RIFAQUIN was a tuberculosis chemotherapy trial in southern Africa including regimens with high-dose rifapentine with moxifloxacin. Here, the application of whole-genome sequencing (WGS) is evaluated within RIFAQUIN for identifying new infections in treated patients as either relapses or reinfections. WGS is further compared with mycobacterial interspersed repetitive units-variable number tandem repeats (MIRU-VNTR) typing. This is the first report of WGS being used to evaluate new infections in a completed clinical trial for which all treatment and epidemiological data are available for analysis.Mycobacterium tuberculosis cultured from patients before and after treatment was typed using 24-loci MIRU-VNTR, in silico spoligotyping and WGS. Following WGS, the sequences were mapped against the reference strain H37Rv, the single-nucleotide polymorphism (SNP) differences between pairs were identified, and a phylogenetic reconstruction was performed.DNA from 36 paired samples of WGS indicated that 32 of the paired samples had a very low number of SNP differences . One pair had an intermediate number of SNP differences, and was likely the result of a mixed infection with a pre-treatment minor genotype that was highly related to the post-treatment genotype; this was reclassified as a relapse, in contrast to the MIRU-VNTR result. The remaining three pairs had very high SNP differences .WGS and MIRU-VNTR both similarly differentiated relapses and reinfections, but WGS provided significant extra information. The low proportion of reinfections seen suggests that in standard chemotherapy trials with up to 24 months of follow-up, typing the strains brings little benefit to an analysis of the trial outcome in terms of differentiating relapse and reinfection. However, there is a benefit to using WGS as compared to MIRU-VNTR in terms of the additional genotype information obtained, in particular for defining the presence of mixed infections and the potential to identify known and novel drug-resistance markers.The online version of this article (doi:10.1186/s12916-017-0834-4) contains supplementary material, which is available to authorized users. Evaluations of drug trials for tuberculosis (TB) are complicated by the fact that a recurrence of disease can either be due to endogenous relapse of disease or to subsequent exogenous infection with a new strain (reinfection). Historically, during the major TB chemotherapy trials of the 1960s to 1980s , spoligotyping and mycobacterial interspersed repetitive units-variable number tandem repeats (MIRU-VNTR) typing [6110 RFLP typing [From the 1980s, a series of genomic-based methods for typing strains of ) typing –4. Some P typing –7 and thMIRU-VNTR became the favoured typing approach because it combined reasonable discrimination with a readout that could both be easily measured and be described in a digital form . More reTwo groups have recently used WGS to evaluate paired samples, comparing SNP differences between the original infections and new infections following treatment , 15. TheThe report by Guerra-Assunção et al.  was not M. tuberculosis recovered before treatment and from those patients showing positive cultures at 6 months, and results were compared with MIRU-VNTR data. Our results agree with the general findings from the two studies referred to above, in that the overwhelming majority of secondary cases were classified as relapses. Importantly, WGS was further able to monitor possible epidemiological connections and sample errors during the trial, which were not detected using MIRU-VNTR. Given the added benefit of WGS in this context, we suggest that WGS should be routinely used as the method of choice in such trials.In this study, we performed WGS and analysed SNPs to compare pre- and post-treatment isolates from the completed RIFAQUIN clinical trial , a studyThe RIFAQUIN chemotherapy trial, in collaboration with six institutions in southern Africa, has been previously described . BetweenControl regimen: 2 months of daily ethambutol, isoniazid, rifampicin and pyrazinamide followed by 4 months of daily isoniazid and rifampicin;4-month regimen: Isoniazid replaced by moxifloxacin daily for 2 months followed by 2 months of twice-weekly moxifloxacin and 900 mg rifapentine; or6-month regimen: Isoniazid replaced by moxifloxacin daily for 2 months followed by 4 months of once-weekly moxifloxacin and 1200 mg rifapentine.M. tuberculosis-positive cultures after 6 months (or at the end of treatment) were selected for WGS.Sputum was examined by microscopy and culture at regular intervals for treatment failure or relapse. Patients had up to 18 months of follow-up post randomisation, with the patients recruited last having 12 months of follow-up post-randomisation. Samples from patients with two or more consecutive M. tuberculosis colonies by sweeping across growth on a Lowenstein–Jenson (LJ) slope. Bacteria were heat-killed and DNA extraction performed using lysozyme and proteinase K digestion followed by phenol-chloroform extraction and ethanol precipitation [The 24-loci MIRU-VNTR typing of these isolates was previously described . Brieflypitation . The 24 For the WGS, 50 μL, containing at least 250 ng, of genomic DNA from each sample was sheared using the Covaris E220 for a target size of 200 bp . Libraries were prepared from sheared DNA using the NEB DNA Ultra kit in accordance with standard protocol . The NEB adapters were substituted for the set described by Kozarewa and Turner . LibrariSequence reads were mapped to the H37Rv reference genome (RefSeq accession: NC_000962) using bwa mem v0.7.3a-r367 , alignme.Spoligotypes were generated using SpolPred . Sub-linFor each isolate sequence a count of the percentage of reads supporting a variant base at each genome position was plotted. Mixed isolates can be identified by the presence of an extra peak, suggesting the presence of two genotype populations in the sequenced sample. Base calls for the majority and minority strains were separated based on the per cent reads and pseudo-sequences were generated and subsequently included in the phylogenetic reconstruction as above.Figure SNP differences were determined between the pairs of isolates, and a comparison with MIRU-VNTR differences is shown in Table Phylogenetic reconstruction of variant SNPs Fig.  showed tThere were also isolates that mapped closely to other patient isolates on the tree, and these merited closer attention to see if there were genuine connections or unexpected problems caused by possible laboratory handling errors.Panels b and c in Fig. Panels d and e in Fig. One patient’s pair of samples (035) displayed 57 SNPs between the pre- (035-1) and post-treatment (035-2) isolates and was therefore initially classified as a reinfection. However, further analysis of the WGS data showed evidence of a mixed infection in the pre-treatment isolate , which would have been an unusual result given that the previous studies had only identified reinfections with very high SNP differences, and nothing at an intermediary level. The observation of mixed genotypes would explain this discrepancy because one of the main filtering criteria in the site-calling algorithm is to remove sites with mixed genotype calls , so the real number of SNP differences between the isolates is likely to be higher. After separating the genotypes, it was estimated that the number of SNP differences between the pre-treatment minority genotype and the post-treatment isolates was 869 SNPs. The pre-treatment minority genotype and the post-treatment isolate appeared to differ by 245 SNPs; however, the genotype separation algorithm used was relatively crude, with filtering based on parameter cut-offs, so it was not possible to completely separate the genotypes at all mixed genome sites, reflecting the overlapping shape of the two distributions Fig. . HoweverFigure The relationship between SNP and MIRU-VNTR differences is shown in Table M. tuberculosis strains have been divided into six global lineages, and further into sub-lineages, some of which may have distinct infection phenotypes [Human enotypes . In addienotypes  was alsoIn silico spoligotyping was also performed , while its pre-treatment isolate partner (004-1) was susceptible to all drugs tested. Therefore, there was no evidence of any acquisition of antibiotic resistance during the trial in the samples that were tested with WGS.Drug susceptibility testing showed that only one post-treatment isolate (004-2) had a drug-resistance phenotype, confirmed by genotyping (RIFesx Type 7 secretion systems (T7SSs) [While most SNPs that arise in a strain between treatment and relapse would be expected to be random, as long as they are not deleterious, it would be a reasonable hypothesis that some SNPs may actively help the bacteria survive. Comparing the relapse pairs, 18 out of 30 SNPs were synonymous and 12 out of 30 were non-synonymous Table . Of the  (T7SSs)   were shown to have very few SNPs (≤5) between pre- and post-treatment On initial inspection, the other four pairs (4 of 36) had significant SNP differences between samples , indicative of reinfection. However, phylogenetic analyses cast doubt on two pairs, in which a single isolate of each pair was highly related to another patient’s isolate in the study. While it is possible that these reflect transmission events, it is difficult to rule out some form of laboratory processing error; indeed, a transmission event so similar to another pair of samples in the trial (in one case the pre-treatment and in the other the post-treatment samples) would be relatively uncommon though not impossible, but such a pattern would be expected if there were a sample processing error and patient samples were swapped. A similar event was suggested by Casali et al. . Indeed,The case with 57 SNP differences between the isolate pair was probably a mixed infection, and while accurate SNP figures could not be obtained, the data were consistent with a relapse from one of the two pre-existing strains. These are described as a major/minor strain within the sequencing data, but that may not accurately reflect the relative levels in the patient; these levels could, for example, be affected by colony size on the LJ slopes, and the actual loop sample taken for DNA preparation. The isolate pair were initially identified as being different from each other by a higher number of SNP differences (57) than would be expected for a relapse, but at an unusually low level of SNPs for a reinfection compared to other reported examples. This is likely to be due to the mixed infection causing many genuine SNPs to be discarded as uncertain by the site-calling algorithm. Reports of similar cases of mixed infections in previous studies , 15, 29 Therefore, from the 36 pairs of isolates sequenced, there was strong evidence that 32 were relapses, one was a mixed infection masking a likely relapse, and three were reinfections, although two of these may have been the result of laboratory processing errors. This proportion (32 of 35 (91%) relapse: 3 of 35 (9%) reinfection; excluding the possible mixed infection) can be compared with previously reported relapse to reinfection proportions of 92:8, also in a chemotherapy trial , and 73:. [The number of SNP differences in the relapse and reinfection groups was comparable to previous pre- and post-treatment studies Table . Casali .  showed l.  demonstrFor 16 of the 32 relapse pairs sequenced, SNPs were identified between the isolates  and reinfection . This is in contrast with the study of Bryant et al.  who repo.  found th.  only exaAchieving consistent results with MIRU-VNTR, which involves 24 multiplexed PCRs, is known to be technically challenging , 36, 37.WGS is technically more straightforward and was comparable in cost in our hands (~£100 per sample), but with reducing costs and whole-genome resolution, it is clearly a superior, more robust method then MIRU-VNTR for strain typing. In addition, WGS can provide additional information by identifying markers associated with drug resistance, which could be useful in the context of relapsing cases in a clinical trial. Sequence data is also more amenable to incorporation into other studies and will provide further information on TB evolution as global databases of genome information grow.M. tuberculosis into different sub-types [Spoligotyping has been widely used for robust division of ub-types , but we The data from this study in combination with the previous two relapse studies , 15 alloThe RIFAQUIN trial was in an area of high endemicity, suggesting that reinfections are not likely to be higher elsewhere due to disease prevalence. Thus, the data presented in this study and previously , 15 indiIn the pre-genomic era, all post-treatment infections were presumed to be relapses, and it could be argued that, due to the low reinfection rates and the increased cost and time required to perform the sequencing, WGS provides only modest gains for the analysis of the primary outcome in a chemotherapy clinical trial of this nature.Nevertheless, in addition to robust genomic evidence for treatment outcome, the added information that WGS provides is scientifically valuable and will become of greater value as more genome sequence data and more information about the genotype–phenotype correlation and its impact on disease and transmission becomes available. Furthermore, future trials for new TB drugs in the development pipeline or novel combination regimens may be held in areas of high TB prevalence where re-infection or mixed infections are more likely, thus making accurate strain discrimination imperative; in these instances, WGS should be the method of choice.


