Phaseolus vulgaris) landraces and six commercial cultivars collected from 19 provinces of Turkey were evaluated for seed antioxidant activity under four environments and two locations. Antioxidant activity was measured using ABTS radical scavenging capacity and mean antioxidant activity in common bean landraces was 20.03 µmol TE/g. Analysis of variance reflected that genotype by environment interaction was statistically non-significant and heritability analysis showed higher heritability of antioxidant activity. Variations in seed color were observed, and a higher antioxidant activity was present in seeds having colored seed as compared to those having white seeds. A negative correlation was found between white-colored seeds and antioxidant activity. A total of 7900 DArTseq markers were used to explore the population structure that grouped the studied germplasm into two sub-populations on the basis of their geographical origins and trolox equivalent antioxidant capacity contents. Mean linkage disequilibrium (LD) was 54%, and mean LD decay was 1.15 Mb. Mixed linear model i.e., the Q + K model demonstrated that four DArTseq markers had significant association (p < 0.01) for antioxidant activity. Three of these markers were present on chromosome Pv07, while the fourth marker was located on chromosome Pv03. Among the identified markers, DArT-3369938 marker showed maximum (14.61%) variation. A total of four putative candidate genes were predicted from sequences reflecting homology to identified DArTseq markers. This is a pioneering study involving the identification of association for antioxidant activity in common bean seeds. We envisage that this study will be very helpful for global common bean breeding community in order to develop cultivars with higher antioxidant activity.Antioxidants play an important role in animal and plant life owing to their involvement in complex metabolic and signaling mechanisms, hence uncovering the genetic basis associated with antioxidant activity is very important for the development of improved varieties. Here, a total of 182 common bean ( The world is facing a great threat of climate change, and extreme events throughout the world are being observed. Besides climate change, rapid growth in the world population is a global issue, and concerns have been raised to produce a sufficient quantity of food in terms of calories and nutrients . A reporPhaseolus vulgaris L.) is one of the most important proteinaceous crops of Latin America. The common bean serves a source of food for more than 300 million people [www.tarimorman.gov.tr). According to Ozturk et al. [The common bean  were used as plant material in this study. Germplasm was collected from 19 geographical regions of Turkey  and mainField experiments were conducted during 2017 and 2018 at two experimental sites—BAIBU  and Cumhuriyet University Sivas . The experiment was laid out according to augmented block design, which has been found to be more effective, precise, time-saving, and trustworthy compared to other experimental designs . In thishttp://www.diversityarrays.com/). DArTseq analysis was carried out as previously described [DNA was extracted from two-week-old seedlings according to CTAB protocol of Doyle and Doyle  with somescribed .Antioxidant activity was measured using ABTS radical scavenging capacity. For the investigation of antioxidant activity, TEAC extract was firstly prepared to calculate trolox equivalent antioxidant capacity (TEAC). For this purpose, dry samples were extracted with acetone, water, and acetic acid solution (70/29.5/0.5), respectively for ten days. For TEAC analysis, 7 mM ABTS  was mixed with 2.45 mM potassium sulfate for 12–16 h as suggested by Saracoglu et al. . This sowww.xlstat.com).An online software for augmented block design developed by Rathore et al.  was usedhttps://tassel.bitbucket.io) software. Linkage disequilibrium (LD) was estimated for all DArTseq markers with r2 based on allele frequencies [p < 0.001) were used. The threshold for LD decay was considered below r2 = 0.1. Results of association analysis reflected that p value (marker) shows whether a marker is associated with a trait, and R2 (marker) defines the proportion of phenotypic variation accounted for by a significant marker [p = 0.01 thresholds were taken significantly associated with antioxidant activity. Statistically significant markers for antioxidant activity were visualized through the Manhattan plot in R 3.4.1 statistical software (http://www.r-project.org/) by using qq-man R Package [p-value distributions  through TASSEL 5.2.50 (https://tassel.bitbucket.io) software.Population structure was carried as described by Nadeem et al. . A mixedquencies ,49. Pairt marker . Both Bo Package . Quantilhttp://phytozome.jgi.doe.gov/pz/portal.html), the legume information system (LIS: https://legumeinfo.org/), and the National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/) databases to detect putative genes homologous to these sequences.Sequences of DArTseq markers associated with the antioxidant activity were used to BLAST-search against common bean genome in Phytozome V.12.1 , while GEI was statistically non-significant for antioxidants activity in this study. Heritability analysis revealed the existence of higher heritability (0.92)  was performed for four environments to understand the effects of genotype, environment, and the interaction of both (GEI) for antioxidant activity in Turkish common bean germplasm . Genotypy (0.92) . Antioxiy (0.92) . At BAIBy (0.92) . At the y (0.92) . Mean TEy (0.92) . Frequeny (0.92) . r2 was 0.47, and 54% was the mean significant LD  containing 117 landraces (59.57%) and population B (green) 71 landraces (37.76%) . The 790icant LD . Mean LDe of 0.5 . Linkagey on LG6 .2) reflected by each marker ranged between 0.094% to 0.156% for DArT-3371498 and DArT-3369938, respectively. The p-value of each marker reflecting significant level of association between marker and antioxidant activity is presented in the form of Manhattan plots .MLM (Q + K) model was applied to investigate marker-trait association for antioxidant activity in studied Turkish common bean germplasm. MLM based on Q + K model showed that out of 7900 DArTseq markers, only four markers  reflected statistically significant association for antioxidant activity . The phean plots . Manhattan plots  reflecteSequences of all four identified linked markers were used as queries to BLAST-search against Phytozome (V.12.1), LIS, and NCBI databases to detect predicted genes homologous to these sequences. A zinc-finger protein family gene (Phvul.009G180200.2) located on Ch Pv07 was retrieved in BLAST using DArT-3369938 marker as a querry . The seaIn this study, a total of 182 Turkish common bean landraces and six commercial cultivars were used to identify marker trait association for antioxidant activity. Results of phenotypic characterization for antioxidant activity showed a great level of diversity ranging from 2.15 to 60.48 with the mean TEAC of 20.03 µmol TE/g . The obsWe also observed variations in seed color and recorded that white color (58% of the accessions) was prevalent in our selected population. Seed color in the common bean is associated with bioactive compounds, such as anthocyanin and tannins which are found in higher quantity in colored seeds as compared to white colored seeds . In thisA total of 7900 markers were employed in this study to investigate population structure and marker-trait association for antioxidant activity. The number of mapped markers ranged from 931 (Chr. 2) to 591 (Chr. 6) . The mear2 was 0.47 while the mean significant LD was 54%  decreases below 0.1 or half of D’ strength (D’ = 0.5) based on curve of nonlinear logarithmic trend line [r2 ≥ 0.2) are used for more precise LD calculation. Our results are in a disagreement with the findings of Campa et al. [r2 plotted vs. p-value between pairs of DArTseq markers the studied germplasm showed a relatively bigger LD decay on LG6  constitute large protein families and have an important role in various plant developmental stages and to biotic and abiotic stress [Triticum aestivum, and stated that these TaZFPs have positive response to high light (44/53), H2O2 (37/53), drought (37/53), and flooding (31/53). Zang et al. [Located only 100 kb upstream of DArT-3369938, on Pv09, c stress . Hichri c stress  stated tc stress  found thc stress  exploredg et al.  confirmeg et al.  identifiPhvul.003G239700) encoding UDP-Glycosyltransferase superfamily protein; a multifunctional protein family in plants. Glycosylation is one of the final steps involved in the biosynthesis of triterpenoid for the production of various plant defensive compounds like phenolic, glucosinolates, salicylates, and anthocyanins [UGT85A5 in tobacco resulted in enhanced salt stress tolerance. Rehman et al. [The BLAST search against DArT-8668387 retrieved a gene . This gene is linked with folate metabolism which plays a positive effects on oxidative stress by its protecting action against cell death and increased folic concentrations stabilize MTHFR enzyme [Phvul.007G171700.1 encodes for PPR containing plant protein (RNA-binding proteins) having a key role in post-transcription via RNA editing, cleavage, splicing, stability, or translation in plastids or mitochondria [R enzyme . Earlierchondria . Zsigmonchondria  revealedchondria  aimed toAs is obvious from the previously discussed evidence, our diverse association panel reflected a wide range of antioxidant activity in common bean seeds. These genetic variations can be utilized for various common bean improvement programs. The role of antioxidants in human health has been universally accepted, and earlier studies revealed that higher antioxidant levels in food can prevent various disorders ,21,22. GA wide range of phenotypic variations were observed for antioxidant activity in Turkish common bean germplasm. Heritability analysis revealed that antioxidant activity is controlled by genotypic effect. A negative correlation was observed between antioxidant activity and white seeds. This is a pioneer study revealing the marker trait association for antioxidant activity in Turkish common bean seeds. A total of four DArTseq markers showed statistically significant association for seed antioxidant activity, and these markers can be used in the future for the development of common bean cultivars with better antioxidant contents.


