The Generation Scotland Scottish Family Health Study (GS:SFHS) includes 23,960 participants from across Scotland with records for many health-related traits and environmental covariates. Genotypes at ~700 K SNPs are currently available for 10,000 participants. The cohort was designed as a resource for genetic and health related research and the study of complex traits. In this study we developed a suite of analyses to disentangle the genomic differentiation within GS:SFHS individuals to describe and optimise the sample and methods for future analyses.We combined the genotypic information of GS:SFHS with 1092 individuals from the 1000 Genomes project and estimated their genomic relationships. Then, we performed Principal Component Analyses of the resulting relationships to investigate the genomic origin of different groups. We characterised two groups of individuals: those with a few sparse rare markers in the genome, and those with several large rare haplotypes which might represent relatively recent exogenous ancestors. We identified some individuals with likely Italian ancestry and a group with some potential African/Asian ancestry. An analysis of homozygosity in the GS:SFHS sample revealed a very similar pattern to other European populations. We also identified an individual carrying a chromosome 1 uniparental disomy. We found evidence of local geographic stratification within the population having impact on the genomic structure.These findings illuminate the history of the Scottish population and have implications for further analyses such as the study of the contributions of common and rare variants to trait heritabilities and the evaluation of genomic and phenotypic prediction of disease.The online version of this article (doi:10.1186/s12864-015-1605-2) contains supplementary material, which is available to authorized users. The GS:SFHS cohort is a family-based study, and a fair proportion of individuals in the population will be related, some unknowingly. This structure will allow the shared variation between individuals within families to be disentangled into its genetic and environmental components and should facilitate accurate estimations of heritability. In addition, genome-wide association studies to be carried using the GS:SFHS cohort will need to adequately account for population substructure. Without proper correction, false-positive results can arise as a consequence of stratification differences, particularly between cases and controls, because of allele frequency differences or differences in LD patterns  a pair marker score that measures how each marker affects the relationship between a particular pair of individuals; and [3] a rarity score to measure the overall amount of rare variants that an individual has. The details of these calculations are provided in the To thoroughly explore the ancestries of the individuals in the GS:SFHS cohort and gain insight into the potential origins of the outliers we analysed the patterns of allele sharing described by genomic relationship coefficients. It is clear from the formulation of the standard estimator  and plotting the frequencies for those alleles in the African, East Asian and European populations of the 1 kG data set. The results are shown in Fig. In the case of an individual carrying an exogenous allele, it is expected that it will increase the  see Eq. . We analrarity score values [rarity scores was 1,071,738 ± 259,736. Using the rarity scores in windows of 50 SNPs, we calculated the number of rare peaks in all the individuals  and 136.7 Mb for the outliers (4 % of the genome). Histograms showing the distribution of the percentage of rare genome and the number of peaks in the non-outliers and the outliers are shown in Additional file Additional file e values  for all These values can help characterise the two groups of individuals. The non-outliers have a few sparse and small peaks in their genome potentially reflecting a point mutation (in most of the cases the window score is due to one single marker at a low frequency). On the other hand, the individuals defined as outliers can reflect a different kind of ancestry: the size of the peaks suggests that these individuals have a more admixed background, due to having a recent foreign ancestor.We checked the self-reported origin of the 74 individuals detected as outliers and although the amount of non-disclosed information is slightly higher than in the whole data set, the recorded origin of their grandparents did not indicate that they came from outside the UK.To provide additional evidence of our results, we analysed a subset of markers in approximate linkage equilibrium with the software ADMIXTURE  to estimAreas in high linkage disequilibrium can drive a lot of variation in the PCs, as we observed with the inversion in chromosome 8 and MHC in chromosome 6, which is responsible of the clustering observed for several PCs , 12. In The plots of PC three and/or four showed a three-cluster pattern for some of the chromosomes. In some cases the three groups were clearly distinguishable , and in some others the pattern was created by combining both eigenvectors together or it became less clear . Sometimes two different clustering patterns could be seen in both the third and fourth PCs   and to the clustering of individuals obtained for the original genome-wide PCA Fig. . This shLactase gene which has been shown to display clines of variation due to selection [A summary of a literature review aiming to identify plausible causes of the other observed three-cluster patterns in the different PCs can be found in Additional file election . In addielection . MHC assIn order to explore the patterns of homozygosity in GS:SFHS, we estimated the length and location of homozygous segments in each individual of the cohort  and markers in chromosome 8 located in the region 8 p23.1, since both areas have been proven to have a big impact on the PCA [R2 ≤ 0.01). This shows that geography has an impact on the genomic variation of GS:SFHS, especially on that contributing to principal components 1 to 3. To further investigate this influence we used the recorded origin of grandparents, in order to obtain a more accurate picture of the individual’s “genetic origin”, so we selected a subset of individuals consisting in those with four grandparents coming from the same area . We used this new subset  to calculate a new regression of the principal components on the geographic coordinates. The results are shown in Table To unravel any possible link between the genomic differentiation and geographic stratification in the sample we created a pruned subset of SNPs in approximate linkage equilibrium with each other in order to capture the structure that reflects geographic origin. We removed all the markers from chromosome 6  and the area of residence of the individuals in a variety of health-related traits, we used two different mixed linear models. We explored the variance explained by a similarity matrix constructed using the values of predicted latitude and longitude . We fitted a mixed linear model as in  includin2, predicted latitude and longitude (as covariates) and the current area of residence when fitted in a mixed linear model with a GRM. Results are shown in Additional file We also explored the effects of sex, age, ageHistoric and demographic events leave their signatures in the genomes of populations. These genomic marks allow us to track relationships, reconstruct introgression events and find different patterns in the populations that can be linked with structural variation or reflect areas in high linkage disequilibrium. This study provides a detailed picture of the genetic structure within the GS:SFHS cohort and some historic demographic events that have shaped the genomes of its individuals.Our analyses showed some expected patterns together with some valuable information regarding GS:SFHS individuals. The first and second principal components of the genomic relationship matrix in the GS + 1 kG population separated the European from the African and East Asian populations respectively. They also showed that a few individuals from the Scottish population have a variable pattern of genes from different ancestries, with part of this group of individuals having more than a 4 % of non-European origin according to the rare allele analysis. These non-European regions are too large to be explained as random mutations or genotyping errors. It is widely accepted that the Eurasian populations were originated from a single “out of Africa” event , and thaThe rest of the population appears to be more homogeneous when analysing the results of the PCA and the rare allele analysis. They show an average proportion of peaks of 0.07 % and a more compact clustering pattern. This 0.07 % of peaks is likely to reflect, at least partially, point mutations that occurred in recent generations. Some of these mutations could also be genotyping errors, but we cannot differentiate them.The pattern of rare alleles detected is highly influenced by the SNPs genotyped so it is likely that the ascertainment bias of the chip has an impact on our results. Whereas with a different chip different variants might have been detected as coming from other populations, we do not expect spurious results arising because of this in the rare allele analysis.The correlation between the estimated proportion of African origin using ADMIXTURE and the percentage of the genome covered by peaks was high (0.94) showing that our estimates correspond well with percentage of African genes in the individuals , it would probably still be useful to correct for some subjacent population structure in further analyses by including the eigenvectors as covariates in the models in future association studies or heritability estimations .Rarity scores showed a negative correlation with the number and total length of ROH as expected, since the more admixed individuals are in general less homozygous.Regarding the homozygosity patterns, the distribution of the number of homozygous segments and their length in GS:SFHS was similar to that described in previous studies in other European populations  , 14. Howrarity scores do not appear as outliers in the population. This implies that there are not big differences in their allele frequencies when comparing to the rest of GS:SFHS . As the architecture of allelic frequencies is the same, we can assume that the underlying architecture of complex traits will be very similar as well. We consider that these individuals could be included for most of the future analyses if the population structure is accounted for using whole genome marker information. Regarding the rest of the European populations, GS:SFHS individuals are located between Britain and Orcadian populations as expected and further away from more southern populations.We identified a group of individuals with Italian ancestry in GS:SFHS through their similarity with the Tuscan individuals in the 1000 Genomes populations Fig. . This isThe genome-wide information in