Recent studies suggested that variants in the MHC region also exhibit significant non-additive interaction effects. However, collective interactions involving large numbers of single nucleotide polymorphisms (SNPs) contributing to such effects are yet to be characterized. In addition, relatively little is known about the cell-type-specificity of such interactions in the context of cellular pathways. Here, we analyzed type 1 diabetes (T1D) and rheumatoid arthritis (RA) genome-wide association data sets via large-scale, high-performance computations and inferred collective interaction effects involving MHC SNPs using the discrete discriminant analysis. Despite considerable differences in the details of SNP interactions in T1D and RA data, the enrichment pattern of interacting pairs in reference epigenomes was remarkably similar: statistically significant interactions were epigenetically active in cell-type combinations connecting B cells to T cells and intestinal epithelial cells, with both helper and regulatory T cells showing strong disease-associated interactions with B cells. Our results provide direct genetic evidence pointing to the important roles B cells play as antigen-presenting cells toward CD4+ T cells in the context of central and peripheral tolerance. In addition, they are consistent with recent experimental studies suggesting that the repertoire of B cell-specific self-antigens in the thymus are critical to the effective control of corresponding autoimmune activation in peripheral tissues.Autoimmune diseases occur when immune cells fail to develop or lose their tolerance toward self and destroy body’s own tissues. Both insufficient negative selection of self-reactive T cells and impaired development of regulatory T cells preventing effector cell activation are believed to contribute to autoimmunity. Genetic predispositions center around the major histocompatibility complex (MHC) class II loci involved in antigen presentation, the key determinant of CD4 PTPN22 and CTLA4 . SN. SNreg cof FOXP3  and idenBAG6, whose multiple appearances in B cell self-interactions  may occur in or near intestinal organs, whose action toward effector cells is suppressed by tTreg and pTreg cells.Our analysis also pointed to two factors whose roles in autoimmunity have been suggested experimentally but their genetic contexts have not been clear. One is ractions  and B ceractions  are readractions . The otheg cells : the indantigens . pTreg cantigens . Based obacteria . The larBAG6 and BTNL2, whose variants had significantly reduced association in the additive component of p-values BAG6 and elopment . In partelopment , BAG6 maMICB in the class I region in MICB and neighboring MICA encode molecules highly expressed in intestinal epithelium  was used for all cases. For data management tasks and LD calculation, we used PLINK [pi-values from GeDI were similar to the PLINK outputs. For consistency with collective inference results, we used the GeDI results for the special cases of independent-SNPs.We used GeDI (genotype distribution-based inference) , availabed PLINK  version l2 penalizer λ, except for pathway scoring, for which we used the mean-field option. Computational costs for the former were of the order of ~1 hr for a single collective inference involving ~100 SNPs on a desktop machine without additional tests. We performed interaction p-value calculations (based on phenotype label permutation) using the Department of Defense High-Performance Computing resources: for m = 100 SNPs, this calculation would consider 100×99/2 = 4,950 SNP pairs, each of which was repeated at least 3,000 times to construct the null distributions, amounting to ~107 h if run on a single desktop computer.For collective inference, we used the pseudo-likelihood method with pi < 10−3 for each disease. We then visually inspected the distribution of bi-allelic raw signals underlying the genotype calls for each SNP and excluded variants with poor clustering as previously described [We obtained the WTCCC case-control data for T1D and RA and built the case and control data sets excluding individuals as specified in the quality control report of the original study . The resescribed . We furtl2 penalizer λ by first selecting a cutoff pc and selecting SNPs with pi < pc using the training set only. The number of SNPs selected in this way varied with each cross-validation run because the training sets were distinct. For each selection, we inferred both additive and interaction parameters involving the selected SNPs using maximum likelihood and predicted disease phenotypes for the test set based on the disease probability given by Bayes’ theorem. These predictions from all cross-validation runs were combined to calculate the AUC. These steps were repeated for different λ values and pc values. We calculated the mean values of the number of SNPs selected for each choice of pc.We performed five-fold cross-validation using the genome-wide data under varying values of an pi value ranks, and 10 SNPs each from PTPN22, CTLA4, INS, IL2RA, and chr-12 loci to form the m = 100 SNP set. Additive and interaction parameters for this SNP selection were inferred under the penalizer value λ = 0.01 suggested by the cross-validation AUC maximum. The p-values associated with the additive single-SNP parameters  were obtained by the asymptotic χ2 null-distribution approximation. For RA, we first selected 255 SNPs in chromosome 6 (MHC region) from the genome-wide data using pc = 10−4. We then calculated LD within this selection of SNPs and used the negative logarithm of LD r2 values as a distance measure to cluster them (k-means clustering) into m = 70 clusters, each with high LD within the group. From each group, we then selected a SNP with the lowest pi value to form the final m = 70 proxy SNP selection. We used λ = 0.01 for collective inference of these RA data, again based on the cross-validation AUC maximum.For T1D analysis, we selected 50 SNPs from chromosome 6 based on p-values were obtained using methods as described [p-value estimates for putatively significant pairs tended to increase. The final sampling depths were such that this convergence reached reasonable saturation.Interaction escribed . Brieflyactive  and inactive  states. We obtained the frequency of active states in each epigenome by calculating the fraction of genomic segments in active state. For the proxy SNP sets of each diseases (T1D and RA), we constructed the group of all known SNPs in LD with each proxy SNP (r2 > 0.5) from 1000 Genomes Project Phase 3 data [bottom) in different epigenomes over these LDed SNP groups.We used genome-wide annotations of epigenetic states in 111 reference epigenomes based on the 15-state hidden Markov model , reducine 3 data . The sizoxy SNP r > 0.5 frp-values in reference epigenomes on the single-SNP level  with fractional numbers rounded up to integers. For the enrichment of interacting SNP pairs, we first selected SNP pairs with interaction p-values below a cutoff (p < 10−3). We then considered all possible combinations of two epigenomes, and counted the number of LDed SNP pairs belonging to the selected proxy SNP pairs where both LDed SNPs were active in each respective cell types. This number was divided by the product of the two LDed SNP group sizes and summed over the proxy set to obtain the effective number of active-state pairs. This number was compared using binomial tests with the background value estimated by the product of active state frequencies in two tissues and the total number of significant SNP pairs.The enrichment NP level  were obtp-values, we enlarged the grid size to 100 kb such that an appreciable subset of grids would have effective SNP pair counts larger than 1. We then compared these effective numbers in each grid points with the epigenome-wide average using binomial tests.To obtain spatially resolved genetic interaction maps , we diviwww.reactome.org on April 19, 2016 and made a list of all non-provisional genes involved in the pathways. From all quality-controlled sets of SNPs, those within the distance of 50 kb from the coding region were assigned to each gene. We then represented each pathway by the non-redundant list of SNPs assigned to the constituent genes. The number of SNPs selected in each pathway ranged up to ~10,000  gene lists from  ~10,000 . For eacd et al.  and Barranalysis .S1 Figr2 > 0.5) to m = 100 T1D proxy SNPs (https://genome.ucsc.edu.Positions of 1000 Genomes Project SNPs with LD (oxy SNPs  are show(PDF)Click here for additional data file.S2 FigSee (PDF)Click here for additional data file.S3 FigSee (PDF)Click here for additional data file.S4 Figr2 > 0.5) to m = 70 RA proxy SNPs (https://genome.ucsc.edu.Positions of 1000 Genomes Project SNPs with LD (oxy SNPs  are show(PDF)Click here for additional data file.S5 Fighttp://epigenomegateway.wustl.edu/browser.The map was generated using the Roadmap epigenome browser at (PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 FigInferences with and without interaction effects are shown together as functions of the number SNPs in each pathway. Pathways containing MHC class II genes are shown in red. Vertical lines are 95% c.i. IL, independent loci inference; CL, collective loci inference.(PDF)Click here for additional data file.S8 Figpi < 10−3).See (PDF)Click here for additional data file.S9 FigData are from summary statistics of studies by Bradfield et al.  and Barr(PDF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.