FADS) gene cluster in chromosome 11 is a very important determinant of plasma PUFA, and that the prevalence of the single nucleotide polymorphisms (SNPs) varies greatly between populations and may constitute a bias in meta-analyses. Previous genome-wide association studies (GWAS) have been carried out in other populations and none of them have investigated sex and Mediterranean dietary pattern interactions at the genome-wide level. Our aims were to undertake a GWAS to discover the genes most associated with serum PUFA concentrations  in a scarcely studied Mediterranean population with metabolic syndrome, and to explore sex and adherence to Mediterranean diet (MedDiet) interactions at the genome-wide level. Serum PUFA were determined by NMR spectroscopy. We found strong robust associations between various SNPs in the FADS cluster and omega-3 concentrations . We also detected a genome-wide significant association with other genes in chromosome 11: MYRF (myelin regulatory factor)-rs174535, p = 1.49 × 10−12; TMEM258 (transmembrane protein 258)-rs102275, p = 2.43 × 10−12; FEN1 (flap structure-specific endonuclease 1)-rs174538, p = 1.96 × 10−11). Similar genome-wide statistically significant results were found for docosahexaenoic fatty acid (DHA). However, no such associations were detected for omega-6 PUFAs or linoleic acid (LA). For total PUFA, we observed a consistent gene*sex interaction with the DNTTIP2 -rs3747965 p = 1.36 × 10−8. For adherence to MedDiet, we obtained a relevant interaction with the ME1  gene (a gene strongly regulated by fat) in determining serum omega-3. The top-ranked SNP for this interaction was ME1-rs3798890 (p = 2.15 × 10−7). In the regional-wide association study, specifically focused on the FADS1/FASD2/FADS3 and ELOVL (fatty acid elongase) 2/ELOVL 5 regions, we detected several statistically significant associations at p < 0.05. In conclusion, our results confirm a robust role of the FADS cluster on serum PUFA in this population, but the associations vary depending on the PUFA. Moreover, the detection of some sex and diet interactions underlines the need for these associations/interactions to be studied in all specific populations so as to better understand the complex metabolism of PUFA.Many early studies presented beneficial effects of polyunsaturated fatty acids (PUFA) on cardiovascular risk factors and disease. However, results from recent meta-analyses indicate that this effect would be very low or nil. One of the factors that may contribute to the inconsistency of the results is that, in most studies, genetic factors have not been taken into consideration. It is known that fatty acid desaturase ( FADS2 (fatty acid desaturase 2) gene and the Delta-5 desaturase, encoded by the FADS1 (fatty acid desaturase 1) gene and elongation steps, as well as oxidation. Among the seven elongases, ELOVL2 (elongation of very long chain fatty acids 2) and ELOVL5 elongation of very long chain fatty acids 5) are PUFA-specific [Polyunsaturated fatty acids (PUFA) are fatty acids having more than one double bond on their carbon chain. Depending on the position of the first double bond close to the methyl end, they are classified as omega-6 or omega-3. These 2 families each encompass fatty acids with varying carbon chain length and degree of unsaturation. Supplemental specific . Docosahspecific ,8,9. Vegspecific ,8,9.Initial studies, both in humans and in animal models, showed several protective effects  of PUFA intake on cardiovascular risk ,14,15,16Abdelhamid et al.  meta-anaNevertheless, recent studies suggest that omega-6 are not as harmful as initially thought ,53. TherFADS genetic polymorphism in determining the effects of dietary and pharmacological PUFA on several cardiovascular risk factors and outcomes, have been investigated by several studies [FADS2 genotype can predict the beneficial effects of the omega-3. Another issue in these meta-analyses is the lack of focus on potential sex-specific differences, as well as overall dietary pattern modulating PUFA interventions. Regarding sex, several human studies as well as research in animal models [Nonetheless, it has been claimed that the lack of conclusive evidence obtained in the meta-analyses may be due to several biases, including not only the poor quality of some of the meta-analyzed trials, but also other relevant factors such as the unmeasured role of genetic polymorphisms influencing the effect of dietary PUFA . The pot studies ,63,64,65l models ,71,72,73l models ,76,77,78FADS1/2 locus in chromosome 11, and some fatty acids observed in all populations analyzed [TRIM58, MCM6, NTAN1/PDXDC1, PKD2L1, PCOLCE2, AGPAT4, HBS1L/MYB, LPCAT3) have also been described [ELOVL2 single nucleotide polymorphisms (SNPs), and the NTAN1/PDXDC1 for the corresponding PUFA). Moreover, in another GWAS undertaken in Chinese participants, followed by a trans-ethnic meta-analysis including European ancestry participants [All these factors should be considered in any new RCTs to be conducted in the field, as well as in the corresponding meta-analyses. In terms of the genetic factors to be analyzed in novel studies related to PUFA, although several genome-wide association studies (GWASs) for the different fatty acid concentrations in numerous tissues have been carried out in several populations, mainly of European ancestry ,84,85,86analyzed ,84,85,86escribed ,82,84,86icipants ,88,89. Iicipants ,81,82,83icipants , no GWASicipants .FADS1/FADS2 cluster, previously identified in initial studies [FADS3 is the enigmatic third member of the FADS cluster [FADS genes greatly varies among populations [FADS1/FASD2 block in southern Europeans [FADS and ELOVL candidate gene polymorphisms and PUFA concentrations [In general, GWASs for PUFA ,84,85,86 studies ,92,93,94ulations ,96,97. Tulations ,96,97,98uropeans . In Spaiuropeans  is stilluropeans ,101. Alttrations ,105,106,Therefore, the potential genetic differences in the Mediterranean population, the urgent need for greater diversity in genetic analysis, and the fact that in previous GWASs, sex-specific differences as well as dietary interactions have scarcely been analyzed, prompted us to carry out this study with the following aims: (1) To investigate, at the genome-wide level, the genetic variants most associated with serum PUFA concentrations  in a Mediterranean population with metabolic syndrome; (2) To analyze, at the region-wide (RWAS) level, the associations between the SNPs in the selected candidate regions and serum PUFA concentrations in this population; and (3) To explore the modulating influence of sex and adherence to Mediterranean diet (as a whole dietary pattern) on these associations.http://predimedplus.com/. This trial was registered at https://doi.org/10.1186/ISRCTN89898870. Participants in the Valencia field center were recruited from several primary care health facilities. These participants were community-dwelling adults  with body-mass index (BMI) ranging from 27 to 40 kg/m2 and having metabolic syndrome [We analyzed participants in the PREDIMED Plus-Valencia study . The Unisyndrome . In thisIn the baseline examination, we assessed socio-demographic variables, clinical and cardiovascular risk factors, and lifestyle variables by validated questionnaires as previously reported . AnthropBlood samples were collected after a 12-h overnight fast. Fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride concentrations were measured as previously described . LeisureSerum was obtained from fasting blood samples as previously detailed . Serum fp < 1.0 × 10−4) were removed. A total of 621,723 SNPs that passed the quality filter remained for further analysis.We isolated genomic DNA from the buffy coat samples with the MagNaPure LC DNA Isolation kit (ROCHE Diagnostics). We measured the concentration and the quality of the isolated DNA by the PicoGreen instrument . After DNA-quality control, high-density genotyping was performed in samples passing this control using the Infinium OmniExpress-24 v1.2 BeadChip genotyping array  at the University of Valencia, Valencia. Genotyping was undertaken according to the manufacturer’s protocol with appropriate quality standards. The Infinium OmniExpress v1.2 BeadChip captures 713,599 markers. Allele detection and genotype calling were performed in the Genome Studio genotyping module . Data cleaning was performed using standard analysis pipelines implemented in the Python programing language using the Numpy library modules combined with PLINK ,119. FroStudent t-tests and ANOVA tests were applied to compare crude means of continuous variables. Triglyceride and fatty acid concentrations (in mmol/L) were log-transformed for the statistical analyses. Fatty acids, expressed as % , reached a normal distribution, and were not log transformed. Chi-square tests were used to compare proportions. Finally, we analyzed the omega-6/omega-3 ratio and we log transformed this variable to achieve normality for statistical testing. We analyzed the associations using both crude models and adjusted multivariate regression models  including potential confounders.For the GWAS, at the genome-wide level, we tested the association between all the SNPs in the array  and serum concentrations (expressed as %) of the different PUFA. We fitted separate models for omega-3 PUFA, DHA, omega-6 PUFA, LA, and total PUFA. Statistical models were sequentially adjusted by sex, age, diabetes, and other covariates including BMI, smoking, medications, physical activity, and adherence to Mediterranean diet (17-item score). Additional adjustment did not change the statistical level of significance. Therefore, for the sake of simplicity, and to compare our results with the previous GWAS carried out in different populations ,84,85,862) were also estimated. To test gene*sex and gene*diet interactions at the genome-wide level, we used the PLINK GxE tool and showed the statistical significance on the interaction terms, as well as the regression coefficients for each stratum. Stratified analyses of interest were additionally carried out when indicated. We used the conventional threshold of p < 5 × 10−8 for genome-wide statistical significance. Likewise, according to the conventional GWAS rules, SNPs with p-values below 1 × 10−5 were also considered suggestive of genome-wide significance. As these thresholds are very conservative for a small sample size, in selected analyses, SNPs with p-values below 5 × 10−5 are also shown in the tables so that they may be compared with other studies that have analyzed similar associations and may be included in meta-analyses. SNPs were rank-ordered according to the minimum p-value in the genetic models.For GWAS, genetic association analyses were performed using PLINK v1.9 ,119. Addp-values [We used R Qqman R library  and Haplp-values  was perfp < 5 × 10−8 for genome-wide statistical significance), we carried out additional RWASs. In these analyses, we previously selected a candidate region of the genome containing the most relevant genes previously associated with PUFA and analyzed the associations with all the SNPs present in the selected region. Two candidate regions were selected [FASD1/FASD2/FASD3 cluster [ELOVL2 and ELOVL5 genes [https://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId, and provided the target gene-ID. The dbSNP-gene type file is used as input argument in the *.bat file for running the Python script. Finally, the Python script produces the corresponding PLINK-formatted couple *.ped and *.map files of genotypes for statistical testing. For the selected regions, we carried out unadjusted and multivariate adjusted analyses determining the serum PUFA, as previously detailed for the GWAS approach. Models adjusted for sex, age, and diabetes were presented. We also used PLINK v.1.9 [p-value <0.05 as statistically significant for the RWAS associations.In addition to the GWAS approach, to explore potential associated loci (candidates based on previous knowledge) which might fail to be identified at the GWAS level due to the strict threshold for the whole genome approach  NK v.1.9 ,119 for p < 0.001), respectively. BMI did not differ significantly in men and women. The women analyzed in this study were all post-menopausal women.p = 0.182 for sex differences) and omega-6 PUFA . Likewise, the percentage of DHA and LA did not differ per sex. We also calculated the omega-6 to omega-3 ratio (no units for the ratio). This ratio was 7.98 ± 0.07 for the whole population and no statistically significant differences were detected between men and women (p = 0.254).Total PUFA was 35.9 ± % of the total fatty acids and did not differ between men and women. Similar percentages of omega-3 PUFA  between men (5.9%) and women (16.7%). There were no participants taking fish oil supplements. These supplements mainly consisted of calcium, folic acid, vitamin D, and some multivitamins (currently we do not have detailed