GS:SFHS allowed us to discover some other variation. The inversion in chromosome 8 is one of the largest polymorphic inversions found in humans (~4.5 Mb) with a frequency of ~20–50 % in European populations, ~59 % in the Yoruba and ~12–27 % in Asians  and it wMajor Histocompatibility Complex in chromosome 6 [The chromosome 8 inversion proves that a linkage disequilibrium region can be detected using genotypes and principal component analysis. The similar clustering patterns in the rest of the chromosomes discovered through the chromosomal PCA suggest regions of between 260 Kb (15q21.3) and ~7.5 Mb (6p22.3-p21.32) in high linkage disequilibrium. This long haplotype patterns could be informative of selective sweeps in the past that have not been broken down by recombination. For some of these areas expected results pointed out the variation due to selection of the lactase gene (chromosome 2) or the mosome 6 . The othAs others, we have shown that the geographic differentiation between populations from different origins  is detected in the PCA but we have also found that the geographic origin within Scotland has a significant impact on the principal components. We were able to predict with relative high accuracy the origin of the individuals from their genomic data. The small number of samples from the islands , and hence their limited influence in the PCA, made that prediction of the origin of these individuals was somehow poorer than that of individuals from regions with more genotyped participants. Most of the families participating in the study were recruited in big cities  and the information from the grandparents’ origin is often not complete. This could affect the results obtained from our origin prediction analysis, for which prediction was more efficient at separating individuals from the South-West from those of the North-East of Scotland .r = 0.63). After adding to the model the current region of residence of the individuals, the predicted longitude of genomic origin was still significant, and similar results were obtained when we replaced current region of residence with region of birth (results not shown). Although these associations with genomic origin as predicted by grandparental birthplace and are not removed by adjustment for either an individual’s place of birth or current place of residence, it is possible that they are the result of undetected stratification in the sample. This could include, for example, persistence of cultural transmission of lifestyle or dietary habits associated with grandparental origins.When we explored the impact of predicted location based on information from the regions of origin of the individual’s grandparents  and current place of residence on four health-related traits , we observed that genomic origin had an impact on these traits. In the four cases, the similarity matrix based on geographic proximity significantly captured some phenotypic variance, irrespective of including current region of residence in the model. Also, when examining the effects of predicted latitude and predicted longitude we observed that longitude had a significant effect for fat and WHR but latitude did not, when both variables were jointly fitted. It has to be noticed that the distribution of the samples over Scotland makes latitude and longitude correlated to each other  and we have characterised some other that can remain in the cohort.The data were obtained from the Generation Scotland: Scottish Family Health Study (GS:SFHS) . Ethical−6. Duplicate samples, individuals with gender discrepancies and those with more than 2 % missing genotypes were also removed. After this quality control, the data set consisted in 9889 individuals  with multiple degrees of kinship (~6000 non related), genotyped for 646,127 SNP spread over the 22 autosomes. The recorded information about the locations of origin of these individuals is shown in Additional file Individuals were genotyped with the Illumina OMNIExpress chip . We used GenABEL version 1.7-6  and PLINIn order to investigate ancestry, the GS:SFHS data set was merged to the data of 1092 individuals of different origins  from Generation Scotland and the samples with European origin in the 1 kG Project  consisted in 879 individuals genotyped for 97,648 markers in approximate linkage equilibrium and with minor allele frequencies (MAF) larger than 0.01.Major Histocompatibility Complex) and those located in area 8p23.1 (to remove the influence of a large polymorphic inversion). Then, we generated a pruned subset of markers in approximate linkage equilibrium by using the default indep-pairwise command in PLINK [grm-cutoff command in GCTA [Relationships were estimated from the genotyped SNP data and summarised into a genomic relationship matrix (GRM) using GCTA version 1.13 . In the in PLINK . We also in GCTA  with a vPrincipal component analysis (PCA) is a widely used method to convert a set of observations for different (and possible related) variables into values of linearly uncorrelated variables called principal components or eigenvectors. If we apply it to the GRM, we can use it to determine, control and correct for population structure, as usually done in genome-wide association studies. Resulting clusters will reflect individuals that group together because of higher genomic similarities . We carr2 as covariates using the linear function in PLINK v1.07 [For some of the analyses, to locate the specific areas of the genome causing the different clustering patterns in the different PCs, genome-wide association analyses of the principal components were performed. We tested for association and estimated the effect of each SNP on the values observed for the eigenvectors of interest. In each analysis, the values of each principal component were analysed as a phenotype in a linear model fitting each SNP in turn and including sex, age and ageNK v1.07 .j and k calculated as:The values in the GRM provide an estimation of the genomic relationships between individuals irrespective of their pedigree relationships. GCTA uses a formula which measures the allele sharing between each pair of individuals, weighted by the frequency of the markers. We have observed that this method obtains accurate relationships due to common alleles, but it inflates some relationships due to rare allele sharing. The GRM elements represent identity by descent between two different individuals i1−pi,j≠kN is the total number of markers, xij is the genotype of individual j at marker i  and pi is the minor allele frequency (MAF) of marker i in the population.Where individual marker score as the individual contribution to the GRM formula per marker as:The formula weights the allele sharing by the allele frequency in the population. This means that some pairs of individuals sharing rare alleles can show an unrealistic inflated value of their genomic relationship coefficient. This happens frequently in individuals known to be closely related according to pedigree records (such as siblings or parent–child relationships) because it is more likely in these relationships to share rare alleles, but it sometimes happens with individuals that are totally unrelated according to the pedigree, but may share alleles due to a common ethnic ancestral origin. We intended to explore further these relationships by selecting those unrelated pairs of individuals with a genomic relationship coefficient larger than expected. We picked those pairs of individuals with an estimated large genomic relationship coefficient (0.17-0.50) when using all available markers but a very low (suggesting “unrelated”) coefficient (0–0.08) when markers with a rare allele were removed and only markers with common alleles used to estimate relationships. For those individuals, we calculated an j and k, their pair marker score, as the relationship per marker between them as :And for each pair of individuals rarity score per individual was calculated. For those markers with at least one rare allele  we summed the inverse of the allele doses multiplied by the allele frequency as:In addition, to measure the overall amount of rare variants in all individuals of the cohort, a pi) contribute to a greater extent to the rarity score. The rarest allele of those markers with low MAF in GS:SFHS are more likely to have come from a different population, and we hypothesise that they have come (“been introgressed”) into the Scottish population from another, distinct, population. To further analyse the structure of rare areas in the genome of GS:SFHS individuals, we estimated the number of rare peaks per individual as follows: we divided each chromosome in non-overlapping windows of 50 SNPs and we calculated the mean rarity score in the window as the mean of all the rarity score values of the all the markers in a window. We defined as rare peaks those windows with a mean rarity score larger than 50 . We assumed that windows with a mean rarity score larger than 50 separated by less than 10 other windows  belonged to the same rare peak that also included all the in-between windows. We tested several window lengths and thresholds and obtained similar results for all the analyses.This makes markers with a low MAF in GS:SFHS  in all individuals of GS + 1 kG.We used the software ADMIXTURE  to estimTo analyse the pattern of homozygosity in the GS:SFHS cohort, a screen for runs of homozygosity (ROH) was performed using PLINK v1.07  with theTo evaluate the correlation between geographic origin of the individuals and the principal components in the analyses performed, we calculated the regression between the different values of the PCs and the values of latitude and longitude assigned to the origin of the individuals. We used the first 20 whole-genome PCs to develop two different sets of analyses. Individuals were given a value of latitude and longitude according to their birth place, as shown in Table Influence of the geographic origin in the genomics of GS:SFHS). Then, for each trait, we used GCTA [We also explored the impact of the predicted genomic origin of the individuals and their area of residence on four health-related traits: body mass index (BMI), fat, waist-to-hip ratio (WHR) and high density lipoprotein (HDL). We used the values of the PCs to predict latitude and longitude of genomic origin in the unrelated individuals subset using the model detailed in the previous section . GRM is a genomic relationship matrix calculated using all autosomal SNPs with a MAF > 0.05.Model 1:2, predicted latitude, predicted longitude and current area of residence . GEO is a similarity matrix based derived from the predicted latitude and predicted longitude. It contains values between 0 and 1 reflecting (geographic) proximity between individuals according to their “genomic” origin. All values in the diagonal are equal to 1.In Model 2:2 as covariates and compared the estimates of σg2 (V(GRM)) and σs2 (V(GEO)) with those obtained fitting the same covariates and including also current place of residence in order to explore if there is a change in the variance explained by those models.In this model we fitted sex, age, age


