FST = 5.4%, P<0.05). We applied robust algorithms for the genomic estimation of individual inbreeding (FSNP) and pairwise relatedness (ΦSNP) as implemented in the programs KING and PLINK, respectively. Estimates of the two parameters from pedigrees (FPED and ΦPED) were computed using the RelaX2 program. Values of the two parameters estimated from genomic and genealogical data were mostly consistent, in particular for the highly inbred animals (e.g. inbreeding coefficient F>0.0625) and pairs of closely related animals . Nevertheless, we also detected differences in the two parameters between the approaches, particularly with respect to the grey Finnsheep. This could be due to the smaller sample size and relative incompleteness of the pedigree for them.Genome-wide SNP data provide a powerful tool to estimate pairwise relatedness among individuals and individual inbreeding coefficient. The aim of this study was to compare methods for estimating the two parameters in a Finnsheep population based on genome-wide SNPs and genealogies, separately. This study included ninety-nine Finnsheep in Finland that differed in coat colours  and were from a large pedigree comprising 319 119 animals. All the individuals were genotyped with the Illumina Ovine SNP50K BeadChip by the International Sheep Genomics Consortium. We identified three genetic subpopulations that corresponded approximately with the coat colours  of the sheep. We detected a significant subdivision among the colour types (a per sample or pairwise-samples basis in cases of complex genealogies or in the absence of genealogical data.We conclude that the genome-wide genomic data will provide useful information on  Genome-wide association studies (GWAS) have been widely used to identify common genetic factors that contribute to variation in complex phenotypes and influence health and disease susceptibility (see the reviews in http://www.sheephapmap.org/), comparisons between the estimates calculated using the two approaches are now realistic. Our results will also advance understanding of the Finnsheep breed in their place of origin Finnsheep, the Finnish native sheep breed, has been the subject of considerable numbers of studies during recent decades. There have been studies of its reproductive and production traits e.g. , within-In this study, we used genome-wide SNP data to characterize genetic variation in a Finnsheep population and compared the results with those derived from analysis of pedigree records. We estimated the pairwise kinship coefficient among all genotyped individuals as well as the individual inbreeding coefficient. The aim of this study was to examine the robustness of a newly developed algorithm for the relationship inference using real genome-wide SNP data and to compare the consistency between results using approaches based on genomic and pedigree information. We were also interested in elucidating the levels of genetic diversity and sub-structuring within the Finnsheep population.FST. In MDS of the identical-by-state (IBS) distance, there were three clusters that corresponded approximately with the coat colours  of the sampled individuals , Bayesian model-based clustering and calculation of ividuals , respectividuals .K) required to explain the observed total sum of within-population genetic variation. The highest average likelihood [Ln(K)] value and its smallest variance between replicates were obtained with K = 3 (data not shown), showing that K = 3 was the optimal number of sub-clusters for the Finnsheep population studied. Therefore, the STRUCTURE analysis found most support for three sub-clusters (or subpopulations) in the Finnsheep population, dominated by the grey, white, and black and brown Finnsheep, respectively , followed by that between the grey and the white sheep , while the lowest value was recorded between the white and the black and brown sheep . Further subdivision indicated a FST value of 1.8% (P<0.05) between the black and the brown individuals. The major component of SNP variation (94.5%) occurred within the subpopulations, with only 5.4%  being diagnostic of differentiation between the three coat colour subpopulations.Model-based clustering was further used to determine the minimum number of subpopulations (ectively . IncreasΦSNP) using the KING program are illustrated in vs. white; grey vs. black or brown) was negative  was estimated either under an assumption of a homogeneous population including the entire sample set or in the presence of a population substructure using various subsamples according to the animals' colour  as indicated by the MDS analysis. Individual inbreeding coefficients (FSNP) were comparable between the two scenarios, but the latter scenario, by incorporating population stratification, always gave systematically lower estimates  for the grey, 0.027  for the black and brown and 0.016 for the white . This suggests that the method in an assumed homogeneous population which consists of the entire samples tends to yield inflated estimates, most likely due to the larger samples or more numerous lineages included in the analyses The impact of within-population stratification on the estimation of inbreeding was reported previously in genome-wide SNP analyses e.g. . In thisstimates . The inbstimates . The regFSNP) was 0.040 for the entire sample, 0.099 for the grey, 0.038 for the black and the brown, and 0.027 (results not shown) for the white Finnsheep under the assumption of a homogeneous population. Of a total of 4851 pairwise kinship coefficients estimated using the genomic data, 411 positive values were within the subpopulations and 77 were between the subpopulations. According to the relationship inference criteria based on the kinship coefficient (Φ) and probability of zero IBD-sharing (π0), 14 pairs were inferred to be the 1st degree relatives (e.g. full-sibs), 22 pairs to be the 2nd degree relatives , and 60 pairs to be the 3rd degree relatives  statistic for the 99 sheep used for the pedigree analyses ranged from poor (e.g. PEC = 0–0.6) to excellent (PEC = 0.9–1). Eighteen sheep had PEC = 1, while the pedigree information for the majority of the samples was incomplete and 8 sheep  had a low level of PEC<0.6, ranging from 0 to 0.57.The pedigree completeness (ΦPED) for a full-sib