology Information (NCBI) B36 assembly Data Rel 28 phase II + III, build 126 (R2 ≥ 0.8). Subsequently, we examined linkage disequilibrium (LD) out of the 19 SNPs covering all common variations in the FADS gene cluster area, using the LD Plot procedure in Haploview V4.2. Most of the SNPs were in LD (R2 ≥ 0.8) and the mean R2 was 0.953, so 19 SNPs were sufficient to cover the entire area. The SIGMA GenElute Gel Extraction Kit  has been used to extract genomic DNA. Selected SNPs of the FADS gene cluster  have been genotyped using validated primers and TaqMan probes  [SNPs in uild 126 . Tagger FADS1), Hs00188654_ml (FADS2), Hs00222230_ml (FADS3) and Hs99999905_ml ) as the housekeeping gene. Assays used the same fluorescent reporter probe (FAM™ dye-labeled) and thus each combination treatment and gene was analyzed in individual wells on a 96-well plate. All samples were run in duplicate on an Applied Biosystems 7500 Fast Real Time PCR System (Applied Biosystems) using the following thermal cycling profile: 50 °C (2 min), 95 °C (10 min), followed by 40 steps of 95 °C for 15 s and 60 °C for 60 s. The RT-PCR results were imported into ExpressionSuite Software v1.0 (Life Technologies). Data were adjusted for the endogenous control (GADPH). cDNA was mixed with TaqMan Universal PCR Master Mix (Applied Biosystems) and a gene-specific primer and probe mixture  in a final volume of 20 μL. The assays used were as follows: Hs00203685_ml  The ALLELE procedure was used to verify the departure from Hardy-Weinberg equilibrium (HWE) and to calculate minor allele frequency (MAF). Variables not normally distributed were log-transformed before analyses. ANOVA and the type III sum of squares were used to look for significant differences in daily energy and nutrient intakes, at prior and after an All SNPs were in HWE except two: rs7935946 and rs174579 (see 2) and had mean plasma TG levels slightly above the cut-point value of 1.129 mmol/L recommended by the AHA for optimal plasma TG levels [Baseline characteristics of study participants are presented in n-3 PUFA period . After the supplementation, the PUFA intake—taking into account food intakes and fish oil capsules used during the supplementation—was significantly increased (p = 0.003). In our study, the 6-week average of fish servings/week (a serving = 75 g of fish) was 0.89 servings/week based on the compliance questionnaire given at the end of the study. Furthermore, subjects were asked to limit their fish consumption to no more than 2 servings/week (maximum of 150 g). Based on these results, subjects who had consumed the maximum quantity of fish permitted per week would have had an extra 0.43 g of EPA + DHA/day. With the fish oil supplementation, the total of EPA + DHA was 3 g/day. Common food items eaten by the subjects were seafood, tuna, trout, haddock and salmon. Daily energy intakes were calculated by a food frequency questionnaire validated for healthy French-Canadian men and women  and are FADS gene polymorphisms and the n-3 PUFA supplementation on plasma TG levels, the MIXED procedure was used in order to test whether the genotype, the supplementation or the interaction (genotype by supplementation) were associated with plasma TG levels. As shown in p = 0.002). In the post-supplementation period, both genotype groups significantly decreased their plasma TG levels  after adjustments for age, sex and BMI (data not shown). No effect of the n-3 PUFA supplementation (p = 0.54) and no gene by supplementation effect (p = 0.56) explained FADS1 gene expression. In a model testing the effect of the genotype, the supplementation or the interaction (genotype by supplementation) on n-3 PUFA supplementation varied according to the presence of common polymorphisms in the FADS gene cluster. 19 SNPs were initially chosen from the FADS gene cluster area covering all common variations of the FADS gene cluster. After genotyping, 17 SNPs were in HWE and thus were analysed in the present study. The FADS gene cluster area has been chosen due to the role of D5D and D6D activity in the n-3 PUFA metabolic pathway. D5D and D6D are essential parts of PUFA biosynthesis that catalyze a series of desaturation processes [FADS1 and FADS2. Also, a Genome-Wide Association Study has shown that the strongest evidence for an association of genetic contributors of plasma PUFA concentrations was observed in the FADS gene cluster area [FADS1 and FADS2 gene promoters because of the head to head orientation. FADS3 promoter was not considered since no desaturase