Zea mays L.) production areas are in temperate regions throughout the world and this is where most maize breeding is focused. Important but lower yielding maize growing regions such as the sub-tropics experience unique challenges, the greatest of which are drought stress and aflatoxin contamination. Here we used a diversity panel consisting of 346 maize inbred lines originating in temperate, sub-tropical and tropical areas testcrossed to stiff-stalk line Tx714 to investigate these traits. Testcross hybrids were evaluated under irrigated and non-irrigated trials for yield, plant height, ear height, days to anthesis, days to silking and other agronomic traits. Irrigated trials were also inoculated with Aspergillus flavus and evaluated for aflatoxin content. Diverse maize testcrosses out-yielded commercial checks in most trials, which indicated the potential for genetic diversity to improve sub-tropical breeding programs. To identify genomic regions associated with yield, aflatoxin resistance and other important agronomic traits, a genome wide association analysis was performed. Using 60,000 SNPs, this study found 10 quantitative trait variants for grain yield, plant and ear height, and flowering time after stringent multiple test corrections, and after fitting different models. Three of these variants explained 5–10% of the variation in grain yield under both water conditions. Multiple identified SNPs co-localized with previously reported QTL, which narrows the possible location of causal polymorphisms. Novel significant SNPs were also identified. This study demonstrated the potential to use genome wide association studies to identify major variants of quantitative and complex traits such as yield under drought that are still segregating between elite inbred lines.The primary maize ( Zea mays L.) is one of the three most important crops of the world along with rice (Oryza sativa) and wheat (Triticum spp.). World production in 2011 was 883 million ton (http://faostat.fao.org [verified 6 May 2013]). Maize is the most important crop in the United States with a production of 301 million tons, and an estimated market value of 77.4 billion U.S. dollars in 2012 . Globally, the most important and highest yielding production areas are in the temperate Midwestern U.S. and other temperate regions throughout the world, which is where the majority of investment in maize breeding is focused ). Second, the false discovery rate (FDR) was also used to correct for multiple testing for all the different analysis and traits in this study but can overcorrect, leading to type II error [For each trait analysed, up to four different phenotypic inputswere used for GWAS analysis to ensure that quantitative trait variants (QTVs) were robust across models, and to identify QTV’s for specific environments. The phenotypic observations used as input for the four GWAS analyses included 1) the entry mean for the different traits collected for all trials; 2) the BLUPs for the combined MET analysis in D matrix ,63,84. TII error .The genetic variance in this experiment was very high. The heritability estimates for the WW and WS trials for grain yield ranged from 0.61 to 0.83 . The lowAcross all environments and all reported traits, heritability estimates ranged from 0.59 for aflatoxin to 0.98 for days to anthesis , based oThe number of hybrids investigated in the experiment resulted in large field tests. Spatial analysis has proven useful in identifying and reducing error in large field studies when checks are replicated throughout the field ,56,92. TA higher genetic correlation across environments would be expected in diverse material with a wide range in flowering, but the diversity in this study was moderated by the use of testcrosses. The modelling of the variance-covariance structure for GEI analysis using There were multiple hybrids that performed statistically as well as, or better than, the elite commercial checks used in this study for grain yield . This wa−1. Testcrosses of the lines bred in tropical and sub-tropical areas generally exhibited decreased aflatoxin susceptibility when compared to testcrosses from temperate materials. These results indicate the presence of favourable alleles for stress tolerance and Aspergillus ear rot disease resistance in exotic material. The presence of favourable genetic variation in exotic germplasm has been previously reported by different authors, including for aflatoxin resistance [For aflatoxin resistance, the best testcrosses  were numsistance ,45–47,96Of the 400 lines, only 346 lines were able to successfully produce seed under conditions of the four Texas nurseries. The genetic diversity analysis between the 346 inbred lines indicated that the majority of the lines are quite distantly related to each other. Between all pairs of lines, the mean and median genetic distance was 0.68 and 0.69, respectively. Only 0.1% of the pairs of entries exhibited a genetic distance of less than 0.2, 2.7% of the entry pairs exhibited a genetic distance of less than 0.5, and 64% of the entry pairs exhibited a genetic distance less than 0.7 . These rThe population structure results were similar to those visualized by the pairwise genetic distance matrix using a PCoA. The first two eigenvectors used to graph the data explained 14% and 13% of the variation, respectively, and separated three general clusters containing tropical lines, B73 types and a mixed group . The mixBased on the heritabilities shown in QTV1, QTV2, and QTV3 were detected under WW and WS trials in 2011 and 2012, respectively . These Qabph1—aberrant phyllotaxy1- gene (Maize B73 RefGen_v2available at www.maizegdb.com/). Gene abph1 is expressed in the shoot apical meristems, and mutations in this gene alters the regular arrangement of leaves and flowers [abph1 locus, and not other linked genes.QTV1 found in this study for grain yield is located in bin 2.03, and the exact SNP location can be found within the locus name in  flowers . Despite2 of plant height and grain yield was determined to be 0.61, indicating the importance of robust tall plants under Southern stressed growing conditions and further supporting the possibility of pleiotropy of this locus [4:5 maize testcross progeny developed by Pioneer Hi-Bred in 1995, identified a QTL in the same chromosome bin, which was detected across seven environments in the Corn Belt.QTV2 is located in bin 7.04, the allelic effect ranged from 0.14 to 0.42, and the percentage of explained phenotypic variation ranged from 4.5 to 5% . In addiis locus  found a is locus  using 10To identify potential candidate genes for QTV2, LD was investigated and found to decay within 0.2 kilobases (kb) upstream and showed no LD with the closest marker 225 (kb) downstream (data not shown). Different authors have reported that LD decays rapidly in maize around 1 to 10 kb; specifically for chromosome seven, linkage disequilibrium has varied from 2 to 5 kb depending on the region and the germplasm ,108–111.QTV3 is located in bin 9.06, the allelic effect ranged from 0.28 to 0.33 ton/ha across environments, and the percentage of explained phenotypic variation ranged from 3.5 to 3.9% . No QTLseff, this SNP warrants further investigation based on consistency and the fact that other authors have reported a linkage QTL for 300 kernel weight, named q300k21, in the same bin [This study did not detect a significant QTV for 500-kernel weight after stringent corrections for multiple testing. However, a peak near significance was consistently observed on bin 9.01 across different trials and both of the MET analysis (data not shown). The effect estimates ranged from 2.3 to 3.6 g per 500 kernels, and explained 3.8 to 4.3% of the phenotypic variation. Although this peak was not significant after correcting for multiple testing using FDR and Msame bin –116. CorThe effect sizes of significant QTV for yield, which ranged from 3 to 5%, appeared larger in magnitude than might be expected for a complex quantitative trait; we believe there are a few potential causes. First, heterosis from the tester would be expected to mask phenotypic variation at many alleles. For all QTVs the tester (Tx714) has the allele of positive effect , suggestTwo QTVs (QTV2 and QTV6) were detected for plant height . QTV2 waFor ear height, only one significant SNP, QTV7 (bin 2.04) was detected after correcting for multiple testing and only in the non-irrigated trial in 2012 . It had Significant peaks associated with SNPs S4_173817044 and S4_173996901 are located in bin 4.07. The effect of the S4_173817044 SNP ranged from 3.8 to 6.5 centimetres, and the percentage of explained phenotypic variation ranged from 4.2 to 5%. The effect of the S4_173996901 SNP ranged from 3.8 to 4.3 centimetres. The amount of phenotypic variation ranged from 4.4 to 4.6%. Similar to the previous QTL for ear height, neither SNP falls within a bin where a QTL has been reported by other authors. Additionally, the LD decay around 100 bp in this area of chromosome four and based on the Maize B73 RefGen_v2 genome, these SNPs seems to be in the promoter regions of different uncharacterized transcripts .For the days to silk and days to anthesis traits, only one GWAS analysis was performed using the entry mean as the phenotype observation. Multiple QTVs were found  with effQTV8 (bin 8.05) was detected for both days to anthesis (effect of 0.9 days) and days to silk (effect of 1 day), explaining 5.8% of the phenotypic variation for both traits. Two different SNPs were found for QTV9 (bin 8.05), S8_123509373 and S8_123511933, separated by 2.5 kb. The effect for these QTL variants ranges from 0.5 to 0.8 and the phenotypic variation ranges from 4.6 to 4.7% . BucklerThis study did not find any significant QTL variants for ASI despite multiple analyses run using the average of the raw data, and the MET analysis described in Eq. A. flavus to proceed under non-wounding inoculation. A QTL for aflatoxin and/or Aspergillus ear rot resistance has been previously reported in bin 3.06 [The GWAS analysis did not find any significant QTLs for the transformed aflatoxin data after correcting for multiple testing  despite bin 3.06 ,36,39. TAspergillus ear rot. Multiple QTL for ear rot resistance have been reported in bin 4.03 for resistance to ear rot diseases such as Fusarium and Gibberella [The SNP: S4_17376432 (bin 4.03) had allele effects of 9.1 ng g−1 and explained phenotypic variation ranging from 5.3 to 5.7%. The transcript GRMZM2G013546 corresponds to this SNP marker and is highly expressed in the pericarp and husk of maize ,41,112, bberella ,41. The bberella  reportedAssociation mapping using candidate genes or at a whole genome level is now routine in many plant species ,121–126 This study used a diverse maize association mapping panel to identify genomic regions associated with grain yield, aflatoxin resistance and important agronomic traits in Southern US environments. Useful variation in diverse germplasm for aflatoxin resistance and drought tolerance was identified. Additionally, this diverse germplasm, when testcrossed to an elite, although older, Texas version of B73, has the potential to out-yield commercial hybrids sold in Texas but bred in the best Midwestern environments. This study found 10 QTVs for grain yield, plant and ear height, days to anthesis and days to silk with some co-localizing to previously reported linkage QTL, while others were novel, demonstrating the utility of GWAS to resolve and discover useful variation. Comparing different statistical adjustment models was useful to maximize the power of the data and to detect consistent QTVs. Once these QTVs are validated, they will be useful for molecular improvement of Southern maize germplasm and, if cloning is pursued, for understanding the basic biology of improvement of these traits.S1 FigThe pairwise genetic distances between lines according to Nei’s 1972 genetic distance were plotted in a heat map. The distribution of pairwise distance values in the upper left also shows the color legend. In the main figure, the dendrogram and relatedness are shown on the top and left. The names of the lines are shown on the right and bottom of the main figure.