Egg weight (EW) is an economically-important trait and displays a consecutive increase with the hen's age. Because extremely large eggs cause a range of problems in the poultry industry, we performed a genome-wide association study (GWAS) in order to identify genomic variations that are associated with EW. We utilized the Affymetrix 600 K high density SNP array in a population of 1,078 hens at seven time points from day at first egg to 80 weeks age (EW80). Results reveal that a 90 Kb genomic region (169.42 Mb ~ 169.51 Mb) in GGA1 is significantly associated with EW36 and is also potentially associated with egg weight at 28, 56, and 66 week of age. The leading SNP could account for 3.66% of the phenotypic variation, while two promising genes (DLEU7 and MIR15A) can be mapped to this narrow significant region and may affect EW in a pleiotropic manner. In addition, one gene (CECR2 on GGA1) and two genes (MEIS1 and SPRED2 on GGA3), which involved in the processes of embryogenesis and organogenesis, were also considered to be candidates related to first egg weight (FEW) and EW56, respectively. Findings in our study could provide worthy theoretical basis to generate eggs of ideal size based on marker assisted breeding selection. Egg weight (EW) is an economically-important trait in chicken and one of the major indexes used by consumers when directly selecting suitable products . However, despite a range of studies in this area, wide confidence intervals for the positions of QTL remain that have rarely been replicated. A new research era was initiated with advances in single nucleotide polymorphism (SNP) chip and sequencing technology, and genome wide association study (GWAS) has become one of the most effective methods to detect genetic variation in livestock. In previous studies, Liu et al. . The whole of this study was approved by the Animal Welfare Committee of China Agricultural University (permit number: SYXK 2007-0023).An 11th generation pure line of Rhode Island Red chicken from Beijing Huadu Yukou Poultry Breeding Co., Ltd. comprised the experimental material for this study. A total of 92 sires and 801 dams contributed to this population which has been selected for egg production and quality over many years. Thus, a total of 1,078 hens of accurate known pedigree were chosen for SNP genotyping in order to collect phenotypic EW-related data. All birds were housed in individual cages of the same area at 13 weeks of age and were provided with free access to feed and water.https://www.r-project.org/).We measured EWs at seven different time points in order to depict genetic architecture across the whole laying period. Thus, first egg weight (FEW) is regarded as the weight of the first egg laid by each bird; however, because of the changes in egg laying performance, we collected fresh eggs at 28 weeks of age, 36 weeks of age, 56 weeks of age, 66 weeks of age, 72 weeks of age, and 80 weeks of age for two successive days during the early laying period (before or at 56 weeks) as well as for three successive days during the late laying period (after 56 week). These time points were selected according to the actual breeding objectives of the company, and average EW for either 2 or 3 days was defined as the phenotypic value for each sample. Descriptive statistics of all phenotypic data were handled using the software R version 3.3.1  method implemented in the software DMU v6.0 , Z is the n × 1 vector of the covariate (random effect), and a and e denote the additive effect and random residual, respectively.In this expression, y denotes the phenotypic value of traits, http://affymetrix.com/) were then applied to calculate SNP quality values; lower quality ones were filtered out using a bespoke R script so that 1,063 individuals and 517,856 SNPs remained. In addition, we discarded SNPs on sex chromosomes because the egg weight is a quantitative trait and the genes that affecting egg weight are mainly located on autosomes. It is very difficult to detect associations between phenotypes and genotypes in these cases. The software PLINK  using the software PLINK to evaluate population stratification prior to GWAS. Considering that clusters of highly correlated SNPs may distort the resulting PCs, all SNPs were pruned to ensure their independence using the indep-pair-wise option in PLINK software, with a window size of 25 SNPs, a step of five SNPs and a r2 threshold of 0.2. And the top five principal components calculated by those independent SNPs were treated as covariates and included in model of GWAS as fixed effects to control for population structure. Considering the possibility of over-conservation inherent to the Bonferroni method, we adjusted the threshold of genome-wide significant We initially performed a univariate GWAS by applying a linear mixed model to account for associations between EWs and effective SNPs using the software GEMMA  used to control population structure, α denotes a vector of corresponding effects that comprise the intercept, x denotes the marker genotypes, β refers to the effects of corresponding markers, u is a vector of random polygenic effects, and ε is a vector of random residuals. We applied the Wald statistical test to evaluate the alternative hypothesis H1: β ≠ 0 vs. our null hypothesis H0: β = 0 for each SNP, since Fwald = In this expression, y denotes the phenotypic values of EWs for We then performed a multivariate association analysis to directly account for the influence of genetic variants on longitudinal EWs. A mixed model in this case was also implemented using the software GEMMA.https://cran.r-project.org/web/packages/gap/) and “qqman” (https://cran.r-project.org/web/packages/qqman/) packages within the software R. A genomic inflation factor (λ) was also calculated using the “GenABEL” package in R  plots for EW traits using the “gap” .We performed a series of linkage disequilibrium (LD) analyses to characterize causative SNPs within strong LD regions by applying the solid spine algorithm in the software Haploview version 4.2  accounts for 3.66% (SE = 0.04) of phenotypic variance. Thus, annotation of the 15 interesting SNPs discussed above using the VEP tool may help us identify promising EW-associated genes. Detailed information regarding the genes identified in this study is summarized in Table P-value 1.58e-6) in our univariate GWAS analysis and are clustered into Block 1 (51 Kb) and Block 2 (3 Kb)  are presented in Figure  Figures . The two Figures ; these dNCAPG has not been identified in our study. We suggest that this region may be associated with egg weights of White Leghorn after we searched QTLs that affecting EW on GGA4 in chicken QTL database . In addition, these workers, however, were mainly concerned with the phenotypes of laying hens younger than 60 weeks of age, a point when egg production yield remains high , just 50% of significant SNPs were located upstream of the CECR2 gene. Previous research has shown that uncovered that CECR2 is expressed during chicken embryonic development and exerts and influence on both somites and neurons  identified by annotating nine significant SNPs can be considered as candidates related to FEW and EW56. Findings in our research could provide valuable breeding theory for the future production of ideal egg size in the context of marker-assisted breeding selection.https://figshare.com/articles/Genome-wide_Association_Analysis_of_Age-Dependent_Egg_Weights_in_Chickens/5844420.The genotype and phenotype data of the samples used in this study are available from the FigShare Repository: NY: Conceived the study and designed the project; ZL: Performed the genetic and bioinformatics analyses; ZL, YY, GL, GW, and AL: All contributed by collecting samples and measuring phenotypic data; ZL: Wrote the manuscript, which was then revised by NY and CS. All authors read and approved the final draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer XSS and handling Editor declared their shared affiliation.