activity is reported  [2R = 1.0), was a dominant SNP in the FADS gene cluster that influences desaturase activity and some evidence emerging from human-based research demonstrates that genetic variation in human desaturase genes affects enzyme activity and, consequently, disease risk factors [n-3 PUFA while homozygotes for the minor allele had the lowest ADA [n-6 PUFA [i.e., FA) availability leads to a reduction of VLDL TG synthesis [FADS gene cluster, especially rs174546, is associated with TG levels and also to ADA, making it a significant SNP when talking about associations between lipids and FA metabolism.In the present study, we observed an independent genotype effect of the SNP rs174546 on plasma TG levels and on C levels . In anot intake) . Another factors . They alwest ADA . This SNn-6 PUFA . Recent n-6 PUFA . Reducedynthesis . FADS gen-3 PUFA metabolism. We tested SNP rs174546 for potential functional significance using MatInspector 8.0 software, but it did not seem to alter transcription factor binding sites. Some SNPs may modulate desaturase activity and lead to changes in n-3 PUFA supplementation, considerable inter-individual variability in plasma TG levels was observed. It appears that some individuals require higher doses to achieve demonstrable benefits, whereas others are highly sensitive to relatively low doses and individuals with certain genotypes may experience adverse responses with respect to specific risk biomarkers, at least at high doses of n-3 PUFA [FADS gene expression may modulate specific risk biomarkers in relation to certain genotype. Overall, the n-3 PUFA supplementation had no effect on FADS gene expression. The SNP rs174546 was a significant predictor of FADS1 gene expression levels. Caslake et al. showed that 31% of all volunteers had no reduction in plasma TG levels after 1.8 g/day of EPA + DHA for 8 weeks [n-3 PUFA supplementation. These results demonstrate that intra-individual variability of plasma lipid levels is an important potential source of error enhancing the importance of genetic testing to identify individuals that are more likely to benefit from such therapies [After the n-3 PUFA . FADS ge 8 weeks . We basiherapies .Canada’s Food Guide to Healthy Eating, they could have reported food consumption differences that slightly changed calculated intakes. However, those differences did not affect significantly BMI nor energy intakes. Because carbohydrates intakes were significantly decreased in the post-supplementation period and this could impact on plasma TG levels, the carbohydrate intakes have been added into the statistical model and results remained unchanged (data not shown).This study presents some limitations. Regarding daily intakes, significant differences for carbohydrates, saturated fats or proteins could be due to recall bias from subjects. Since subjects were asked to follow recommendations drawn from the FADS gene cluster, especially SNPs from FADS1, are major determinants of plasma TG levels. SNP rs174546 may be an important SNP in the FADS gene cluster associated with plasma TG levels and FADS1 gene expression independently of a nutritional intervention with n-3 PUFA. These results need to be replicated in other independent studies. A better understanding of the phenomenon could allow the development of personalized dietary advice for prevention of CVD. In summary, our data support the notion that the


Single nucleotide polymorphism (SNP) validation and large-scale genotyping are required to maximize the use of DNA sequence variation and determine the functional relevance of candidate genes for complex stress tolerance traits through genetic association in rice. We used the bead array platform-based Illumina GoldenGate assay to validate and genotype SNPs in a select set of stress-responsive genes to understand their functional relevance and study the population structure in rice.indica accessions compared with other rice sub-populations based on different population genetic parameters. The inferred ancestry of 16% among rice genotypes was derived from admixed populations with the maximum between upland aus and wild Oryza species.Of the 384 putative SNPs assayed, we successfully validated and genotyped 362 (94.3%). Of these 325 (84.6%) showed polymorphism among the 91 rice genotypes examined. Physical distribution, degree of allele sharing, admixtures and introgression, and amino acid replacement of SNPs in 263 abiotic and 62 biotic stress-responsive genes provided clues for identification and