is always greater than or equal to 1/4 because full-sib kinship is 1/4 in an outbred population, but there can be common ancestral relatedness that increases the kinship coefficient. A kinship coefficient of Φ = 1/4 assumes that parents are unrelated. Accordingly, Φ values should be ≥1/8 or 1/16 for the 2nd and 3rd degree relatives, respectively (ΦPED), 13 pairs were inferred to be 1st degree relatives (e.g. full-sibs), 36 pairs to be 2nd degree relatives , and 125 pairs to be 3rd degree relatives  were compared with those calculated using SNP data (FSNP) in a homogeneous population or in the presence of population substructure  and 0.2065 in the case of a homogeneous population, and between −0.1263 and 0.1845 when population substructure was accounted for. The proportion of animals having an inbreeding coefficient greater than 6.25%, which is the level reached by cousin mating, was 25.3% (25/99) by genomic data, 15.2% (15/99) by pedigree data and 14.1% (14/99) in both the pedigree and genomic estimations ; see st degree relative from the SNPs was not supported by the pedigree data. Out of twenty-two 2nd degree relatives  inferred by the genomic data {kinship coefficient Φ = 1/8, the inference criteria is between ; see rd degree relatives and unrelated pairs (i.e. the degree of relationship that lower than the 3rd degree), the kinship coefficient based on pedigree information was generally higher than that based on genomic data. The average difference for the values of pedigree- (ΦPED) and SNP-based (ΦSNP) kinship coefficients  for first, second and third degree relatives and ‘non-related’ individuals are 0.021±0.0522, 0.018±0.075, 0.019±0.061 and 0.017±0.0249, respectively. Of the total 4851 [(99×98)/2] pairs of within- and between-subpopulation relationships estimated by the two approaches, 4725pairs showed a consistent degree of relationship , while 126 pairs disagreed between estimations of relationships.We also compared pairwise kinship coefficients between those estimated using the pedigree and genomic data (F) of an individual is often unknown or inaccurate when calculated using pedigree information. Here we presented an empirical example where the individual's F was estimated using high-density SNP genotype data from a genome-wide SNP study (FSNP), and compared the results with F values calculated using pedigree data (FPED). We established a reasonable correlation between the genomic estimator and the pedigree-based estimator. However, we also recorded differences in the estimates of F using the two approaches. There are several explanations for the differences: (i) incorrect pedigrees links due to all sorts of errors such as mislabelling, farmers not recording matings correctly, lambs being adopted by other mothers before they are tagged etc - i.e. we may have perfect knowledge of what we think the pedigrees are for these animals, but they represent incorrect links; (ii) the pedigree completeness, a parameter that describes the quality of available pedigree information and is of great importance in assessing inbreeding (see iii) the in silico estimates of inbreeding are biased downward by the ascertainment bias due to being under-representative of the whole population or genealogies; and (iv) the requirement for a large number of samples and a denser set of SNPs to obtain accurate results.In a long-term isolated animal population such as the Finnsheep in Finland, where close relative matings are very likely, there exist complex genealogies with unknown historical loops. Therefore, the exact inbreeding coefficient  for the 1st and 2nd degree relative pairs  a large set of molecular markers (>10 000) at the genome-wide level has only recently become accessible; and (ii) the cost of comprehensive genotyping is too high. With the present approach, where only a fraction of individuals were genotyped, we were able to examine the quality of pedigrees in the Finnsheep population.As discussed above, we conclude that genome-wide SNPs provide more accurate information on genetic diversity of the Finnsheep than do the pedigrees. In particular, the sampling variance of SNP sharing even for full-sib pairs can be pretty huge, so accounting for true (genetic) genome sharing rather than expected (pedigree) genome sharing in linkage and genome-wide association studies can surely only improve the estimates. Nevertheless, pedigree information has been and will continue to be used in estimating population genetic parameters in the Finnsheep and other domestic animal breeds. The reasons are: (FSNP) indicated higher estimates in a homogeneous population than under population stratification. The explanation for the higher values can be that the larger sample size in a homogeneous population will always inflate the number of observed homozygotes and expected homozygotes by chance specifically for SNPs with very low MAF  among SNPs with the same underlying allele frequencies. In practice, a small proportion of SNPs deviate from the LE due to reasons including genotyping errors, recent admixture in a mixed population or removing Mendelian errors from families Inbreeding coefficients calculated using genomic data (ΦSNP) inference using only information from the two individuals under comparison. The inference is invariant to inclusion of any additional samples and to use of different SNP panels, producing reliable results using genotypes from GWAS or from studies of rare variants alone ΦSNP) in a single homogeneous population and under population stratification.The robust algorithm in the KING program performs pairwise relatedness  indicated strong and significant sub-structuring among the groupings of Finnsheep of different coat colours. However, the spanning of white vs. black and brown sheep in the kinship coefficient estimated using genomic data indicated closer genetic similarity between these subgroups (see i) the geographical isolation of the grey Finnsheep in the province of Kainuu in northeastern Finland, where they were discovered; (ii) a partly different genetic origin of the grey Finnsheep; and (iii) the inheritance of coat colours in the Finnsheep, where the different colour types may share the same alleles and have similar ranges of allele frequency (e.g. white vs. black and brown Finnsheep) at