Genome–phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases.In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx.ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1).We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer’s disease  contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) have proven an effective strategy for the detection of variants statistically associated with disease or traits. Since 2005, thousands of single nucleotide polymorphism (SNP)-trait associations have been identified, most of which were deposited in the GWAS Catalog . In receAs a proof of concept, the first PheWAS genotyped 6005 European–Americans in Vanderbilt’s biobank using five SNPs that had been previously reported with disease associations in GWAS . After gAnother challenge is how to improve the interpretation of the associations in the PheWAS Catalog. Previous studies have not systematically examined biological or functional annotations associated with those SNPs. Although one alternative PheWAS approach is to focus on variants with expected function (such as damaging variants with stop-gain and stop-loss) , this apIn this study, we performed a comprehensive investigation of the functional regulation of variants derived from the PheWAS Catalog through an integrative functional genomics framework Fig. . SpecifiP value for the difference in the frequency of functionally annotated SNPs between all the reported SNPs and the SNVs from the 1000 Genomes project.We downloaded all the SNP-phenotype association results from the GWAS Catalog  . In total, we collected the position information of 1772 motifs for 662 TFs. Promoter/enhancer information was obtained from FANTOM5 (http://fantom.gsc.riken.jp/data/), Roadmap , and ENCODE . In the GTEx analysis, cis-eQTLs were calculated for all the SNPs within ± 1 Mb of the transcriptional start site (TSS) of each gene. Each eQTL is defined as a SNP being significantly cis-associated with the expression difference of at least one gene by false discovery rate (FDR) ≤ 0.05. SNVs from the final phase of the 1000 Genomes project were retrieved from the EBI FTP Site in VCF format (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/).We collected three types of functional annotation information: motif, promoter/enhancer, and eQTL. Motif data were extracted from the ENCODE-motif that was available from the MIT Computational Biology Group ( Browser ). We dowhttps://www.broadinstitute.org/mpg/snap/) to search for the proxy SNPs of each reported SNP in the PheWAS Catalog and GWAS Catalog based on linkage disequilibrium (LD), confined to the CEU population of HapMap 3 (release 2). For each of the reported SNPs, we obtained those SNPs that were in its strong LD (r2 ≥ 0.8) within the 500-kb flanking region of each side (upstream and downstream) of the SNP.We used the online SNP Annotation and Proxy (SNAP) tool . For each tissue, we regarded those genes with RPKM ≥ 1 in > 80% samples as tissue-expressed genes and the remaining genes as tissue-unexpressed. To quantify the expression significance of tissue-expressed gene i in tissue t, we calculated the average expression 〈E(i)〉 and the standard deviation δE(i) of a gene’s expression across all considered tissues [i in tissue t is defined asWe downloaded the gene expression data of 32 tissues from GTEx V6 release . We illustrated the network graphs using Cytoscape (v2.8.1) [All the statistical analyses were performed using R v3.2.3 ((v2.8.1) .http://phewascatalog.org, which included 215,108 significant disease-SNP associations (P < 0.05) connecting 1354 disease terms and 3107 SNPs. As a comparison, we downloaded data from the GWAS Catalog (data downloaded on 27 April 2015), including 28,829 significant disease-SNP associations (P < 1.0 × 10–5) connecting 1290 disease terms or traits and 15,442 SNPs from 2153 published papers. Then, we performed systematic localization for the disease SNPs in various functional regions including TF-motifs, promoters, enhancers, and eQTLs based on the data from FANTOM5 [We developed an integrative functional genomics framework to examine the functional regulation and tissue-specific regulatory circuits for large-scale disease-associated SNPs reported in the GWAS Catalog and PheWAS Catalog Fig. . To exam FANTOM5 , ENCODE  FANTOM5 , NIH Roa FANTOM5 , and GTEHNF4A: p.T139I; and rs1057910, CYP2C9: p.I359L, Additional file IL4R: p.Q576R; and rs11906160, MYH7B: p.A25T, Additional file CYP2C9: p.I359L is reported to be related with deep vein thrombosis by PheWAS and with warfarin maintenance dose by GWAS [First, we annotated each SNP with RefSeq gene information using ANNOVAR . From th by GWAS . HoweverAmong the 585 exonic SNPs from the GWAS Catalog, there were eight SNPs located at ligand-binding sites or within their two-residue flanking regions  with the reported SNP in the CEU population (Utah residents with Northern and Western European ancestry from the CEPH collection) of HapMap 3 (release 2) [Interestingly, a previous study has suggested that a functional SNP with the strongest experimental evidence is often not the reported SNP itself in GWAS; rather, it is a SNP within LD of the reported SNP . Inspirelease 2) . We perfP = 0.035, Fisher’s exact test), moderate enrichment for promoter/enhancer , and strong enrichment for eQTLs , respectively, in the PheWAS Catalog. Similarly, comparison of the GWAS Catalog with the 1000 Genomes data revealed 1.14-fold enrichment of TF-motif (P = 1.4 × 10–10), 1.37-fold enrichment for promoter/enhancer (P = 1.0 × 10–133), and 13.2-fold enrichment for eQTLs (P < 2.2 × 10–226), respectively  and detected 202 associations for 102 SNPs and 87 phenotypes [PBRM1 and ITIH1 in the GWAS Catalog, PheWAS suggested they might be associated with lipoma. In addition, 109 (54%) were either replications or associations with phenotypes related to associations in the GWAS Catalog. For example, PheWAS replicated the associations for four functional SNPs in the gene CDKN2B − AS1 to coronary atherosclerosis. Figure In order to search for novel associations, Denny et al. used a FDR < 0.1  genome-wide mapping of promoters and enhancers; (2) linking TFs to promoters and enhancers; and (3) linking enhancers and promoters to target genes based on data from the FANTOM5 consortium, as described in a previous study . For eacP = 9 × 10–18) [MAPT and CRHR1. MAPT, encoding the microtubule-associated protein tau that binds and stabilizes microtubules, plays an important role in neuronal polarity and signal transduction. Mutations on MAPT have been associated with several neurodegenerative disorders such as Alzheimer’s disease (AD), Pick’s disease, frontotemporal dementia, cortico-basal degeneration, and progressive supranuclear palsy [For breast cancer, we identified two significant SNPs involved in TF targeting promoter/enhancer with strong confidence by altering gene expression in disease-associated genes. One interesting discovery is SNP rs242557, which was found to be associated with progressive supranuclear palsy from the GWAS Catalog (× 10–18) . While iar palsy . In cancar palsy .TNFSF15. This SNP may affect the binding motif of multiple transcriptional factors and lead to the dysfunction of TNFSF15. A clinical investigation has indicated that high levels of TNFSF15 were associated with increased survival rates of breast cancer patients [2 = 0.857) with SNP rs2885805, a functional SNP located in the enhancer region of CD53, which is a prognostic gene signature in breast cancer [Another example is SNP rs6478109 located in the promoter region of patients , 52. We patients . There ht cancer , 55. A rt cancer , suggestP values in PheWAS for all four brain-related diseases. For schizophrenia, one novel associated SNP identified by PheWAS is SNP rs339969 with P = 0.046 based on 60 cases. In the GWAS Catalog, it is reported that this is associated with Gamma-glutamyl transferase from a previous study [RORA. The retinoic acid-related orphan receptor alpha (RORA), which is a ligand-dependent orphan nuclear receptor, acts as a transcriptional regulator and has been previously identified as a novel candidate gene for autism spectrum disorders [We next examined the tissue-specific regulatory circuits (TF-target gene regulatory network) for four brain-regulated diseases: schizophrenia; AD; bipolar disorder; and Parkinson’s disease. Figure us study . Functioisorders .P = 0.015). Although there is no functional evidence for this association from the annotation of this SNP itself, we identified another SNP rs7194347, in perfect LD (r2 = 1) with SNP rs7197475, which strongly supports this discovery. SNP rs7194347 overlaps with an enhancer region and may perturb the expression of STX1B whose methylation and expression changes are associated with Parkinson’s disease [STX1B showed highly brain-specific expression with its Z-score = 5 when compared to the other 31 tissues, suggesting a potential functional gene for AD  with this SNP. SNP rs9898916 is involved in the regulation of SKA2 whose methylation is associated with decreased prefrontal cortical thickness and greater post-traumatic stress disorder (PTSD) severity among trauma-exposed veterans [SKA2 mediates vulnerability to suicidal behaviors and PTSD through dysregulation of the hypothalamic pituitary adrenal axis in response to stress [SKA2 in PTSD. Two previous studies have reported that the ZIC1 gene encodes a TF that binds and trans-activates the apolipoprotein E gene and further plays an important role in neuronal maintenance and repair [ZIC1 is highly expressed in brain  [We next built a brain-specific TF-target gene regulatory network for AD as shown in Fig.  disease . STX1B sveterans . In addio stress , suggestd repair , 64. GTEr (ADHD) .Fig. 5A ZBTB38) and asthma. Later this was confirmed by one GWA study [RASA2, ZBTB38, RNF7, and SLC25A36) in a 1-Mb region centered by SNP rs6763931. Functional evaluation showed that RASA2 (103 Kb away from SNP rs6763931) was highly differentially expressed between children with asthma and healthy individuals while the host gene (ZBTB38) showed no evidence of differential expression [IL1RL1) is a variant affecting the quantity of eosinophil in pleiotropic multifunctional leukocytes, which is involved in inflammatory and immune responses observed in asthma, eczema, rhinitis, and other inflammatory diseases [P = 0.0015).We further built a lung-specific TF-target gene regulatory network for asthma in Fig. WA study . In totapression . SNP rs1NFKB whose target genes include HLA-DRB1, HLA-DRB5, and HLA-DPB1  is one of the most variable and gene-dense regions in the human genome with potential effects on innate and specific immunity. In the MHC region, PheWAS reported an association between asthma and SNP rs660895. This SNP may alter the binding enhancer of n sample . In addin sample .CSF3 (encoding Colony Stimulating Factor 3). CSF3 is a protein-coding gene that is important for the survival and proliferation for neutrophils and macrophages. GTEx data showed that CSF3 is a lung-specifically expressed gene with Z-score = 5  on the colon-specific TF-target gene regulatory network. We found that one of the new network-predicted IBD genes in our reconstructed colon-specific TF-target gene regulatory network  for follow-up validation. This strategy has been demonstrated as being effective in previous studies [In PheWAS, large-scale multiple testing is needed to control the FDR. However, standard FDR control procedures, such as the Benjamini–Hochberg procedure , are typ studies , 76. TakReported results in multiple GWASs have highlighted a number of pleiotropic effects. Compared to GWAS, one promising advantage of PheWAS is to examine pleiotropy by measuring genetic associations of one variant with thousands of diseases or phenotypes simultaneously. Variants demonstrating pleiotropy may confer tissue-specific effects on multiple genes , some ofIn this study, we proposed an integrative functional genomics framework that maps 215,107 significant SNP traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map. By incorporating various functional annotation data from four major functional genomics databases—FANTOM5, ENCODE, NIH Roadmap, and GTEx—we showed that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. We demonstrated that integration of functional annotations significantly improves the power of detecting novel associations in PheWAS and we further found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Furthermore, we performed a tissue-specific regulatory circuit analysis through integrating the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. We uncovered several promising tissue-specific regulatory TFs or genes for AD (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1) in our case studies. In summary, this study offers powerful functional genomics tools and network methodology for exploring the functional consequences of variants generated from genome–phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.Additional file 1: Table S1.List of PheWAS SNPs located at ligand binding sites. (XLSX 9 kb)Additional file 2: Table S2.List of PheWAS SNPs located at phosphorylation sites. (XLSX 9 kb)Additional file 3: Table S3.List of GWAS SNPs located at ligand binding sites. (XLSX 9 kb)Additional file 4: Table S4.List of GWAS SNPs located at phosphorylation sites. (XLSX 12 kb)Additional file 5: Table S5.List of SNPs in different types of functional categories. (XLSX 546 kb)Additional file 6: Figure S1.Figure S2. A colon-specific TF-target gene regulatory network for inflammatory bowel disease. (PDF 1073 kb)SNP annotation and enrichment analysis in different types of functional data.