targeted mapping of trait-associated genomic regions. We assessed the functional and adaptive significance of validated SNPs in a set of contrasting drought tolerant upland and sensitive lowland rice genotypes by correlating their allelic variation with amino acid sequence alterations in catalytic domains and three-dimensional secondary protein structure encoded by stress-responsive genes. We found a strong genetic association among SNPs in the nine stress-responsive genes with upland and lowland ecological adaptation. Higher nucleotide diversity was observed in SNPs validated in biotic and abiotic stress-responsive rice genes can be used in association analyses to identify candidate genes and develop functional markers for stress tolerance in rice. Single nucleotide polymorphisms (SNPs) represent a robust class of molecular markers . SNP marindica (93–11) and japonica (Nipponbare)  of these loci were used to classify the 91 rice genotypes into one of three clusters: (i) homozygous AA (japonica “Nipponbare”), (ii) homozygous BB (indica “93–11”), and (iii) heterozygous AB  of 32,942 genotype calls were identified as missing data. The remaining 32,155 yielded successful genotype calls giving a high average call rate of 98% per valid SNP for rice genotypes. When we increased the Gen-Train score cut-off value to ≥0.4, the average missing data rate per successful SNP in rice genotypes decreased to ≤0.5%. Three hundred twenty-five (90%) of the 362 SNP loci, which produced 29,575 genotype calls, showed polymorphism . SNP loci that differentiated any two individual genotypes belonging to two different groups were considered polymorphic between the groups to which they belonged. Based on this criterion, 254 (78%) were polymorphic between O. nivara and O. sativa, 168 (52%) between O. rufipogon and O. sativa, and 28 (9%) between O. rufipogon and O. nivara  between O. nivara and indica, 215 (66%) between O. nivara and long-grained aromatics, and 76 (23%) SNPs between O. rufipogon and short-grained aromatics. Within O. sativa, the most polymorphism was observed between indica and japonica  and the least was between japonica and short-grained aromatics   and lowest between the two japonica genotypes   transitions and 118 (36%) transversions, were validated using the “rice OPA-1” high-throughput bead array-based assay. The higher frequency of transitions versus transversions in validated SNPs was comparable to the indica and two genotypes of wild species from 30 lowland indica and 19 aromatic rice genotypes. Four of these SNPs — found in a MYB family transcription factor, a sucrose transporter, a calcium dependent protein kinase and a WRKY family transcription factor  and NB-ARC domains (LOC_Os04g30930) differentiated the bacterial leaf blight (BLB) resistant indica rice genotypes Aditya from BLB susceptible aromatic rice genotypes. Another two SNP loci found in genes containing serine threonine protein kinase (LOC_Os09g37949) and leucine-rich repeat (LOC_Os04g19750) domains differentiated eight rice blast resistant indica genotypes  from the susceptible indica genotypes  followed by transcription factors  — showed amino acid sequence change in DNA binding/signal sensing domains (Table OsWRKY35V2), which was found substituted by serine (S) in the lowland genotypes. This possibly resulted in decreased binding affinities of WRKY domains to invariant ‘TGAC’ core of W box. It is likely that substitution mutations in WRKY impart differential hydrophobic interactions and beta sheet’s stability thus creating a varied zinc finger and DNA binding protein structure between drought tolerant upland and sensitive lowland rice genotypes  compared with long (0.40) and short (0.32) grained aromatics, japonica (0.2841) and aus/wild species (0.25) was evident. We found the average nucleotide diversity (PIC =0.44) across 263 SNP loci in candidate abiotic stress response genes to be higher specifically in upland indica genotypes than that observed in the 62 candidate biotic stress-related genes (PIC = 0.27).The polymorphism information content (PIC) based on SNPs in the stress-related rice genes varied widely across the 89 indica and japonica/aromatic , two wild species O. rufipogon and O. nivara. Molecular genetic variation among and within these four sub-populations (determined by pair-wise estimate of divergence (mean FST) and genetic distance (Dij) based on 325 SNP loci), revealed a wide quantitative genetic differentiation in O. sativa   and minimum between aromatics and japonica (0.28)  