(PDF)Click here for additional data file.S1 TableAspergillus flavus sporulation on the ears; NoKerRow1, NoKerRow2, NoKerRow3—the number of kernel rows on each of three randomly selected hand harvested ears; type:$—a visual rating of the kernel type (flint or dent); the amount of Aflatoxin in ng g−1; Log(Aflatoxin+10)—the log of aflatoxin in ng g−1 plus 10.The raw phenotypic data is provided to allow genetic mapping using this panel with SNPs discovered in the future. Column headings are as follows: Rows—The number of rows in a plot; Row Width—the Row width in centimeters; Range Length—the plot length in meters; Loc—the location of the test; Year—the year of the test; Trial:$9—a unique combination of location, year, and treatment; Trial:$92—a unique combination of location, year, and test; Pedigre:$160.—the pedigree of the testcross hybrid inbred lines which were crossed to Tx714; Common name—another version of the inbred pedigree; Entry:$ a coded entry for analysis; Entry_No—the entry number; Panel:$15.—a stock number for the inbred; Inbredtag:$15.—a stock number for the testcross hybrid seed that was planted; Tagtrial:$15.- the unique barcode/ plot location in the field; Lox:$—the knocked out lipoxygenase gene in the Tx714 isogenic tester (must be either lox-4 or lox-5); Plot:$—the plot number, only unique within a trial; Row—the range in the field, only unique within a field location; Column—the row in the field, only unique within a field location; Pos—an identifier of row and column only unique within a field location; Rep—the replication only unique within a trial; DTA—days from planting to anthesis; DTS—days from planting to silk; ASI—anthesis silking interval; PH—plant height in centimeters; EH—ear height in centimeters; STD—the stand count in number of plants; PlotWt_gr—the total plot weight from the combine and hand harvest in grams; Moist—the percentage of moisture in the grain; kernel500Wt—the weight of 500 kernels in grams; Yield_plot—the yield in kilograms per hectare; Yield_BuAc—the yield in bushels per acre; PlotWt_Combine_gr—the plot weight from the combine in grams; Wt_Shelled_grs—the weight from hand shelling hand-harvested ears in grams; EarCount—the number of ears hand-harvested and hand shelled; poln_per—the percentage of pollinated kernels on hand-harvested ears; abortion_per—the percentage of kernel abortion on the ears; flav_per—a visual rating of the amount of (XLSX)Click here for additional data file.


Joint analysis of multiple phenotypes has gained growing attention in genome-wide association studies (GWASs), especially for the analysis of multiple intermediate phenotypes which measure the same underlying complex human disorder. One of the multivariate methods, MultiPhen , employs the proportional odds model to regress a genotype on multiple phenotypes, hence ignoring the phenotypic distributions. Despite the flexibilities of MultiPhen, the properties and performance of MultiPhen are not well understood, especially when the phenotypic distributions are non-normal. In fact, it is well known in the statistical literature that the estimation is attenuated when the explanatory variables contain measurement errors. In this study, we first established an equivalence relationship between MultiPhen and the generalized Kendall tau association test, shedding light on why MultiPhen can perform well for joint association analysis of multiple phenotypes. Through the equivalence, we show that MultiPhen may lose power when the phenotypes are non-normal. To maintain the power, we propose two solutions (ATeMP-rn and ATeMP-or) to improve MultiPhen, and demonstrate their effectiveness through extensive simulation studies and a real case study from the Guangzhou Twin Eye Study. Genome-wide association studies (GWASs) have emerged as a common tool for identifying the genetic variants for numerous complex diseases. The conventional GWASs focus on a single phenotype, aiming to identify the associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype –3. HowevIn recent years we have witnessed an increasing interest in multiple phenotypes GWASs. Among the numerous multivariate methods that have been proposed, some commonly used ones include canonical correlation analysis (CCA) , MANOVA Despite the promising performance of MultiPhen, the properties of MultiPhen are not well understood. One exception is a recent work by Wang  that offThe rest of this paper is organized as follows. First, we establish the equivalence between MultiPhen and the generalized Kendall’s tau association test, and demonstrate that the MutiPhen may lose power for non-normal phenotypes. Second, we propose two association tests for multiple phenotypes (ATeMP) that perform well even when the phenotypes are non-normal. Finally, extensive simulations and real GWAS data are used to evaluate the performance of ATeMP.n subjects in an association study. Let  denote the observed data of the ith subject, where Yi = T is a vector of K phenotypes of the ith individual and Gi is the genotypic score. For simplicity, we consider a single variant and the genotypic score is coded as 0, 1, or 2, corresponding to the number of minor alleles in a biallelic locus.Suppose that there are Gi as a function of the multiple phenotypes,α’s are regression coefficients. Under this setting, the score test statistic is [G with values of 0, 1, and 2, respectively. The statistic S follows a chi-square distribution with degrees of freedom df = K.MultiPhen uses the proportional odds logistic regression to model the probability distribution of an individual’s genotype istic is S1=1n(1-fg(⋅) and fk(⋅) are kernel functions. Two popular choices of the kernel function are the identity function and the sign function. For clarity, let fg be the sign function because G is in an ordinal scale, and let fk(⋅) be the identity function. Then, statistic U can be simplified asS2 is equal to the score test statistic S1 of MultiPhen. Given the earlier work on the generalized Kendall’s tau, it is not surprising that MultiPhen works well for the multiple phenotypes association studies under various circumstances.The generalized Kendall’s tau is one of the earliest association tests for multiple phenotypes . Becauseucted as S2=UTvarThe MultiPhen used the classic technique in genetic analysis  by condiRk1, ⋯, Rnk) be the rank vector of the k dimensional phenotypic vector . Next, we can employ the inverse normal transformation, and transform Yik into ATeMP-rn: The idea is to replace the original phenotypes with their normalized ranks, a common approach to transforming non-normal data , 22. LetGi and and the transformed phenotypes:Yik when the kth phenotype  is ordinal [When a phenotype is in an ordinal scale, the sign function is more suitable as the kernel function. And, if we assume the genetic effect is additive, the generalized Kendall’s tau statistic in  ordinal . Hence, Yik into ATeMP-or: For a non-normally distributed phenotype, we employ the ordinal residual transformation as described above, and transform Gi is the causal variant with minor allele frequency of 0.2, Ei is a random effect, and ϵ is the random error following N. Varying the distribution of Ei among several non-normal distributions yields a variety of non-normal phenotypes. Specifically, we set βG1 = 0.1 and βG2 = 0, or 0.05, or 0.1, and considered the following different distributions for Ei: (1)N, (2)t(3), (3)Laplace and (4) Gamma. We chose suitable values of βE1, βE2 and σ2 such that the variances of both Yi1 and Yi2 are equal to 1 and the between-phenotype correlation, r, varies from -0.8 to 0.8 in an increment of 0.4.We conducted simulation studies to systematically evaluate the efficiency as well as the robustness of ATeMP. We generated bivariate traits under the bivariate linear model−4. This nominal level of significance is much higher than the typical level of significance in GWAS to reduce the computational time in simulation. However, we believe it is small enough for the purpose of comparing the power of MultiPhen, ATeMP-rn, and ATeMP-or.To evaluate the statistical power, we simulated 1000 datasets under each simulation scenario above. Each simulated dataset consisted of 2000 unrelated individuals. The significance level was fixed at 5 × 10−4 in this simulation study. To assess the asymptotic approximation, we also considered relatively small sample sizes of 300 and 500.We assessed the Type I error of these tests by letting MAF be 5%. 