While year after year, conditions, quality, and duration of human lives have been improving due to the progress in science, technology, education, and medicine, only eight diseases have been increasing in prevalence and shortening human lives because of premature deaths according to the retrospective official review on the state of US health, 1990-2010. These diseases are kidney cancer, chronic kidney diseases, liver cancer, diabetes, drug addiction, poisoning cases, consequences of falls, and Alzheimer's disease (AD) as one of the leading pathologies. There are familial AD of hereditary nature (~4% of cases) and sporadic AD of unclear etiology . Therefore, sporadic AD is no longer a purely medical problem, but rather a social challenge when someone asks oneself: “What can I do in my own adulthood to reduce the risk of sporadic AD at my old age to save the years of my lifespan from the destruction caused by it?” Here, we combine two computational approaches for regulatory SNPs: Web service SNP_TATA_Comparator for sequence analysis and a PubMed-based keyword search for articles on the biochemical markers of diseases. Our purpose was to try to find answers to the question: “What can be done in adulthood to reduce the risk of sporadic AD in old age to prevent the lifespan reduction caused by it?” As a result, we found 89 candidate SNP markers of familial and sporadic AD . In addition, rs768454929, and rs761695685 correlate with sporadic AD as a comorbidity of short stature, where maximizing stature in childhood and adolescence as an integral indicator of health can minimize (or even eliminate) the risk of sporadic AD in the elderly. After validation by clinical protocols, these candidate SNP markers may become interesting to the general population . While year after year, conditions, quality, and duration of human lives have been improving due to the progress in science, technology, education, and medicine, only eight diseases have been increasing in prevalence and shortening human lives because of premature deaths according to the retrospective official review on the state of US health, 1990-2010 .Escherichia coli BL21 (DE3) cells transformed with the pAR3038-TBP plasmid  as described by Pugh (Recombinant full-length human TBP (native amino acid sequence) was expressed in  by Pugh  with two by Pugh .32P labeling of both strands by means of T4 polynucleotide kinase  with subsequent annealing by heating to 95°C (at equimolar concentrations) and slow cooling (no less than 3 h) to room temperature. Equilibrium dissociation constants (KD) for the TBP–ODN complex were measured using a conventional protocol . Combining a fixed concentration (0.1 μM) of ODNs with various concentrations  of the above TBP, we analyzed six time-series of the fluorescence expressed in conventional units using high-resolution spectrometer SX.20 . These experimental data were input into the Dynafit software  whose output was the above K2 until the desired level of confluence. The proximal core promoter 177 bp long containing either the ancestral allele or minor allele of the selected candidate SNP marker rs201381696  was cloned into the pGL 4.10 vector  and cotransfected with pRL-TK using Screen Fect A (InCella) as described by Wolfe et al. (Cell line hTERT-BJ1 (human fibroblasts) was cultivated in a complete medium consisting of Dulbecco's modified Eagle's medium/Nutrient mixture F-12 Ham, supplemented with 10% (v/v) of fetal bovine serum (Sigma), penicillin (100 U/mL), and streptomycin . The culture was maintained at 37°C in a humidified atmosphere containing 5% of COe et al. . Next, tFigures in silico ” in front of these references found.In Figure MAPT, APP, PSEN1, PSEN2, and APOE—by the same method because these genes have been clinically most reliably implicated in familial AD  of sporadic AD   Table  is consiP) Table . Near thIL1B geneThe human  (interleukin 1β) promoter has an SNP marker (rs1143627) causing overexpression, which is clinically associated with virus-induced cancers in the liver and in the stomach as well as with non-small cell lung cancer, with Graves' disease, major recurrent depression, obesity, gastric ulcer, and chronic gastritis  contains a known clinical SNP marker , which can cause overexpression of this gene associated with resistance to methotrexate treatment in children with acute lymphoblastic leukemia  that reduces this gene's expression and, thus, lowers the risks of psoriasis, systemic sclerosis, and chronic asthma in children and in smokers  contains a known SNP marker (rs72661131) of variable immunodeficiency, preeclampsia, and stroke as one can see in our review : its promoter contains a known SNP marker (rs7277748) of familial amyotrophic lateral sclerosis (ALS) because this SNP causes SOD1 excess  are the most thoroughly characterized in terms of clinical regulatory SNP markers, seven of which (Table APOE gene (apolipoprotein E) whose SNPs can prevent malaria  of hemolytic anemia and neuromuscular diseases manifesting themselves as a deficiency of this enzyme  contains the −35A→C substitution reducing the expression of this gene, which is a known SNP marker of fatty liver in childhood  carries a known SNP marker (rs13306848) of thrombosis  and contains a known SNP marker (rs563763767) of myocardial infarction and thromboembolism caused by F3 excess , where an undocumented SNP (the a→c substitution at position -35 upstream of the transcription start site of this gene) has been clinically implicated in moderate bleeding caused by proconvertin deficiency  contains two biomedical SNP markers (rs10465885 and rs35594137) of arrhythmia and cardiovascular events  contains a biomedical SNP marker (rs11568827) of short stature , which can cause the somatotropin excess and hence serve as candidate SNP markers of the lower risk of sporadic AD (Table Two base pairs away from this known SNP marker rs11568827), we found an unannotated SNP (rs796237787), which also represents a deletion of G with the same effect on the same gene expression  of type 1 diabetes (T1D) after neonatal diabetes mellitus , in whose promoters none of biomedical SNP markers have been found yet. In our previous work   has no known biomedical SNP markers within its promoters, but we first found two candidate SNP markers (rs553179073 and rs11872014) of deficient expression of this gene and another marker (rs374878846) of its overexpression  of the significant overexpression of this gene and two candidate SNP markers (rs756747509 and rs561135968) of the significant deficiency of β-amyloid peptides  contains no known biomedical SNP markers supported by clinical data within its promoters. Nevertheless, for the first time, we found three candidate SNP markers  of the significant APOE overexpression and another one (rs758379972) of significant APOE underexpression  of a significant excess of presenilins and seven candidate markers  of a significant presenilin deficiency  of candidate SNP markers predicted here decrease expression of five genes associated with familial AD Table  whereas 2 of 28 oMAPT, APP, PSEN1, PSEN2, and APOE is well-consistent with the manifestation of damage to these genes as familial AD only at the age of over 65. This is when the humans accumulate a number of pathologies that may not be associated with AD and whose interactions with these damaged genes allow these pathologies to manifest themselves as AD. Moreover, due to this robustness of these genes in childhood, adolescence, and adulthood, the human body can respond to deadly stressors  by borderline slowly progressing pathologies  that develop only in the elderly as sporadic AD or other diseases of aging as noted previously . Figure TPI1 gene promoter. As one can see in Figure D values significantly correlate according to a number of statistical tests such as linear correlation (r), Spearman's rank correlation (R), Kendall's rank correlation (τ), Goodman–Kruskal generalized correlation (γ), and χ2 and Fisher's exact (p) tests. These robust correlations between our predictions and experimental data also support the candidate SNP markers  predicted here.Because different computational methods have their own advantages and disadvantages in predicting functional SNPs, to gain good-quality knowledge, a comparison between computer-based predictions and experimental data as an independent commonly accepted uniform platform is still needed in familial and sporadic ADs. That is why we selected some of the 89 candidate SNP markers predicted here—rs563763767, rs33980857, rs34598529, rs33931746, rs33981098, rs35518301, rs1143627, rs72661131, rs7277748, and rs1800202—and measured equilibrium dissociation constant Kin vitro in real time on a high-resolution spectrometer SX.20  in the case of the selected candidate SNP-marker rs1800202. As one can see, our prediction in silico that this SNP is manifested as reduced affinity of TBP binding to the human TPI1 gene promoter (Table ex vivo verification using the human cell line hTERT-BJ1 (human fibroblasts) cultured and transfected with the pGL 4.10 vector carrying a reporter LUC gene  predicted here.In addition to the above-mentioned widely used conventional EMSA, we applied two modern high-performance methods. Figure er Table  is consine Table . As the ne Table , fits thThis comparison between the computer-based predictions of the candidate biomedical SNP markers and proper experimental measurements as an independent commonly accepted uniform platform are the novelty of this work relative to other computer-based methods and tools in this active field of research.Currently, the OMIM database  for the binding of human TBP to a 26-bp synthetic duplex DNA identical to the SNP in question; the constant is expressed in nanomoles per liter, nM . After that, such SNP markers may be applicable to clinical practice. To help with this validation, we