levels revealed that at K value of 2 all genotypes were classified into two distinct sub-populations, r Figure . At thisr Figure . The ricindica (possibly upland aus type) and the wild Oryza species while between japonica and long-grained aromatics it was ~ 8%. Tripura Medicinal Rice had maximum (54%) admixtures of japonica followed by that of indica (18%), traditional Basmati (15%), short-grained aromatics (5%) and the wild (2%) species  followed by chromosomes 7 (59%) and 1 (57%); chromosome 6 contained maximum introgression (68%) of indica. In contrast there was equal sharing (~ 49%) of genomic regions in chromosomes 2 and 8 of japonica and indica. Introgression frequency (based on the number of recombination events) was the most in chromosome 1  showed strong association with ecological adaptation at P < 0.01 and R2 = 1.00.Genetic association analysis using genotyping data of 325 SNPs in stress-responsive genes and phenotypic information of 91 rice genotypes belonging to different ecosystems revealed 18 non-synonymous SNPs associated with ecological adaptation at Illumina GoldenGate platform has shown exceptional performance with regard to throughput, reproducibility, genotype call rate and assay development success rate among several SNP genotyping assays involving human and a few plant species, ,34,43. Iindica genotype 93–11 contributing to false SNPs at 37 of 362 loci for which genotype calls were made. With the recent availability of high quality genomic sequence from multiple genotypes , which is expected to be much more informative in plant genomes [The intensity data for each SNP were normalized and cluster positions were assigned using Illumina BeadStudio Genotyping software module. The quality scores represented by GenCall and GenTrain scores were estimated for each SNP call that reflected the degree of separation between homozygous and heterozygous clusters for each SNP locus and placement of individual SNP call for each genotype within a cluster . Minimum genomes . All allThe SNPs in stress-responsive rice genes were categorized according to nucleotide substitutions as either transitions (C/T or G/A) or transversions , and their frequency of occurrence determined individually in different rice genotype groups. The physical position of each validated SNP locus showing allelic variation was determined based on their annotations on the 12 rice chromosomes as described above. The physical locations (bp) of validated SNPs on the 12 chromosomes were used in Graphical GenoTypes  Version O. sativa and wild rice genotype groups using the “preferred” and “unpreferred” concepts of codon usage pattern in Oryza[ab intio three dimensional secondary protein structure and active catalytic domain binding sites with ligands. The high quality protein model of correct topology and protein-ligand complex active binding sites was selected based on high confidence (C≥−1.5) and binding site (BS≥0.5) cut-off scores.The divergence of coding sites in each of the variant SNP loci based on derived non-synonymous substitutions (degree of amino acid changes) was analyzed individually for  in Oryza. Amino a in Oryza to deter in Oryza,78 for pDNaSp 4.0 [ij) across the genotypes. The genotypic data were used in a model-based program “STRUCTURE” [indica, aromatic, japonica and aus/wild rice genotype groups at α value less than 0.2 was selected. Twenty independent runs with K = 4 were carried out to determine the consistency of results obtained. Various population genetic parameters including fixation of different SNP loci in different sub-populations and their efficiency for detecting genetic variability (FST) and degree of admixture within and between groups were estimated.The SNP loci validated in the stress-responsive gene sequences across 91 diverse rice genotypes were aligned using CLUSTALW multiple sequence alignment tool in MEGA  4.0 and NaSp 4.0  to calcuNaSp 4.0  and geneRUCTURE”  to deter2 value ≥0.90 were selected for further analysis.Genotyping data of validated SNP loci in stress-responsive rice genes and phenotypic information of 91 rice genotypes belonging to three different ecosystems namely, upland, medium land and lowland  domain (LOC_Os04g19750) showing differentiation between eight known blast resistant indica and 30 blast susceptible indica rice genotypes.Click here for fileOptimization of number of sub-populations  varying from K = 2 to K = 5 to determine the best possible population structure for 91 rice genotypes.Click here for filePair-wise estimates