Drosophila melanogaster, pigmentation of the abdomen and a trident‐shaped region on the thorax are genetically correlated. Here, we used a pooled replicated genomewide association approach (Pool‐GWAS) to identify the genetic basis of variation in thoracic trident pigmentation in two Drosophila melanogaster populations. We confirmed the previously reported large effect of ebony and the association of the cosmopolitan inversion In(3R)Payne. For the first time, we identified tan as another major locus contributing to variation in trident pigmentation. Intriguingly, the regulatory variants of tan that were most strongly associated with female abdominal pigmentation also showed a strong association with trident pigmentation. We validated this common genetic basis in transgenic assays and found qualitatively similar effects on trident and abdominal pigmentation. Further work is required to determine whether this genetic correlation is favoured by natural selection or reflects a neutral by‐product of a shared regulatory architecture.Traits with a common genetic basis frequently display correlated phenotypic responses to selection or environmental conditions. In  The effects of variants around ebony are only loosely correlated between female abdominal and trident pigmentation. In contrast, the effects of variants around tan on the two traits are highly correlated. We investigated three SNPs within the tMSE that are highly associated with female abdominal pigmentation in transgenic flies and observed qualitatively similar effects on both traits, despite the relative contributions being variable.Artificial selection for female abdominal pigmentation identified a genetic correlation between abdominal and trident pigmentation  to 4 (very dark) similar to David et al. , and Bolzano, Italy (2011), as described in Bastide et al. . The flid et al. , and forWe used female flies not selected for pigmentation traits to estimate the allele frequencies prior to phenotyping   with barcoded TruSeq Single Index adapters . All libraries were size‐selected for an insert size of 280 bp using AMPureXP beads  and amplified with 10 PCR cycles. The libraries were sequenced on a HiSeq2000 as 100‐bp paired‐end reads.After fragmentation with a Covaris S2 device , approximately 1.5 g DNA of each sample were used to prepare paired‐end libraries using the components of the NEBNext2.3fastqc , Wolbachia pipientis (AE017196.1), Lactobacillus brevis (CP000416.1), Acetobacter pasteurianus (AP011170) and phage phiX174 (NC_001422.1) without seeding and allowing for two gap openings (‐o 2), an alignment distance threshold leading to loss of <1% of reads assuming a 2% error rate (‐n 0.01), and at most 12 gap extensions (‐e 12 ‐d 12).After quality control using Andrews, , the seqAndrews, . MappingAndrews,  against samtools , the reads were realigned around short insertions and deletions and indels from the Drosophila Genetic Reference Panel  using gatk . Simple low complexity regions were not considered (repeatmasker option: ‐nolow).After all filtering steps, the coverage varied among replicates between 54‐ and 112‐fold for autosomal chromosomes and correspondingly 50% less for the X chromosome   phase 2 (multiple African and French lines) and 3 (Zambian lines) downloaded from the Drosophila genome nexus . Only lines genotyped at all three focal SNPs were used.For estimating linkage between the three focal SNPs in the 2.4popoolation2 as previously described .Association of variants with trident pigmentation was tested using a Cochran–Mantel–Haenszel (CMH) test implemented in p‐value > 10−2). Some functions for plotting were adapted from scripts by Stephen Turner (http://www.gettinggeneticsdone.com).Only polymorphic sites with a minimum coverage of 15 for each sample, and a minimum overall minor allele count of 8 in each population were considered for testing. The 2% most highly covered sites were excluded from the calculations. All variants were further filtered for a minimum average distance from the ends of supporting reads (average distance >8) and strand bias  >0.1 or Fisher's exact test on alternative vs. reference strand balance p‐value of the CMH test depends on the counts at each SNP and the sequencing depth of the autosomal chromosomes is on average twice that of the X chromosomes in males. To be able to compare p‐values across all chromosomes, we downsampled SNP counts at each autosomal position to 50% for calculation of the p‐values (random sampling without replacement), but used full sequencing depths for odds ratios and allele frequency estimates.The snpeff v4.1 g  of 0.05 derived from an empirical null distribution as described in Bastide et al. . For ann2.5tMSE variants described in . From this template, all possible combinations of SNPs were derived using site directed plasmid mutagenesis techniques  using PhiC31 integrase‐based transgenesis  and 255 (black). The trident of ten males was quantified for each tMSE haplotype.Flies were immersed in 75% Ethanol and pinned through the abdomen on a silicon substrate. Their thoraces were imaged with a binocular equipped with Leica DC480 digital camera using the Leica IM50 Image Manager software. An annular lamp was used to ensure homogeneous lighting. All pictures were taken with identical settings during the same session. To quantify the intensity of the trident, we delimited the trident using a polygon based on the suture anterior to the scutellum and the base of each dorsocentral macrochaetes using Rasband,   and zero sum constraints on factor contrasts (option contr.sum) as previously described ) considering the population (Vienna and Bolzano) and the extreme pool (light and dark) as categorical predictors. The model was fitted using the lm function of R and the p‐value for the coefficient of the extreme pool corrected for multiple testing using the Bonferroni method of the p.adjust function.To identify significant associations between cosmopolitan inversions and trident pigmentation, the frequency of each inversion in each pool was estimated as the median frequency of marker SNPs Payne Payne was significantly associated with trident pigmentation , with higher frequencies in the light pools and being almost absent in the dark pools c, which partially overlaps In(3R)Payne . However, the frequency differences between light and dark pools were not as marked as In(3R)Payne Payne  test. With a 5% false discovery rate (FDR), we find a large number of SNPs (739) to be significantly associated with trident pigmentation. Most of them (578) are located on chromosome 3R within or close to the region of the cosmopolitan inversion tan gene , which were most strongly associated with female abdominal pigmentation in multiple populations  Payne, these results are hard to interpret. The whole genomic region covered by the inversion shows an elevated background and multiple peaks of significant variants, especially close to the inversion breakpoints. As recombination rates are lowest around the breakpoints, variants located there are more likely to be in linkage disequilibrium Payne have also been identified as inversion‐specific markers Payne comes from the comparison of the inversion and the strongly associated SNPs. Both differ about 10% in frequency between light and dark pools, which suggests linkage disequilibrium, but not necessary causality. In contrast, some candidate SNPs around the ebony locus differ up to 50% in frequency Payne and tan, we find several significant associations on the X chromosome that were not previously linked to trident or abdominal pigmentation , a gene involved in Yellow clearance and in cuticle melanization , suggesting that variants around ebony do not contribute strongly to the correlation of the two traits in our populations.As the same populations were used to map the genetic basis of female abdominal pigmentation  . SNP2, on the other hand, which shows the strongest effect in the transgenic flies, does not reach significance . This surprising discrepancy could at least partially be explained by the haplotypic configurations inferred from the reads in the combined base population Payne and the ebony locus Payne shows marked differences for the trident and abdominal pigmentation GWAS. In contrast to its role in trident pigmentation, the inversion is not associated with female abdominal pigmentation. While variants around ebony, which are associated with abdominal pigmentation, also influence trident pigmentation, the variants most strongly associated with trident pigmentation have almost no effect on the female abdominal pigmentation GWAS payne have a stronger effect on trident than on female abdominal pigmentation and that some variants around ebony are exclusively affecting trident pigmentation.The genomic region around In(3R)Payne, we mainly find associations on the X chromosome. The high density of candidate regions on the X chromosome is remarkable. Since we only scored males, it is possible that hemizygosity of the X chromosome provides penetrance to low frequency recessive variants, which would be difficult to detect in females. A few of these regions overlap genes that have been connected to pigmentation phenotypes—though not in association studies—such as Crag, CG15772 and Megalin, illustrating the power of the Pool‐GWAS approach.Apart from the variants within tan, most notably around Crag, Bap11 and mgl, we note that the significant effects were detected in the Italian population, which has more replicates and a higher frequency of the candidate alleles. In combination with similar logOR in both populations, this suggests that the different significances could reflect power, rather than biological differences between the populations. Nevertheless, further follow‐up experiments are needed to resolve this question.In our study, we combined data from two populations, which were maintained at two different temperatures. Because the CMH test is designed for consistent signals across replicates, this is a conservative approach targeted to find strong effects that are not dependent on the population surveyed or the culture conditions. The drawback of this approach is that population‐specific effects are difficult to disentangle from temperature‐specific effects. For some of the variants on the X chromosomes outside tan, which was previously only known to modulate variation in female abdominal pigmentation, also harbours segregating variation affecting trident pigmentation. The effect of tan is more strongly correlated between abdominal and trident pigmentation than for ebony. This indicates a common regulatory architecture of the two traits at tan but not ebony. Further inspection of the three SNPs in the tMSE, which are most highly associated with abdominal pigmentation in females, showed that all three SNPs also influence trident pigmentation in males. While these effects are qualitatively consistent with the ones on abdominal pigmentation, they differ drastically in the ranking of their effect size. Contrasting the effect size from Pool‐GWAS to results from transgenic constructs showed that the SNP with the strongest effect in transgenes exhibits the lowest association with the trait. Similar to female abdominal pigmentation, where the effect of SNP2 was estimated in the wrong direction, we also attribute this discrepancy to the linkage structure in the mapping population. This is further supported by in silico simulations, which show that the pronounced haplotype structure of the three regulatory SNPs leads to the apparent reversal of the allelic effect of SNP2 and the strongest association for SNP3. We conclude that allelic effects estimated from GWAS studies can be incorrect when multiple sites are contributing to the phenotype and their linkage structure is not considered.The most surprising result of our study is that tan and ebony encode enzymes with antagonist activities. Thus, the amount of melanin produced is directly linked to the expression ratio of ebony and tan, and as ebony is epistatic over tan (because Ebony's product is Tan's substrate), ebony must be expressed for tan to have an effect . CorrelC.S. and V.N. conceived the study. L.E. and J.M.G. analysed the data. J.M.G. and V.N. performed the experiments. All authors contributed to writing the manuscript.https://github.com/luenling/TridentPigmentation2018. Sequencing reads are available at the European Nucleotide Archive (ENA) under the accessions PRJEB3292 (reference populations) and PRJEB25181 (trident pigmentation extreme pools).Custom scripts are available under  Click here for additional data file. Click here for additional data file. Click here for additional data file.