accompanied each predicted candidate SNP marker by our prediction of the equilibrium dissociation constant .LS, ID, ES, and DZ designed the study. IC compiled data. NK created and maintained the software operation. EK performed experiments. DR and PP analyzed data. MP wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Elite parental lines are more likely to breed fine varieties, but knowledge about elite parents and their genetic backgrounds is limited. In this paper, we investigated the pedigree relationships of potato varieties bred worldwide and in China. Several elite parents were identified, and these parents were more frequently used as parents in breeding programs across different time periods and countries. We next used 2b-RAD, a reduced-representation sequencing method, to genotype the elite parent Mira and 24 of its offspring. These cultivars span 5 generations, making this lineage the longest continuous pedigree among Chinese bred potatoes. A total of 47,314 tetraploid single nucleotide polymorphisms (SNPs) identified by FreeBayes were used to trace the conserved segments of the Mira genome. The conserved segments had identical or similar allele-specific SNPs across the analyzed genotypes. In Mira, 3,788 segments comprising over 10,000 bp, or 20.8% of the genome, were defined as conserved segments. These segments contain genes involved in crucial biological processes that are of special interest to breeders. These regions, which have been conserved across generations of highly selective breeding, may be helpful for further breeding and performing genome-wide breeding by design. Solanum tuberosum L.) were first cultivated in China between 1573 and 1620 . Genome assembly after resequencing autotetraploids, which is a precondition for genotyping, is a huge challenge, and no autotetraploid assembly has yet been obtained (aaaa), simplex (aaab), duplex (aabb), triplex (abbb), and quadruplex (bbbb) genotypes , aaab (simplex), aabb (duplex), abbb (triplex), or bbbb (quadruplex) using DAx software . The heterozygous groups aaab, aabb, and abbb were distinguished based on the relative peak height of each nucleotide in the sequence trace file.Candidate sites were randomly selected for validation, including approximately equal numbers of heterozygous and homozygous SNP calls. Primers were designed using BatchPrimer3 based on the PSGC assembly to amplify a 200 to 400-bp fragment flanking each target site . Two strategies were employed. First, all progenies of Mira were assumed to have the same pedigree status, which ignores the fact that these progenies are from different generations. In practice, we calculated all the SNP inherited ratio as described above, and then averaged the ratio among progenies site by site. For the second strategy, pedigree status was taken into account. Mira and its progeny span 5 generations, which led to different minimum inherited ratio between generations. For instance, the minimum inherited ratio of polymorphic sites from Mira in first generation offspring is 0.5, and in second generation offspring the minimum ratio is 0.25. In actuality, we calculated inherited ratio for all direct progeny of Mira for all SNP sites, and then averaged the ratio site by site as described above. If one progeny also served as a parent, we also calculated the inherited ratio of its progeny and multiplied the ratio among generations for each site. Then we averaged the ratio among collateral lines. Ratios were calculated for both diploid and tetraploid SNPs. Frequency calculation, correlation analysis, and generation of the QQ plots were performed in SPSS Statistics 22 . A diagram of chromosomal segments with different inherited ratio was drawn using matlab7 .The conserved and variable segments were detected based on the inherited ratio of polymorphic sites from Mira to its progeny  as a cut-off to identify the conserved segments, but a very similar ratio of 0.96875 resulted in the inclusion of a large proportion (6.8%) of all segments; therefore, we decided to use 0.96875 as a threshold. Segments with inherited ratio > 0.96875 may be highly conserved and were named highly conserved regions. Segments with inherited ratio > 0.9 were named conserved regions. We used the inherited ratio < 0.306  as a cut-off for identification of the named variable segments. The genes in these regions were identified, and gene ontology (GO) enrichment analysis was performed as described previously .Table 1) and were used to breed 655 out of 4,397 (14.9%) cultivars (Table 2 and Supplementary Table S2), and 295 genotypes were listed as a direct parent ≥5 times (Table 2 and Supplementary Table S2). Katahdin, which was bred in 1932 in the United States and has 97 direct progenies in the potato pedigree database, was the most frequently used parent worldwide between 1841 and 2013. The ancestors of Katahdin include Early Rose, Erste von Fromsdorf, Paterson’s Victoria, Rural New Yorker 2, and Sutton’s Flourball. The direct progenies of Katahdin include Houma, Wauseon, Sebago, and Pontiac; the offspring of Katahdin include Shepody, Atlantic, Innovator, Desiree, Favorita, Spunta, and Maris Piper. These results indicate that some genotypes were used as parents more frequently in potato breeding.An online potato pedigree database, developed by Wageningen University and consisting of over 8,000 genotypes bred in 68 countries, reflects most of the potato breeding history worldwide . A totalTable 1), were used to bred 76 out of 510 (14.9%) cultivars (Table 2 and Supplementary Table S2), and 63 genotypes were listed as a direct parent ≥3 times (Table 2 and Supplementary Table S2). Agria, which has 27 direct progenies, was one of the most frequently used parents for potato breeding from 2003 to 2013 and has 62 direct progenies among all 4,397 cultivars developed between 1841 and 2013. Agria was bred in Germany in 1985, and its ancestors include Industrie, Jubel, Binjie, Jaune d’Or, and Clivia, which are quite different from the ancestors of Katahdin’s ancestors. These results indicate that the parents preferred in breeding changed over time.To more specifically reflect the modern breeding program, 510 cultivars bred worldwide between 2003 and 2013 were investigated. Five genotypes, which were listed as a direct parent >10 times (Table 3), were used to breed 143 out of 523 (27.34%) cultivars (Table 2 and Supplementary Table S2), and 81 genotypes were listed as direct parents ≥3 times (Table 2 and Supplementary Table S2). To determine the changes in preferred parents during different periods, we further divided these 523 Chinese cultivars into two groups: cultivars bred before 2000 and those bred after 2000. Before 2000, 193 cultivars and 5 genotypes, which were listed as the direct parent >10 times (Table 3), were used to breed 89 out of 193 (46.11%) cultivars (Table 2 and Supplementary Table S2), and 25 genotypes were listed as direct parents ≥3 times (Table 2 and Supplementary Table S2). After 2000, 330 cultivars and only 2 genotypes were listed as a direct parent >10 times (Table 3), and 57 genotypes were listed as the direct parent ≥3 times (Table 2 and Supplementary Table S2). These results indicate that some genotypes were used as parents more frequently in potato breeding and that more germplasm was used in potato breeding after 2000.Because the Chinese bred potato varieties were not included in the potato pedigree database, we collected cultivar validation information up to 2016, and 523 bred cultivars with detailed information were used for pedigree investigation. Eight genotypes, which were listed as a direct parent >10 times . This indicates that excellent parents appeared in different periods; some of the offspring of elite parents were also excellent parents, and some of elite parents shared the same ancestors, implying that some genes were inherited during artificial selection. So we can infer that the ideal models for studying the conserved genome segments of elite parents are the excellent parents that appeared in different generations of the same family.We further investigated the pedigree relationships of the elite parents. In the 1850s, the first generation of elite parents, such as Rough Purple Chili, Jaune d’Or, and Paterson’s Victoria, appeared in the potato pedigree database. These parents were crossed with each other or with other parents to gradually form a new generation of elite parents, and this process was repeated. Several elite parents in early generations included Early Rose, Jubel, and Industrie. From the 1930s–1950s, China imported elite parents such as Katahdin, Epoka, Mira, Anemone, and Schwalbe, and the elite parents selected from imported lines, such as Xiaoyezi and Duozibai. In later years, some of the offspring of these parents also served as elite parents, such as Kexin 2, Gaoyuan 7, Zaodabai, and Zhongshu 3 . Progeny derived from Mira, such as Kexin 2 and Gaoyuan 7, have also served as excellent parents. The Mira lineage spans 5 generations and is the longest continuous pedigree in China, making Mira and its progeny ideal candidates for dissecting the conserved genome segments of elite parents. The reduced-representation genotyping method, 2b-RAD, was used to generate libraries for sequencing on the HiSeq X-Ten platform . In these tags, 113,157 sites showed SNPs. The number of alleles unique to a cultivar ranged from 0 to 2,433, and in general the parents had fewer unique alleles (Table 4). A total of 104,732 diploid SNPs were identified using SOAP2  , and 52,239 tetraploid SNPs were identified using FreeBayes  . A total of 3,066 diploid SNPs and 1,781 tetraploid SNPs exhibited polymorphism against the reference genome but were not polymorphic among samples. We randomly selected 220 tetraploid SNPs identified in the samples for Sanger sequencing to validate the FreeBayes genotyping, and 