of genetic variance (FST) among four O. sativa sub-populations.Click here for fileDomesticated and wild Oryza genotypes used in the study and their inferred ancestry coefficients in population genetic structure analysis.Click here for fileGraphical genotyping using 325 SNP loci validated through Illumina GoldenGate assay across 91 rice genotypes based on their ascending order of physical location (bp) on 12 rice chromosomes giving allele sharing maps of individual rice genotypes.Click here for fileGenomic constitution of rice genotypes based on introgression of indica and japonica alleles on the 12 rice chromosomes.Click here for fileGraphical genotyping of 12 Tripura Medicinal rice chromosomes.Click here for file


Plasma DNA obtained from a pregnant woman contains a mixture of maternal and fetal DNA. The fetal DNA proportion in maternal plasma is relatively consistent as determined using polymorphic genetic markers across different chromosomes in euploid pregnancies. For aneuploid pregnancies, the observed fetal DNA proportion measured using polymorphic genetic markers for the aneuploid chromosome would be perturbed. In this study, we investigated the feasibility of analyzing single nucleotide polymorphisms using targeted massively parallel sequencing to detect such perturbations in mothers carrying trisomy 21 fetuses.DNA was extracted from plasma samples collected from fourteen pregnant women carrying singleton fetuses. Hybridization-based targeted sequencing was used to enrich 2 906 single nucleotide polymorphism loci on chr7, chr13, chr18 and chr21. Plasma DNA libraries with and without target enrichment were analyzed by massively parallel sequencing. Genomic DNA samples of both the mother and fetus for each case were genotyped by single nucleotide polymorphism microarray analysis. For the targeted regions, the mean sequencing depth of the enriched samples was 225-fold higher than that of the non-enriched samples. From the targeted sequencing data, the ratio between fetus-specific and shared alleles increased by approximately 2-fold on chr21 in the paternally-derived trisomy 21 case. In comparison, the ratio is decreased by approximately 11% on chr21 in the maternally-derived trisomy 21 cases but with much overlap with the ratio of the euploid cases. Computer simulation revealed the relationship between the fetal DNA proportion, the number of informative alleles and the depth of sequencing.Targeted massively parallel sequencing of single nucleotide polymorphism loci in maternal plasma DNA is a potential approach for trisomy 21 detection. However, the method appears to be less robust than approaches using non-polymorphism-based counting of sequence tags in plasma. Prenatal screening and diagnosis of fetal aneuploidies, such as trisomy 21 (T21), is an established part of modern obstetrics care. Conventional prenatal screening is built on parameters such as maternal age, sonographic and biochemical markers Maternal plasma DNA contains a mixture of fragmented maternal and fetal genomic DNA http://www.ncbi.nlm.nih.gov/projects/SNP/), conventional non-targeted MPS would only include SNP alleles in a small proportion of sequence reads. In our previous publication, by using a hybridization-based targeted MPS platform, we demonstrated the enrichment of DNA molecules within the targeted regions, as well as the preservation of the allelic ratios of the targeted SNPs in maternal plasma after target enrichment An alternative approach for NIPD of T21 is to measure the proportion of chromosome 21 (chr21)-derived DNA molecules in a maternal plasma sample. If a mother is carrying a T21 fetus, the additional copy of chr21 from the fetus would contribute an additional amount of chr21 DNA molecules to the maternal plasma sample, leading to an increased proportion of chr21 sequences Approvals were obtained from the institutional review board of the Joint Chinese University of Hong Kong - New Territories East Cluster Clinical Research Ethics Committee. All participants gave informed written consent.We recruited fourteen pregnant women with singleton fetuses  from the Department of Obstetrics and Gynaecology, Prince of Wales Hospital, Hong Kong. Maternal peripheral blood samples were collected prior to CVS. Following CVS, fetal genomic DNA samples were obtained from the chorionic villi. The aneuploidy or euploidy status were confirmed by full karyotyping. Among the fourteen cases, seven were T21 fetuses and the rest were euploid.Maternal peripheral blood samples (5–10 