the colour genes FST = 2.5–8.2%) reported within sheep breeds such as Dorset, Dorpers, Suffolk and Texel FST value based on SNPs could be also due to some of the SNP markers being linked to genes affecting the economically important production traits including the coat colour and pattern, and wool quality such as like fiber diameter and its coefficient of variation, staple length and staple strength etc. This opens the possibility that the set markers in the SNP panel can be used for genome-wide association analysis to identify the genomic regions and mutations that underpin e.g. the coat colour trait in sheep.In order to examine the degree of within-population genetic sub-structuring in the Finnsheep, the distribution of SNP variation was examined as a function of membership of subpopulations with different coat colours. Within-population genetic differentiation (oups see . These fvs. black) or selection criteria (meat vs. wool) tended to cluster together i.e. different parts of Finland) for the samples. The differences in SNP allelic frequencies found between the three Finnsheep subpopulations could be explained on the basis of the positive assortative breeding associated with wool colour. Further tests for Hardy-Weinberg equilibrium (HWE) at the candidate loci for sheep coat colour and patterns will provide evidence of assortative mating in history of the breed. The proportion of animals with inbreeding coefficients greater than the critical level of 6.25% The MDS and STRUCTURE analyses of the Finnsheep showed a consistent pattern of within-population genetic subdivision corresponding with the different coat colours, although with some overlapping  or genetF) were low in the present study. Similar low levels of inbreeding in the Finnsheep population were also recorded in previous studies based on microsatellite and blood protein loci in-situ or ex-situ live conservation populations and germplasm cryogenic gene banks should be considered.The individual inbreeding coefficients .n = 54; grey, n = 14; black, n = 16, brown, n = 14, black/white spotted, n = 1) was extracted from whole blood using standard methods. DNA samples were subjected to SNP genotyping via the Illumina technology , using the ovine SNP50K BeadChip as coordinated by the International Sheep Genomics Consortium (ISGC). Details on SNP discovery, design of the ovine array and genotyping procedures can be found in the ovine SNP50 HapMap dataset (http://www.sheephapmap.org/hapmap.php) and Genomic DNA from a total of 99 individual Finnsheep of different coat colours ; thus, 47 693 SNPs on a total of 26 autosomes were maintained in the subsequent analysis.Markers were excluded from the analysis if they were annotated by Illumina as having either atypical X-clustering, a nearby polymorphism, compression, intensity values only, evidence of a deletion or some combination of these assay abnormities, if their genotypes were discordant between experiments, if they showed Mendelian inconsistencies within the AgResearch International Mapping Flock created nearly a decade earlier , pedigree-based inbreeding coefficients (FPED) and pedigree-based pairwise kinship coefficients (ΦPED) using the RelaX2 program PEC) was computed, and the degree of completeness of pedigree was assessed using the index proposed by Csire and Cdam are contributions from the paternal and maternal lines, respectively. In equation (2), gi is the proportion of ancestors being present in generation i and d is the total number of generations taken into account. In this study, 5 ancestral generations were considered in the calculation of PEC and more details were also described in Φ) between animals predicts the future level of the inbreeding coefficient. We calculated pairwise coefficients of kinship between all genotyped animals.Pedigree analysis included calculation of individual pedigree completeness (i.e. based on the observed vs. expected number of homozygous genotypes). Individual inbreeding coefficients (FSNP) estimated from genomic data were calculated using the option –het in the data set that was pruned to be in approximate linkage equilibrium using the –indep-pairwise option  implemented in PLINK ΦSNP) were estimated using the KING program through the parameter –kinship. Both programs used genomic information from all genotyped animals or subsets when appropriate. Furthermore, we used the KING algorithms (KING-robust) to screen pedigree errors. Potential pedigree errors can be also viewed through graphical display, in which the inferred kinship coefficients are plotted against the estimated probability of zero-IBD. Both the kinship coefficient and the probability of zero-IBD are estimated from SNPs.Given a large number of SNPs in a homogeneous sample, it is possible to calculate inbreeding coefficients (P<0.01) deviation from HWE as estimated by using the option –hwe (significance level P = 0.01) in PLINK ij = 0 rather than IBDij = 1 or 2 between two individual indexed by i and j can result in IBSij = 0 , the probability of zero IBD was estimated using the KING program We calculated pairwise identical-by-descent (IBD) values between each pair of individuals for all the samples. We estimated the IBD statistics by use of the average of identical-by-state (IBS) and the estimation of sample-level allele frequencies at individual SNPs assuming Hardy-Weinberg equilibrium (HWE) D) for all the 99 animals. The calculation of D is described as follows as well as earlier in N is the number of loci tested. We performed the calculations using the PLINK program (http://pngu.mgh.harvard.edu/purcell/plink/). The extent of population substructure was further explored using STRUCTURE v 2.2 ] and its variance between replicates was used to search for the optimal number of subpopulations (see http://cmpg.unibe.ch/software/arlequin3/) was used to calculate the global and between-subpopulation genetic differentiation using the estimate of FST.We performed classical multidimensional scaling (MDS) on the IBS matrices of genetic distances (RE v 2.2 . All 99 ions see . ARLEQUITable S1Results of relationship inference based on pairwise kinship coefficient (Φ) and probability of zero IBD-sharing (π0) as estimated by genomic or pedigree data.(DOC)Click here for additional data file.