information about their composition).In these subjects with metabolic syndrome, consumption of antihypertensive drugs was high (78.4%). Consumption of lipid-lowering drugs (64.3%), consisting of mainly statins, was also high. No significant differences by sex were detected regarding for antihypertensive, lipid-lowering, or antidiabetic medications. In addition to the main medications presented in p = 0.145). We defined 2 groups of adherence to the Mediterranean diet as either “Low” adherence (0–8 points), or “High” adherence (9–17 points). This cut-off point has been previously used by our group for genome-wide-diet interaction studies [Mean adherence to the Mediterranean diet was 8.1 ± 0.1 point in the 17-item score. No statistically significant differences were detected between men and women , using both a GWASs approach and a RWASs approach as detailed in Methods.p < 5 × 10−8), replicating previous findings in other populations. p-value (−log10 p) of each SNP analyzed as well as indicating both thresholds for significance . Supplemental FADS1-rs174547 with p = 7.02 × 10−15 in the unadjusted model  in the Illumina Human OmniExpress array and the 6 PUFA determined. For omega-3 PUFA, we obtained several very strong associations at the genome-wide statistical level of significance . Also, at the genome-wide level of significance, we detected other genes, close to the FADS1/FADS2 cluster. These genes and SNPs being: MYRF (myelin regulatory factor)-rs174535, p = 1.49 × 10−12; TMEM258 (transmembrane protein 258)-rs102275, p = 2.43 × 10−12; FEN1 (flap structure-specific endonuclease 1)-rs174538, p = 1.96 × 10−11; MYRF-rs108499, p = 4.21 × 10−11; MYRF-rs174532, p = 1.20 × 10−08. Previous studies also have detected these SNPs as top-ranked in different populations . The SNPs near the FADS1/FADS2 locus, were also highly associated with the FADS1/FASD2 SNPs. MYRF-rs174532, and the FADS2-rs2727270, the other SNPs were highly correlated among them and with the lead SNP (FADS1-rs174547).FADS1-rs174547 polymorphism. In this Mediterranean population, the T allele was the major allele . Minor allele carriers presented significantly lower serum omega-3 PUFA concentrations . This strong genetic association did not vary significantly even after multivariate association , smoking, physical activity, and adherence to the Mediterranean diet. A previous GWAS in the Framingham study also showed a slight effect of the dietary adjustments in the SNP-PUFA associations [FADS1-rs174547 polymorphism and serum omega-3 concentrations (p = 2.90 × 10−13).ciations . Additiop-value of GWAS significance (p < 5 × 10−5). Among these genes, we would like to highlight the signal at the AHI1 (Jouberin) gene, located on chromosome 6, close to the MYB  gene and the HBS1L  gene, both of which have previously been reported in the Framingham study as new genes associated with PUFA [p-value of GWAS significance, were found for the ELOVL candidate genes on chromosome 6 for omega-3 PUFA in our Mediterranean population.In addition to the SNPs found at the genome-wide level of significance, we also detected several SNPs at the suggestive ith PUFA . Howeverp = 1.13 × 10−8). The MAF for this SNP was 0.193. In addition to this hit, we found 7 other SNPs  reaching p-values < 1 × 10−5 for the interaction terms with sex. Future studies should investigate these loci in more detail.We next explored gene*sex interaction in determining serum omega-3 PUFA concentrations at the genome-wide level and only found a statistically significant interaction at the genome-wide level of statistical significance between the rs17097464-intergenic at chromosome 12 and sex . ME1  gene significantly interacted (at p < 1 × 10−5) with adherence to Mediterranean diet with p-values ranging from 2.15 × 10−7 to 9.4 × 10−6 . ME1 gene is located on chromosome 6 and it is a plausible candidate for gene-diet interactions, taking into account that this gene is involved in fatty acids synthesis in the liver [ME1-rs3798890 (p = 2.15 × 10−7) in the genome-wide interaction analysis with Mediterranean diet. For this SNP as well as for the other SNPs top-ranked in the region, FADS cluster [13 SNPs in the he liver ,124 and he liver . Figure  cluster , where dp-values for the top-ranked SNPs in the models..FADS1 gene  were the hits, having p-values of 1.28 × 10−11, 1.50 × 10−11, and 1.66 × 10−11). These SNPs were in very high LD. Likewise, 4 SNPs in the FADS2 with p-values ranging from 2.87 × 10−11 to 3.47 × 10−10; 1SNP in FEN1-rs174538 (p = 3.47 × 10−10), 1 SNP in TMEM258 (p = 8.79 × 10−10), and 2 SNPs in MYRF, with p-values of 3.89 × 10−10 and 1.22 × 10−9, were statistically significant at the genome-wide level. Despite their significance, the genetic effect for DHA was lower than that observed for total omega-3. Here, the top-ranked SNP (the FADS1), only explained the 10.6% of the variability, versus a 13.4%, explained by the same SNP for total omega-3. This indicates that here are other omega-3 fatty acids (not determined in this project) for which the genetic association effect should be higher.−7. Likewise, consistent gene-sex interactions were found at p < 1 × 10−5 with several genes also detected at this level for the analysis on omega-3 PUFA. Among these genes, similar interaction results at (p < 1 × 10−5) have been found with LOC105372018-rs8091414; NHEJ1 (non-homologous end joining factor 1)-rs1402508 and the intergenic-rs11081343.In the exploratory analysis of the genome-wide interaction by sex in determining serum DHA concentrations, we found as top-ranked the same SNP that reached statistical significance at the GWAS level for the omega-3 PUFA. In this case, the interaction between the intergenic-rs17097464 on chromosome 12 and sex was 1.31 × 10ME1 gene in determining serum DHA concentrations. The top-ranked SNP for gene*Mediterranean diet interactions in the ME1 was the rs1180204-ME1 at p = 5.34 × 10−7.Regarding the exploratory analysis of genome-wide interactions with adherence to Mediterranean diet, we also detected several interactions with the p < 5 × 10−5. FADS genes, but in other genes. After adjustment for age, sex, and diabetes, the top-ranked SNP  is located in the ISG20 (interferon stimulated exonuclease gene 20) gene, followed by an intergenic SNP (rs2551402) in chromosome 18 and another SNP  located in the BCL2 (BCL2 apoptosis regulator) gene, also in chromosome 18.DNTTIP2  gene located at chromosome 1. The top ranked SNP was the DNTTIP2-rs3747965 at p = 1.07 × 10−7. Other SNPs in the DNTTIP2 gene also achieved statistical significance at p < 5 × 10−5. Regarding, the exploratory analysis of gene*Mediterranean diet interactions on serum omega-6 concentrations, the top-ranked SNP was the LOC105376941-rs977905 at p = 1.17 × 10−6.When we analyzed gene–sex interactions at the genome-wide level on omega-6 PUFA concentrations, we found no interaction at the GWAS significance level. However, the most significant interaction was obtained with the C22orf42 and the DAB1 (DAB adaptor protein 1) presented association at the GWAS level. This gene, involved in brain signaling in the endothelium, is an integrative signal essential for neuro-glia-vascular communication [−10 for rs5994479 and 5.69 × 10−10 for DAB1-rs11207162. Although the variability explained by the top-ranked SNP is greater for LA (9.5%) than for total omega-6, it is still lower than for DHA. It is also striking that none of the candidate genes in the FADS or ELOVL genes are among the top-ranked.nication . Supplemp < 1 × 10−5 with SNPs in the DNTTIP2 gene. This is highly consistent with that observed for total omega-6. Likewise, the top ranked gene–sex interaction for serum LA concentrations was observed with the gene DNTTIP2-rs3747965 at 1.04 × 10−7 level. For the genome-wide interactions with adherence to Mediterranean diet on serum LA, the top-ranked SNP was LOC105374410-rs1906292 at p = 8.88 × 10−7. This SNP was also the hit for omega-6 PUFA.In the genome-wide interaction study to analyze the interactions with sex, we obtained several significant associations at the p < 5 × 10−5 in the sex-, age-, and diabetes-adjusted model. None of them reached the level of GWAS significance and the p-values were higher than 3.12 × 10−6. This may reflect the greater heterogeneity of this variable. Among the most significant SNPs are those already detected as top-ranked for omega-6 or LA, as omega-6 are the most abundant PUFA. These SNPs were: Intergenic-rs7047109 (3.12 × 10−6), DNAH11 -rs10256021 (p = 4.8 × 10−6); ISG20-rs4491485 (p = 7.18 × 10−6) and intergenic-rs252181 (p = 9.69 × 10−6).In the GWAS for total PUFA, we only detected 4 statistically significant SNPs at DNTTIP2-rs-3747965, already obtained in the previous analyses for omega-6 and for LA, but this time, reaching the level of statistical significance of GWAS (p = 1.36 × 10−8). Other SNPs in the DNTTIP2 gene and nearby, also reached the genome-level of significance. DNTTIP2-rs-3747965.Interestingly, in the study of genome-wide interaction with sex, the top-ranked SNP determining the concentrations of total PUFA was the According to this interaction, the minor allele of this SNP (MAF: 0.345) was associated with reduced PUFA in females (beta: −0.634) and with increased PUFA in males (beta: 1.158).FADS1/2 cluster) showed statistically significant associations with the omega-6/omega-3 ratio at the GWAS level. The top-ranked SNP was the FADS1-rs174547 T > C in both models. Specifically, in the multivariate adjusted model, the minor allele of this SNP was associated with a higher omega-6/omega-3 ratio .We also explored at the GWAS level the SNPs associated with the omega-6 to omega-3 ratio in this population. FADS1-rs174547 polymorphism. Homozygous subjects for the minor allele (CC) presented significantly higher ratio. This strong genetic association changed little even after multivariate adjustment , smoking, physical activity, and adherence to the Mediterranean diet. Additional adjustment of the model containing all of these variables for more medications , did not substantially change the statistical significance of the association (p = 1.71 × 10−13). Furthermore, we analyzed the interaction between the adherence to the Mediterranean diet and the SNPs at the genome-wide level in determining the serum omega-6/omega-3 ratio (ME1 gene. More studies are needed to replicate and to characterize this interaction.-3 ratio . We obtaFADS cluster) and in region B (ELOVL2/ELOVL5).To explore potential associated loci which might fail to be identified due to the strict threshold for GWAS associations, we carried out a RWAS investigating 2 regions previously reported to be genetic determinants of the SNPs analyzed. FADS1/FADS2/FADS3 region. We obtained statistically significant associations at p < 0.05 with 15 SNPs , including SNPs detected at the GWAS level and other SNPs detected at the nominal level of significance. In addition to the FADS1 and FADS2 SNPs, we detected a significant association (p = 0.012) with the FADS3-rs174450 SNP and with the FADS3-rs174455 (p = 0.027). p < 0.05. However, no statistically significant association even at this level with FADS3 SNPs was observed.FADS1/FADS2/FADS3 region was poorly associated with their serum levels in this population even after considering the nominal p-value. FADS2-rs174618 and FADS2-rs174611, reached the significance at p = 0.037 and p = 0.047, respectively. However, for LA concentrations, 11 SNPs in the analyzed FADS1/FADS2/FADS3 region, reached statistical significance at p < 0.05 (FADS2-rs174611 (p = 0.0005).For omega-6 PUFA, the p < 0.05 . The mosFADS1/FADS2/FADS3 region revealed 6 SNPs  statistically associated with serum levels at p < 0.05.For total PUFA, the RWAS at the ELOVL2 and ELOVL5, we did not detect any statistically significant association with serum omega-3 PUFA  on total serum omega-3 PUFA [p < 5 × 10−8 for several SNPs in/near the FASD1 and FASD2 genes. The top-ranked SNP was the FADS1-rs174547 T > C. This SNP was strongly associated with serum omega-3 concentrations even after additional adjustment for covariates . Further adjustments for medications, diet, and other lifestyle variables did not substantially change the statistical significance of the association. Likewise, some previous GWAS [FADS1-rs174547 T > C top-ranked SNP  was associated with decreased serum omega-3 PUFA concentrations. This is consistent with functional studies showing a functional association between the FADS1-rs174547 and delta-5 desaturase activity [FADS1-rs174547 SNP in FADS1 C allele carriers [In this first GWAS carried out in a Mediterranean population composed of subjects with metabolic syndrome in Spain, we confirmed the large contribution of the a-3 PUFA . Despiteous GWAS ,83,96 onactivity . Moreoveactivity  has repocarriers . The faccarriers ,86,93,98FADS1 and FADS2 in agreement with the previous GWAS analyzing DHA concentrations, with FADS1-rs174547 SNP also being