50000 datasets were simulated and the significance level was set to be 5 × 10U1, ⋯, UK)T follows multivariate normal distribution with mean 0 and covariance matrix Σ. A gradient of strong to low levels of correlation for Σ is simulated; that is, ρij = 0.8i−j∣∣. Under the alternative hypothesis, we assumed that the genetic variant is associated with one third of the phenotypes. We simulated independent ɛk from one of the following distributions: (1) N; (2)t(3); (3)Laplace; (4)Gamma. Finally, a was set to be 0.4 and the number of phenotypes K was set to be 5 and 10.To further evaluate the efficiency and robustness of ATeMP, we considered high dimensional phenotypes. The phenotypes are generated using a linear additive model−4. The minor allele frequency of the causal variant G is set to be 0.3. The genetic variant explains 0.3% of the phenotypic variations when ɛk follows the normal distribution, and 0.6% for the other distributions. We assessed the Type I error by simulating 50000 datasets, and the sample sizes were set to be 300, 500 and 1000.To evaluate the statistical power, we simulated 1000 datasets under each simulation scenario above. Each simulated dataset consisted of 1000 unrelated individuals. The significance level was fixed at 5 × 10Here, we applied MultiPhen, ATeMP-rn, and ATeMP-or to evaluated 38 candidate SNPs which are identified from three large GWASs , 25 for The current data are from the Guangzhou Twin Eye Study. A detailed description has been published elsewhere . The GWATo offer a practical guide, we summarize the order of superiority between different methods. When the phenotypic distribution is heavily-tailed, such as the t distribution or the Laplace distribution, ATeMP-or is the most powerful approach in all of the considered simulation settings as can be seen clearly from −4. We can observe that the Type 1 error rates of ATeMP-rn and ATeMP-or are very close to the nominal values, indicating that these methods can control Type I error well in the considered simulation settings. The Type 1 error rates of MultiPhen is inflated for the t distribution when the sample size is 300 or 500. We do not observe inflated Type 1 error rate for MultiPhen when the sample size is 2000. −4) is identified by the ATeMP-or. For the SNPs with p-value < 0.01, most of the p-values from ATeMP are smaller than those from MultiPhen, suggesting again that transforming phenotypes is helpful in this real data analysis. These results confirm the observations from the simulation studies. For SNPs with p-value > 0.01 , indicating that part of candidate SNPs of refractive error are associated with its two major intermediate traits. Our real data analysis confirmed that ATeMP tests are superior to MultiPhen, underscoring the usefulness of transforming the non-normal phenotypes prior to association testing, despite the fact that MultiPhen is distribution-free.We first note thatS1 FigThe simulation settings are the same as the simulation studies for bivariate phenotypes in Section 1.5. To alleviate the computational burden, the sample size was set to be 500, and the significance level was set to be 0.05.(EPS)Click here for additional data file.S1 Table−4, and the sample sizes are set to be 300, 500 and 100, respectively.The nominal significance level is set to be 5 × 10(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.


Parus major) pedigree with simulations, we assess how heritability estimates derived from social pedigrees change depending on the mode of the relationship between EPP and the focal trait. We show that the magnitude of the underestimation is typically small (<15%). Hence, our analyses suggest that quantitative genetic inference from pedigrees derived from observations of social relationships is relatively robust; our approach also provides a widely applicable method for assessing the consequences of nonrandom EPP.Quantitative genetic analysis is often fundamental for understanding evolutionary processes in wild populations. Avian populations provide a model system due to the relative ease of inferring relatedness among individuals through observation. However, extra‐pair paternity (EPP) creates erroneous links within the social pedigree. Previous work has suggested this causes minor underestimation of heritability if paternal misassignment is random and hence not influenced by the trait being studied. Nevertheless, much literature suggests numerous traits are associated with EPP and the accuracy of heritability estimates for such traits remains unexplored. We show analytically how nonrandom pedigree errors can influence heritability estimates. Then, combining empirical data from a large great tit ( Estimating the heritability of traits in wild populations is fundamental in determining responses to selection, as well as understanding patterns of genetic variation  population for which both a detailed social pedigree (gained through observations) and genetic pedigree  was available , of paternity in multiple ways. Therefore, first, we analytically derive how the trait values of the social and genetic fathers may influence heritability estimates from father–offspring regression. Second, we explore how animal model estimates of heritability are influenced by the degree, and mode, of nonrandom EPP within authentic populations using a permutation approach. As wild populations may differ in pedigree structure from both animal breeding designs and simple simulated pedigrees, which may also influence heritability estimation  can be used in assessing heritability of traits that may correlate with paternal misassignment in wild pedigrees.z is the trait value of the social father and o the trait value of the offspring. Under the assumptions that (1) the social father's trait does not have a paternal effect; (2) there is no genetic correlation between the trait and female preference; and (3) there is no inbreeding, then  cov = cov /2, where a is the breeding value of the offspring's genetic father. If we assume that mating behavior is determined directly by the studied phenotype, thenz' is the phenotype of the genetic father. Denoting EPP events as δ=1 (δ=0 otherwise ), where δ is also a random variable:e is the phenotype of the extra‐pair male (EPM), which is not defined when δ=0. It can be shown (see Supporting Information) thatδ¯ is the mean EPP rate, γ is the ratio of the variance in phenotype of cuckolded males compared to all males, βe|z is the regression of EPM phenotype on social father phenotype, βδ|z is the regression of being cuckolded on the social father phenotype, and Δ the average difference between the phenotypes of EPMs and the social fathers they cuckold. Perhaps surprisingly, equation βe|z=0), nor a relationship between social father phenotype and the probability of being cuckolded (βδ|z=0) and so necessarily γ=1. This result has been used previously to estimate δ¯ before the advent of cheap molecular markers  when patterns and rates of EPP are associated with the cuckolded male's phenotype. This can happen through two broad, but interacting, mechanisms. First, the phenotypes of EPMs and those they cuckold could be correlated, with a positive correlation (βe|z>0) reducing bias and a negative correlation (βe|z<0) increasing bias. However, a lower bound onβe|z is −1 if the variances in trait value for all males, EPMs, and cuckolded males are approximately equal, and so heritability estimates from animal models are unlikely to be biased downwards by more than twice the EPP rate, and most likely substantially less.However, the bias is not simply determined by the EPP rate (βδ|z<0) and the phenotype of EPM is larger than the average (Δ>0) or vice versa. It should be noted that even if EPP is independent of EPM phenotype these two quantities will have different signs: if social fathers with low trait values get cuckolded (βδ|z<0) then by necessity EPMs (which are drawn randomly from the population) will have larger trait values (Δ>0). However, if EPP also depends on EPM phenotype then this is not necessarily the case: as an extreme example, imagine two types of male with phenotypes 0 and 1 and that males with phenotype 1 neither gain nor lose EPP, but males with phenotype 0 lose all their paternity (to each other). Under this example βδ|v=−1, yet Δ=0. Under these conditions E[h2^]=h2 (because γ=0) and there is no bias; this makes intuitive sense because the social father of every offspring has the same phenotype as the genetic father. In contrast, if males with phenotype 0 lost all their paternity to males with phenotype 1 then Δ=1 and E[h2^]=0 generating large biases.A second mechanism is if the probability of being cuckolded depends on the social father's phenotype. Here, bias will increase when the probability of being cuckolded decreases with trait vale  of 9193 Single‐nucleotide polymorphism (SNP) . Following quality control, a linkage map of the great tit genome was constructed, with 4701 SNPs mapped to autosomes . For computational reasons, a set of 1700 of the mapped SNPs were chosen for parentage analysis by selecting a third of evenly spaced SNPs on each chromosome to reduce interdependence. CERVUS 3.0  was employed to confirm social paternal pedigree links and identify previously unknown genetic links. For each offspring, the pool of candidate fathers included all genotyped males at least one year older than itself. Using 10,000 simulated mating events, paternity was assigned to the male with the highest paternity likelihood if they were assigned with high confidence (>99%) in CERVUS.As this study was aimed at examining the possible effects of misassigned paternities, rather than data completeness :m and n) observed that year. To incorporate the likelihood that there are spatial constraints on the occurrence of EPP, dij was defined as the distance between the potential EPM and the social father, and λ as the rate of decline of the probability of EPP with distance. We investigated five EPP scenarios with different functional forms for f, where z is trait value:i, that is, EPM trait values increase the probability of gaining EPP: f=zi.The trait of male j, that is, social father trait values decrease the probability of being cuckolded: f=−zj.The trait of male f=zi−zj.A combination of (i) and (ii): f=|zi−zj|.The absolute difference in trait values between the EPM and social father increases the probability of EPY within this brood: f=−|zi−zj|.The absolute difference in trait values between the EPM and social father decreases the probability of EPY within this brood: We used simulation techniques to assess how the estimates of traits with heritabilities of 10, 30, 50, and 70% (a range spanning estimates often made for traits in wild populations) were influenced when linked to EPP through the five different mating scenarios. All analyses were run in R  of one so that β can be roughly interpreted as a standardized selection gradient (note that β must be multiplied by two in scenario [iii] to get the standardized selection gradient).Under each scenario, β was set as either 0, 0.1, 0.2, 0.4, 0.8, or 1.6; these values range from no association between the trait and EPP (or “zero selection”) to strengths of association (or “selection”) well above the range typically encountered in wild populations.βe|z,βδ|z, and Δ identified as important in the analytical model (eq. A number of extra‐pair (EP) events  were then drawn from the distribution defined by equation odel eq. . FurtherVA) and residual variance (VR) , EPP scenarios (n = 5), and different strength of these scenarios (n = 6), 100 simulated pedigrees were generated and analyzed.For the social, observed genetic, and simulated genetic pedigrees, the additive genetic variance , our genetic pedigrees simulated with no association between the male trait and EPP  did not differ from the observed genetic pedigree in measurements of pedigree structure that could be influenced by such techniques Fig. . This enβe|z) becomes more negative in this scenario, yet, when males cuckold others most similar from themselves (scenario [v]), βe|z increases  is only generated when this was directly simulated  song  is very close to the average EPP rate of socially monogamous bird species (∼11%, Griffith et al. In conclusion, although pedigree errors in wild populations only result in minor underestimation of heritability of particular traits (Keller et al. Supplementary InformationClick here for additional data file.