The parent FC1 always produced more big nodules, yet fewer nodules in total than the parent FC2 in the field. Furthermore, nodulation in FC1 was more responsive to environmental changes than that in FC2. Broad-sense heritability (2bh) for all BNF traits varied from 0.48 to 0.87, which suggests that variation in the observed BNF traits was primarily determined by genotype. Moreover, two new QTLs for BNF traits, qBNF-16 and qBNF-17, were identified in this study. The qBNF-16 locus was detected under all of the four tested conditions, where it explained 15.9–59.0% of phenotypic variation with LOD values of 6.31–32.5. Meanwhile qBNF-17 explained 12.6–18.6% of observed variation with LOD values of 4.93–7.51. Genotype group analysis indicated that the FC1 genotype of qBNF-16 primarily affected nodule size (NS), while the FC2 genotype of qBNF-16 promoted nodule number (NN). On the other hand, the FC1 genotype of qBNF-17 influenced NN and the FC2 genotype of qBNF-17 impacted NS. The results on the whole suggest that these two QTLs might be valuable markers for breeding elite soybean varieties with high BNF capacities.Soybean is an important economic and green manure crop that is widely used in intercropping and rotation systems due to its high biological nitrogen fixation (BNF) capacity and the resulting reduction in N fertilization. However, the genetic mechanisms underlying soybean BNF are largely unknown. Here, two soybean parent genotypes contrasting in BNF traits and 168 F As an important economic crop, soybean is a main source of edible oil and protein for human around the world due to the high oil (20–25%) and high protein (42–45%) contents in the seeds . At the 2. BNF is a process in which plant unavailable atmospheric N2 is converted into readily available ammonia (NH3) in nodules formed through symbiotic associations between plants and microbes (Nitrogen (N) is one of the most limiting factors in crop production. In order to obtain high yields, farmers tend to supply excessive amounts of N fertilizers, which not only increases input costs, but also causes potentially adverse effects on the environment, including air and water pollution . Althougmicrobes .Chemical synthesis provides 118 million metric tons of fertilizer N each year . In compSoybean BNF capacity is influenced by many environmental factors. Among them, rhizobial strains play critically roles in the nitrogen fixation capacity of soybean nodules , and whiqBNF-C2, qBNF-O and qBNF-B1, are co-located with yield trait loci  bred from their progeny were grown in the field to evaluate the effects of N level and rhizobial inoculation on nodulation and BNF traits, as well as, to identify associated QTLs.Despite the volume of research devoted to mining QTLs for soybean BNF capacity in the field, fine mapping of QTLs regulating nodulation traits in soybean still remain largely unknown. In the present study, two soybean genotypes contrasting in nodulation traits and 168 F9:11 RILs was derived from a cross between two accessions contrasting in BNF capacity, FC1 (a cultivar accession with high BNF capacity) and FC2 (a semi-wild landrace with low BNF capacity), using the single seed descent (SSD) method. This population was further used to construct a genetic linkage map to detect QTLs associated with BNF traits, as well as, to evaluate effects of N level and rhizobial inoculation on BNF traits. A field trial under four environmental conditions was carried out at the Zhaoxian experimental farm  of the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences. The four environmental conditions included with (+R) or without rhizobial inoculation (-R) at two field sites contrasting in long-term nitrogen fertilization . Three highly effective rhizobium strains, BXYD3, BXBL9, and BDYD1, which were previously identified belonging to Bradyrhizobium elkanii based on morphological and 16s ribosomal DNA sequence analysis (Fluventic Ustochrept soil. Basic characteristics of the top 20 cm of soil in the HN and LN field are shown in Table -1 of (NH4)2HPO4 and 187.5 kg⋅ha-1 of urea as base fertilizer, and 75 kg⋅ha-1 of urea as additional fertilizer during the elongation stage in the HN field. No fertilizer was applied during soybean growth. Field management  followed local farmer practices. Parental genotypes and RILs were planted in a split plot design with plots arranged in randomized complete blocks within each block of split plots. There were two plots for each RILs in each treatment, and in total, were 1,360 plots in the field. Thirty seeds were sown per plot in three 3 m rows spaced 0.5 m apart.In this study, a soybean population consisting of 168 Fanalysis , were usAt the R4 stage, three representative plants from each plot were harvested, and in total, six plants were harvested for each line in each treatment. Plant roots were manually extracted from the soil to ensure the integrity of the root systems. All nodules were removed and cleared carefully from roots prior to drying in a 37°C oven. Three nodulation traits, namely NN, NW, and NS, were investigated for subsequent analysis. NS was calculated by the quotient of total NW and NN. NN and NW were derived from individual plant. The plants were dried at 60°C, and then measured dry weight and total N content (TNC) using a continuous flow analyzer as described by Genomic DNA of the two parents were extracted using commercial extraction kit, DNeasy Plant Mini Kit , following the manufacturer protocol. Library construction and sequencing were performed on the Illumina sequencing platform. Briefly, genomic DNA was fragmented to 100–300 bp size and two replicate genomic DNA re-sequencing libraries (100–300 bp insert) were prepared for two entire lanes sequencing on Hiseq X10 PE150 sequencing system. Genotyping by sequencing (GBS) libraries were constructed using the EcoRI and NIaIII enzymes modified from the Elshire’s protocol . The simGlycine max Wm82.a2.v1 with the settings ‘mem 4 -k 32 -M’ (2) tests were conducted for all SNPs to detect segregation distortion. The sliding window (1 kb) approach was used to evaluate groups of consecutive SNPs for utility in genotyping and avoidance of false positive population based SNPs. Qualified bins of SNP markers were then used to construct genetic linkage maps using Join Map 4.1. The regression algorithm and Kosambi mapping function were used in marker distance calculation. The LOD value was set according to the chromosome numbers. A Perl SVG module was used to draw the linkage map. Bin markers were named as Ch“x”.“y” according to results of SNP calling, “x” and “y” represent “chromosome number” and “physical position,” respectively.For SNPs calling, the Burrows-Wheeler Aligner (BWA) was used to align the clean reads from each plant against the reference genome k 32 -M’ . Genotypk 32 -M’ . PolymorP-values <0.05 was used to verify LOD scores. QTLs for which the LOD score simultaneously exceeded 4.5 and the genome wide LOD, were considered as high confidence QTLs in MQM mapping.Multiple-QTL model (MQM) implemented in MapQTL6.0  was used2bh) was estimated for each trait according to: 2bh = VG/(VG + VE), with VG as the variance between RILs and VE as the variance within RILs. The Student’s t-test was used to test the significance of the effects of N level and rhizobial inoculation on each trait in RILs. Multi-factor and multivariate analysis of variance (ANOVA) was also performed using SPSS 19  was implemented in the QTL ICIMapping V4.1 software . Parenta SPSS 19 .P < 0.001) and NS (P < 0.01), as well as, a significant interaction between rhizobium inoculation and genotype on NW (P < 0.05)  Table –C. Howev5) Table . Interes5) Table . Nodules5) Table , indicatIn addition, N level significantly influenced nodulation on FC1 but not FC2. NN increasing by 535.71% or 41.94%, NW increasing by 323.70% or 235.96%, and NS increasing by 12.90% and 181.63% in response to rhizobium inoculation in LN and HN treatments, respectively Figures –F. These9:11 soybean RILs under different environments. As expected, among the RILs, high N supply significantly suppressed nodule formation and development as indicated by declines of 33.31% and 30.91% in NN, 63.72% and 63.72% in NW, and 29.87% and 51.98% in NS, with or without rhizobial inoculation, respectively  for all the traits under the tested environments varied from 0.48 to 0.87, with generally higher values being observed in LN than in HN plots  . In ordeqBNF-16 significantly affected NN and NS, but not NW and TNC  into ammonia with the assistance of rhizobia in symbiotically derived organs known as nodules  and BNF capacity , and theSince effective rhizobial strains play critical roles in facilitating soybean production with high nitrogen fixation capacity, lots of commercial rhizobial strains have been isolated and applied in the field . However2 fixation can provide the nitrogen needed for plant growth, symbiotically fixed N is not always sufficient for producing high yields . These QTLs are distributed on linkage groups D1a(2), C1(1), A1(1), C2(4), A2(1), K(1), B1(1), E(2), L(2), and I(3). None of these QTLs co-locate or are closely linked with QTLs identified in our study, suggesting that Any related SNPs data can be available on request by contacting the corresponding author directly.HLi designed the experiments and critically revised the manuscript. QY and YY analyzed the data and wrote the manuscript. QY, YY, RX, and HLv carried out the experiments.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