the top-ranked.In our GWAS, in addition to the association with serum omega-3 PUFA, we have studied the association with serum concentrations of DHA, an omega-3 fatty acid derived primarily from fish. For DHA we have also obtained statistically significant associations at the GWAS level with several SNPs in the genes FADS1/FADS2 cluster SNPs at the GWAS level with total serum omega-6 PUFA or LA (omega-6). This result differs from other GWAS studies [FADS1/FADS2 SNPs with serum omega-6 may be due to several factors. One of them may be related to the age of our population. We have studied older subjects (mean age 65 years old), and it has been reported that aging decreases biosynthetic activity regulated by the FADS1 polymorphisms [FADS1/FADS2/FADS3 desaturases is the ethnic background [FADS1/FADS2/FADS3 cluster. This is similar to our results. However, in the INCHIANTI study, the associations were mainly observed for some omega-6 PUFA [FADS1, in determining plasma arachidonic acid (AA) concentrations (p = 5.95 × 10−46). In our study, we did not analyze the association with AA, because the NMR platform used for measuring serum fatty acids [However, we did not detect statistically significant associations between the  studies ,84,85,86orphisms , so poteckground . Our Spackground ,84,85,86ckground ,83,96 asckground . Nowadayckground ,101. Amockground , focuseda-6 PUFA . Thus, tty acids ,115,116 ty acids , the autty acids . It has ty acids . In genety acids . MoreoveAnother factor that may be related to some differences among studies when comparing GWASs is the medication use in the different populations. In previous GWAS ,84,85,86FADS1/FADS2/FADS3 [ELOVL2/ELOVL5 [FADS1/FADS2/FADS3 SNPs and LA concentrations also in this population, but of a lower magnitude than the associations found for DHA or total omega-3 PUFA. Regarding the ELOVL region [ELOVL2 SNPs presented significant associations at p < 0.05 with total serum omega-6 and LA.In addition to these limitations, sample size is another limitation for our study. Considering the strict threshold for statistically significant results at the GWAs level, some potential associations with relevant loci, might fail to be identified. Therefore, we conducted a RWAS, focused on previously identified regions considered as relevant for the genetic associations with PUFA ,94. The L region ,82,83,84FADS1/FASD2 genes has been adapting to changes in the diet, with the aim of achieving the highest concentrations of long chain PUFA [These results emphasize the need to carry out specific studies to know which genetic variants are most relevant in each population, because the results of the most significant associations in a population or in a meta-analysis are not always those that are actually observed in a different population . In the ain PUFA . Severalain PUFA ,96,97,98ain PUFA ,96,97,98ME1 gene in determining serum omega-3 concentrations. Several SNPs in this gene, having different MAF, reached statistical significance for the interaction term at p < 5 × 10−5. According to this interaction, the association between the ME1 genotypes and serum omega-3 depends on the adherence to the Mediterranean diet pattern. Moreover, when we further analyzed the Mediterranean diet interaction at the GWAS level in determining the serum omega-6/omega-3 ratio, we also obtained the ME1 as the top-ranked gene, replicating the results for omega-3 PUFA. This is the first time that this interaction is reported in humans and subsequent studies are needed to replicate and characterize the modulation. However, existing literature initially supports a potential mechanism behind this interaction. The ME1 gene encodes a cytosolic, NADP-dependent enzyme that produces NADPH for fatty acid biosynthesis [ME1 gene has been implicated in PUFA biosynthesis [ME1 gene expression is strongly regulated by the dietary fat content [ME1 polymorphisms on serum omega-3 PUFA (as well as on the omega-6/omega-3 ratio), contributing to explain the statistical signal. Our findings are exploratory and additional replication studies are needed to confirm this interaction.Some GWAS have considered dietary intake in adjustments carried out in the statistical analysis, indicating a modest influence . Howeverynthesis . Likewisynthesis ,137,138. content ,139. FatDNTTIP2 gene (rs3747965) in determining serum total PUFA (mainly omega-6 PUFA), at the genome-wide level of significance (p < 5 × 10−8). Again, this is the first time that this statistical interaction has been reported, and more studies are needed to replicate and characterize this interaction. The DNTTIP2 gene has the alternative name of “estrogen receptor-binding protein gene”, as well as the short name: “TdIF2” . The TdIF2/estrogen receptor α-binding protein (ERBP) is a multifunctional nucleolar protein. It promotes ribosomal RNA transcription by still partially unknown mechanisms [DNTTIP2 is scarce, but in a recent GWAS, the DNTTIP2 gene has been linked to ischemic stroke [Likewise, genetic modulation by sex may be another relevant factor explaining some differences in the effect of the genetics of PUFA. Some PUFA are specifically required for reproduction . It has chanisms . Thus, tchanisms , explainc stroke , an outcFADS1/FADS2 locus and omega-3 PUFA  in this population. The associations of this locus with omega-6 were much weaker, but also present at the RWAS level. Likewise, we have observed a strong genetic influence of the FADS1/FADS2 cluster in determining the omega-6/omega-3 ratio, at the GWAS level, in this population. We have also detected some new interactions with sex (on omega-6 PUFA) and adherence to the Mediterranean diet (on omega-3 PUFA), which will have to be replicated and functionally characterized in other studies. This underlines the need for these variables to be studied in all specific populations, so as to better understand the complex metabolism of PUFA and their repercussions on health.In conclusion, our results allowed us to define the genetic variants most associated with serum PUFA concentrations in a Mediterranean population with metabolic syndrome and to confirm the strong association between the


Haplotype information is essential for many genetic and genomic analyses, including genotype-phenotype associations in human, animals and plants. Haplotype assembly is a method for reconstructing haplotypes from DNA sequencing reads. By the advent of new sequencing technologies, new algorithms are needed to ensure long and accurate haplotypes. While a few linked-read haplotype assembly algorithms are available for diploid genomes, to the best of our knowledge, no algorithms have yet been proposed for polyploids specifically exploiting linked reads.The first haplotyping algorithm designed for linked reads generated from a polyploid genome is presented, built on a typical short-read haplotyping method, SDhaP. Using the input aligned reads and called variants, the haplotype-relevant information is extracted. Next, reads with the same barcodes are combined to produce molecule-specific fragments. Then, these fragments are clustered into strongly connected components which are then used as input of a haplotype assembly core in order to estimate accurate and long haplotypes.Hap10 is a novel algorithm for haplotype assembly of polyploid genomes using linked reads. The performance of the algorithms is evaluated in a number of simulation scenarios and its applicability is demonstrated on a real dataset of sweet potato. Polyploids are organisms that possess three or more copies of each chromosome. There are numerous cases of polyploidy in the animal kingdom, including fish, amphibians and reptiles . In planSeveral algorithms for polyploid haplotyping have been developed in recent years for diploid and polyploid haplotyping , 6. In tThe above-mentioned algorithms are developed solely for short reads generated by Illumina DNA sequencing machines. These produce reads that have a low sequencing error rate (~ 0.1%) but do not provide long-range information, which is key in reconstruction of long haplotypes. Over the last years, a novel category of sequencing technology characterized by long-read sequencing was developed and commercialized by Pacific Biosciences and Oxford Nanopore . HoweverRecently, 10X Genomics developed a linked-read sequencing library preparation strategy, commercialized through their Chromium platform, as a complementary technology to Illumina devices. This platform has the potential to provide long fragments at both low error rate and cost. In brief, the input genomic DNA, as little as 1 ng, is sheared into molecules of ~ 10–100 kbp. Subsequently, these molecules are isolated, partitioned into fragments, tagged with a unique 16 bp barcode, and amplified on beads in an emulsion. The resulting material is then sequenced by normal Illumina paired-end technology, which results in high-throughput reads that contain long-range genomic information through these barcodes . The 10XHowever, no polyploid haplotyping algorithm is available at this moment, precluding the application of 10X-based haplotyping to a number of commercial crops and animals. Current polyploid haplotyping algorithms can be used on the obtained reads, ignoring the barcode information, but obviously the reconstructed haplotype blocks would be shorter than possible.Exploiting the barcode information for haplotyping is possible by leveraging the so-called “fragment file” format. This format is used in preprocessing steps in several haplotyping algorithms , 20, 22.To tackle this computational problem, we designed Hap10 – a first haplotyping software package specifically tailored for 10X linked reads generated from a polyploid genome. We provide a general framework based on SDhaP that allows haplotyping at the chromosome scale. Furthermore, we propose a novel optimization method that generates more accurate haplotypes with almost the same block length.We have developed the Hap10 package to reconstruct haplotypes from a polyploid genome using linked reads. Prior to haplotyping, several processing steps on sequencing reads are required. These include barcode handling, read alignment and variant calling, which are discussed in "First, the 16 bp 10X barcode is removed from the beginning of each paired-end read generated by the Illumina device. The barcode is stored as a read tag for further use. The possibility of sequencing errors in the barcode calls for an error correction scheme based on the known set of barcodes. Next, the reads are aligned to the reference genome using the barcode information. The barcodes contain long range information that can help provide a better alignment, particularly in repetitive genomic regions. These steps are performed using the LongRanger package (version 2.2.2)  providedHap++ is a fast program to reconstruct haplotypes in polyploids by exploiting linked read information. It consists of three main steps:1) extracting haplotype-relevant information from input BAM and VCF files;2) extracting molecule-specific fragments;3) extracting strongly connected components of fragments.The output of the last step can then be used by SDhaP to assemble the haplotypes. The three steps are described below.We first extract data relevant for haplotyping from the BAM and VCF files. As only heterozygous SNPs are informative for haplotyping, we filter out the homozygous variants from the VCF file. Next, we remove reads that cover fewer than two SNPs, since these do not provide any information for haplotyping. Subsequently, we extract the alleles of SNP sites of each read stored in the BAM file. In order to exploit long-range information provided by the barcodes, we combine the obtained fragments originating from the same 10X bead, i.e. with the same barcode. This results in long barcode-specific fragments. If there are two mismatching alleles for a SNP site corresponding to a specific barcode, we choose the one with the higher base quality. The result is a compact fragment file, similar to the output file of extractHAIRS , 24.The reads generated from the molecules in the 10X bead have identical barcodes. In an ideal case, the microfluidic device is expected to produce one molecule within each bead. In reality however there are, on average, 10 molecules per bead that originate randomly from one of the haploid chromosomes . TherefoIt is crucial to have a decent reference genome, because read alignment to the reference is upstream of haplotyping "" SectionG in which fragments are considered as vertices v ∈ G. The weight wij of the edge eij =  between two nodes is calculated as the number of shared SNPs between two corresponding fragments, inspired by SDhaP [To prevent such problems, we first create a graph by SDhaP . As a deG, we provide a generic solution based on extracting strongly connected components of fragments. To this end, we exploit an iterative bipartitioning method based on the normalized