This region is believed to carry the Fhb1 gene for response to Fusarium head blight. In this study, 266 wheat accessions were evaluated in three environments for Type II FHB response based on the single floret inoculation method. Hierarchical clustering (UPGMA) based on a Manhattan dissimilarity matrix divided the accessions into eight groups according to five FHB-related traits which have a high correlation between them; Group VIII comprised six accessions with FHB response levels similar to variety Sumai 3. Based on the compressed mixed linear model (MLM), association analysis between five FHB-related traits and 42 molecular markers along the 3.1-Mb region revealed 12 significant association signals at a threshold of P<0.05. The highest proportion of phenotypic variation (6.2%) in number of diseased spikelets (NDS) occurred at locus cfb6059, and the physical distance was about 2.9 Kb between umn10 and this marker. Haplotype block (HapB) analysis using a sliding window LD of 5 markers, detected six HapBs in the 3.1-Mb region at 2r>0.1 and P<0.001 between random closely linked markers. F-tests among Haps with frequencies >0.05 within each HapB at 2r>0.1 and P<0.001 showed significant differences between the Hap carried by FHB resistant resources, such as Sumai 3 and Wangshuibai, and susceptible genotypes in HapB3 and HapB6. These results suggest that Fhb1 is located within HapB6, with the possibility that another gene is located at or near HapB3. SSR markers and Haps detected in this study will be helpful in further understanding the genetic basis of FHB resistance, and provide useful information for marker-assisted selection of Fhb1 in wheat breeding.A previous study provided an in-depth understanding of molecular population genetics of European and Asian wheat gene pools using a sequenced 3.1-Mb contig ( Fusarium graminearum Schwabe (Giberella zeae Schw. and Petch), is a devastating worldwide fungal disease of wheat (Triticum aestivum L.). FHB infection is favored by warm humid conditions during the flowering and early stages of kernel development Fusarium head blight (FHB), or scab, caused mainly by The use of host resistance is an economically and environmentally effective strategy for controlling FHB. So far, only a few highly resistant wheat cultivars have been identified from different geographic regions, including Asia, South and North America, and Europe ce through linkage analysis Fhb1Fhb2Fhb4Fhb5Fhb1 (syn. Qfhs.ndsu-3BS) that derived from Sumai 3 was originally identified by RFLP analysis in a recombinant inbred population gwm493 and gwm533sts3B.189, sts3B.206UMN10Fhb1. Almost all the markers were identified in the bi-parental populations through linkage mapping.During the past decade, numerous studies in wheat have focused on molecular mapping FHB resistanFhb1 in European and Asian wheats through association analysis in a candidate region in contig ctg954 (Genebank accession number: FN564434) on the short arm of chromosome 3B. Haplotype diversity and its relationship to FHB-related traits were also analyzed in the 3.1-Mb genomic region. Such an association analysis should provide useful information for marker-assisted selection of Fhb1 in wheat breeding. The ultimate aim in the study was to discover potential resistance sources for use in wheat breeding and genetic improvement.Association mapping, a powerful approach to unravel the genetic architecture of complex traits in crops 2r<0.2 at P<0.001 along the 3.1-Mb region for the two sub-groups. The European sub-group had stronger LD than the Asian sub-group. These results were consistent with the previous study on 376 accessions using the same genotyping dataset Based on genotyping datasets of 70 genome-wide microsatellites, principal coordinate analysis of the 266 accessions revealed that the European and the Asian materials were independent sub-groups . Re-evalBesides evaluation of genetic structure of the test genotypes based on the 70 genome-wide SSRs with major allelic frequencies (MAF) >5% at the population level, the same sets of genotyping data were used to calculate relative kinship between pairs of individuals in the current study. This revealed the approximate identity between two given individuals over the average probability of identity between any two random individuals 2h) was calculated for each trait , indicating that this trait was less affected by environmental factors than the other four.Using the phenotypic data from three environments and their average values, comparisons were performed across environments for each FHB-related trait . Obviousch trait . The h2 P<0.01) positive correlations between all FHB resistance traits. All pairs of phenotypic correlation coefficients ranged from 0.778 to 0.970, and the highest value was observed for the correlation between the PDS and DI. This was consistent with the interrelationship among these parameters at the statistical level.Pearson correlation coefficients between environments were calculated for each FHB-related trait using SPSS v12.0 . There wIn order to infer possible classifications for the test genotypes, hierarchical clustering (UPGMA) of all wheat accessions based on Manhattan dissimilarity matrix with DARwin v5 software using the five FHB-related traits was used . The hieP<0.01) resistance compared to the other subgroups, indicating that these six accessions might therefore have potential as resistant genetic resources for improvement of FHB resistance in wheat breeding around the world. Detailed information for the six accessions in subgroup VIII is provided in A comparative analysis of the five traits between clusters was made based on the average values for the three environments . ClusterP<0.05 (cfb6059 accounted for the highest amount of phenotypic variation (6.2%) for NDS, whereas cfp5062_S38 explained the lowest (1.6%). Most markers had significant associations with traits in a specific environment, and only three significant associations  occurred in two environments. In total, four loci including three SSR  and one SNP (cfp5062_S38) were significantly associated with four FHB-related traits, and for each locus, there were at least two significantly associated traits. Locus cfb6059 was associated with four traits, indicating the relatedness between the traits or a situation of multiple traits determined by one gene.Given the strong population clustering of the test genotypes, the compressed mixed linear model (MLM) P<0.05 . No signcfb6059 and cfp5062_S38 loci. Significant phenotypic differences (P<0.05) were found between the two alleles at cfb6072 only for