P < 0.05 and directionally consistent) on TGs at visit 4 (TG4) for five loci, including rs645040 (DOCK7), rs964184 (ZPR1/ZNF259), rs4765127 (ZNF664), rs1121980 (FTO), and rs10401969 (SUGP1). Across these loci, we identify three with strong evidence of an indirect genetic effect on TG4 through HDL, one with evidence of pleiotropic effect on HDL and TG4, and one variant that acts on TG4 indirectly through a nearby methylation site. Such information can be used to prioritize candidate genes in regions of interest, inform mechanisms of action of methylation effects, and highlight possible genes with pleiotropic effects.Even though there has been great success in identifying lipid-associated single-nucleotide polymorphisms (SNPs), the mechanisms through which the SNPs act on each trait are poorly understood. The emergence of large, complex biological data sets in well-characterized cohort studies offers an opportunity to investigate the genetic effects on trait variability as a way of informing the causal genes and biochemical pathways that are involved in lipoprotein metabolism. However, methods for simultaneously analyzing multiple omics, environmental exposures, and longitudinally measured, correlated phenotypes are lacking. The purpose of our study was to demonstrate the utility of the structural equation modeling (SEM) approach to inform our understanding of the pathways by which genetic variants lead to disease risk. With the SEM method, we examine multiple pathways directly and indirectly through previously identified triglyceride (TG)-associated SNPs, methylation, and high-density lipoprotein (HDL), including sex, age, and smoking behavior, while adding in biologically plausible direct and indirect pathways. We observed significant SNP effects ( Lipid traits, such as triglyceride (TG) and high-density lipoprotein (HDL) cholesterol concentrations, are highly heritable; estimates range from 20 to > 70%, with common variants estimated to explain approximately 30 to 33% of the variance for these traits , 2. GenoThe purpose of our study was to demonstrate the utility of the structural equation modeling (SEM) approach to inform our understanding of the pathways by which genetic variants lead to disease risk. With the SEM method, we can examine multiple pathways directly and indirectly through previously identified TG-associated SNPs, methylation, and HDL, including sex, age, and smoking behavior, while adding in biologically plausible direct and indirect pathways. Although SEM has been used to examine the influence of genetic variants on disease through environmental exposures , on geneGAW20data were provided by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. Individual genetic and phenotypic data are drawn from a total of 1105 adults from 188 families. Of these, 810 individuals from 172 families have been genotyped on the Affymetrix 6.0 .Our analyses focused on fasting TG as the primary outcome measure. Secondary outcomes included DNA methylation and HDL. Of those with genotype data, 707 participants had whole-genome methylation data measured from CD4+ T cells at visit 2. The HM450K array was used to measure DNA methylation  following bisulphite conversion. The platform detects methylation status of 485,577 CpG (cystine-phosphate-guanine) sites by sequencing-based genotyping of bisulphite-treated DNA. The methylation score for each CpG is reported as a β value, ranging from 0 (nonmethylated) to 1 (completely methylated), according to the intensity ratio of detected methylation. We calculated principal components (PCs) using methylation β values across all CpGs in R (v3.3.1) and used the first four PCs to adjust to control for cell purity and batch effects prior to performing association analyses. Covariates included sex, baseline age, and study center. Additionally, we adjusted for baseline smoking status, as had been done in previous genetic and methylation association analyses –15.We selected established TG-associated SNPs , 16 avaiP > 0.2) were removed from the model and reevaluated for model fit. For all loci, model fit criteria were met following this step. Figure P < 0.05) and displayed directionally consistent effects with previous GWAS findings.For the SEM, we defined a structure a priori based on hypothesized pathways to evaluate how certain covariates directly and indirectly influenceTG4.Our SEM framework estimates all plausible pathways simultaneously, so each estimate can be considered conditional on all other possible pathways modeled within each locus. A lag effect for TG and HDL was included to allow current values to relate to the time point immediately prior, thereby allowing for covariates to have an indirect effect on TG and/or HDL through their impact on a previous time point. Huber-White sandwich estimation (HSE) was used to account for family relatedness and correlation within a household . Root meOur sample included up to 707 participants of the GOLDN study (50% women); the average age of participants at the time of the baseline examination was 48 years. It is worth noting that all participants in the GOLDN study received treatment with fenofibrates between visits 2 and 3, which resulted in a decrease in mean TG and a decrease in the variance .P < 0.05 and directionally consistent) on TG4 for five loci (Table DOCK7), rs964184 (ZPR1/ZNF259), rs4765127 (ZNF664), rs1121980 (FTO), andrs10401969 (SUGP1). We did not identify any significant direct effects of any SNP on TG4 after accounting for indirect effects through early measured TG, HDL or CpG methylation. For both rs645040  indirect effect on TG4. Both variants were associated with TG in previous GWAS; rs1121980 was also associated with HDL [R2 = 0.904), has been associated with HDL. For both of these loci, the effect previously observed on TG and HDL does not appear to be a result of pleiotropy, but rather an indirect effect of the SNP on TG through its effects on HDL. Similarly, rs10401969, which displays a significant indirect effect through HDL at visit 1 (HDL1) on TG4, does not display a significant independent direct effect on TG. Even though this locus has not been associated with HDL, it has been associated with TG, low-density lipoprotein (LDL) cholesterol, and total cholesterol (TC).We observed significant indirect SNP effects , which was previously associated with both HDL and TG [P < 0.05 and directionally consistent) effect on TG4 , as does the total effect of the SNP on TG4 accounting for all proposed pathways. Contrary to the three loci mentioned above, we do find evidence of an independent direct association for rs964184 with both TG and HDL. This suggests a model whereby a common single mechanism affects multiple lipoprotein concentrations, which is indicative of true pleiotropy. Indeed, the complexity of lipoprotein metabolism means that genes and biochemical pathways can be involved in metabolism of several lipoprotein classes [For rs964184 ZPR1/ZNF59, whichTG4 Fig. . Also, t classes .P value < 0.05) and directionally consistent indirect effect for rs4765127 (ZNF664) on TG4through a nearby CpG (cg02647265), which lies 4 kb upstream of our tag SNP in the 5′UTR (untranslated region) of CCDC92, the gene adjacent to ZNF664. While our tag SNP lies within ZNF664, variants within CCDC92 also have been associated with multiple lipid levels and lipoprotein size, also suggesting CCDC92 as a candidate gene for this region [CCDC92and ZNF664), further investigation into the association of this CpG with expression is warranted to further elucidate the causal gene underlying this GWAS association signal.Finally, we also observed a nominally significant (s region , 23. EveWe aimed to highlight the utility and flexibility of SEM for adding to our understanding of the genetic underpinnings of disease risk by investigating pathways between GWAS-established SNPs and a disease risk factor, TG, through correlated phenotypes and epigenetic markers simultaneously. There is substantial interest in the field for approaches to integrate multiple types of phenotypic and omics data so that a better understanding of disease mechanisms can be achieved. Using the GAW data, we were able to determine if the previously observed SNP effects on TG could be explained by an indirect effect of the SNP through HDL and nearby CpG sites. We identified three loci where associations with TG were indirect through HDL and one locus where the effects of SNPs on TG were mediated through methylation. Such information can be used to prioritize candidate genes in regions of association, inform mechanisms of action of methylation effects, and highlight possible genes with pleiotropic effects.Although the examples highlighted herein demonstrate the utility and flexibility of SEM to inform mechanistic underpinnings of GWAS loci, our study is limited by the small set of variables available to investigate in the complex SEM. For example, only direct genotypes, methylation, HDL, and TG values were available. Lastly, it is also possible for nominally significant associations between CpGs and TG to be mediated through HDL, but because of model complexity and limited sample size, we did not test this explicitly.The majority of investigations into the genetic underpinnings of TG do not take advantage of the wealth of longitudinal data available in many large cohort studies. Additionally, there is a dearth of comprehensive studies that incorporate genetic, epigenetic, and correlated phenotypic data to investigate pathways through which genetic variants influence trait variance. To address this important research gap, we capitalized on extant genotypic data at known TG-associated loci, longitudinal assessments of TG and HDL, smoking exposure, and methylation available through the GAW20 to explore the utility and flexibility of the SEM framework for informing mechanistic insights at GWAS loci. In future investigations, the proposed approach can be easily extended to accommodate additional and longitudinal omics data, ultimately assisting researchers in better identifying the mechanist pathways through which genetic variants influence trait variance.