mL) were centrifuged at 1 600 g for 10 min at 4°C. The plasma portion (2–5 mL) was recentrifuged at 16 000 g for 10 min at 4°C. We removed any residual plasma from the blood cell portion by recentrifugation at 2 500 g for 5 min http://genome.ucsc.edu) with the aid of SOAPaligner/soap2 (http://soap.genomics.org.cn). Two mismatches were allowed during alignment. The range of fragment sizes of paired-end reads was defined as 50–600 bp. Duplicated paired-end reads  were considered clones of the same original plasma DNA template. All but one of the duplicated reads were filtered, leaving only 1 copy for subsequent bioinformatics analysis as previously described As the plasma DNA molecules were already fragmented in nature, no additional fragmentation step was required. 10–30 ng plasma DNA for each case was used for DNA library construction by the Genomic DNA Sample Preparation Kit (Illumina) as previously described 21) and the reference chromosomes (expressed as FSRRef). In this study, reference chromosomes included all autosomes except chr21 in the non-targeted sequencing data analysis, and included chr7, chr13 and chr18 in the targeted sequencing data analysis. If a mother is carrying a euploid fetus, the ratio between FSR21 and FSRRef  and heterozygous in the fetus . In this scenario, the B allele is the fetus-specific allele and the A allele is the allele shared by the mother and fetus. Informative SNPs were identified according to maternal and fetal genotyping information. The fetus-specific allelic counts and shared allelic counts for the informative SNPs were obtained from the plasma DNA sequencing data, and were used to calculate the ratio between the fetus-specific alleles and the shared alleles (expressed as F-S ratio) for chr21 . The parental origin of the additional copy of chr21 in seven T21 fetuses was determined using microarray analysis of the chorionic villus samples in which the allelic signal intensities for SNPs on chr21 were analyzed. If the fetus has paternally-derived T21, the signal intensity of the fetus-specific allele  should be 2-fold higher than the shared allele , because the fetal genotype would become ABB on chr21, and vice versa. Using this approach, one fetus was identified as a paternally-derived T21, while six were maternally-derived T21.Computer simulation was employed to investigate the accuracy of the F-S ratio analysis for T21 detection. The statistical model is based on the assumption that the numbers of fetus-specific and shared allelic counts should follow a binomial distribution, according to the fractional fetal DNA concentration in both the paternally- and maternally-derived T21 models. For example, assuming the fractional fetal DNA concentration in the reference chromosomes (chrRef) is f, the probability of detecting the fetus-specific allele for an informative SNP would be f/2 on chrRef irrespective of the aneuploidy status of the fetus. On the other hand, the probability of detecting the fetus-specific allele on chr21 would be f/2 if the mother is carrying a euploid fetus, 2f/(2+f) if the mother is carrying a paternally-derived T21 fetus, and f/(2+f) if the mother is carrying a maternally-derived T21 fetus . For illWe defined sequencing depth as the mean number of times each base had been sequenced in a particular region. On average, 3.1 million and 2.9 million paired-end reads in non-enriched and enriched samples, respectively, were mapped uniquely to the reference human genome (Hg18). After filtration of the duplicated paired-end reads, we calculated the sequencing depth of the targeted region by dividing the total number of sequenced bases within the targeted region by the length of the targeted region. The mean sequencing depth was 0.12 time for the non-enriched samples and 27 times for the enriched samples, indicating a mean enrichment of 225-fold . This fiWe calculated the fractional fetal DNA concentrations  in the non-enriched samples by using the informative SNPs from all autosomes except for chr21, according to the equation: fractional fetal DNA concentration=fetus-specific allelic counts ×2/ ×100%. The median fractional fetal DNA concentration for the fourteen cases analyzed in this study was 15.5% (range: 9.1%–19.7%).21=2 to 19, SC21=87 to 213) and chrRef . We then calculated the FSR21, FSRRef and Based on maternal and fetal genotyping information, informative SNPs were identified on chr21 (range amongst the samples: 1 044 to 1 775 SNPs), and chrRef which included all autosomes except chr21 (range