DS. However, at cfb6110, significant differences (P<0.01) were detected between alleles 289cfb6110 and 294cfb6110 for traits NDS, PDS, LDR and DI. Accessions with 289cfb6110 included Sumai 3 and those sharing the same Hap with Sumai 3 had lower mean phenotypic values than those with 294cfb6110 for all four traits. Taking PDS as a trait example  for 42 markers along the contig indow LD , Table 62r>0.1 and P<0.001, those Haps with frequencies >0.05 in the studied population, i.e. 6 in HapB1, 6 in HapB2, 3 in HapB3, 4 in HapB4, 3 in HapB5, 2 in HapB6, were selected for tests of association with the five FHB-related traits  for HapB3 were very clear. Moreover, the accessions with HapB6-2 and HapB3-2, such as Sumai 3, had the lowest values for the five FHB-related traits. Therefore, a genetic effect analysis of haplotype combinations of HapB3 and HapB6 was performed for the five FHB traits  on the short arm of chromosome 3B was constructed using Chinese Spring wheat (cfb6078 and cfb6061) might include the Fhb1 locus (B. Gill pers. comm.). Association study of this target region revealed that cfb6059 was significantly associated with FHB resistance. Importantly, this marker was very close to umn10 as a marker widely used for MAS in FHB-resistance breeding Physical map of 42 molecular markers from sequencing the contig ng wheat . On the Fhb1 gene was originally located in the distal region of chromosome 3BS between SSR loci gwm493 and gwm533cfb6061 and cfb6011 were mapped the interval between gwm493 and gwm533. Moreover, the physical distance between cfb6061 and cfb6059 was 376.3 Kb, while 373.4 Kb between cfb6061 and umn10. Comparative analysis of markers mapped in both genetic and physical maps revealed that cfb6059 significantly associated with FHB resistance was really located on the region contained Fhb1 locus, and also very close to the umn10 marker.As we all known, cdo981, and was designated as Fhb1 (syn. Qfhs.ndsu-3BS) Qfhs.ndsu-3BS) linked with the marker gwm493. This QTL was verified by a series of mapping reports in bi-parental populations. Zhou et al. gwm533 and barc147. At about the same time, Buerstmayr et al. gwm533 and gwm493 on 3BS using a large DH population of CM-82036×Remus. Other intervals or linked markers with the major QTL in this genomic region were barc133gwm533barc133 and gwm493barc147 and gwm493gwm533 and barc147gwm533 and gwm493sts3B.80 and sts3B.142sts3B.189 and sts3B.206umn10gwm493 and gwm533During the past decade, numerous studies have focused on genetic mapping of FHB resistance in wheat, and knowledge on the genetic control of FHB resistance has continually increased with time cfb6078 and cfb6061, Fhb1 locus (B. Gill pers. comm.). In the present study, after diversity detection of SSR markers between the two parents, loci cfb6061 and cfb6011 were mapped between gwm533 and gwm493 using a RIL population of Nanda 2419×Wangshuibai (cfb6059 that explained the highest proportion of phenotypic variance (6.2%) was between cfb6078 and cfb6061, and very close (261 Kb) to the mapped marker cfb6061. Comparing the physical map of SSR markers constructed using Chinese Spring  between cfb6059 and umn10 was 2r = 0.28 (P<0.001) in despite of their closely linkage, revealing that there was a complementary relationship between these two markers in marker-assisted selection of FHB-resistant breeding.In addition, this significantly associated marker ubgroups , but fivubgroups , except β-amylase gene Bmy1 of cultivated barley (Hordeum vulgare L.); Stracke et al. Bymovirus resistance in barley; and Gore et al. Association mapping based on linkage disequilibrium (LD), representing next-generation plant genetics With the increasing number of studies on association mapping using grouped molecular markers or several haplotypes, the efficiency of the two methodologies has greatly improved. In the present study, both marker association , Table 4P<0.05 (cfb6059 explained the highest proportion of variation (6.2%) for the trait NDS mapped in the interval between gwm493 and gwm533 . On the other hand, HapB3 did not involve any associated locus, suggesting a complementary relationship between haplotype analysis and simple association mapping. The locus cfb6110 significantly associated with scab-related traits was very close physically to haplotype block HapB3. It can be predicted that Fhb1 should be within the HapB6, but we cannot exclude the possibility of another gene located around HapB3. This study therefore provides useful information and genetic markers for cloning Fhb1 and for marker-assisted selection in breeding.As suggested by Garner and Slatkin c region , Table 6Two hundred and sixty six wheat accessions comprising 157 Asian and 91 European genotypes were used in this study. They were chosen according to their heading date in China from those included in the previous study on genetic diversity and linkage disequilibrium in 376 Asian and European bread wheat accessions FHB responses were assessed in three environments, viz. Jiangpu Experimental Station, Nanjing Agricultural University in 2009 , experimental field of Jiangsu Academy of Agricultural Sciences in 2009  and Jiangpu Experimental Station, NAU in 2010 . Each accession was planted in two replicates with fifteen seeds evenly distributed in a 1.5 m row, with 25 cm between rows.F. graminearum conidial suspension containing four virulent strains with a concentration of 5×104 conidial/ml was produced as described by All accessions were evaluated for FHB response based on the single floret inoculation method. A mixed ctg954 (Genebank accession number: FN564434) umn10 designed by Liu et al. In the previous study, Hao et al. 2r) and significances of each pair of loci using the 32 polymorphic SSRs and 10 SNPs in the approximate 3.1-Mb region on chromosome 3BS with the dedicated procedure of the TASSEL v3.0 software Additional analyses included population structure  with STRUCTURE v2.2 software In order to define the degree of genetic covariance between pairs of individuals, the relative kinship matrix (K) was calculated using genotypic data of genome-wide SSR with SPAGeDi software http://www2.maizegenetics.net/) P values) between markers and traits and phenotypic variation (2R) explained by the markers associated with traits were obtained for further study. Markers were defined as being significantly associated with traits according to their P values .The software program TASSEL v3.0 (2h) of each test trait in different environments, defined as the proportion of genetic variance over the total variance, was calculated according to the formula 2h = σa2/(σa2+σe2) with the MLM options of no compression and re-estimation for each marker. Here, σa2 means genetic variance, and σe2 indicates the residual variance. Allelic effects were evaluated in comparison to the “null allele”  for each locus Based on the procedure of the TASSEL software, only using phenotypic data and the kinship matrix, the heritability  was statistically calculated for each HapB in Excel.To determine the number and types of haplotype blocks (HapB) along the contig Figure S1Hierarchical clustering (UPGMA) of screened accessions based on a Manhattan dissimilarity matrix using five FHB-related traits. Blue line means negative control, and red line indicates positive control.