Brassica oleracea var. botrytis) is a vernalization-responsive crop. High ambient temperatures delay harvest time. The elucidation of the genetic regulation of floral transition is highly interesting for a precise harvest scheduling and to ensure stable market supply. This study aims at genetic dissection of temperature-dependent curd induction in cauliflower by genome-wide association studies and gene expression analysis. To assess temperature-dependent curd induction, two greenhouse trials under distinct temperature regimes were conducted on a diversity panel consisting of 111 cauliflower commercial parent lines, genotyped with 14,385 SNPs. Broad phenotypic variation and high heritability (0.93) were observed for temperature-related curd induction within the cauliflower population. GWA mapping identified a total of 18 QTL localized on chromosomes O1, O2, O3, O4, O6, O8, and O9 for curding time under two distinct temperature regimes. Among those, several QTL are localized within regions of promising candidate flowering genes. Inferring population structure and genetic relatedness among the diversity set assigned three main genetic clusters. Linkage disequilibrium (LD) patterns estimated global LD extent of 2r = 0.06 and a maximum physical distance of 400 kb for genetic linkage. Transcriptional profiling of flowering genes FLOWERING LOCUS C (BoFLC) and VERNALIZATION 2 (BoVRN2) was performed, showing increased expression levels of BoVRN2 in genotypes with faster curding. However, functional relevance of BoVRN2 and BoFLC2 could not consistently be supported, which probably suggests to act facultative and/or might evidence for BoVRN2/BoFLC-independent mechanisms in temperature-regulated floral transition in cauliflower. Genetic insights in temperature-regulated curd induction can underpin genetically informed phenology models and benefit molecular breeding strategies toward the development of thermo-tolerant cultivars.Cauliflower ( In cauliflower, curd development is regulated by temperature since vernalization is obligatory to promote the transition from the vegetative to the generative phase and thus, to induce the formation of the edible curd as marketable plant organ. Vernalization requirements can only be fulfilled after passing through a juvenile vegetative phase during which plants are insensitive to vernalization . The lenArabidopsis thaliana  and VERNALIZATION 2 (VRN2) play a pivotal role in the regulation of the transition from vegetative to generative phase , FLOWERING LOCUS D (FD), and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 , together with VERNALIZATION 1 (VRN1) and VERNALIZATION INSENSITIVE 3 (VIN3) is induced, which mediates repression of FLC and thus releases flowering genes FT, FD, and SOC1 from FLC suppression (LEAFY (LFY) and APETALA 2 (AP2) that promote floral induction  for flowering time that mapped to genomic regions displaying synteny to the region of Arabidopsis chromosome 5 harboring several flowering genes  have advanced as a promising approach recently emerged in crop improvement to identify genes and distinct genetic variants controlling complex traits with respect to natural variation . GWAS hapathways  but alsopathways .The present study aims at genetic dissection of temperature-related curd induction in cauliflower by combining genome-wide association mapping and gene expression analysis conducted on a cauliflower diversity set, consisting of 111 accessions. In particular, the main objectives of the study are (i) assessment of phenotypic variation of curd induction in dependence on temperature, (ii) detection of genomic markers significantly associated with flowering time by GWAS, (iii) identification of promising QTL regions and putative candidate genes, (iv) evaluation of genetic diversity and linkage disequilibrium (LD) patterns among the cauliflower diversity set, and (v) transcriptional analysis of flowering time genes to examine its functional relevance during vernalization and curd induction in cauliflower. Results will give insights in genetic regulation of temperature-related curd induction in cauliflower and further contribute to the elucidation of molecular pathways underlying floral transition. Especially, as interest in the development of genetically informed models describing flowering is recently raised , the higB. oleracea var. botrytis commercial parent lines was used. The diversity panel included lines from the temperate zone (n = 99), comprising early (n = 5), medium (n = 63) and medium-long (n = 31) time to harvest varieties and accessions from the subtropical (n = 3) and tropical (n = 9) zones. These materials display variation in temperature-related curd induction and harvest time reliability. For genotyping, DNA was extracted from freeze-dried leaf tissue and hybridized to an Illumina Infinium iSelect (B. oleracea 20k) array according to the manufacturer’s protocol. Array hybridization resulted in 14,385 polymorphic SNP markers used for genotyping, covering the whole cauliflower genome and being equally distributed among all linkage groups (O1–O9).For the experiments, a diversity set consisting of 111 2O5, 25% K2O; Everris, The Netherlands) with increasing concentrations from 5 to 26 g x l-1. Plants were treated with SpinTor  and KARATE ZEON  against cabbage root fly and cabbage whitefly. Temperature was recorded hourly with a Tinytag Ultra 2 data logger . Experimental trials were performed with four plants per accession in a randomized complete block design with two replications. Plants were phenotyped frequently, at least three times a week, until visible curd induction occurred. The onset of visible curd initiation was recorded by the number of days after sowing (DAS). For accessions which did not induce a curd at the end of the experiment, the termination date of the experiments (105 DAS for GH-T1 and 112 DAS for GH-T2) was assigned, since data elimination of non-curding genotypes under high temperatures might limit GWAS power in terms of detecting key loci. Accessions with less than three plants per line where excluded from data analysis.Two independent greenhouse trials under distinct temperature regimes were carried out on the whole cauliflower diversity set, conducted under: (i) semi-controlled conditions, where plants were cultivated in the greenhouse under natural light and photoperiod conditions with average daily minimum and maximum temperature of 15.0 ± 4.0°C and 30.7 ± 6.3°C, respectively (GH-T1) and (ii) controlled temperature conditions applying high ambient stress temperature, consistently not lower than 22.5°C with a photoperiod of 12/12 h day/night (GH-T2). In GH-T1, plants were sown on 6th May 2013, in GH-T2 on 10th October 2013, in trays filled with modular tray substrate  and cultivated in the greenhouse. After four weeks, plants were potted in 1.7 l pots filled with clay substrate . Plants were fertigated frequently including application of Universol Orange fertilizer  was carried out with statistical software R1 and variance components were calculated as functions of the mean squares estimated from the ANOVA. Broad-sense heritability (h2) was calculated according to Statistical analysis of phenotypic data was performed by Kruskal-Wallis test with statistical software SPSS Statistics 22. Statistical significance was assumed at 2G is the genetic variance, σ2GxE is the genotype x environment interaction variance, σ2ε is the residual variance and e and r are the numbers of environments and replications per environment, respectively.where σK = 1–10 clusters with five replications for each K-value comprising 100,000 burn-in period iterations followed by 100,000 Markov Chain Monte Carlo iterations while using a population admixture ancestry model. The determination of the most probable number of subpopulations (K) within the diversity panel was based on the calculation of ΔK according to Q). Admixture was assumed if the Q-value for the probability to belong to one subgroup was < 100%. The cluster analysis was performed with the neighbor-joining algorithm (The population structure of the diversity panel was estimated with STRUCTURE software 2.3.4  applyinglgorithm  implemenlgorithm .Q-matrix) and kinship (K-matrix), calculated with TASSEL, as a random effect matrix accounting for genetic relatedness to reduce false-positive results. For performing association analysis, SNPs were filtered according to minor allele frequency (MAF) and call rate, excluding those with a MAF < 0.05 and a minimum call rate < 0.9, finally resulting in 4,758 out of 14,385 SNPs considered in GWA mapping. Significant marker association with the phenotypic trait was assumed for markers exhibiting a P-value < 0.01. Candidate gene search was performed by using the publicly available B. oleracea var. capitata genome database . LD as represented by squared allele frequency correlations (2r), between each pair of marker loci  and for unlinked loci . LD decay was estimated by using second-degree locally weighted polynomial regression  was estimated to determine critical 2r value, as a population-specific threshold above which LD is assumed to be caused by genetic linkage  analysis was performed with TASSEL 4.0  by applyker loci  was estiker loci . Only alnsidered . Pairs on LOESS; , obtaine linkage . Criticaware 4.2 .For qRT-PCR analysis, fresh leaf samples were collected at seven time points, starting after germination until the onset of visible curd induction. In detail, sampling dates were 22, 36, 43, 57, 64, 71, 78 DAS. At each time point, samples of ∼100 mg of fresh tissue of the lateral tip of the second fully developed leaf were harvested, immediately frozen in liquid nitrogen and stored at -80°C. Standardized sampling conditions were applied in order to avoid circadian or diurnal variation in gene expression.