93.19% SNPs had the correct genotype. The longest distance between diploid SNPs was 315,022 bp, with an average distance of 7,110 bp, and the longest distance between tetraploid SNPs was 483,592 bp, with an average distance of 13,769 bp. In potato, the LD decay value is at least 600 Kb and between 2 and 4 Mb on average; therefore, the SNP density was sufficient for our study . We found that although the ratio have a distribution close to normal, ratio were not normally distributed based on the Kolmogorov–Smirnov test . QQ plot was used to identify which inherited ratio deviated from a normal distribution . The inherited ratio of tetraploid SNPs and diploid SNPs at the same sites were significantly correlated at the 0.01 level (r = 0.763). There is a small difference in SNP inherited ratio because the ab heterozygous polymorphism sites in diploid SNPs became abbb, aabb, and aaab in tetraploid SNPs. We also calculated the ratio while assuming all progenies had the same pedigree status . We found that the highly conserved sites (inherited ratio > 0.96875) identified by this method included the sites identified when considering pedigree status . The number of highly conserved sites was reduced drastically when we considered pedigree status. This is largely because the minimum rate of transfer of parent DNA polymorphisms to different generations of offspring is different, and these differences are ignored when assuming all progenies have the same pedigree status.To trace the conserved chromosome segments, the inherited ratio were calculated based on information about SNP sites and pedigree relationships between Mira and its progenies. Because the progenies were selected from different generations, the pedigree relationships between different generations were also considered. All tetraploid SNPs and diploid SNPs were separately used to calculate the inherited ratio . These chromosome segments accounted for 11.8% of the Mira genome and included 2,575 chromosome segments with sizes over 10,000 bp. The longest segment was 358,489 bp, with segments 31,507 bp on average. There were 97 to 408 highly conserved segments distributed on each chromosome . An average of 1.84 genes was included in each highly conserved segment. The conserved segments with inherited ratio > 0.9 accounted for 20.8% of the Mira genome and included 4,037 chromosome segments with sizes > 10,000 bp. The longest segment was 358,489 bp, with an average segment length of 35,692 bp. There were 175 to 509 conserved segments distributed on each chromosome . A 36.49 to 130.93% increase in the number of conserved segments was observed when 0.9 was used as a threshold compared with 0.96875. An average of 1.94 genes was included in each conserved segment. These results indicate that some parts of the Mira genome remained highly conserved after several generations of selection. Lines are selected during breeding if they have the traits of interest, thus the conserved chromosome segments may contain genes that are of interest to breeders.During artificial selection on potato, different alleles contained in parental chromosome segments may have different fates, including conservation or variation because the resulting traits do or do not meet the needs of breeders . The tenFigure 5A). Genes with similar functions were not concentrated in a specific region or clustered together. The transcripts of 7,632 genes in conserved segments were found in DM or RH89-039-16 (hereafter referred to as RH) samples in PGSC . We also found that 181 genes were highly expressed under biotic and abiotic stress (>10-fold) . Furthermore, we found some characterized functional genes in these regions that are of interest to breeders (Table 5). Several genes related to photosynthesis were located in conserved segments, such as the plastidial Calvin cycle protein CP12, which regulates Calvin-Benson cycle enzymes (PGI1), which provides phosphate sugar metabolites for the transient accumulation of starch in photosynthetic leaves . Other genes involved in carbohydrate metabolism and transport were also found in conserved segments, including, α-glucosidase (Agl), disproportionating enzyme (Dpe-P), starch synthase III (SSIII), UDP-glucose pyrophosphorylase (UGPase), pyrophosphate-fructose-6-phosphate α subunit (Pfp-α), sucrose synthase 3 (Sus3), adenylate transporter (Ant), plasma membrane H+-ATPase (Pha1), pentose-5-phosphate 3-epimerase (Ppe), inorganic phosphate transporter 1(Pt1), Enolase (Eno), and nucleoside diphosphate kinase (Ndpk)  .Figure 4C). 1142 variance chromosome segments with sizes over 10,000 bp were found in the genome, and 54 to 147 segments were distributed on each chromosome . A total of 1328 genes with annotated functions were found in the variance chromosomal segments, and 1122 of these genes were annotated as belonging to the categories of molecular function, biological processes, or cellular components. Transcripts of the 2,150 genes in variable segments were found in DM or RH samples in PGSC (G6pdh (Glucose-6-phosphate dehydrogenase), which is associated with the trait of chip quality, and Gap C , and GMPase (GDP-mannose Pyrophosphorylase), which are involved in carbohydrate metabolism . New generations of elite parents were largely the progenies of earlier ones. In China, imported cultivars such as Katahdin, Epoka, Mira, Anemone, Schwalbe, Xiaoyezi, and Duozibai, and some of their offspring, such as Kexin 2, Gaoyuan 7, and Zaodabai, served as elite parents . This implies that elite parents passed on genes that were selected during breeding.In this paper, we investigated the pedigree relationships of potatoes cultivated worldwide and those bred in China . We founFigure 3). Thus, we chose to use the reduced-representation genotyping method, 2b-RAD, to identify SNPs . The pedigree status of different generations of Mira offspring was considered when we calculated the inherited ratio instead of assuming all progeny had the same pedigree status . We did not utilize the common sequence diversity parameters of Watterson’s 𝜃, Tajima’s π, or Tajima’s D because we sequenced only a small portion of the genome, and this sequencing method generated SNPs equally distributed across the genome . An elite parent line should demonstrate many traits that meet the needs of breeders. Genes we found in conserved segments were involved in many crucial biological processes, including characterized genes of special interest to breeders . These results are consistent with other studies in rice, and maize, in which conserved regions containing genes of interest to breeders were also distributed throughout the genome (In this study, we considered not only the challenges of assembling the highly heterozygous autotetraploid potato genome, but also the importance of obtaining an even distribution of polymorphic sites at a sufficient density given LD decay (ify SNPs . The cone genome . Moreovee genome . In contIn this study, we sequenced only one family using a reduced-representation method. More detailed information will be obtained when additional elite genotypes are sequenced with advanced technology. When the genome conservation patterns of cultivated potato are established, other genomic regions could be possibly filled with the alleles of genes with crucial functions, such as disease, virus, pest, and drought resistance . MoreoveXL designed the study, conducted the experiments, performed the data analysis, and wrote the manuscript. JX designed the study and wrote the manuscript. SD and CB designed the study and participated in the data analysis. JH participated in the data analysis and wrote the manuscript. HS designed the study. GL designed the study, performed the data analysis, wrote the manuscript, and supervised the research. LJ designed and supervised the research. All authors have read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Japonica rice mainly distributes in north of China, which accounts for more than half of the total japonica rice cultivated area of China. High yield, good grain quality and early heading date were the main breeding traits and commercial property in this region. We performed re-sequencing and genome wide variation analysis of one typical northern japonica rice variety Longdao24 and its parents (Longdao5 and Jigeng83) using the Illumina sequencing technology. 53.17 G clean bases were generated and more than 96.8% of the reads were mapped to the genomic reference sequence. An overall average effective depth of 43.67 × coverage was achieved. We identified 420,475 SNPs, 95,624 InDels, and 14,112 SVs in Longdao24 genome with the genomic sequence of the japonica cultivar Nipponbare as reference. We identified 361,117 SNPs and 81,488 InDels between Longdao24 genome and Longdao5 genome. We also detected 428,908 SNPs and 97,209 InDels between Longdao24 genome and Jigeng83 genome. Twenty-two yield related genes, twenty-two grain quality related genes and thirty-nine heading date genes were analyzed in Longdao24. The alleles of Gn1a, EP3, SCM2, Wx, ALK, OsLF and Hd17 came from the female parent Longdao5. The other alleles of qGW8, SSIVa, SBE3, SSIIIb, SSIIc, DTH2, Ehd3 and OsMADS56 came from the male parent Jigeng83. These results will help us to research the genetics basis of yield, grain quality and early heading date in northern rice of China. Thus, there is a need for an increase in rice yield, because rice is the world’s most important staple food crop. To substantially increase rice yield, Japan initiated a super high-yielding rice breeding program in 1981 that was targeted to raise rice yield 50% within 15 years. However, this target has not yet been realized . Next, teriments . The cor panicle . Althoug panicle , but thejaponica (Nipponbare) and indica (93–11), were released [japonica rice variety, Longdao24, with high quality, super high yield and