The northwards expansion of barley production requires adaptation to longer days, lower temperatures and stronger winds during the growing season. We have screened 169 lines of the current barley breeding gene pool in the Nordic region with regards to heading, maturity, height, and lodging under different environmental conditions in nineteen field trials over 3 years at eight locations in northern and central Europe. Through a genome-wide association scan we have linked phenotypic differences observed in multi-environment field trials (MET) to single nucleotide polymorphisms (SNP). We have identified an allele combination, only occurring among a few Icelandic lines, that affects heat sum to maturity and requires 214 growing degree days (GDD) less heat sum to maturity than the most common allele combination in the Nordic spring barley gene pool. This allele combination is beneficial in a cold environment, where autumn frost can destroy a late maturing harvest. Despite decades of intense breeding efforts relying heavily on the same germplasm, our results show that there still exists considerable variation within the current breeding gene pool and we identify ideal allele combinations for regional adaptation, which can facilitate the expansion of cereal cultivation even further northwards. Hordeum vulgare L.) is, alongside wheat, one of the dominating cereal crops in the Nordic region1, used primarily for feed and malt but with a growing demand for human consumption  which have been associated with early flowering in spring barley, a phenotype first discovered in the Finnish cultivar Tammi  which promotes spikelet initiation  on chromosome 1H is a homolog of Arabidopsis thaliana gene ELF3  is the result of an induced mutation in the cultivar Bonus and was released 1960 with the cultivar Mari (matura = early and rigida = stiff)  on chromosome 5H is involved in vernalization requirement and interacts with Vrn-H2 on chromosome 4H. A wild type recessive vrn-H1 and a functional Vrn-H2 always result in a winter growth habit  . The day equator . Vrn-H1 th habit . Several deleted .uzu1) (semi-dwarf 1 (sdw1/denso) (breviaristatum-e (ari-e) (hcm1)  , semi-dw1/denso) , breviar (ari-e) , and sho) (hcm1) . The uzu arm 2HL . Modern dwarfing . Plants th habit .High-throughput genotyping has developed as a feasible alternative to traditional genotyping with molecular markers, such as AFLPs. The high-throughput method utilizes single nucleotide polymorphisms (SNP) spread across the genome at an even distribution . The devLinkage disequilibrium (LD) is the non-random co-segregation of alleles at two loci . LD is gBetter understanding of the allelic diversity in the Nordic breeding material will enable the application of marker-assisted selection for beneficial allele combinations and speed up the breeding process. Detailed understanding of loci controlling earliness and straw stability will enable fine-tuning of cultivars better adapted to northern latitudes. We screened a panel of 169 barley lines from the Nordic breeding pool at eight locations for the traits heading day, maturity day and straw stability with the aim of performing a genome-wide association analysis to identify loci responsible for the quantitative traits earliness and straw stability in multiple environments.A panel of 169 spring barley lines, representing the Nordic breeding gene pool, were included in the study, selected by each of six different Nordic barley breeding entities . The mai2 field plots. Lodging and straw breaking were not recorded in row sowings, since it could have yielded a different result compared with field plots.Multi-environment field trials (MET) were performed in eight locations for 2–3 years, resulting in a maximum of 19 distinct environments , rangingPlants were phenotyped for early spring growth in four environments  . The meaDNA was extracted using a standard CTAB protocol (Cetyl Trimethyl Ammonium Bromide) with DNA extraction kits as described earlier by 2  on the barley reference genome  were ret2 .All traits were continuous except for the discrete character row type. For the continuous characters, a normal distribution was expected. To check for deviations from the expected normal distribution, data for all trials and traits were plotted in distribution plots using Excel Add-In XLSTAT v. 19.2. Pearson correlations were calculated to describe the relationships between traits and between field trials using Microsoft Excel 2016.Descriptive statistics for all environments, separately and combined, were computed with the psych software package v. 1.8.12  using thTo evaluate the relative contributions of genotype, environment, and genotype by environment interactions in the data set, each trait was analyzed with mixed linear modeling using PROC MIXED in SAS v. 9.4 (SAS Institute Inc.). This initial analysis assumed genotypes, environments, and the genotype by environment interactions to be fixed effects and replications and blocks within replications of the individual trials as random effects.Best linear unbiased estimates (BLUEs) to be used in the GWAS analyses were calculated using the lmer function in the “lme4” R package , assuminyijk is the kth observation of the ith genotype in the jth environment, μ is the common intercept, iG is the effect of the ith genotype, jen is the effect of the jth environment, (j)kr is the effect of the kth replication in environment j, and ijke is the corresponding error.where Population structure was evaluated with the software STRUCTURE v.2.3.4  and by p3. The STRUCTURE analysis was previously run for the PPP169 panel in For the STRUCTURE analysis, the SNP genotype data was run 10 times with a burn-in period of 9999 followed by 9999 iterations from K = 1 to K = 12. To identify the optimal number of genetic clusters (subpopulations), ΔK values were calculated as proposed by Genome-wide association analysis was performed using TASSEL v. 5.2.31  and usinQ-values from STRUCTURE K = 2; (3) EMMA using kinship matrix with eigenvalues from the PCA analysis; and (4) EMMA using only the kinship matrix. In addition, the models in TASSEL were compared with the following models using GAPIT v. 3.0: (5) MLM using the van Raden kinship; and (6) MLM using the van Raden kinship with eigenvalues from the PCA analysis. The significant allelic effect estimate is given in relation to the minor allele in the GAPIT output. The MLM was run as optimum level and the P3D (estimated once) variance component estimation. Quantile-quantile (Q-Q) plots were created by comparing expected and observed chi-square values and Manhattan plots showing positions of associated markers across the genome were constructed for each trait using the R package CMplot4. Quantile-quantile plots (Q-Q plots) were plotted to assess the goodness of fit of the model for each trait. Large deviations from the expected distribution mean that the model does not fit the data. The models were evaluated trait-wise by comparing the Q-Q plots and checking the narrow sense heritability values, where a higher heritability indicated that the model had a higher predictive value.After filtering 5710 SNP markers remained in the analysis of the PPP169 panel, or 73% of the total number of 7864 SNPs. When the PPP124 panel was filtered to remove markers below 5% polymorphism, 5037 SNP markers remained. Kinship matrices were constructed based on the filtered set of markers using the scaled identity-by-state (IBS) method  for both10(p) = 5.00 for the PPP124 panel and -log10(p) = 5.06 for the PPP169 panel. This is a very stringent method (10(p) = 3.70 for PPP124 and -log10(p) = 3.76 for PPP169, which is considered as the significance threshold for the marker-trait associations identified in this study. Both thresholds are indicated in the Manhattan plots.GWAS was performed trait-wise on the calculated BLUEs from all trials with the PPP169 panel, as well as on the PPP124 panel which includes only the two-rowed lines. A threshold value was calculated to estimate a significance level for the association analysis. The Bonferroni method based on the total number of markers for each panel and a significance level of 0.05 gave a -logt method  considerr2-values earlier reported for each chromosome and each panel (The length of the quantitative trait loci (QTL) were decided by calculating the LD between the most significant markers at each intra-chromosomal locus using the TASSEL software. The ch panel  were useAllele combinations were constructed for the traits Ht34, HSHD, HSMD, StL, and SB in the PPP169 panel. The most significantly marker-trait associated SNP marker was used for each QTL and for practical reasons, the total combination of SNPs used to construct the allele combinations was limited to three. The effect of each allele combination with at least five observations (lines) was calculated based on BLUE values and the significance of the effects was tested using the lm  function in R. Allele frequencies were calculated for the SNP markers used in construction of the allele combinations, as well as previously described SNP markers for earliness traits .Summary statistics were calculated for all nineteen environments, individually and combined , S3, andp < 0.05) except for environment effect in Ht32 in the PPP124 panel (p < 0.01), but with negative correlations to the straw properties and the early vigor , these were in turn positively intercorrelated.For all traits in both panels the genotype, environment, and genotype by environment interaction were significant , 108 significant markers with known genetic position were found with 50 and 45 markers unique for the PPP169 and PPP124 panels, respectively , S8. In Ppd-H1, HvCO1, HvCO3, HvFT1, and denso/sdw1 loci , the pattern was less clear.Ppd-H1 and HvCEN locus, which could explain why these loci were not detected in the GWAS , the short and long arm of chromosome 2H (Ppd-H1 and HvCEN), and chromosomal arm 7HS (HvFT1 and HvCO1)  . Here weF3 locus . QDEV.16HvFT1 locus  was found on chromosomal arm 7HS. The two most significant markers  at the 7H-2 QTL, 12_30894, and 12_30895, were both located within the T1 locus . The patT1 locus . Althougr, BK_03 , showed denso/sdw1, in the PPP169 panel (denso/sdw1 locus has been speculated to have an effect on both earliness and height (Another QTL with strong association (QDEV.169.3H-1) to early vigor, heading, and maturity was detected on chromosomal arm 3HL nearby the known flowering and semi-dwarf locus, 69 panel , 4. The d height , and thed height .HvAP2, previously shown to influence tiller number and plant height  and maturity (PPP124) we found a QTL located nearby the locus HvCry1b , which pHvCry1b , and hasHvCry1b .When the most significantly associated markers for each trait were combined into allele combinations, we identified combinations with strong effect on earliness traits . Especiadenso/sdw1 locus on chromosome 3H in the PPP169 panel (Hv20ox2), involved in biosynthesis of gibberellic acid (GA), has been identified as a candidate for the denso/sdw1 gene which could explain its effect on plant height  near the 69 panel , with ant height . This QTt height .HvDWF11 locus (Another significant QTL (QDEV.169.2H-4), also with an effect of 9 cm, was found on 2H in the PPP169 panel. This is located close to the 11 locus , which h11 locus .In the PPP124 panel three QTL, located on chromosomes 1H, 2H, and 5H, were found associated with StL . The peaHvGID2  population has been produced.The BLUE distributions showed a considerable difference between the two panels, with the PPP169 panel having a greater range of diversity for all traits. This is, at least for the earliness traits and the early vigor, most likely due to a small number of extremely early six-rowed barley lines from Iceland, that all headed earlier than 50 days in the field trials see . The lowHvFT1 and HvCEN genes are fixed in the two-rowed lines but there is diversity among the six-rowed lines. Based on our results we could identify ideal allele combinations for regional adaptation to the unique day length and climate conditions in the extremely northern latitude, which could help push the margin for barley cultivation both in the north and possibly at other marginal areas.We here report the first GWAS of developmental traits focusing exclusively on Nordic spring barley from all five Nordic countries including both two- and six-rowed cultivars. Previous studies have found Nordic barleys to carry allelic diversity in many loci affecting early heading and early maturity . This waAJ, MV, MA, LR, GB, JO, BG, RH, MG, and BE were involved in the planning and experimental design of the study. LK, RH, RA, MI, MJ, LR, TC, MG, and JH were managing the field experiments and phenotyping in field. JO, GB, and MG were managing the laboratory experiments. TB, MG, JO, ML, GB, and JHH performed data and statistical analyses. MG, TB, JHH, and ML wrote and critically reviewed the manuscript, made the figures and finalized the tables. All authors contributed to the discussion of the results and the editing and approval of the final manuscript.JO and AJ were employed by Nordic Seed A/S, Denmark. TC, ST, and BG were employed by Lantmännen Lantbruk, Sweden. LR and MA were employed by Graminor, Norway. RA, MI, and MV were employed by Boreal Plant Breeding Ltd., Finland. LK, RH, and BE were employed by Sejet Plant Breeding, Denmark. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MUC5B with increased survival, suggesting instead a significant association with decreased survival. In re-analysis of a study of Crohn’s disease prognosis, four regions remain associated at genome-wide significance but with increased standard errors.Following numerous genome-wide association studies of disease susceptibility, there is increasing interest in genetic associations with prognosis, survival or other subsequent events. Such associations are vulnerable to index event bias, by which selection of subjects according to disease status creates biased associations if common causes of incidence and prognosis are not accounted for. We propose an adjustment for index event bias using the residuals from the regression of genetic effects on prognosis on genetic effects on incidence. Our approach eliminates this bias when direct genetic effects on incidence and prognosis are independent, and otherwise reduces bias in realistic situations. In a study of idiopathic pulmonary fibrosis, we reverse a paradoxical association of the strong susceptibility gene  Different from GWAS for susceptibility to disease, GWAS for prognosis or survival may be vulnerable to selection bias. Here, Dudbridge et al present an approach to reduce index event bias in simulated and realistic situations, and apply it to GWAS of survival with idiopathic pulmonary fibrosis and Crohn’s disease prognosis. However, such studies are not necessarily informative on the course of disease, so their results cannot immediately be utilised to identify therapeutic targets or inform clinical management2. Among a few published GWAS of survival, associated single-nucleotide polymorphisms (SNPs) have tended to differ from those associated with susceptibility8. With many collections of disease cases now genotyped by studies of susceptibility, more GWAS of severity, prognosis and survival are expected in the coming years.The majority of genome-wide association studies (GWAS) conducted to date have studied susceptibility to disease. They have provided insights into biological mechanisms leading to disease, enabled Mendelian randomisation studies of risk factors and shown promise for population screening9. This is one of several types of selection bias whose relevance to genetic epidemiology has recently been discussed11. Independent causes of disease become correlated when selecting only the cases of disease, creating indirect associations between causes of disease with subsequent events  and re-analyse a GWAS of Crohn’s disease prognosis.X is linear in the coded genotype G, the combined common causes U of incidence and prognosis, and causes EX unique to X:Y is linear in G and U with an additional main effect of X:X or Y are binary, we continue to argue from linear models by observing that logistic and probit link functions are approximately linear for small effects (Methods).For a single SNP, we assume that incidence βGY, conditional on incidence X and confounders U. In practice, however, the relevant confounders may not be observed and we can only estimate the SNP effect conditional on incidence, denoted by βGY plus a bias that is linear in the effect on incidence βGX:βGX is negative if the confounder effects on incidence and prognosis, βUX and βUY, have the same sign and positive if they have opposing signs.The effect of interest is the direct SNP effect on prognosis U is constant  as a pre-processing step. However, this is only required to obtain a valid estimate of the slope X and Y, the genetic component of U may not be constant across SNPs and the assumption of a constant regression slope b is violated. This problem could be avoided by conditioning the prognosis associations on the major genes, thereby estimating the bias through all confounders except those genes. The resulting correction is then appropriate for all SNPs including those in major genes .Firstly, we simulated 100,000 independent SNPs of which 5000 (5%) had effects on incidence only, 5000 had effects on prognosis only and 5000 had effects on both incidence and prognosis. Incidence and prognosis were simulated as quantitative traits under additive models with 50% heritability (Methods), with a non-genetic confounder  simulated to explain 40% of variation in both incidence and prognosis. No direct effect of incidence on prognosis was simulated 2. Results for the family-wise error were more pronounced and followed the same pattern.Table Table We then repeated the simulation with no non-genetic confounding, so that bias only arises through genetic correlation violating our independence assumption. Table Tables MUC5B) with incidence26. We reanalysed 612 UK cases and 3366 UK controls that we had contributed to that meta-analysis, obtaining an odds ratio of 5.64 for the SNP rs35705950 in MUC5B , and conducted a GWAS of survival time in 565 of the cases (Methods). Similar to previous studies27 the risk allele of rs35705950 was associated with increased survival in our study . This apparently paradoxical result could arise from index event bias, given the strong odds ratio for incidence. We applied our regression-based adjustment using 140,092 LD-pruned SNPs with imputation MUC5B; full results of the survival GWAS will be reported separately.A recent GWAS meta-analysis of idiopathic pulmonary fibrosis (IPF) confirmed the strong association of mucin 5B . The very wide confidence interval reflects high standard errors on individual SNP effects. Nevertheless, the coefficient is significantly negative, which implies that there are common causes of incidence and prognosis that have the same net direction of effect.The regression of survival log hazard ratios on incidence log odds ratios gave a coefficient of −0.025. The sign of this coefficient changed under the Hedges–Olkin based adjustment for regression dilution, which is implausible because regression dilution bias is the ratio of two variancesP-value of order less than 10–4. The empirical 95% confidence interval for the log hazard ratio was 11.58–126.56, suggesting an extremely strong effect on survival. However, in view of the substantial uncertainty in The asymmetry in the confidence interval suggests that a normal approximation for mentclass2pt{minimMUC5B on incidence, it is possible that it dominates the estimation of index event bias and that our approach over- or under-corrects the bias in MUC5B itself  by a subsequent study by Lee et al.6. The latter study identified four regions associated with prognosis at genome-wide significance Odds ratios for Crohn’s disease have been published by the International Inflammatory Bowel Disease Genetics ConsortiumWe reanalysed the summary statistics using our regression-based adjustment with 29,715 LD-pruned SNPs with imputation MHC region was attenuated to just short of genome-wide significance. However, another MHC SNP, which was genome-wide significant in the index study, did remain so after adjustment  consortium30, which aims to identify risk factors for recurrent coronary events in patients with coronary heart disease. In a simulation study15, we showed that index event bias could be small in GWAS. Here, we have confirmed this for Crohn’s disease, but have shown an example in IPF where a strong effect on susceptibility appears to create a substantial bias that reverses the survival effect. This illustrates how index event bias can have variable effects in different studies, and reinforces the need to adjust for it to be confident in any genetic associations with prognosis.Awareness of index event and related biases31. For phenotypes related to cumulative effects of long-term exposures, such as CVD but also perhaps some psychiatric traits, such shared pathways may be common. But for conditions in which prognosis depends upon the response to an initiating event, as perhaps in cancer or infectious diseases, the determinants of prognosis are conceivably independent of those for incidence. Even in CVD, determinants of arterial plaque development may be independent of those for plaque rupture. For immune-mediated disease, where a break in immunological tolerance is the key event at disease initiation, it is expected that other pathways drive disease course, since tolerance can only be broken once to a particular antigen. Also, where developmental mechanisms contribute to predisposition to late-onset disease, the determinants of prognosis may plausibly be independent. Some have argued that independently pleiotropic effects are likely to be typical for complex disease25: for most pairs of traits, the genetic effects on the first are independent of the corresponding effects on the other. However, our independence assumption precludes any meaningful analysis of genetic correlation between incidence and prognosis.The critical assumption of our approach is that direct genetic effects on prognosis are independent of those on incidence. Since GWAS of susceptibility have been motivated by the discovery of novel treatment targets, our assumption may seem incompatible with the premise of GWAS. Indeed, shared pathways of incidence and prognosis have been observed in coronary heart disease, in which statins have proved effective in preventing both initial and recurrent events32 while allowing a high degree of index event bias. In smaller studies, however, such as our IPF survival GWAS, our adjustment may have high variance resulting in more severely reduced power.Our simulations showed reduced type-1 error rates for our procedure compared with an unadjusted analysis, except in the case of strong negative genetic correlation between incidence and prognosis or no non-genetic correlation. Power is slightly reduced overall, but may be considerably increased for some individual SNPs. Again our approach performed more poorly under strong negative genetic correlation, which is arguably less likely than positive correlation. We simulated genetic architectures that were typical of complex diseasesMUC5B with increased survival may be due to index event bias, and that in fact this gene may well cause decreased survival. It has been hypothesised that carriers of MUC5B risk alleles experience a milder form of disease, in line with the clinical heterogeneity of IPF27. While associations with prognosis can be explained by disease heterogeneity, they remain susceptible to index event bias whether or not disease subtype is accounted for  could lead to an over-representation of patients with ileal disease, for whom surgery is more commonly used because the operation carries lower morbidity than colonic surgery and does not leave a permanent stoma. Considering location as a disease subtype, adjustment for location might modify the prognosis associations for SNPs with effects on particular disease locations 34 would assume that at least half of the SNPs considered have no direct effect on prognosis. Alternative approaches related to parallel work in Mendelian randomisation are a promising area for further development.We may draw on the analogy with MR-Egger to contemplate other approaches based on the ratio of prognosis effects to incidence effects. Such approaches would entail other assumptions that require careful consideration. For example, a counterpart of the median ratio estimatorU and is therefore accounted for by our regression procedure. Indeed our simulations used the same subjects in both GWAS, and obtained the correct type-1 error rates when expected.Our approach is robust to the use of the same subjects in the prognosis GWAS as in the incidence GWAS. This is because any correlation in prognosis and incidence phenotypes is by definition included in 17. However, in practice our approach only requires that the bias is linear