® RNA plant isolation kit  according to the manufacturer’s instruction. In addition to the standard protocol, a second DNA digestion step with DNase I  was added to completely remove genomic DNA. Absence of genomic DNA contamination was confirmed by PCR analysis using the isolated RNA as a template as suggested by 28S and 18S rRNA bands under UV light in a denaturing agarose gel stained with ethidium bromide. RNA concentration was measured using a Nanodrop ND-1000 spectrophotometer4. Complementary DNA (cDNA) was synthesized from 2.5 μg of total leaf RNA using the RevertAid First Strand cDNA Synthesis Kit  following the manufacturer’s protocol. qRT-PCR of temperature-related flowering time genes BoFLC2 and BoVRN2 was performed using the CFX96 Real Time PCR Detection System and SYBR fluorescence  for detection. To normalize gene expression, the constitutively expressed 18S was selected as internal reference . Primers were synthesized by Biolegio . To exclude cross-binding to other targets than to the genes of interest, in silico designed primer sequences were again subjected to BLAST analysis in the B. oleracea var. capitata genome database and cross-validated with the B. oleracea var. italica Short Read Archive. Additionally, high resolution melting curve analysis was performed after each qPCR assay to verify that qPCR assays have amplified single, specific products and thus to ensure gene-specificity of the primers. Melt curve was generated within the range of 65–95°C in increments of 0.5°C per 0.05 s. Gel electrophoresis further was conducted to reconfirm that a single amplicon was amplified.Total RNA was isolated from leaf tissue using the NucleoSpineference . Gene-speference  were desTM SYBR Green qPCR Master Mix kit . Three technical replicates of qPCR assay were performed for each gene, while comprising three biological replicates, respectively. The data were analyzed using the Bio-Rad CFX Manager software . Relative gene expression was calculated according to the ΔΔCT method. The average CT of the reference control gene was used to normalize gene expression data; CT values of candidate genes were subtracted by CT of the reference gene (ΔCT). To calculate ΔΔCT, ΔCT of interest was subtracted by the ΔCT of control. These results were transformed to log2 scale to obtain the fold-change values.The qRT-PCR reaction mixtures were prepared according to the protocol of MaximaTable 1. Lines which did not induce a curd at the end of the experiment or lines with less than three plants were excluded, resulting in 107 lines from greenhouse trial GH-T1 and 88 lines from GH-T2 remained for final data analysis. With regard to distinct temperature regimes, mean time to curd induction among all accessions of the diversity set was lower in GH-T1 with 61.9 ± 9.7 DAS compared to 79.7 ± 15.5 DAS in GH-T2, performed under high ambient temperature of constantly not lower than 22.5°C. Minimum and maximum time to curd induction ranged from 32.0 to 84.0 DAS in GH-T1 compared to 40.3 to 106.3 DAS in GH-T2. The latter generally showed higher phenotypic variation in time to curd induction . Broad-sense heritability for time to curd induction was high as estimated with h2 = 0.93.Phenotypic data is summarized in Figure 2). In particular, mean time to curd induction was shortest for those accessions selected in the tropical and subtropical climate with 45.9 ± 7.2 and 47.7 ± 8.6 DAS for GH-T1 and 57.4 ± 10.9 and 62.3 ± 10.1 for GH-T2, respectively. Mean time to curd induction of lines from the temperate zone was generally higher.Classifying the plant material into tropical, subtropical and early, medium, and medium-long harvest time lines from the temperate zone, the mean time to curd induction was higher under the high ambient temperatures of GH-T2 for all groups . Thereby, a distinct separation of accessions from the temperate zone (clustering in G1 and G2) from those of the tropical/subtropical zone (G3) could be observed. In detail, G1 (n = 61) was composed of early (n = 5), medium (n = 27) and medium-long (n = 29) term varieties from the temperate zone, whereas G2 (n = 38) mainly comprises medium varieties from the temperate zone (n = 36) and two medium-long term accessions. G3 (n = 12) includes only tropical and subtropical lines. A total of 42 accessions (37.8%) showed a clear relationship each with one single cluster based on their inferred ancestry value of 100%, while the remaining 69 accessions (62.2%) were categorized as admixtures. Neighbor-joining analysis based on Nei’s genetic distance was performed to display the genetic relationships among the 111 cauliflower accessions . Neighbor-joining analysis showed similar classification into three subpopulations widely in consistency with STRUCTURE analysis. The neighbor-joining tree consists of three main branches. Two of the main branches nearly exactly display the subpopulations G1 and G2 of the STRUCTURE analysis. The third main cluster consists of two distinct branches, one of them comprises all of the G3 genotypes, the other one consists of five genotypes, belonging to G1 and G2. When looking at the estimated membership coefficients (Q) for those genotypes, it becomes obvious, that they display a relatively high degree of admixture.Analysis of population structure by admixture model-based simulations, determines 2r) of interchromosomal (unlinked) and intrachromosomal (linked) marker pairs . Average global LD was estimated at 2r = 0.06 with 20.71% interchromosomal marker pairs in significant LD (P < 0.05). Average intrachromosomal LD for significant marker pairs values at 2r = 0.23 (P < 0.05), while ranging from 0.18 to 0.28 among the linkage groups. LD estimates were significant for 38.40% intrachromosomal marker pairs (P < 0.05), with 2.87% marker pairs that were completely linked (2r = 1). However, 2r decreased as the physical distance between loci increased, indicating that the probability of LD was low between distant marker pairs. Estimation of LD between interchromosomal markers (unlinked) was inferred to determine critical 2r value, as a population-specific threshold above which LD is assumed to be caused by genetic linkage  for curd induction under both temperature regimes. Chromosomal position, P-level of significance and allele effects for all detected markers significantly associated with temperature-related curd induction are summarized in Supplementary Table P < 0.01) on chromosomes O1 (n = 5), O2 (n = 2), O3 (n = 2), O4 (n = 17), O6 (n = 3), O8 (n = 4), and O9 (n = 4). For GH-T2, 7 significant markers on O3 (n = 1) and O8 (n = 6) associated with days to curd induction were detected. Genetic linkage between physically neighbored SNPs was assumed at 2r = 0.33 . By taking genetic linkage between intrachromosomal markers into account, it finally resulted in an adjusted number of 16 significant QTL regions for curd induction in GH-T1 (P < 0.01), localized on chromosomes O1 (n = 1), O2 (n = 1), O3 (n = 2), O4 (n = 4), O6 (n = 2), O8 (n = 3), and O9 (n = 3), and two QTL one each on O3 and O8 for GH-T2 . Among them, QTL positions on O3 and O8 in GH-T1 were not similar or overlapping with those detected in GH-T2. Local LD analysis for all detected significantly associated markers showed that suites of markers on O1, O2, O4, O6, O8, and O9 are in high intrachromosomal LD and could be assigned to a total of seven haplotype blocks  spanning marker intervals from 1 to 346 kb. In detail, haplotype blocks could be assigned to markers on O1 , O2 , O4 , O6 , O8 , O9 . In addition, LD analysis indicates genetic linkage among two significant QTL for GH-T1 detected on chromosomes O4 and O8 with 2r = 0.7. In contrast, detected QTL on O3 and O8 in GH-T2 were not in LD and represent two individual QTL. A summary of the most promising QTL regions and putative candidate genes is shown in Table 2. A total of 9 QTL, detected in GH-T1 and GH-T2, could be found in genomic regions close to candidate genes involved in flowering regulation and vernalization response, located on chromosomes O1, O2, O3, O4, and O8. Among them, QTL for GH-T1 were found to be co-localized with, e.g., BoFLC2 on O3, BoVRN1 on O1 and BoFLD on O8. The QTL for GH-T2 on chromosome O3 was found in the genomic region close to BoVIN3.Association mapping revealed a total of 44 significant marker-trait associations (FLOWERING LOCUS C 2 (BoFLC2) and VERNALIZATION 2 (BoVRN2) and validate its functional relevance in temperature-related curd induction in