(DOC)Click here for additional data file.Figure S2Association studies of five FHB-related traits with molecular markers in the 3.1-Mb genomic region. (a) Dot plots of compressed mixed linear model (MLM) for percentage of diseased spikelets (PDS). Negative log10-transformed P values in a sequenced contig (ctg954) of 3.1-Mb are plotted against position along the contig. Blue horizontal dashed line indicates the chromosome-region significance threshold. (b) Quantile-quantile plot of compressed MLM for PDS. (c) Dot plots of compressed MLM for disease severity (DS), as in a. (d) Quantile-quantile plot of compressed MLM for DS. (e) Dot plots of compressed MLM for disease index (DI), as in a. (f) Quantile-quantile plot of compressed MLM for DI.(DOC)Click here for additional data file.Figure S3Comparison of allelic effects of four loci significantly associated with FHB-related traits NDS (a), LDR (b), DS (c) and DI (d).(DOC)Click here for additional data file.Table S1FHB-related traits of controls in different environments.(DOC)Click here for additional data file.Table S2Correlation analyses of five FHB-related traits.(DOC)Click here for additional data file.Table S3FHB-related traits of wheat accessions clustered into the same subgroup as Sumai 3 by UPGMA based on a Manhattan dissimilarity matrix.(DOC)Click here for additional data file.Table S4umn10 in both European and Asian wheat gene pools.Comparison of FHB-related traits between the two alleles at (DOC)Click here for additional data file.Table S5Passport data of 266 wheat accessions with detailed pedigree information and four controls included in the study.(XLS)Click here for additional data file.Table S6FHB resistance evaluation data in three environments and theirs mean value.(XLS)Click here for additional data file.Table S7ctg954 BACs contig.The sequences of molecular markers and theirs physical positions on (XLS)Click here for additional data file.Table S8Genotyping data of all accessions for 43 3B-specific markers and 70 whole-genome SSR markers.(XLS)Click here for additional data file.


As several rare genomic variants have been shown to affect common phenotypes, rare variants association analysis has received considerable attention. Several efficient association tests using genotype and phenotype similarity measures have been proposed in the literature. The major advantages of similarity-based tests are their ability to accommodate multiple types of DNA variations within one association test, and to account for the possible interaction within a region. However, not much work has been done to compare the performance of similarity-based tests on rare variants association scenarios, especially when applied with different rare variants pooling strategies.Based on the population genetics simulations and analysis of a publicly-available sequencing data set, we compared the performance of four similarity-based tests and two rare variants pooling strategies. We showed that weighting approach outperforms collapsing under the presence of strong effect from rare variants and under the presence of moderate effect from common variants, whereas collapsing of rare variants is preferable when common variants possess a strong effect. We also demonstrated that the difference in statistical power between the two pooling strategies may be substantial. The results also highlighted consistently high power of two similarity-based approaches when applied with an appropriate pooling strategy.Population genetics simulations and sequencing data set analysis showed high power of two similarity-based tests and a substantial difference in power between the two pooling strategies. Similarity-based tests are motivated by the fact that haplotypes carrying the same causal mutation are more related compared with those without causal mutations; so, case haplotypes are expected to share longer stretches of DNA identical by descent. One of In this article, we compared the performance of the four similarity-based tests  appliedFor each test, 1000 permutations were performed to assess the significance of association. To make sure the empirical type-1 error is controlled, we ran the analysis of simulated data under the null model. As can be seen from Additional fileFigure Finally, we investigated the performance of the tests in the “Mixed Rare” scenario which incorporated both risk and protective variants within a region Figure , Panel 4We also analyzed the simulated data after excluding all common variants defined as those with MAF > 1% Figure . Since ahttp://www.1000genomes.org). The dataset consists of 697 unrelated individuals from six populations (Centre d'Etude du Polymorphisme Humain (CEPH) samples, Tuscan, Chinese, Japanese, Yoruba and Luhya). The complex phenotype model incorporates environmental covariates  and both common and rare causal SNPs from genes in particular pathways. Totally 200 replicates of several quantitative traits and case–control status were simulated under the phenotype model. A more detailed description of the simulations can be found in Almasy et al.