in the incidence effect, which we argue is approximately true for polygenic traits. This linear relationship is estimated from data, and while our theory provides an interpretation for it under some assumptions, our approach requires only that such a relationship exists. The data in our examples used log odds ratios and log hazard ratios, and our simulations suggested the linear approximation was acceptable in these cases.Our analytic result is derived from linear regression models, and is inexact for traits generated under other models18. In our simulations, we found no systematic difference between the case/control and case-only analysis. Note that our approach could be applied in conjunction with the case/control analysis, and possibly with further adjustment for measured confounders of incidence and prognosis. This would have the desirable effect of reducing index event bias through several complementary approaches at once.When the incidence trait is binary, we have mainly considered a case-only analysis of prognosis. Other approaches are possible, such as setting the prognosis to a degenerate value for controls and then analysing cases and controls together, with adjustment for case/control status20, but cannot immediately be corrected by our approach because it would require knowledge of the effects of all confounders :\documend X Eq. ):\documementclass2pt{minimG, U, EX and EY each have mean zero and hence also G on Y conditional on X, but not on U. If Assume without loss of generality that 2, except that we allow for the direct effect of X on Y in Eq. then the asymptotic ordinary least squares estimate ismentclass2pt{miniminto Eq. \documentβGX across many SNPs. In a finite sample, this yields an estimate 28, we can obtain a bias-reduced estimate as 35. We find that the Hedges–Olkin estimatorAs noted in the Results, we may argue that 23, this approach can have large variance, and can lead to implausible negative adjustments for regression dilution, as we found in our IPF data. Therefore we follow Bowden et al.23 in proposing simulation extrapolation24 (SIMEX) for the analysis of real datasets. Briefly, this approach simulates new estimates λ. The linear regression of Eq.  and prognosis6 (2734 cases) from the internet and analysed 7,908,787 autosomal markers present in both datasets. Our re-analysis of these results is covered by the existing ethical approvals and informed consent reported for those studies. To estimate our regression adjustment we selected a set of 29,715 LD-pruned SNPs, from a total of 1,370,154 SNPs with imputation R2 values than the Haplotype Reference Consortium reference.We downloaded summary statistics for incidenceSupplementary informationpeer review fileDescription of Additional Supplementary FilesSupplementary data 1


The fungal mycelium represents the essence of the fungal lifestyle, and understanding how a mycelium is constructed is of fundamental importance in fungal biology and ecology. Previous studies have examined initial developmental patterns or focused on a few strains, often mutants of model species, and frequently grown under non-harmonized growth conditions; these factors currently collectively hamper systematic insights into rules of mycelium architecture. To address this, we here use a broader suite of fungi , all isolated from the same soil, and tested for ten architectural traits under standardized laboratory conditions. We find great variability in traits among the saprobic fungal species, and detect several clear tradeoffs in mycelial architecture, for example between internodal length and hyphal diameter. Within the constraints so identified, we document otherwise great versatility in mycelium architecture in this set of fungi, and there was no evidence of trait ‘syndromes’ as might be expected. Our results point to an important dimension of fungal properties with likely consequences for coexistence within local communities, as well as for functional complementarity . Understanding basic “rules” of mycelium structure is therefore of fundamental importance for gaining insight into fungal biology and ecology.The mycelium comprises the entirety of the hyphae of a fungus, representing its nutrient-capture and interaction interface, and the infrastructure for transport within the fungal individual. This structure is designed for a dynamic exploratory lifestyle with its ability to reconfigure, fragment and fuse, and represents the very essence of the fungal lifestyle3, revealing some general hyphal growth patterns: emerging from a spore, a hypha extends at an exponential rate followed by a constant linear phase until the formation of a new branch is initiated; each new branch itself follows this exponential–linear phase pattern. Additionally, hyphae show negative autotropism and radial orientation away from the colony center3, eventually giving rise to the characteristic circular (in 2D) or spherical (in 3D) shape of “colonies” or fungal individuals which can be observed on homogenous growth media (e.g. agar).The initial development of the mycelium starting from germinating spores has been extensively studied e.g.Neurospora crassa branching angles decrease after approximately 22 h while hyphal extension rate and diameters increase. Ultimately, the mycelium establishes a hierarchy in which hyphae of higher branching order have decreased hyphal growth rate and diameter in relation to the parental hyphae from which they emerged. As a consequence, the space-filling capacity of the mycelium increases4, leading to a maximum surface area while investing in a minimum of hyphal length5.Mycologists have also examined the kinetics and branching behavior of fungi and revealed that fungal mycelia undergo changes in growth behavior due to differentiation. In the model organism 7. Additionally, the formation of anastomoses decreases the number of hyphal tips, hence influencing colony growth rate8.Modelling approaches further contributed to our understanding of how the growth of fungal mycelia is influenced by internal restructuring processes. Internal resource recycling and translocation affects mycelium formation by e.g. increasing hyphal growth rate while decreasing branching rateRhizoctonia solani and Neurospora crassa) or mutants of the same species, mainly derived from fungal culture collections11; with only few notable exceptions e.g.12. Thus, we are currently lacking a systematic comparison of a range of architectural features, measured under the same, standardized laboratory conditions, on a larger set of fungi from a common ecological context. This is why we currently only have limited knowledge about tradeoffs governing mycelium architecture that could give insight on structural “rules”. So far, insights into such tradeoffs come from studying single species or mutants, and this frequently has not resulted in a consensus. For example, the relationship between hyphal diameter and growth rate can be positive (e.g. Bortrytis) or neutral 13. The same discrepancy holds true for the relationship between hyphal branching frequency  and hyphal growth rate, which was examined in multiple studies focusing on Neurospora strains and mutants: some studies found a positive relationship14 and others did not15.The majority of studies exploring the rules of mycelium structure draw their conclusions from a limited suite of fungal strains with a typical focus on few fungal model strains , Basidiomycota (four strains) and Mucoromycota (seven strains) . A set of 31 fungal strains were isolated from soil samples as described in Andrade-Linares ns) Fig. .Figure 121 to split the rRNA sequences into the different subregions ITS1, 5.8S, ITS2 and LSU. Each region was aligned independently using AlignSeqs in the R package “decipher” v.2.023. Aligned subregion sequences were concatenated. Pairwise distances from sequences were calculated using the JC69 evolutionary model and used to construct a neighbor-joining tree, with the dist.ml and NJ functions respectively, of the R package “phangorn” 2.5.524. Root was placed at the midpoint of the longest path between any two tips. Taxonomic annotations of the fungal isolates were based on each subregion. We used the Naive Bayesian Classifier25 as implemented in the R package “dada2”26 against UNITE database for ITS1 or ITS227, and against the RDP LSU database for LSU28. A confidence threshold was calculated with bootstrap analysis and an annotation was deemed valid when supported in 80% of the bootstraps. The best resolved taxonomic annotation among the regions was chosen. In the case of a conflict of taxonomic annotations between regions, priority was given to ITS1 or ITS2 because UNITE is more complete than the RDP LSU database. We followed the phylum classification by Spatafora et al.29  and a part of the large rRNA subunit (LSU). DNA was extracted using the Qiagen DNeasy PowerSoilKit (100) or MasterPure Yeast DNA Purification Kit , following the manufacturer’s instructions. Amplification of the ITS and partial LSU regions were performed with primers ITS1F and LR5, while internal primers ITS4 and NL4 were used for sequencing with Sanger technology. Sequences were assembled and manually edited to resolve ambiguous base calling using Sequencher 5.4.6 . We used ITSxin vitro system with potato dextrose agar  as growth substrate, focusing on traits that were measurable for all strains, and on methods that worked for all. We needed to use a translucent agar growth medium to enable investigation of fungal hyphae; this study would not have been possible in soil with the currently available methods. Furthermore, the application of a single growth medium guaranteed consistent, standardized environmental conditions for the trait measurements16. However, results cannot be extended to situations in which other environmental parameters prevail, such as different growth media with distinct sources of nutrition .We conducted two studies to collect traits for the 31 fungal strains. In order to allow meaningful comparisons of collected traits, all studies were performed in the same Due to the high variability in colony growth among our 31 fungal strains, the set of traits and locations within the colony that could be measured was naturally restricted. The only reliable inoculation technique that works for this diverse set of fungi uses a colonized poppy seed (as described below), since some of our fungi do not readily sporulate. Since spores are unavailable, we cannot use a single spore as the starting point of mycelial development, as done in studies with selected model strains: we already start with a small mycelium. This, in turn, necessitated a focus on the colony edge since in most of our fungal strains the mycelium behind this peripheral zone was too dense to be resolved for identification of single hyphae . Hence, it was not possible to investigate hyphal branching hierarchies. Additionally, some basic hyphal traits  were only measurable for a subset of strains  and hence were not further considered.in vitro system. We focused on hyphal growth traits and traits representing hyphal arrangements within the mycelium by using fractal dimension. We believe these traits are also informative for key ecosystem functions related to mycelium architecture, such as soil aggregation17. For the hyphal growth traits, we focused on measuring hyphal branching angle (BA), internodal length (IL) and diameter (D). For this, in the first study, the fungal strains were grown on single concavity slides carrying 150 µl of PDA; a widely used standard growth medium for filamentous, saprobic fungi. We chose to reduce the concentration of PDA to 10% to obtain nutrient reduced growth medium for reduced mycelial density. This was necessary to be able to identify single hyphae in very densely growing fungi. To guarantee solidification of the medium, we added agar (Panreac AppliChem) to reach 15 g L−1 concentration. The growth medium was flattened by placing a cover slip on the liquid medium drop until it solidified. A pre-sterilized poppy seed carrying the target fungal strain was positioned in the center of the concavity. The slide was placed in a 9 cm Petri dish filled with a 5 mm layer of water agar to maintain high air humidity. Plates were sealed and stored at room temperature (22 °C) in the dark until the fungal colony covered half of the concavity area. For each fungal strain (n = 31), five slides were prepared and placed in separate Petri dishes. Hence, we had five replicates for 31 fungal strains yielding 155 concavity slides for analyses. For the