cut (NC) [LN) of G based on the corresponding weight matrix W:D is the degree matrix of the graph, a diagonal matrix with LN, we use the eigenvector (E2) that corresponds to the second smallest eigenvalue in order to bipartition the graph. In [NC value is equivalent to minimization of a Rayleigh quotient, E2 indicates the affiliation of fragments to either subgraph G1 or G2. Then, we calculate the NC value:As errors other than those due to gaps can lead to spurious edges in cut (NC) . We calcraph. In , it is sd in (2) . The sigNC is greater than a pre-specified threshold t, we stop the bi-partitioning procedure; otherwise, we continue bi-partitioning for each remaining partition. We set t to 0.03 for all simulations throughout the paper. When this step is finished, we output all strongly connected components of fragments as individual fragment files for processing by the assembly core, SDhaP. The Hap++ pipeline is depicted in Fig. If K haplotypes H = {h1, …, hK} from N aligned fragments R = {r1, …, rN} generated by DNA sequencing of a K-ploid organism. This definition is universal and applies to different sequencing data types. Each ri is assumed to originate from a single haplotype, as is the case for Illumina reads. As we discussed earlier, in linked read technology, we use molecule-specific fragments as ri in our pipeline.The Hap10 pipeline leverages the Hap++ pipeline and adds a novel optimization as the assembly core. The goal of a haplotype assembly algorithm is to reconstruct I.i and j calculated as.Construct a fragment graph =At is a matrix with the same dimensions as X of zeroes with a 1 in the -th element. Then, (6) can be rearranged toWe exploit dual theory in optimization to solve the semidefinite programming problem (3). Note that the identity matrix is a positive definite matrix, and all its elements are nonnegative. Thus, the identity matrix belongs to the interior of the optimization domain. Thus, the optimization is strictly feasible. Therefore, Slater’s condition is satisfied for the optimization, which immediately results in strong duality (section 5.2.3 of ). To derX, we should make it bounded. To this end, the weight of the affine function should be zero. Thus, the maximization (6) can be simplified toSince the second term is affine in To achieve an unconstrained optimization, we define the augmented Lagrangian function of the optimization as :9\documK-cut problem then becomes:A novel iterative optimization scheme for solving the max- objp and objd are the value of primal and dual objective functions, respectively [X is found using complementary slackness conditions . We stoectively ) falls b5.5.2 of ). To imp5.5.2 of .Solanum tuberosum) as a basis. We first simulated data based on an arbitrarily selected region of one million base pairs (1 Mb) starting from position 5,032,020 on chromosome 1 and subsequently used the full chromosome 1 sequence (88.6 Mb). We introduce SNPs in the reference at a rate of one per 100 or 1000 (for the 1 MB region) and one per 100 for the full chromosome. We generate synthetic triploid and tetraploid genomes as FASTA files by combining K = 3 resp. K = 4 mutated copies of the reference sequence using the haplo-generator routine from the Haplosim package [K true haplotypes in a text file, including the genomic positions of SNPs and the corresponding alleles, which are used for evaluation  from the NCBI database (accession SRX4706082) [To the best of our knowledge, there is no publicly available, real dataset for a polyploid organism containing true haplotype sets, which makes it hard to determine accuracy. To obtain an impression of the distribution of haplotype block lengths and runtimes, we download 10X raw read data of hexaploid sweet potato (4706082) .reconstruction rate, a measure of local accuracy; and vector error rate, a more global measure. Given reconstructed haplotypes H = {h1, …, hK}, the reconstruction rate is defined as:L is the haplotype length and DH is the Hamming distance function, which counts the number of mismatch elements between its arguments. Additionally, p is a permutation on the set {1, …, K}, and pk is the k-th element of p. We calculate this criterion for each haplotype block and report the average. The vector error rate is calculated by finding the minimum number of switches needed in haplotype segments in order to match H; this number is then divided by the haplotype length [To evaluate the length of the reconstructed haplotypes, we calculate and report the mean value over all haplotype blocks. To assess the accuracy of each algorithm, we consider two criteria: e length .Minimum Error Correction (MEC) score, has been frequently used in the literature [Ri is the i-th pre-processed read  data. The basis of Hap10 is a set of pre-processing steps called Hap++. After application of Hap++, SDhaP  can be uTo obtain an impression of the performance of SDhaP, Hap++/SDhaP and Hap10, we performed extensive simulations based on real-world data, the potato genome. This allows us to investigate accuracy "" SectionWe first applied the various algorithms on 10X data simulated based on a relatively short stretch of the potato genome, of 1 Mb "" SectionAs a first test, we applied SDhaP to the simulated read data with and without taking the barcode information into account. The program has no problem dealing with data for a region of this length. Without linked-read information, the reconstruction rate and the vector error rate are relatively good, but the reconstructed haplotype blocks are very short, 11.8 SNPs on average , but drastically improves performance over SDhaP. The reconstruction rate increases, in particular for the triploid simulations, and the vector error rate drops to below 0.1 for almost all simulations where for SDhaP it can reach as high as 0.6. This indicates that the spurious connection problem discussed before occurs in practice and seriously impacts results. It is clear that the SNP rate has a large influence on performance: at low SNP rates, average haplotype block lengths are shorter and accuracy is higher, again particularly for the triploid simulations.Figure K-cut randomized approach (part of the assembly core) is theoretically guaranteed to converge to near the optimal value . HoweveIdeally, the 10X technology ensures each unique barcode is assigned to fragments that originate from a single, long DNA molecule. In practice however, fragmentation is imperfect, leading to shorter molecules, and more than one molecule may receive the same barcode see "" SectionFigure We also varied the length of the 10X molecules in the simulations, from 30, 50 and 100 to 150 kb. Figure accurate mode (using Hap10 optimization) with high haplotype quality, or in fast mode (using Hap++) with reasonable quality, depending on user preference.Figure As a final test, we generated linked read data for the full chromosome 1 of the potato genome, simulating a tetraploid genome at a SNP rate of 0.01. The coverage is 10 per haploid genome. Results are reported in Table To obtain an idea of the applicability of Hap++ and Hap10 to real data, we ran the pipeline to reconstruct the six haplotypes of chromosome one of sweet potato (with the length of 36 Mb) based on 10X data available in the NCBI Short Read Archive.The length distribution of the reconstructed haplotypes is displayed in Fig. We developed a first haplotyping pipeline specifically for linked-read data generated from a polyploid genome. It makes haplotyping full chromosomes of complex genomes feasible. The proposed Hap++ preprocessing pipeline improves on the accuracy of immediate application of SDhaP by approximately 30% (resp. 20%) on simulated 10X data of triploids (resp. tetraploids) at the cost of a decreased haplotype block length. Our framework builds on SDhaP, a typical Illumina haplotyping algorithm, using a standard fragment file as input. Any improvement in SDhaP or similar algorithms thus may immediately enhance linked-read (SLR) haplotyping. The proposed novel optimization scheme, Hap10, provides even more accurate haplotypes, albeit at significant computational cost.K-cut clustering problem [t for the normalized cut algorithm and the convergence threshold used in the optimization step.One topic for future research is to consider different optimization techniques for the max- problem . A new m problem  may provAdditional file 1: Figure S1. Description of the fragment file format using an example SAM and VCF input. A) In the SAM format, each line corresponds to a read (except for the header lines). The fourth column shows the genomic position of the first base of the aligned read. As a simple example, for each read 10 bases are shown in the 10th column. The next column shows the Phred quality of each base. Finally, the BX tag shows the barcode of each read provided by LongRanger software. For more information on the SAM format, see http://samtools.github.io. B) In the VCF format, the second column shows the genomic position of the variant in each line (except for the header lines). The third and fourth columns contain the reference and alternative alleles, respectively. The last column shows the genotype of the variant, in this example for a triploid. For more information on the SAM format see http://samtools.github.io. C) In fragment file designed for short reads, the first column shows the number of consecutive alleles  in the fragment, the second column the id of the fragment, the third column the start position of the first part, followed by the alleles of the part. The position is reported as the index of the variant in the VCF file, starting from 1. If there are more parts, they will appear next. The last column shows the Phred quality scores of all alleles in all parts consecutively. D) To include the barcode information for haplotyping, the barcodes in the SAM file BX tag are provided in the third column of the fragment file. The other columns are shifted accordingly. E) In Barcode-specific fragment file, reads with the same barcode are combined, as discussed in step one of "F) Molecule-specific fragments file is the output of step one of "G) A schematic of the mentioned procedures is illustrated here. Eight fragments are presented in the image, colors indicating barcodes. In all three boxes, row corresponds to a line in the corresponding file.Additional file 2: Figure S2. A graph indicating overlap between fragments. Red dots are vertices (corresponding to the fragments), grey lines are edges drawn when two fragments have at least one SNP in common. The depicted graph is for a case with 5 mb reference genome containing an N-region of 50 kb. The coverage is 15 per haploid and the SNP rate is 0.01. The average length of 10X DNA molecules for this simulation is set to 50 kb. Few fragments originate from a DNA molecule larger than 50 kb. The resulting graph has two separate subgraphs connected by a single edge. Note that one barcode-specific fragment connecting two read blocks is not sufficient for connecting the corresponding haplotypes. This phenomenon decreases the quality of reconstructed haplotype. The figure is generated using Cytoscape (www.cytoscape.org).Additional file 3: Table S1. An example of the haplotype output format. We report the reconstructed haplotypes as a text file with a specific format similar to that of HapCUT2. Each haplotype block starts with a line describing the length of the haplotype, number of reads corresponding to the block and the minimum error correction (MEC) score. From the next line, each row corresponds to each variant. The first and second columns show the 1-based index and variant position, respectively. Then, the next 2 ∗ ploidy columns are haplotypes and quality scores. For each allele of haplotypes, a quality score is provided. As a metric for quality, we use the number of matching reads at each position that are estimated for each haplotype.Additional file 4: Table S2. The impact of the convergence threshold on Hap10 performance. A triploid genome of 230 kb with a SNP rate of 0.001 is simulated. The average molecule length and number of molecules per bead are 50 k and 10, respectively.Additional file 5: Table S3. SDhaP with and without linked-read information. For the latter, the input data is considered as regular Illumina reads and barcodes are not used. The dataset is simulated using 1 Mb of chromosome one of potato genome with a SNP rate of 0.01. The coverage is 10. The results are averaged over 5 independent simulations. For the third row, we split the 1 Mb region into three independent parts of the same size. The last two rows present results of Hap++ and Hap10 on the same data.Additional file 6: Table S4. Performance of SDhaP at different coverage levels, for a triploid genome with SNP rate of 0.001. The average molecule length and number of molecules per bead are 50 k and 10, respectively. The results are averaged over 5 independent simulations.