[The GAW17 data set is a large scale exome sequencing data set with genotypes from the 1000 Genomes Project , and R is the matrix of ten principal components of genotype matrix obtained using the software Eigenstat[˜Ei = Ei-RRTEi, i = 1,2,3. Next, covariates are regressed out of adjusted phenotypes using the regression models:We performed an association analysis of causal genes that affect two quantitative traits, Qg et al.. Let G bigenstat. The corĞ of the causal genes. The type-1 error was set at 0.05, and 1000 permutations were performed for each of the 200 phenotype replicates to assess the power. To assess the empirical type-1 error rate for all the statistical tests, we ran the analysis with randomly permuted adjusted phenotypes obtained from the regressions (1). The resulting type-1 error rates are presented in Additional filesThe residuals from the regression models (1) were dichotomized  and tested for association with adjusted genotype Figure In this article, we compared the performance of the four similarity-based tests together with two rare variants pooling strategies using population genetics simulations and the GAW17 real data set. The results suggest that weighting may be a much better strategy than collapsing under the assumption of strong effect from rare variants, and moderate or low effect from common variants. Collapsing, in turn, showed much better performance when common variants possessed a strong effect. The absolute power difference of a statistical test when applied with collapsing and weighting pooling strategies may be substantial. From our study, it follows that if researchers are inclined to believe in the association of rare variants within a region, weighted pooling should be applied with similarity-based tests, whereas collapsing is more appropriate if common variants are suspected to be associated with phenotype. Additionally, under strong rare variants effect size in one direction when common variants were excluded from the analysis, collapsing performed equally good or better than weighting. Finally, both SKAT and KBAT had consistently high power compared with other considered similarity-based tests when applied with the appropriate pooling strategy.Recently, Basu and Pan comparedwlp, l = 1, … L, where the power value p varied from 0 to 1. So, p = 1 corresponded to the beta weights applied in our study, whereas p = 0 corresponded to the analysis without weights. Additional filep. It is clear that for all significance levels, the power of MDMR monotonically decreased with higher values of p, which corresponded to higher relative weights for rare variants. In the Additional fileFrom our results, the MDMR test does not seem to perform well when applied with weighting pooling strategy. To have a more detailed picture, we applied weighted MDMR test to the “Risk Rare” data sets with modified weights p-values, as many genes with low or no association signal can be dropped out after a few thousand permutations. For highly significant genes, permutation procedure can be split into several parts and performed in parallel on a cluster.One limitation of the current study is that the minimum significance level in population genetics simulations was 0.001. For genome-wide significance, the number of permutations needed to reliably estimate the significance is very large. This makes the comparison of the similarity-based tests at the genome-wide level prohibitive. In real GWAS studies, only few highly-significant genes will require a very large number of permutations to estimate The performance of similarity-based tests applied with two rare variants pooling strategies was investigated. Population genetics simulations and sequencing data set analysis showed consistently high power of two similarity-based tests and a substantial difference in performance of the two rare variants pooling strategies.N individuals (NA cases and NU controls), and within a genomic region L SNPs (both common and rare) were called. Let us denote the genotype matrix G = {gnl, n = 1,…,N l = 1,…,L} coded as minor allele counts, and the phenotype vector Y = {yn, n = 1,…,N} with the elements valued 1 for cases and −1 for controls (except when otherwise specified). The N × N similarity matrix is defined as K = {s}n,m = 1N, where gn is a multi-site vector of genotype {g1n,…,gLn} for nth individual, and s  is a similarity function. There is a variety of examples of similarity functions published in statistical genetics literature  = ∑ l = 1Lwlgnlgml for some fixed weights wl,l = 1,…,L the weighted quadratic kernel s = (1 + ∑ lLwlgnlgml)2, and the weighted IBS kernel s = ∑ l = 1Lwl(2 − |gnl − gml|). For our analysis, a popular exponential similarity measure[Assume that an association study involves u et al., Wessel u et al., and Muku et al.). Howevenalyses”. Thus, w measure was usedThe choice of similarity was motivated by the need to analyze quantitative genotype obtained as a result of population stratification adjustment (see Results section). As the exponential similarity is a function of the Euclidean distance between two multi-site genotypes, we consider this similarity to be more appropriate compared with, for example, another popular similarity measure, identity-by-state, which ww = {wl, l = 1,…,L}. In general, they may be derived from observed minor allele frequency (MAF) or prior information. Here, we adopted the weights proposed by Wu et al.[wl = Beta2, where mafl is MAF of lth SNP, Beta  is the beta density distribution function with parameters b and c evaluated at point a. The weight function monotonically increases as MAF decreases, while, as noted by the authors, “putting decent nonzero weights for variants with MAF 1%–5%”. As noted by Wu et al.[0 ≤ b ≤ 1 and c ≥ 1 allows for an increase in the weight of rare variants and a decrease in the weight of common variants. Thus, any values of parameters and from the specified range are acceptable. For the three tests , the weights are incorporated via the calculation of similarity matrix. Specifically, the weights incorporating similarity function sw for the similarity matrix Kw is as follows:Here we consider the two major ways of rare variants pooling: weighting and collapsing. The SNP weights will be denoted as u et al.: wl = Beu et al., settingFor the KBAT test statistic, the weights were incorporated differently  as the test does not use the multi-site genotype similarity.gn(L+1),n = 1,…,N as follows:The collapsing of rare variants was performed as described in Thalamuthu et al., namely,gn(L+1),n = 1,…,N, and a similarity matrix is constructed using common variants and the super-locus.In general, this type of collapsing preserves more information than an indicator of at least one rare variant being present, as suggested by Li and Leal. The colN x N identity matrix 1N and a vector of 1 of size N as 1N. Following Wessel and Schork[Let us denote d Schork, the tesH = Y(YTY)-1YT, where upper T denotes transposition.1. Phenotype projection matrix D = {dij}i,j = 1N = 1N1NT − K, where K is a similarity matrix defined above.2. Dissimilarity matrix G = (1N − 1N1NT/N)A(IN − 1N1NT/N), where3. Gover’s centered matrix MDMR = tr(HGH)/tr((IN − H)G(IN − H)), where tr is matrix trace.4. The test statistic Large values of the test statistic indicate a deviation from the null hypothesis of no association of a genotype with a phenotype.Y = {yn,n = 1,…,N} is coded as 1 for cases and 0 for controls. The mean phenotype vector is defined asSKAT test statistic under the null hypothesis is asymptotically distributed as the weighted sum of chi-squared random variables with one degree of freedom. Thus, the significance level can be assessed theoretically. Permutations can also be used to estimate the p-value empirically.For this test, the phenotype vector u et al., the tesU1 and controls U0 is defined as follows:The average similarity score between pairs of cases Knm, n, m = 1,…,N are the elements of the K similarity matrix . The U-test statistic is defined as U = (U1 − U0)2. Note that Shaid et al.