amongst the samples: 99 581 to 106 950 SNPs). Within the above informative SNPs, fetus-specific allelic counts (expressed as FC) and shared allelic counts (expressed as SC) were determined for chr21  and chrRef  , informative SNPs  were identified for each case. The number of sequenced reads with the fetus-specific and shared alleles were determined for chr21 (FCo 5 557) . As showWe used computer simulation to determine the parameters that would further improve the accuracy of the allelic ratio approach for T21 detection. First, we fixed the fractional fetal DNA concentration at 15% and gradually increased the numbers of informative allelic counts on chr21 and chrRef to investigate the detection accuracy for fetal T21. Additional informative allelic counts would improve the detection accuracy in both the paternally- and maternally-derived T21 models. In order to obtain accurate detection , more informative allelic counts were required for the maternally-derived T21 detection  than the paternally-derived T21 detection  . If the In this study, we investigated if the analysis of chromosome-specific allelic ratio would be useful for the NIPD of fetal chromosomal aneuploidy. The paternally-derived T21 case was successfully detected in both non-targeted and targeted sequencing data. For the maternally-derived T21 cases, this approach became less effective. Although the mean ived T21 . Since fSince maternally-derived T21 accounts for the majority of T21 cases in clinical practice, it is necessary to investigate how one could improve its detection accuracy using the allelic ratio method The number of informative SNPs is determined by two factors: the total number of SNPs on the chromosome of interest for detection and the frequency of informative SNPs. The latter is the percentage of detectable informative SNPs amongst a group of analyzed SNPs. As a proof-of-principle study, we directly used the fetal genotype information, as determined by microarray-based analysis of chorionic villus DNA, to maximize the frequency of informative SNPs. Amongst the fourteen cases recruited for this study, the mean proportions of SNPs that were informative were 11.9% (range: 11.3% to 12.1%) in all autosomes and 12.9% (range: 11.0% to 15.0%) in targeted SNP loci. However, the fetal genomic material would not be available beforehand for actual NIPD. Therefore, informative SNPs would need to be deduced indirectly, for example, by selecting SNPs in which the parents are both homozygous but with different alleles . In this scenario, the frequency of informative SNPs would decrease, because such an approach would exclude any SNPs in which the father is heterozygous.In terms of targeted analysis of SNPs, we demonstrated in this study the feasibility of the hybridization-based enrichment system. In this proof-of-principle study, we only employed a relatively small number of probes to capture 2 906 SNP loci on the target chromosomes. However, it is possible to increase the number of analyzed SNP loci. For example, if we design probes to cover all chr21 SNP loci (12 930 SNPs) on the Genome-Wide Human SNP Array 6.0 (Affymetrix), we could increase the number of informative SNPs on chr21 to approximately 1 500 SNPs for each case in the current dataset. If we obtain the same number of informative SNPs on chrRef and sequence the plasma DNA to a depth of 87 times, we would harvest approximately 130 000 informative allelic counts (1 500×87) on chr21 and chrRef, respectively. Such numbers would allow the relatively robust classification of maternally-inherited T21 from the euploid cases, assuming a fractional fetal DNA concentration of 15% or above . Given tIn order to obtain a sufficient number of allelic counts, an alternative approach is to sequence more deeply each locus with a relatively small number of informative SNPs. According to our previous publication, we demonstrated that each milliliter of plasma contained approximately 1 000 genome-equivalents of DNA, which would give rise to 2 000 allelic counts for each locus The targeted massively parallel sequencing of SNP loci in plasma DNA is thus a feasible method for the NIPD of fetal aneuploidies. However, when compared with published work based on tag counting of nonpolymoprhic loci for T21 detection Table S1Sequencing data and allelic information for plasma DNA libraries with and without target enrichment.(DOC)Click here for additional data file.Table S2Information of SNPs used for targeted sequencing.(XLS)Click here for additional data file.