B. juncea), which is a major edible oilseed crop of low input ecologies in Indian subcontinent. Breeding manipulation for these traits is difficult because of the involvement of multiple interacting genetic and environmental factors. Here, we report a genetic analysis of these traits using a population comprising 92 diverse genotypes of mustard. These genotypes were evaluated under deficient (N75), normal (N100) or excess (N125) conditions of nitrogen (N) application. Lower N availability induced early flowering and maturity in most genotypes, while high N conditions delayed both. A genotyping-by-sequencing approach helped to identify 406,888 SNP markers and undertake genome wide association studies (GWAS). 282 significant marker-trait associations (MTA's) were identified. We detected strong interactions between GWAS loci and nitrogen levels. Though some trait associated SNPs were detected repeatedly across fertility gradients, majority were identified under deficient or normal levels of N applications. Annotation of the genomic region (s) within ± 50 kb of the peak SNPs facilitated prediction of 30 candidate genes belonging to light perception, circadian, floral meristem identity, flowering regulation, gibberellic acid pathways and plant development. These included over one copy each of AGL24, AP1, FVE, FRI, GID1A and GNC. FLC and CO were predicted on chromosomes A02 and B08 respectively. CDF1, CO, FLC, AGL24, GNC and FAF2 appeared to influence the variation for plant height. Our findings may help in improving phenotypic plasticity of mustard across fertility gradients through marker-assisted breeding strategies.Timely transition to flowering, maturity and plant height are important for agronomic adaptation and productivity of Indian mustard ( Brassica juncea: AABB; 2n = 36) is an oilseed crop with adaptations all over the globe. It is a facultative long day (LD) plant which flowers early under long day length conditions and late during short days (SD). East European germplasms are adapted to LD conditions, while most Chinese winter type mustards are naturally biennial. Indian germplasms are winter annuals with a very short vegetative phase. However, flowering time variations exist within these geographic groups. Days to flowering can range from 20 to 145 days during Indian winters. Flowering along with the plant height are the key determinants of productivity. These are genetically complex but interrelated traits which directly or indirectly affect ecological and agronomic adaptations in plants. Both these traits are also influenced by Nitrogen (N) nutrition. Timely transition to flowering is central to the reproductive fitness as it enables reproductive phase to coincide with the conditions favourable for fruit development. Initiation of flowering requires a well-choreographed interplay of many genetic and epigenetic factors which function in concert with photoperiod, temperature, nutrient and moisture availability in the soil2. A prolonged exposure to low temperatures  is essential for flowering in temperate plants, which respond to increasing day length conditions. In contrast, tropical plants flower at the onset of short days. This adaptive strategy prevents precocious flowering and damage to the reproductive structures. Over one hundred flowering genes are known in Arabidopsis6. Each of which interacts differently with the environment. FLOWERING LOCUS T (FT) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) are regulatory pivots. These genes initiate transition to flowering by increasing the expression of meristem identity genes: APETALA 1 (AP1), LEAFY (LFY) and CAULIFLOWER (CAL). Upstream of FT and SOC1, in the flowering pathway, are FLOWERING LOCUS C (FLC) and CONSTANS (CO), which control floral integrators (FT and FD). Both acts differently; a stronger expression of FLC represses FT and SOC1. In comparison, CO promotes expression of FT and SOC1. Expression of floral integrators is regulated by flowering pathways, which sense environmental and developmental cues, including gibberellin levels. FRIGIDA (FRI) is a regulator of FLC expression8. FLC functions in a dosage-dependent manner and delays flowering9. Mutation in FRI reduces FLC expression and leads to early flowering10. Winter annuals possess functional copies of both FRI and FLC, whereas summer annuals show mutations in either FRI or FLC or both12. Allelic differences at these loci account for the maximum flowering time variation in Arabidopsis13. Many other genes associated with the photoperiod, vernalization, gibberellins and the autonomous pathways are also known.Indian mustard  population of B. napus, developed from parents differing for vernalisation responses27, led to the identification of flowering time QTLs in chromosomes A02, A03, A07, and C06. These included homologues of known Arabidopsis flowering genes: VERNALISATION INSENSITIVE 3, AP1, FLC, FLT, CURLY LEAF, SHORT VEGETATIVE PHASE, GA3-OXIDASE and LEAFY. Upadhyay et al.28 have reported 17 significant marker trait associations (MTA's) for six quantitative characters, including days to flowering and plant height in B. juncea. These explained 3.0–33.2 percent of the phenotypic variations. Availability of high-density linkage maps in B. juncea31 have also facilitated identification of QTLs linked to flowering time and plant height in both A- and B-genome chromosomes32. GWAS is an excellent alternative to biparental mapping populations as it is scalable and can detect linkage disequilibrium (LD) between molecular markers and the gene candidates. For the present studies, we used an SNP genotyped association panel of mustard to investigate genetic factors controlling variation for flowering phenology and plant height in response to external nitrogen (N) application. Phenotyping was carried out by measuring seven flowering related traits at three levels of N application and repeated over two crop seasons. Extensive variation was recorded for flowering time, maturity and plant height. Genotypes × nitrogen interactions were highly significant. Genotypes generally flowered and matured earlier at N75 as compared to N100 and N125. Genome wide association studies (GWAS) identified 282 single nucleotide polymorphism (SNP) markers that were associated with the test traits. Annotation of associated genomic regions predicted the role of several important genes related to the light perception, circadian pathway, floral meristem identity, flowering regulation, gibberellic acid pathway and general plant development. We were able to predict multiple copies of as many as six flowering genes. These outcomes emphasized the robustness of our strategy of multiple layers (N-levels in present context) of evaluating the same set of germplasm.Quantitative trait loci (QTLs) with large flowering effects have been mapped to different genomic regions in About 148 million clean GBS reads (56.7 Gb) were used to develop SNP genotypes for the diversity set. Various filtration steps narrowed the number to 16,250,575 SNPs with the base quality of 30. The SNPs with minor allele frequency (MAF) < 0.05 were deleted to reduce false positives. After quality control and imputation only 406,888 SNPs remained, with an overall missing rate of 20%. Final data set was then transformed to numeric values for population structure with minimal remaining missing data filled using the genotypic means of the lines. SNPs were counted at 1 Mb window size for the called genotypes along the pseudo-chromosome as displayed in Supplementary Fig. r2 between selected set of 66,835 SNP marker genotypes in the association panel , and decayed to approximately 0.21 for SNPs as distant as 200 Kb. The mean r2 dropped below 0.1 when inter-marker distance increased beyond 1 Mb.The genetic components of the diversity set were established by using STRUCTURE K = 1 to 10). The value of Evanno’s ΔK peaked at K = 3. Structure , DFL (days to fifty percent flowering), DCF (days to complete flowering)}, MRT (days to maturity), GDD (growing degree days), HTU , PTU  and PH (plant height) at three levels of N application Fig. a. AnalysCYCLING DOF FACTOR 1 (CDF1)-AT5G62430 in the vicinity. 10 MTA's involving chromosome A08 were found associated with FD-AT4G35900 at N100 for DFL. CONSTANS (CO)-AT5G15840 was envisaged on chromosome B08. CRY1 (CRYPTOCHROME 1)-AT4G08920 was predicted through 15 MTA's, identified in the region 338,948–346,586 on B03. These MTA's involved two flowering traits  and were common across three levels of N application. Two Circadian pathway genes, CKB4-AT2G44680 and AT1G22770-GI (GIGANTEA) were envisioned for DFL at N75 for chromosomes A05 and A09, respectively. These associations explained 9.79–11.79% of phenotypic variation for flowering traits.GWAS was conducted to identify marker trait associations (MTA's) based on the multi-environment phenotyping data Table  using GAAGL24 (AGAMOUS-LIKE 24)–AT4G24540, AP1 (APETALA1)–AT1G69120, AP2 (APETALA2)–AT4G36920 and JMJ14 (JUMONJI 14)–AT4G20400 were predicted. SNPs associated with these genes explained 2.63–13.19 percent of the phenotypic variation observed for flowering and derived traits. SNPs annotating three copies of AGL24-AT4G24540 were located in genomic regions on A02 , A03  and B02 . AP1-AT1G69120 was envisaged close to the genomic region harboring 13 SNPs associated for HTU and PTU. This gene was envisaged close (0.04 kb) to the peak SNP on the chromosome A02. Other copy of the same gene was predicted on chromosome B02. AP2-AT4G36920 and JMJ14-AT4G20400 were identified for DFL on chromosomes B02 and B03 respectively. GWAS led to the prediction of 15 genes involved with flowering regulation. Majority of these MTA's were associated with A-genome. KHZ1-KHZ2-AT5G06770, FLOR1-AT3G12145, FLC-AT5G10140 and FLM (FLOWERING LOCUS M)—AT1G77080 were envisaged on chromosome A02. KHZ1-KHZ2-AT5G06770 and FLOR1-AT3G12145 appeared to influence variation for GDD, HTU and PTU. These genes were envisaged at the distances of 2.56 and 2.99 kb from the respective peak SNPs. FLOWERING LOCUS C (FLC)-AT5G10140 was predicted at a distance of 10.61 kb from the SNP A02_1762477. FLM-AT1G77080 was annotated in vicinity of 28 SNPs associated with DFL, GDD, HTU and PTU. A distance of 0.82 kb separated FLM from the peak SNP. FLOWERING LOCUS D (FLD)-AT3G10390 appeared close (0.94 kb) to the SNPs located on chromosome A03 . AGL6 (AGAMOUS-LIKE 6) and SOC1 (SUPPRESSOR OF OVEREXPRESSION OF CO1) also known as AGL20 (AGAMOUS-LIKE 20) were detected on chromosome A05. Associated SNPs were detected for DCF and MRT at N75. AT2G32950-COP1 was predicted < 2 kb away from peak SNP (N75) present on chromosome A05 . AT1G68050-FKF1 (A07) could explain variation for DFI at N100. We predicted two copies of