cauliflower. Transcriptional profiling was conducted in four genotypes (A–D) comprising medium (A), medium-long (B) and early (C) variety from temperate zone and tropical climate (D) with diverging flowering phenotype with mean time to curd induction of 59.3 ± 4.0, 104.5 ± 3.5, 78.5 ± 5.9, 67.5 ± 5.4 DAS under high ambient temperature regime (GH-T2), respectively. These genotypes include both parental lines of the DH population on which previous QTL study has been conducted (unpublished data), referred to as genotype A (reliable parent) and genotype B (unreliable parent). Relative gene expression was analyzed in regard to temporal variation under high ambient temperature (GH-T2) at seven different time points ranging from 22, 36, 43, 57, 64, 71, 78 DAS . Genotype A (reliable parent) with the shortest time to curd induction under high ambient temperature (GH-T2) exhibits increased expression levels of both, BoFLC2 and BoVRN2 until the onset of visible curd induction (59.3 DAS), while BoFLC2 transcript level was 9.88- to 6.5-fold higher in comparison to the control time point (22 DAS). In genotype C (early cultivar), similar expression patterns of BoFLC2 and BoVRN2, possessing higher transcript levels until the onset of curd induction, were observed. After visible curd induction has occurred, BoFLC2 and BoVRN2 expression in genotype A and C decreased to a level similar to those of the control time point. In contrast, in genotype B (unreliable parent with the longest time to curd induction under high ambient temperature), relative gene expression of BoFLC2 and BoVRN2 was consistently reduced, while showing highly diminished values ranging between 0.01 and 0.59-fold expression compared to the control time point. In genotype D no general expression trends among BoFLC2 and BoVRN2 could be demonstrated.Quantitative real time PCR was carried out to analyze relative expression patterns of flowering time genes Phenotypic analysis on curd induction under distinct temperature regimes in controlled greenhouse experiments displayed considerable variation in curd induction among the cauliflower diversity set. Mean time to visible curd induction was shorter under natural temperature regime (GH-T1) than under high ambient stress temperature (GH-T2). Similarly, time range for curding was smaller in GH-T1 when compared to GH-T2, consequently showing higher phenotypic variation and delayed curd induction under high ambient temperatures, which is consistent with available vernalization models, generally describing delayed vernalization with higher temperature . Delay iThe observed variation in curd induction among the cauliflower diversity set regarding prior classification according to distinct geographical habitats corresponds well with the population structure estimated with STRUCTURE analysis. Tropical and subtropical accessions could be assigned to one cluster that clearly distinguished from accessions from the temperate zone, which clustered in two distinct groups. These results reconfirm that variation in flowering might be primarily based on genetic differences arising from artificial selection in different geographical areas or distinct environmental niches. The neighbor-joining dendrogram confirmed the STRUCTURE results by revealing similar clusters. Additionally, neighbor-joining analysis showed high genetic distance for tropical/subtropical accessions to accessions from the temperate zone, presenting less genetic relatedness between those accessions that might explain the high phenotypic variation in curd induction among those groups. For accessions from the temperate zone, a closer genetic relationship was observed, although two subgroups could be classified. However, a relatively large number of accessions were categorized as admixtures, with different proportion of mixed ancestry among the cauliflower population.B. oleracea chromosomes O1, O2, O3, O4, O6, O8, and O9 under temperature regime GH-T1 and 2 QTL one each on O3 and O8 under high ambient temperature (GH-T2). Thereby, QTL position on chromosomes O3 and O8 was not consistent among both temperature regimes suggesting that the QTL, solely expressed in GH-T2, might be specific for floral transition in terms of high ambient stress temperature. The results show, that under natural temperature conditions (GH-T1), curding and floral induction is controlled by several individual QTL, all of which might contribute with variable allelic effect. That is consistent with the concept that flowering is a multigenic trait, which is controlled by multiple QTL often with small genetic effects  to several bp (346 kb). Consequently, high-resolution mapping of the identified QTL regions should be applied to reveal candidate genes putatively accounting for flowering time variation. But, as candidate gene search revealed, evidence is given for several flowering time genes, among them FLC and VRN as putative candidates to be involved in the regulation of floral transition and vernalization response in cauliflower, since some of the detected QTL could be found in genomic regions harboring those flowering genes.To dissect genetic variation in temperature-related curd induction, GWAS was conducted on the cauliflower diversity set. GWA analysis identified 16 QTL for temperature-related curding under natural temperature regime (GH-T1), whereby, under high ambient temperatures (GH-T2), only two loci significantly associated with the trait could be detected. In particular, genomic loci conferring temperature-regulated floral transition in cauliflower were mapped on  effects . Previou, and O9 . In a cr, and O9 . Axelsso2r = 0.06, while 20.71% of all marker pairs showing significant LD estimates (P < 0.05). In other crops, for instance, global LD has been reported with an average of 2r = 0.019 to 0.029 in wheat populations  with average values of 2r = 0.23 and 2r = 0.06, clearly shows that the major cause for LD in the cauliflower genome is intrachromosomal linkage. Estimation of LD decay, i.e., the rate of return to random association between two given alleles, revealed LD decay in the cauliflower population is relatively slow for cross-pollinating cultivars but comparable to crops with a similar breeding system. Studies in maize reported LD decay ranges from less than 1 kb  as well as in the early accession, both exhibiting short time to curding. In contrast, in medium-long accession (unreliable parent), transcript levels of BoFLC2 and BoVRN2 were generally lower. Thus, results indicate a positive relation between BoVRN2 expression and reliability in curd induction under high ambient temperature. That could be attributed to higher capacity of suppressing floral inhibitors like BoFLC2; although BoFLC2 expression levels were generally high. FLC is the major repressor of flowering by inhibiting transcription of downstream floral integrator genes, such as FT and AP  that largely accounts for flowering time variation in cauliflower. BoFLC2 transcription was reduced with vernalization, inversely with BoFT-expression, which is a floral integrator gene. However, no significant contribution of other homologs than BoFLC2 to flowering time could be proofed yet. Hypothesis that other genes than BoFLC might be responsible for flowering time variation in cauliflower could be strengthened by previous QTL study on flowering time variability in a B. oleracea DH population primarily based on differences in vernalization response (BoFLC locus with flowering time could be observed. FLC-independent vernalization pathways, like already reported in Arabidopsis (BoFLC-transcription patterns, generally correlating with variation in curd induction among different genotypes, could be detected.Gene expression analysis of flowering time genes T and AP . In contpression . In the reported . Recentlresponse , althougbidopsis  might exThe present study indicates high phenotypic variation in temperature-related curd induction among the cauliflower diversity set. GWA mapping revealed significant loci associated with floral transition with respect to different temperature regimes, which possesses basis for selection of genomic loci useful for marker-assisted breeding. Thereby, the investigation of global and local LD patterns defined resolution power of association mapping. Promising QTL regions and candidate genes putatively being involved in vernalization and floral transition were identified and further candidate gene approaches can help to dissect allelic diversity and thus, to elucidate genetic variation in temperature response and floral transition in cauliflower. Understanding of vernalization-induced flowering would potentially accelerate predicting and managing harvest traits in cauliflower production and enforce breeders to integrate marker-assisted strategies that benefit the development of elite cauliflower cultivars adapted to a wide geographical range of cultivation.The Associate Editor Thomas Debener declares that, despite being affiliated with the same institute as the authors Yaser Hasan and Ralf Uptmoor, the review process was handled objectively. 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.