measurements, slides were examined under the microscope . Per slide, we randomly chose five hyphae as subsamples; for each of these hyphae we measured at the colony edge the last developed branching angle, the internodal lengths between this last and second-to-last branch and the hyphal diameters within this youngest internodal segment. For analyses, we used the image processing software ImageJ30. For each replicate for each fungal strains, we calculated a mean value and a coefficient of variation (CV) from the subsample data. These represented two aspects of a trait: the average value and its variability. The trait data used in statistical analyses were the average of mean values and CVs of the five replicates (i.e. n = 31).We found ten traits to be measurable for all 31 fungal strains in the established 31 to cover the hyphal arrangement. For this, we applied the same approach as in the first experiment but with eight replicates per fungal strain yielding 248 concavity slides for analyses. At harvest, the slides were examined under the microscope  focusing on the outer 200 µm of the growing zone to investigate the “surface fractals”32. Camera (Leica DFC290) settings were chosen to generate grayscale photos with high contrast. For each slide, we photographed three randomly chosen fields of view at the colony edge. These settings and further image processing in ImageJ30 and Adobe Illustrator  were necessary to guarantee comparable and unbiased photos that can be processed by image analysis software. First, photos were converted to 8-bit binary images in ImageJ and subsequently hyphae were skeletonized. For this, a thinning algorithm repeatedly reduced pixels from the edge of the target object until a one-pixel wide shape was reached33. In Illustrator, the skeletonized hyphae were reconnected and image artifacts excluded, if necessary. Line thickness was adjusted to mean diameter trait values derived from experiment one. The final processed images were loaded into the ImageJ plug-in “FracLac”34 to measure fractal dimensions. We chose box counting dimensions (Db) as a measure of structural complexity (i.e. the degree of detail or amount of parts a pattern consists of), and lacunarity (L) as a representative of structural heterogeneity . In FracLac, we used overall default settings but for the grid design we allowed for multiple rotational orientations in the analyses. Finally, subsample data were used to calculate CVs for box counting dimension and lacunarity. The subsample data were then merged to one mean and CV trait value per replicate. Additionally, we verified if implementing diameter data altered fractal dimension data by correlating skeletonized and adjusted diameter data for both box counting dimension and lacunarity .In the second study, we investigated the complexity and the heterogeneity of fungal mycelia by applying fractal analysis – a technique used to assess self-similarity and space-filling capacity of fungal hyphae35 in the package “ade4”38. Only the first axis was significant, hence we included PC axis 1 and 2 in the visualizations without losing information from the excluded axes. Next, we conducted kernel density estimation to assess species occurrence probability following the procedure presented by Diaz et al.39. Briefly, we used two most important PC axes (axis 1 and 2) to run a two-dimensional kernel estimation by applying the kde function in the “ks” package. To do so, the optimal kernel smoother had to be chosen by using the Hpi function in the package “ks” on the two PC axes40; following Diaz et al.39, we chose unconstrained bandwidth selectors. We constructed the contour plots from the estimated kernel density distribution. The depicted contour lines (by contourLevels function) and corresponding color gradient represent the 0.5 and 0.95 quantiles of the probability distribution.We analyzed the relationships between the ten architectural trait variables derived from 31 saprobic fungal strains represented by both mean value and coefficients of variation (CV) (n = 31). First, to evaluate fungal distribution in ten-dimensional trait space, we ran a principal component analysis using the function prcomp in the package “stats” with z-transformed data. Significance of PC axes was determined via the function testdim41 and “ape”42 using the functions pic and match.phylo.data. We evaluated if the assumptions of the Brownian motion model were satisfied by our data43. For that, we investigated the standardization of the contrasts via diagnostic regression tests to evaluate the relationship between absolute standardized contrasts and (i) the square root of their standard deviation44 and (ii) the node height (i.e. node age46). Identified influential nodes (node 37 (strains RLCS02 and RLCS15) and node 57 (strains RLCS21 and RLCS22)47) were excluded, following the threshold of absolute studentized residuals greater than 348. To satisfy the Brownian motion model assumption, we further used log transformed trait values.Second, to test for phylogenetic signal in our trait variables we used Moran’s I statistic, a measure for phylogenetic autocorrelation, as implemented in the package “phylosignal”. We accounted for phylogenetic relatedness among species  by calculating phylogenetically independent contrast of our trait variables with the packages “picante”49. Analyses were done for original  and phylogenetically corrected data .Third, multiple pairwise correlations using Pearson’s rho were conducted and plotted with the function corrplot in the eponymous packagehttps://github.com/cran/quantreg). Under most ecological conditions, linear regressions tend to over- or underestimate relationships due to a focus on the mean of the response distribution. Especially in wedge-shaped data distributions, indicating unmeasured limiting factors, quantile regressions are more informative since they test the relationship between response and predictor variable at their maxima51. Both regression analyses were run on z-transformed data and model residuals were tested for homogeneity and normal distribution. Additionally, we ran multiple pairwise regressions for both original and phylogenetically corrected data to provide graphical information on data distributions of all trait combinations . These were generated by the function ggpairs of the package GGally52.Fourth, we ran linear regressions and further investigated the relationships by quantile regression with the package “quantreg“ (53) and plots were created with the graphics package ggplot254 and its extension GGally.All analyses were conducted in R  influences the expression of the traits we examined here.Overall, we found variability among strains for all traits Fig. . The appAfter establishing the trait database, we investigated the trait space generated by the collected fungal architectural features. To do this, we applied principal component analyses.For our 31 fungal strains, the sole significant first principal components accounted for 34% of the variability in the ten architecture traits Fig. . In thisCV . Hence, we needed to account for phylogenetic relations for these two traits among our 31 fungal strains by applying phylogenetically independent contrast in the following analyses.For this, we first tested all ten traits for a phylogenetic signal to evaluate if the phylogenetic relatedness among fungal strains can influence any trait relationships we want to investigate. Applying Moran’s I statistics Table , we founCV in Fig. CV in Fig. CV in Fig. CV - BACV in Fig. CV), hyphal diameter and the coefficients of variation of lacunarity (D - LCV), as well as between internodal length and the coefficients of variation of lacunarity (IL - LCV), internodal length and branching anlge (IL - BA) and the coefficients of variation of mycelium complexity (L - DbCV) and also branching angle and the coefficients of variation of intermodal length (BA - ILCV)  Figs  and S5. CV in Fig. CV in Fig. For structural complexity (as represented by box counting dimensions) and branching angle, we detected a negative relationship between their mean values and CVs , no clear ecological or evolutionary explanation is available. It is noteworthy that these traits were of low statistical significance. It is thus necessary to challenge these trait relationships in future works to unravel their true significance.It is interesting that there were no sharp boundaries in the sense of architectural “syndromes” or clear groups of traits, but rather relatively gradual changes in trait values within the set of fungal isolates we examined. This illustrates the relative versatility of the mycelial growth form in evolutionary terms, at least in the peripheral growth zone of the fungus under experimental conditions, which we examined here. We clearly show that there are limits to how a mycelium can be constructed, since some trait combinations are evidently not favorable . However, fungi have evidently otherwise filled the trait space within the constraints of such fundamental tradeoffs, even seen in a sample of 31 species. It will be interesting and necessary, especially for broader external validity beyond the environment from which these strains came, to compare our results to other sets of fungi once such data are available: our results suggest key parameters on which to focus. Ultimately, new knowledge will be gained by the synthesis of the combined research efforts which may yield insight into a hyphal/mycelium architecture spectrum.CV, LCV) in response to environmental conditions.One of the most fundamental decisions a growing hypha has to make is when to branch. Thus, it is maybe not surprising that internodal length was a highly influential variable (aligned with PC axis 1, Fig. 62 by offering fungi different ways to forage and colonize the soil environment. On the other hand, such trait divergence can also mediate functional complementarity, for example in efficient decomposition of organic material or soil aggregation17.Mycelial architecture is a fundamental property of filamentous fungi, governing the way these organisms explore their substrate. Using a set of fungi co-occurring in the same soil, we show that architectural features vary strongly and reproducibly among different isolates under the same laboratory conditions. It is therefore highly likely that such differences contribute to enabling coexistence within fungal communitiesSupplementary InformationSupplementary dataset


Brassica oleracea, heterosis is the most efficient tool providing impetus to hybrid vegetable industry. In this context, we presented the first report on identifying superior heterotic crosses for yield and commercial traits in cauliflower involving cytoplasmic male sterile (CMS) and doubled haploid (DH) lines as parents. We studied the suitability of genomic-SSRs and EST-SSRs based genetic distance (GD) and agronomic trait based phenotypic distance (PD) for predicting heterosis in F1 hybrids using CMS and DH based parents. 