FT (FLOWERING LOCUS T)-AT1G65480 on the chromosomes A07 and B06. These explained variation for DFL and MRT at N100. ARP6-AT3G33520 was envisioned on chromosomes A08 for its role in the heredity of DCF. One copy each of FVE was recognized on chromosomes A09 and B04 at respective distances of 3.43 kb and 0.66 kb from the peak SNPs. Two copies of AT4G00650-FRIGIDA (FRI), an important flowering regulator, were recognized on chromosomes A10 and B08. Six SNPs on B06  were found associated at the distance of 46.85 kb from MAF5-AT5G65080. HAM1 (HISTONE ACETYLTRANSFERASE OF THE MYST FAMILY 1)—AT5G64610 was also predicted at a distance of 0.61 kb from key SNP on chromosome B07. A gibberellin pathway gene, AT3G05120-GID1A (GA INSENSITIVE DWARF 1A) seemed to influence variation for DFI, DFL and DCF at N75 and N125 levels of N application. Another gibberellin pathway gene, AT4G02780-GA1 (GA REQUIRING 1), was recognized on chromosomes B03. ATGA3ox3 (ARABIDOPSIS THALIANA GIBBERELLIN 3-OXIDASE), also predicted on chromosome B03, was significant contributor to variation observed for DFL trait at N100 level. Three genes involved in development processes of plants were envisaged: AT5G56860-GNC (GATA NITRATE-INDUCIBLE CARBON-METABOLISM-INVOLVED), ATBPC2-AT1G14685, FAF2 (FANTASTIC FOUR 2)–AT1G03170 and ATJ3 (ARABIDOPSIS THALIANA DNAJ HOMOLOGUE 3)-AT3G44110. The chlorophyll biosynthetic gene AT5G56860-GNC was significant for PH on A03  at N75 and N100 levels of N application. The same gene was detected for DFI trait close to the SNP B08_14118234 (5.71 kb) at N100. Only one SNP, B03_115277 was identified 0.06 kb away from FAF2 or FTM5 (FLORAL TRANSITION AT THE MERISTEM 5) which is responsible for regulating shoot meristem size in A. thaliana. Similarly, ATJ3-AT3G44110 was detected at B05. We also estimated LD blocks for adjacent SNPs present within 1 Mb of the predicted genes (Table r2) values > 0.20. Size of these LD blocks ranged from 0.01 to 225.16 kb. Longest LD block (225.16 kb) was identified on chromosome A02 from 34,557,898 to 34,783,055 bp for gene AP1-AT1G69120. It also included the maximum number of SNPs (366). In contrast, numbers of significant SNPs (78) were located in terminal region of chromosome A02 from 1,202,251 to 1,217,865 bp within the LD block of 23.75 kb.Four floral meristem identity genes: 33. N availability affects plant phenotypes by genome-wide changes in the expression pattern(s) of the genes associated with different metabolic processes36. Testing of genotypes at three levels of N application in the current studies produced highly significant G × Y, G × N and G × N × Y interactions. This was an expected as phenotypic expression of a genotype is only one among many phenotypic manifestations’ realizable under different environments. Although the test genotypes varied for their individual responses, most flowered and matured earlier at N75 in comparison to N100 and N125. Lower N availability induces early flowering39, while high N delays it40. Genotype-specific flowering time variations in response to the applied N have been observed earlier in A. thaliana41. Population structure and kinship analysis allowed clustering of germplasm into three broad groups. Most of the resynthesized and exotic (largely east European) genotypes fell in to the groups distinct from the one harbouring indigenous B. juncea lines with some admixing. This kind of population structure suggested an independent evolution of Indian and east European genotypes. Admixing may have resulted from extensive inter-varietal hybridizations practised by earlier and present-day plant breeders. We used GWAS, an LD based method, to investigate trait genetics. The resolution with which a QTL can be mapped depends upon the speed of LD decay, which is the outcome of multiple genetic recombination’s across the genome43. Mustard is a self-pollinated crop, with varying amount of cross pollination. LD was distributed unevenly among chromosomes and two sub-genomes in our test panel. It decayed to approximately 0.21 for SNPs at a distance of 0.20 Mb. Mean r2 also dropped below 0.1 beyond 1 Mb inter-marker distance. A rapid LD decay in our association panel confirmed its suitability for conducting GWAS. There is no past report about LD decay estimated using a large number of biallelic markers in B. juncea. However, present outcomes are consistent with those in related allotetraploids of B. napus44 and B. carinata45.Days to flowering, maturity and plant height have been the major targets of selection during domestication due to their importance for the reproductive success, uniform ripening and ease of harvesting. Breeding for these traits is important even now as new cultivars must fit into newly emerging cropping systems and geographic niches. Optimum transition to flowering is also important to correct climate change-induced “phenological mismatches” that are creeping into many crops and their current ecosystems. However, phenological alterations are difficult to accomplish due to the complex genetics and strong genotype × environmental interactions. These hinder selection gains. N is an important external cue as it is both a plant nutrient and a signalling moleculeCO, CRY1) and circadian pathways in Arabidopsis46. Photoperiod pathway also interacts with the gibberellin acid and autonomous pathways to modulate nitrate-regulated floral transition47. Nitrates repress flowering time via gibberellin pathway48. Loss of function or overexpression of many genes in these pathways cause major flowering time changes49. The annotated genes included more than one copy each of six important flowering genes . Multiple copies of each of the flowering genes are expected as Brassicas are ancient polyploids50. The resultant functional redundancy can impact phenotypic diversity if multiple gene copies act in an additive or dosage-dependent manner51. FD, predicted on chromosome A08, encodes a bZIP transcription factor which is a positive regulator of flowering. It promotes flowering following interaction with FT52. CRY1 was detected on chromosome B03 at all three levels of N application. It is one of the two key factors involved in N-regulated flowering time control in Arabidopsis thaliana, other being ferredoxin-NADP+-oxidoreductase. Loss-of-function mutants of CRY1 are insensitive to N availability46. CRY1 also acts through N signal pathway to regulate flowering output genes CO and GI. These are upregulated if plants are grown under limiting nitrate conditions46. CO was envisioned on chromosome B08. Overexpression of CO and FLC eliminates the influence of nitrates on flowering47. Circadian clock controlled flowering pathway is the timekeeper of photoperiodism where GI promotes the expression of flowering-time genes, CO and FT53 as well. AP1 and AP2 were predicted close to the SNPs identified significant at N75 on chromosomes A02 and B02 respectively. Two more copies of AP1 were envisaged on the chromosomes A09 and B02 at N100. AP1, AP2 and AGL24 regulate initial stages of flower development and redundantly act to control floral meristem identity54. AGL24 is upregulated in the inflorescence apex during floral transition55. Three copies of this gene were predicted on chromosomes A02, A03, and B02. Associated SNPs were identified at N100 and N125 but not under N deficiency (N75). Another gene JMJ14 was predicted on chromosome A07, in vicinity of SNPs found significant at N125. It encodes a histone H3K4 demethylase which prevents early flowering through repression of the floral integrators FT, AP1, SOC1 and LFY during the vegetative growth phase56. FT, predicted on A07 and B06, is the core of photoperiodic flowering pathway and it is present downstream of the GI and CO57. SOC1/AGL20 was predicted on the chromosome A05 for the traits DCF and MRT under N deficient conditions. It integrates signals from the photoperiod, vernalization and autonomous floral induction pathways58. SOC1 is upregulated under N deficiency38.A large number of SNPs were identified as significant for association with flowering, maturity, plant height and derived traits. These SNPs differed for their chromosome locations and effects over N-levels. Identified SNPs explained significant proportions of the phenotypic variations recorded for the evaluated traits. In silico annotation within a threshold window of genes within 100 kb (50-kb upstream and 50-kb downstream) of the peak SNP allowed prediction of 30 candidate genes belonging to light perception, circadian pathway, floral meristem identity, flowering regulation, gibberellic acid pathways and plant development. This threshold window was selected on the basis of low levels of overall LD (200 kb) in our association panel. Majority of identified genes were predicted from the associations found significant at N75 and N100. Nitrate availability is known to alter the expression of major genes belonging to photoperiod (e.g. FLC1 (A02) and FT  at N100. FLC is important for the initiation timing of flowering. Three FLC homologs are known in Brassica59. It represses FT and SOC1, to prevent the conversion of apical meristem into the reproductive structures60. Significant downregulation of FLC and upregulation of FT, LFY, and AP1 has been reported under N deficient conditions37. Another predicted gene ATJ3 (B03) acts downstream of various floral pathways and mediates the transcriptional regulation of FT and SOC1, during switch to flowering via a known flowering repressor SVP61. FRI up regulates the expression of FLC to accelerate the transition to flowering after the vernalization62. A copy each of this gene was identified on chromosomes A10 and B08 under N75 and N125. FVE is a key regulator of the autonomous pathway that reduces FLC expression. A similar gene ARP6 that encodes actin-related protein, a putative component of a chromatin-remodelling complex, is required for both histone acetylation and methylation of the FLC chromatin in Arabidopsis and controls its expression63. HAM1, identified on B07, affects the flowering time by epigenetic modification of FLC64. Other flowering genes, FLOR1, FLR1 and FTM4 were identified on chromosome A02. FLOR1, predicted under high N conditions, interacts with the MADS box transcription factor AGAMOUS to delay flowering. Its role is partially redundant with SOC1 and FUL65. MAF1 or FLM, which is related to FLC was identified on A02. It acts as the negative regulator of flowering. FLD located on A03, plays a key role in regulating the reproductive competence of the shoot by repressing FLC66. AGL6, present on A05, act as a floral promoter with roles in inhibition of FLC/MAF genes and promotion of FT67. COP1 modulates the circadian rhythm and flowering. It encodes a RING-finger E3 ubiquitin ligase