early heading date in Heilongjiang province. Our results provided several candidates account for the super high yield, good quality and early heading date of rice in Heilongjaing province. The results therefore lay the groundwork for long-term efforts to uncover important genes and for future variety improvement.To meet the global food demands, achievements in molecular genetics of complex traits in rice will be important for increasing yield in the post-genomics era . The drad indica 3–11, werreleased ; and latreleased . The widreleased . These creleased . Severalreleased , Shanyoureleased  and Shenreleased  has beenjaponica rice Longdao5 and Jigeng83. Longdao5 is a leading cultivar widely cultivated in the south and east part of Heilongjiang province and has high resistance to disease and super high yield. Jigeng83 is a leading cultivar widely cultivated in the middle part of Jilin province and has super high yield.Longdao24  was seleGenomic DNA was prepared from bulk harvested leaves of a single young plant of Longdao24, Longdao5 and Jigeng83 using a modified CTAB method . Whole gjaponica Nipponbare reference genome—the unified-build release Os-Nipponbare-Reference-IRGSP-1.0 [The clean sequencing reads were aligned to the temperate RGSP-1.0 , using tRGSP-1.0 . AverageFirstly, we used GATK tools software  to detecThe annotation of SNPs and Indels were performed by SnpEff software . The locGenome sequencing of Longdao24, Longdao5 and Jigeng83 yielded 64.19, 53.93 and 70.60 million reads, most of them being 300 bp paired end reads. After cleaning, 19.17 G (Longdao24), 16.07 G (Longdao5) and 21.11 G (Jigeng83) clean bases were generated and 96.80%, 99.27% and 98.99% of the reads were mapped to the genomic reference sequence. The overall effective depth coverage of Longdao24, Longdao5 and Jigeng83 was 47 ×, 32 × and 52 ×, respectively. The 1 ×, 5 × and 10 × coverage ratio of Longdao24 to the reference genome was 97.87%, 96.11% and 95.08%, respectively. The 1 ×, 5 × and 10 × coverage ratio of Longdao5 to the reference genome was 97.21%, 95.44% and 93.19%, respectively. The 1 ×, 5 × and 10 × coverage ratio of Jigeng83 to the reference genome was 98.40%, 97.32% and 96.63%, respectively. The state information of Longdao24 and its parent were showed in We then examined genome-wide variations. 420,475 SNPs, 95,624 InDels, and 14,112 SVs were yielded from the Longdao24 genome. The frequency distributions of SNPs and InDels on Longdao24 genome were showed in 22,250, 65,858, 137,848 and 107,288 SNPs were found in intergenic region, intron region, upstream region and downstream region respectively. 9,054 SNPs were located in the 5’UTR and 3’UTR region. 1,958 SNPs were detected near the splice site. A total of 76,214 SNPs were located in the CDS regions, among which 31,308 were synonymous coding sequences (Syn CDS) and 42,115 were non-synonymous coding sequences (Nonsyn CDS) .5,623, 17,609, 34,717 and 27,132 InDels were found in intergenic region, intron region, upstream region and downstream region respectively. 4,465 InDels were located in the 5’UTR and 3’UTR region. 426 InDels were detected near the splice site. A total of 5,602 InDels were located in the CDS regions, among which 3,401 caused frame shift and 2,092 caused codon change .A total of 14,112 Structure Variation (SVs) were detected in Longdao24 including 7,795 Insertion (INS), 3,273 Deletion (DEL), 217 Inversion (INV), 317 Intra-chromosomal Translocation (ITX) and 2396 Inter-chromosomal Translocation (CTX). A total of 11,285 SVs were located in the CDS regions, among which 2,969 were detected in exon, 1,697 were located in intron and 6,619 were located in intergenic region. And 2,677 Copy number variation (CNVs) were also detected in Longdao24.16,831 variations including 10,615 non-synonymous SNP mutations, 3,727 frame shifts and 2,489 SVs caused by indels, transversions, and transitions in 12,772 genes might influence the expression of the relevant protein. All tested and functional annotated genes were classified into gene ontology (GO) categories , which mGn1a (related to spikelet number), EP3 (related to erect panicle and spikelet number), SCM2 (related to lodging resistance and spikelet number) and qGW8 . Four non-synonymous SNPs and a codon insertion InDel were identified in Gn1a locus. Two non-synonymous SNPs and a codon change + deletion InDel were identified in SCM2 locus. Three non-synonymous SNPs and a codon deletion InDel were identified in qGW8 locus. One frame shift InDel were identified in EP3 locus. The allele of Gn1a, DEP3 and SCM2 were come from the female parent Longdao5. The allele of qGW8 were come from the male parent Jigeng83.We investigated 22 cloned yield related genes  which miGBSSII (granule-bound starch synthase II), SSIIIb (Soluble starch synthase IIIb) and SSIIc (Soluble starch synthase IIc) locus between Longdao24 and Nipponbare. The GBSSII in Longdao24 had only one SNP. The SSIIIb and SSIIc of Longdao24 had six SNPs plus one Indel and four SNPs, respectively. The other three SNPs were detected in SSIVa and SBE3 locus among Longdao24, Longdao5 and Jigeng83. The allele of GBSSII locus was same among Longdao24, Longdao5 and Jigeng83, but different from Nipponbare. The other four allele of SSIVa, SBE3, SSIIIb and SSIIc were come from the male parent Jigeng83.We investigated 22 cloned grain quality related genes  in LongdDTH2, OsLF, Hd17, Hd3a, Hd1, Hd2, Ehd3 and OsMADS56) from 39 heading date genes had 24 variations (19 SNPs and 5 Indels) among Longdao24, Longdao5 and Jigeng83 . Hd2, Ehd3 and OsMADS56 in Longdao24 all be identified with 3 non-synonymous SNPs. OsLF in Longdao24 all had one non-synonymous SNP and one Indel. Only one non-synonymous SNP was detected in Hd3a and Hd17. The SNPs and Indels in Hd3a, Hd2 and Hd1 locus were detected between Nipponbare with the other three varieties. The allele of OsLF were come from the female parent Longdao5. The other fourth allele of DTH2, Hd17, Ehd3 and OsMADS56 were come from the male parent Jigeng83.Eight  is expected to provide higher efficiency, reduced cost and shorter duration of the breeding scheme, compared with conventional methods. But most important complex traits in rice, including yield, quality and stress tolerance, are controlled by quantitative trait loci (QTLs). Isolating and characterizing genes involved in QTLs has been the key step for MAS. Whole genome re-sequencing technology has provided the possibility of genome wide variation analysis in rice breeding varieties. Longdao24 had been used widely as breeding parent, because of its high yield, good quality, early heading date and strong stress tolerance. Genome sequencing of Longdao24 with 47 × coverage effective depth and 95.08% 10 × coverage ratio was achieved. A total of 420,475 SNPs, 95,624 InDels, and 14,112 SVs were yielded from the Longdao24 genome. We identified 361,117 SNPs and 81,488 InDels between Longdao24 genome and Longdao5 genome. We also detected 428,908 SNPs and 97,209 InDels between Longdao24 genome and Jigeng83 genome. These different variations will be used for developing new molecular markers for genetics research and MAS.Gn1a, EP3, SCM2 and qGW8) having genetic variations in Longdao24. The first one is Gn1a, which controlled cytokinin accumulation in inflorescence meristems and increases the number of reproductive organs, resulting in enhanced grain yield [Gn1a allele from Longdao24 and Longdao5 was similar to Habataki, which has been reported having the increasing Gn1a allele. The second one is EP3, which controlled panicle architecture and enhance the grain yield in rice [EP3 allele from Longdao24 and Longdao5 was a novel allele. The third one is SCM2, which showed enhanced culm strength and increased spikelet number [SCM2 allele from Longdao24 and Longdao5 was same as Habataki, which has been reported having the increasing SCM2 allele. The last one is qGW8, which showed enhanced culm strength and increased spikelet number [qGW8 allele from Longdao24 and Longdao5 was same as HJX74, which has been reported having the increasing yield haplotype. So, the super high yield formed by the large half erect panicle and lodging resistance of Longdao24 would be caused by these four increasing alleles of high yield genes including Gn1a, EP3, SCM2 and qGW8.Developments of whole genome re-sequencing technology have provided new tools for discovering and tagging novel useful alleles for improving target traits and for manipulating those genes in rice breeding program through MAS. We identified four yield related genes  in Nipponbare was changed to Lys (AAC). The Hd1 allele in Longdao24, Longdao5 and Jigeng83 was also a novel allele. Four InDels and six SNPs was identified by comparison of the DNA sequences of Hd1 between the cultivars. Three long insertion and four nucleotide changes resulting in amino acid variation were identified in the first exon; and a 2-bp insertion and two nucleotide changes resulting in amino acid variation were identified in the second exon , we analyzed 39 cloned heading date related genes.  in rice . Compari3 allele . This Hdond exon . The Hd2th exons . These vJigeng83 . This SNJigeng83 . But onlLongdao5 . All theSSIVa, SBE3, SSIIIb and SSIIc between the parents of Longdao24. All four allele of SSIVa, SBE3, SSIIIb and SSIIc were come from the male parent Jigeng83. These genes represented starch synthases  and branching enzymes (SBE3). These enzymes are involved in amylopectin biosynthesis in rice endosperm. They had low to medium effects on variation in starch trait [Starch is the major component of rice grain, which is mainly composed of amylose and amylopectin. The starch synthesis pathway is an ideal system for examining the evolution of biochemical pathways. Over 20 genes involved in the rice starch synthesis pathway have been identified so far . We invech trait . So we n