Detection of disease-causing mutations using Deep Sequencing technologies possesses great challenges. In particular, organizing the great amount of sequences generated so that mutations, which might possibly be biologically relevant, are easily identified is a difficult task. Yet, for this assignment only limited automatic accessible tools exist.http://www.tau.ac.il/~nshomron/GenomeGems.We developed GenomeGems to gap this need by enabling the user to view and compare Single Nucleotide Polymorphisms (SNPs) from multiple datasets and to load the data onto the UCSC Genome Browser for an expanded and familiar visualization. As such, via automatic, clear and accessible presentation of processed Deep Sequencing data, our tool aims to facilitate ranking of genomic SNP calling. GenomeGems runs on a local Personal Computer (PC) and is freely available atGenomeGems enables researchers to identify potential disease-causing SNPs in an efficient manner. This enables rapid turnover of information and leads to further experimental SNP validation. The tool allows the user to compare and visualize SNPs from multiple experiments and to easily load SNP data onto the UCSC Genome browser for further detailed information. The sequencing of the human genome was the highlight of many years of international laborious efforts. Since 2004, new technologies termed “Deep Sequencing” or “Next-Generation Sequencing” have been developed to reduce timelines and costs of subsequent re-sequencing of additional human genomes,2. TheseSeveral tools exist to facilitate the data interpretation stage, each focusing on a different aspect of the analysis: EagleView, for exaGenomeGems, was developed in order to provide systematic means to reduce inconsistency in selecting which genetic variances or mutations should be further investigated. We developed a unique interface which includes analysis and visualization (via the widely used UCSC Genome Browser) leading to prioritization of data generated by Deep Sequencing runs. One way to facilitate variance calling from genetic sequences is putting them in context with other sequenced samples]. G]. G[[,[2GenomeGems suggests additional external useful links: Polymorphism Phenotyping v2( PolyPhen-2)[GenomeGems to enable direct referral to a specific entry in the databases, based on the user’s SNP selection.For further investigation and annotation of specific SNPs and of the impacts of amino acid changes encoded by the mutant gene on a human protein, yPhen-2) , ServeryPhen-2), PredictyPhen-2) and UnivyPhen-2),  ability to integrate data from several Deep Sequencing runs on a standard PC; (ii) assimilation with the UCSC Genome Browser and Microsoft Excel; (iii) applicability for any Deep Sequencing data (given the correct input file format) (iv) power to compare and analyze a large number of samples. GenomeGems' main virtues allow: (i) reducing variability in selecting which mutations should be further investigated; (ii) facilitating genomic research via clear and accessible presentation of processed Deep Sequencing data; (iii) assisting rapid turnover of information and a quick lead to further experimental mutation detection.GenomeGems lies our main objective of facilitating genomic research by processing Deep Sequencing data in a comprehensive and accessible fashion. This enables rapid turnover of information and leads to further experimental SNP validation. The tool allows the user to compare and visualize SNPs from multiple experiments and to easily load SNP data onto the UCSC Genome browser for further detailed information.Behind the implementation of GenomeGems, development of additional elements for further analysis is underway. GenomeGems was designed using a modular approach, enabling easy extension of its capabilities. Continuous dialogue with potential end-users of GenomeGems, and constant search for improvements, ensures that more advanced features will be added to the current implementation. A few examples are listed below.In addition to the currently implemented features of 1. Indel AnalysisAt the moment, the tool does not support data files containing indels. An extension of the tool will include indel analysis and an algorithm for determining whether an indel causes the appearance of a nonsense mutation in the sequence analyzed.2. Full Genome AnalysisGenomeGems enables analysis of a single chromosome specified by the user. In the next version of GenomeGems we intend to enable full genome analysis and full genome comparison between samples.At the moment 3. Additional Visualization CapabilitiesGenomeGems enables SNP visualization by means of UCSC’s Custom Tracks. In subsequent versions a convenient visualization within the application and without the need to connect to the Internet will be included.The current version of 4. Further mutation AnalysisGenomeGems lacks an independent feature for prediction of the impacts amino acid substitutions (caused by SNPs) on the structure and function of human proteins. Instead, external free tools providing this information are suggested. In subsequent versions this feature will be included as an integrated function of GenomeGems.The current version of GenomeGems.Project Name: http://xwww.tau.ac.il/~nshomron/GenomeGems.Project Home Page:Operating System: Microsoft Windows.Programming Language: MATLAB 2009.Other Requirements: installation of an ActiveX Control and “MCR Ver 7.10” on the users' workstations.SNPs: Single Nucleotide Polymorphisms; Indels: Insertion-Deletions; MAQ: Mapping and Assembly with Quality; UCSC: University of California Santa Cruz; NCBI: National Center of Biotechnology Information; SNVMix: Small Nucleotide Variants; PgSNP: Personal Genome SNP; CDS: Coding Sequence; NA: Not available; SOAP: Short Oligonucleotide Analysis Package; ACE: Archive Compression Extension; AFG: Auxiliary File Generator; EGI: Embedded Gateway Interface; SNVMix: Small Nucleotide Variants; CIGAR: Compact Idiosyncratic Gapped Alignment Report.The authors declare no competing financial interest.NS conceived the need for the software, SB, AG and NS designed the tool, SB and AG wrote the software, SB, AG and NS wrote the paper. All authors read and approved the final manuscript.