which along with SUPPRESSOR of phyA-105 (SPA) proteins, represses photoperiodic flowering by regulating proteasome-mediated degradation of CONSTANS (CO)68. MAF5/AGL68 also regulates the flowering time during the vernalization. Nitrates are known to modulate the expression of TEM1. This gene control floral transition by repressing FT and GA-dependent flowering pathways by regulating GA3ox1 and GA3ox269. FKF1, a flavin-binding kelch repeat F box protein was identified on chromosome A07 under variable N levels. It is clock-controlled gene that regulates transition to flowering70. It is repressed by nitrate and is involved in the induction of CO and FT71. Mahmood et al.72 had identified a stable QTL for flowering traits on linkage group B06. This QTL simultaneously influenced flowering, maturity and plant height. Ramchiary et al.30 also reported QTLs for plant height on chromosomes, A01, A02 and B06 in B. juncea. GA is important for promoting flowering under non-inductive photoperiod conditions73. This pathway is affected by N availability. Low N increases GA concentration while high N reduces it38. GA1 was envisaged on chromosomes A09 and B02 at all three levels of N application. GID1A, a positive regulator of flowering and a stable soluble gibberellin receptor74, was predicted on A05 and B02 for the traits DFI, DFL and DCF under N75 and N125. GID1A is a soluble gibberellin (GA) receptor and is involved in GA signaling that controls root growth, seed germination and flower development. ATGA3ox3 identified on chromosomes A08 and B03 catalyses the final step in the synthesis of bioactive gibberellins (GAs). The gene is expressed in stamen filaments, anthers, and flower receptacles to promote their growth75. GATA transcription factors GNC  and GNL/CGA1 (GNC-LIKE/CYTOKININ-RESPONSIVE GATA FACTOR1) prevent flowering by directly repressing SOC1 expression in Arabidopsis gene SOC176. One copy each of GNC was predicted on chromosomes A03 and B08 respectively. These may explain part of variation recorded for DFI and PH at N75 and N100. GNC has a major role in the chlorophyll biosynthesis and it is upregulated by N and repressed by GA signalling77. Plant height is a major yield component in mustard. Though plant height is positively associated with yield, tall plants are more prone to lodging, especially as they approach maturity. Lodging can strongly reduce yield and impair grain quality. Genotypes with reduced height can withstand lodging better and are more suitable for mechanical harvesting. In the present studies, 18 SNPs on A09 in the genomic region of 52,234,633–52,238,177 were significantly associated with both plant height and flowering time. As described earlier, majority of the associated SNPs were located within the LD blocks. Identification of a fairly number of SNPs in high LD is important as any SNP can be used as a tag SNP. However, the tag SNPs are required to be selected independently if associated SNPs are present in more than one group with little intergroup LD.We predicted CRY1, CO, SOC1/AGL20, AGL6, AGL24, GI, AP1, AP2, FLC, FT, ARP6, FVE, ATGA3OX3, FLOR1, GID1A, etc. Majority of these were located within a distance of 25 kb from respective peak SNPs. Broadly, our results are consistent with flowering QTLs reported earlier for chromosomes A02, A03, A05, A06, A08, A09, B02, B03, B06, B07 and B08 in B. juncea72. The information on marker-trait associations and validation of candidate genes predicted during our studies may promote marker aid breeding (MAB) in Indian mustard.Summarising, we used multiple phenotypic data  and multilayer evaluation to detect a large number of MTA's showing low to moderate contributions to respective trait variations. Most of the associated SNPs were distributed around key flowering genes: B. juncea (DJ), resynthesized determinate mustard (DTM), introgression lines (IL). All the genotypes included in the association panel had very high (> 90%) and euploid chromosome number expected for B. juncea . The details information about the genotypes included in the association panel is available elsewhere78. The trials were conducted at the farms of Punjab Agricultural University at Ludhiana  for two crop seasons  in alpha lattice design with two replications and three levels of nitrogen (N) {low dose , standard dose  and high dose }. Urea (46% N) was applied in two splits, half at the time of seeding and the second half, 22 days after sowing. We raised each genotype in a plot of four rows, each row measuring two meters in length. The rows were spaced 30 cm apart. The crop was sown during second fortnight of October and it was harvested during second week of April every year. The crop was flood irrigated at three times during the growing season. Standard agronomic practices were followed for the other crop inputs. Days to flowering were counted three times: between sowing of the crop till the commencement of flowering (DFI), when 50% of plants had started to flower (DFL) and culmination of flowering (DCF). We counted days from seeding when crop had attained physiological maturity (DM). Physiological maturity was reached when 90 percent of pods had turned light brown. Plant height (PH) was measured in centimetres from base of the plant till the tip of the main shoot at maturity. We also estimated growing degree days (GDD), referred to as sum of mean daily temperatures (°C) above a defined temperature threshold of 5°C79.Association panel comprised 92 germplasm lines maintained for seven generations of selfing as per single seed descent method. The panel included advanced breeding lines (BL) or cultivars (CV), germplasm from east Europe, Australia (EM), derived resynthesized The growing degree day value for a given day was considered zero, if the average temperature fell below the temperature threshold or the base temperature (5 °C).80.We computed Helio-thermal unit (HTU) and photo-thermal units (PTU). HTU is the product of GDD and corresponding actual sunshine hours of that day and PTU is the product of GDD and corresponding day lengthGDD, PTU and HTT values were computed at the initiation of flowering.The agro-meteorological data for everyday temperature, sunshine hours and day length were sourced from the Department of Climate Change & Agricultural Meteorology, Punjab Agricultural University, Ludhiana, Punjab, India.81. The analysis was carried out using SAS version 9.4. Basic equation was:yijk denotes values for the observed traits for ith treatment in the k-the blocks within j-th replicate (superblocks), ti is the fixed effects of the ith treatment ; rj is the effects of jth replicate (superblocks) ; bjk is the effects of the kth incomplete blocks within the jth replicate  and eijk is an experiment error associated with the observation of the ith treatment in the k-th incomplete block within j-th complete replicate.Analysis of variance (ANOVA) was implemented to test significance of variation owing to genotypes, nitrogen levels, crop seasons and their interactions. GLM  was implemented in alpha lattice designPearson correlation coefficients were first estimated and these were plotted by R-package “corrplot”.82. Total genomic DNA was extracted from young leaves of 92 genotypes using a standard CTAB method with minor modifications. DNA samples were quantified by visual comparison to λ-DNA standards on ethidium bromide-stained agarose gels. The purity and concentration of the samples was checked with spectrophotometer readings at 260 and 280 nms. High quality DNA samples were genotyped by sequencing (GBS) on the ILLUMINA HiSeq platform, which was outsourced to Novogene (HK) Company Limited, Hongkong. Bioinformatic analyses were conducted with publicly available software’s. The reference genome of B. juncea v1.5 was used for alignment of whole genome sequence (25x) of a commercial B. juncea genotype, PBR357 with the software "bowtie2". SNP calling was implemented in NGSEP-GBS pipeline83. Total SNPs were replaced in B. juncea genome reference using a perl script, pseudomaker.pl implemented in the SEG-Map to construct mock-up pseudomolecule reference. All 92 inbred lines of the diversity set were then aligned on pseudomolecule genome reference and SNPs were identified with NGSEP-GBS pipeline. We improved the SNP density of through imputation, using a mustard reference SNP set with 570,764 high quality SNPs. This reference set was developed through re-sequencing (10–12 Χ genome coverage) of a mustard germplasm core set comprising 96 genotypes. Software minimac2 was used for imputation84. A total 406,888 SNPs were retained for GWAS after filtration. The filtration parameters were MAF > 0.05 and maximum heterozygosity < 10%. A sub-set of 66,835 SNP set, available with stringent filtration (MAF > 0.10) and prior to imputation, was used to determine population structure and linkage disequilibrium. Software’s PGDSpider v2.1.1.3 and ‘GAPIT’85 were used for converting SNP genotypes into the structure and numeric formats, respectively.The diversity set was genotyped by sequencing (GBS)K). Ten runs for each K were performed by a model assuming admixture and correlated allele frequencies. The run length was 10,000 period followed by Markov Chain Monte Carlo (MCMC) repetitions, set to 1,00,000 replications. The optimum number of subgroups (K) was selected on the basis of the log probabilities LnP(D) and ad hoc statistic ∆K method86. Second order rate of change of the likelihood function for K was determined using Structure Harvester87. Linkage disequilibrium (LD) was estimated with TASSEL v5.088 based on squared allele frequency correlations (r2) between all pairs of SNP markers.Population structure was developed with the software STRUCTURE 2.3.4. and 1–10 subgroups  implemented in GAPIT3. SNP-trait associations were classified as significant on the basis of an arbitrarily chosen threshold of –log10 (P) ≥ 3.0. Bonferroni threshold or a P value adjusted by a false discovery rate of 0.05 was considered too restrictive stringent. Pairwise LD values for the trait associated SNPs were estimated within 1 Mb window using the software Plink1.09 (https://www.cog-genomics.org/plink2) with parameters: -noweb -all -blocks -no-pheno-req -ld-window-kb 1000.Phenotypic data was normalized by Johnson transformation as implemented in Minitab v16.0. We used software GAPIT3Arabidopsis thaliana database using Blast2GO v5.0 tool90. Functions of the possible candidate genes were checked in the literature to determine their relevance for the trait in question.50-kb flanking regions surrounding the peak SNPs were scanned to predict candidate genes. The predicted gene and its orthologous sequences were then annotated by BLAST analysis against the Supplementary Figures.Supplementary Tables.