120 F1 hybrids derived from 20Ogura based CMS lines and 6 DH based testers were evaluated for 16 agronomic traits along with the 26 parental lines and 4 commercial standard checks. The genomic-SSRs and EST-SSRs based genetic structure analysis grouped the 26 parental lines into 4 distinct clusters. The CMS lines Ogu118-6A, Ogu33A, Ogu34-1A were good general combiner for developing early maturity hybrids. The SCA effects were significantly associated with heterosis suggesting non-additive gene effects for the heterotic response of hybrids. Less than unity value of σ2A/D coupled with σ2gca/σ2sca indicated the predominance of non-additive gene action in the expression of studied traits. The correlation analysis of genetic distance with heterosis for commercial traits suggested that microsatellites based genetic distance estimates can be helpful in heterosis prediction to some extent.In  Brassicaceae holds a great agronomic, scientific and economic significance and comprises of more than 372 genera and 4060 species  x 100, BPH% (Better parent heterosis) = [(F1-BP)/BP] x 100, where MP is mid-parent and BP is better-parent performance and testing of significance was done at probability of p < 0.05, p < 0.01 and p < 0.01 through F test. The narrow-sense heritability  estimates were categorized into three classes viz., high (> 30%), medium (10–30%) and low (< 10%) [2b× phenotypic standard deviation × K, where K value is 2.06, which is a standardized selection differential constant at 5% selection intensity [The agronomic data recorded for each parent, 120 Fsion 9.4 . The linsion 9.4  through sion 9.4  based on (< 10%) . The GA ntensity . The parntensity . They weThe Euclidean distance (ED), hereafter referred as phenotypic distance (PD) was calculated based on sixteen phenotypic traits using R software . The sim2) indicated high variability percentage (>70%) for all the traits except curd length, curd diameter, core length and curd size index. The higher R2 value also suggests a higher significance of the model. The line × tester analysis of variance (ANOVA) for combining ability revealed highly significant differences among the treatments and parents for all the studied traits  for both lines and testers was found lower as compared to the SCA variance (σ2sca) for all the studied traits except for Days to 50% curd maturity. The value of dominance variance (σ2D) was greater as compared to the additive component of variance (σ2A) for all the traits except for days to 50% curd maturity. The greater than unity value of degree of dominance for all the traits indicated dominant nature of these traits except for days to 50% curd maturity. The ratio of additive to dominance variance (σ2A/D) coupled with predictability ratio (σ2gca/σ2sca) was less than unity for all the traits suggesting preponderance of non-additive gene action. The estimation of heritability is associated with selection efficiency. We observed the lowest estimate of narrow-sense heritability (h2ns) for curd length (3.44%) and the highest h2ns value was recorded for days to 50% curd maturity (49.21%). Generally, moderate level of h2ns estimates was found for majority of the traits except for CL, CD, CSI, and HI. The higher estimates of genetic advance (GA) at 5% selection intensity were observed for GPW, MCW, NCW and LSI, while lower estimates of GA were recorded for all the remaining traits.The estimation of genetic components of variance revealed that nature of gene action, heritability, genetic advance and degree of dominance is presented in The estimates of combining ability are effective for early generation selection of inbred lines and identifying heterotic crosses. The GCA estimates of parental lines and testers are summarized in The highest SCA effect in the desirable negative direction for curd initiation was recorded in the hybrid Ogu307-33A × DH-18-8-3 followed by Ogu34-1A × DH-53-1 and Ogu309-2A × DH-53-9. For the days to 50% CM, the highest SCA effect in desirable negative direction was observed in the cross Ogu22-1A × DH-53-10 followed by Ogu125-8A × DH-53-6 and Ogu1A × DH-18-8-3. Among the 120 testcross progenies, 31 exhibited high significant positive SCA effects for PH. The highest positive significant SCA effect for PH was observed in the cross Ogu122-1A × DH-18-8-3 followed by Ogu13-85-6A × DH-53-1, Ogu13-85-6A × DH-18-8-3 and Ogu118-6A × DH-53-9. For the commercial traits viz. GPW, MCW, NCW, CL, CD, CoL and CSI, out of 120 crosses, 39, 32, 38, 10, 26, 33 and 24 crosses exhibited significantly high SCA effects in the desirable direction. For the vegetative traits, among 120 crosses, 44, 37, 33 and 48 crosses showed significantly high positive SCA effects for LL, LW, NoL and LSI, respectively. For the HI and TMY, among 120 hybrids, 18 and 32 hybrids displayed significantly high positive SCA effects, respectively. The cross combination Ogu22-1A × DH-53-6 exhibited highest positive significant SCA effect for GPW followed by Ogu307-33A × DH-18-8-3 and Ogu122-5A × DH-53-10. For the MCW, hybrid Ogu33A × DH53-1 showed the highest positive significant SCA effects followed by Ogu122-5A × DH-53-10 and Ogu1-6A × DH-53-1. The highly significant positive SCA estimate for NCW was observed in the cross Ogu33A × DH-53-1 followed by Ogu1A × DH-53-9 and Ogu22-1A × DH-53-6. For the total marketable yield, the highest significant SCA estimate in desirable positive direction was observed in the cross, Ogu33A × DH-53-1 (good combiner × good combiner) followed by Ogu122-5A × DH-53-10 (good combiner × good combiner) and Ogu1-6A × DH-53-1 (good combiner × good combiner) .The mean performance of the parental CMS and DH lines along with standard checks is presented in Ho) ranged from 0.03  to 0.19 (for the loci BoSF2294a). The mean expected heterozygosity (He) was 0.68, with a range of 0.27 (primer cnu107) to 0.83 (for primer Na12F03a and BoESSR041) and had a higher mean value than Ho. The mean polymorphic information content (PIC) for 87 loci was 0.63. The PIC content ranged from 0.24 for the primer cnu107 to 0.80 for the primer Na12F03a  between lines and testers were computed from 16 phenotypic traits  and GD wΔK reached to peak at k = 4  showed highest significant MPH for percent HI followed by Ogu126-1A×DH-18-8-3 (49.16%) and Ogu12A×DH-18-8-3 (41.99%). For the total marketable yield, the highest significant heterosis over mid-parent in desirable positive direction was observed in the testcross Ogu126-1A×DH-18-8-3 followed by Ogu122-5A×DH-53-10 and Ogu307-33A×DH-18-8-3. Among the top ten crosses also wide range of MPH was recorded for TMY from 92.76% (Ogu33A×DH-53-1) to 172.31% (Ogu126-1A×DH-18-8-3).The Pearson correlation coefficient of genetic distances with heterosis, combining ability, and combining ability with heterosis for ten commercial traits is presented in 2sca than σ2gca of lines and testers for the majority of traits. As the response to natural and artificial selection relies on additive genetic variance, the narrow sense heritability (h2ns) holds a great promise in plant breeding as it provides the basis to precise selection of genotypes based on phenotypic variance because of additive genetic components [2ns was observed for majority of vegetative and commercial traits suggesting non-additive genetic control of these traits, which might be due to large epistatic effects. We also observed moderate estimates of h2ns for antioxidant traits in cauliflower in our previous study [The analysis of variance depicted highly significant differences among all the treatments for all the 16 agronomic traits, indicating considerable genetic differences among parents and their testcross progenies. The success of any crop breeding programme relies on genetic variation present in the germplasm. High genetic divergence among genotypes was reported by Garg and Lal , Verma amponents . In thisus study , the resus study  for mineus study , 10. It us study , 76. Furus study , 9, 73. us study . The majus study , 77. Fewus study , 77. ConB.oleracea [The morphological and molecular diversity is vital in selecting desirable parents for hybrid breeding. The identification of heterotic groups and analyzing existing genetic variation in CMS lines is the preliminary requisite for efficient use of elite CMS lines in heterosis breeding. Study of genetic diversity at morphological and molecular level has been regarded as potential tool in identification of promising parental lines for developing heterotic hybrids in oleracea , 78–80. Brassica crops with different set of germplasm and molecular markers [Brassica oleracea comprising 51 cultivars of cauliflower by Zhao et al. [We also observed high allele frequency of overall 511 alleles through 87 genomic-SSR and EST-SSRs loci in 26 parental CMS and DH lines with an average allelic frequency of 5.87 alleles per locus. Allelic diversity could be due to variation in germplasm used in the study, difference in methods of detection of markers and number of markers from transcribed regions of genome etc. , 81, 82. markers , 79. The markers , 83 and  markers , 83. In  markers  and 0.60o et al. . The DH B. oleracea var. italic L.) by Hale et al. [Numerous studies in different crops have revealed the utility of the genetic distances in prediction of heterotic crosses , 25–29, e et al.  using DHe et al. , 62, 93.e et al. , 62, 93.Ogura cytoplasm-based CMS lines and DH based testers. We also presented the first comprehensive report on predicting the association of genomic SSRs and EST-SSRs based GD and morphological traits-based PD with heterosis involving CMS and DH parental lines in snowball cauliflower. Analysis of variance of parents and their testcross progenies revealed the presence of significant genetic variability. Present investigation also emphasizes the relevance of both GCA and SCA in the selection of elite parents for the improvement of yield and commercial traits and predicting appropriate breeding strategies for the crop genetic improvement, developing high yielding hybrids, synthetics and composites in cauliflower. High and significant correlation among SCA with heterosis suggested the role of non-additive gene effects in heterosis. It was also evident that development of DH lines could broaden the genetic base of any crop through creating more diversity in the existing population. The findings of our study further suggested that genetic distances based on genomic and EST-SSRs can be used as a predictor of heterosis for commercial traits in CMS and DH based F1cauliflower. Contrasting results obtained in different earlier studies regarding the efficacy of genetic distances in the prediction of heterosis, invites further investigation with different sets of large number of molecular markers covering entire genome, and a different set of parental germplasm, in multiple standard environments.The current study is first of its kind in determining the heterotic groups based on combining ability for morphological, yield and commercial traits using S1 FigDCI: days to 50% curd initiation, DCM: days to 50% curd maturity, PH: plant height, LL: leaf length, LW: leaf width, GPW: gross plant weight, MCW: marketable curd weight, NCW: net curd weight, CSI: curd size index, NoL: number of leaves, HI: harvest index, TMY: total marketable yield, CL: curd length, CD: curd diameter, CoL: core length, LSI: leaf size index. The upper and lower lines outside the box stand for maximum and minimum adjacent value, respectively. The median value is represented by line inside the box. The lower and upper hinge of the box stands for 25% and 75% percentile, respectively.(TIF)Click here for additional data file.S2 Fig(A) Corrplot depicting correlation of phenotypic distance with heterosis, (B) Corrplot of association of genetic distance with combining ability, (C) Corrplot depicting association of phenotypic distance with combining ability.(TIF)Click here for additional data file.S1 TableAmong the 350 microsatellites, 87 primers showed clear cut polymorphism and used for allelic diversity analysis.(DOCX)Click here for additional data file.S2 Table* = significant at 5% probability, ** = significant at 1% probability, *** = significant at 0.1%, **** = significant at 0.01% probability through F test, Rep = Replication, Blk = Block, Trt = Treatment, Rep (Blk)Adj = Rep (Blk) Adjustable Days to 50% CI = Days to 50% curd initiation, Days to 50%CM = Days to 50% curd maturity, PH = Plant height, GPW = Gross plant weight, MCW = marketable curd weight, NCW = net curd weight, LL = leaf length, LW = leaf width, NoL = No of leaves, CL = curd length, CD = curd diameter, CoL = core length, CSI = curd size index, LSI = leaf size index, HI = harvest index, TMY = total marketable yield, R2: coefficient of determination.(DOCX)Click here for additional data file.S3 Table* = significant at 5% probability, ** = significant at 1% probability, *** = significant at 0.1%, **** = significant at 0.01% probability through F test, CD = critical difference, Days to 50% CI = Days to 50% curd initiation, Days to 50%CM = Days to 50% curd maturity, PH = Plant height, GPW = Gross plant weight, MCW = marketable curd weight, NCW = net curd weight, LL = leaf length, LW = leaf width, NoL = No of leaves, CL = curd length, CD = curd diameter, CoL = core length, CSI = curd size index, LSI = leaf size index, HI = harvest index, TMY = total marketable yield.(DOCX)Click here for additional data file.S4 Table*commercial standard checks.(DOCX)Click here for additional data file.S5 TableL: CMS lines; T: DH testers.(DOCX)Click here for additional data file.S6 Table* = significant at 5% probability, ** = significant at 1% probability, *** = significant at 0.1%, **** = significant at 0.01% probability through F test, MPH: Mid parent heterosis, BPH: better parent heterosis, SCA: specific combining ability .(DOCX)Click here for additional data file.S7 Table* = significant at 5% probability, ** = significant at 1% probability, *** = significant at 0.1%, **** = significant at 0.01% probability through F test, MPH: Mid parent heterosis, BPH: better parent heterosis, SCA: specific combining ability .(DOCX)Click here for additional data file.