[U is similar to the single SNP U-test statistic proposed by Shaid et al.[p-value.where d et al. considerd et al., but it Kl = {(Kl)nm}n,m = 1N as a single SNPs similarity matrix for lth variant. Similar to the notations of the U-test subsection, Ul1 and Ul0 are the average similarity scores for pairs of cases and controls, respectively, calculated from Kl, and let Ul = (Ul1 + Ul0)/2. Following Mukhopadhyay et al.[Let us denote y et al., consideKBAT = ∑ l = 1LBSSl/∑l = 1LWSSl. Since the test does not utilize the multi-site similarity matrix, but only single SNP matrices Kl, the weighted test statistic KBATW = ∑ l = 1LwlBSSl/∑l = 1LwlWSSl is used here. A large value of the KBAT statistic indicates a deviation from the null hypothesis. Permutations are used to assess the significance.where the two groups are case-case and control-control pairs. The test statistic is Population genetics simulations were performed based on the code provided by King et al. with demThe authors declare that they have no competing interests.SZ, AS and AT conceived the study. SZ and AT designed the experiments. SZ conducted the experiments and performed the analysis. SZ wrote the manuscript. SZ, AS and AT approved the manuscript.Empirical type-1 error estimate for population genetics simulations (Table S1), detailed description of population genetics simulations, and considerations for possible reasons for MDMR power loss when applied with weighting pooling strategy.Click here for filePower as a function of significance level for the four similarity-based tests with IBS kernels and two rare variants pooling strategies. Panel 1: “Risk Rare” Scenario; Panel 2: “Risk Both” Scenario; Panel 3: “Risk Common” Scenario; Panel 4: “Mixed Rare” Scenario.Click here for fileEmpirical type-1 error rates for dichotomized adjusted quantitative phenotype in GAW17 data set at the theoretical level of 0.05 .Click here for fileEmpirical type-1 error rates for dichotomized adjusted case–control status in GAW17 data set at the theoretical level of 0.05.Click here for filePower to identify an association with dichotomized adjusted case–control status in GAW17 data set for some of the causal genes.Click here for filePower to identify an association with dichotomized adjusted case–control status in GAW17 data set for some of the causal genes.Click here for fileImpact of power value on MDMR test performance in a “Risk Rare” scenario.Click here for file


KDR gene provided by Genetic Analysis Workshop 17. Based on the likelihood of logistic regression models and considering noninformative uniform prior probabilities on the coefficients of the explanatory variables, we used a random walk Metropolis algorithm to simulate the distributions of deviance and deviance difference. The distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift depended on the genetic factor. For the variant with the highest minor allele frequency and for any rare variant, standard logistic regression showed a higher power than the novel approach. For the two variants with the strongest effects on Q1 under a type I error rate of 1%, the integrated approach showed a higher power than standard logistic regression. The advantages and limitations of the integrated Bayesian/likelihood approach should be investigated using additional regions and considering alternative regression models and collapsing methods.Aitkin recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple. We have applied this method, which does not rely on informative prior probabilities or large-sample results, to investigate the evidence of association between disease and the 16 variants in the  Association studies are substantially contributing to our knowledge of the genetic basis of human disease, but for most complex diseases the proportion of explained heritability remains at best modest . Large vKDR gene provided by Genetic Analysis Workshop 17 (GAW17).Likelihood plays a central role in Bayesian, frequentist, and likelihood theory as a measure of strength of evidence. Based on the posterior distribution of the likelihood first described by Dempster, Aitkin has recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple ,5. BayesKDR variants influence a quantitative risk factor Q1 with larger effects among smokers. Q1 correlates with a quantitative risk factor Q2 and with a normally distributed latent liability trait that increases with age and is higher in smokers. The dependent variable investigated in the present contribution (y = disease affection status) is a function of Q1, Q2, the liability trait, and additional risk factors.Analyses were performed with knowledge of the underlying simulating model. Details on data simulation are provided by Almasy et al. . In shory depends on age (continuous variable), smoking status (dichotomous), and ethnicity (five categories). Model 2 includes age, smoking status, ethnicity, and a single genetic factor. Table We consider a baseline logistic regression model model 1), where , where yi ~y Bernoulli (iπ) with:Under the two models, xi contains the elements of the corresponding design matrices and β contains the model parameters . We consider noninformative, improper uniform prior probabilities for β. As a consequence, the joint posterior distributions of β are proportional to the likelihoods from the two logistic regression models, that is,where N = 697 persons in the present exercise. We use a random walk Metropolis algorithm to make 10,000 draws from the posterior distribution under model 1 and 10,000 independent draws under model 2. The deviance is defined as minus twice the logarithm of the likelihood. Random draws from posterior distributions are used to simulate the distributions of deviance and deviance difference.with fixed Power calculations rely on the probability values and on the proportions of positive deviance differences for the 200 replicates in the GAW17 data. To compare standard logistic regression with the novel approach, we use Wilcoxon rank sum tests on the null location shift and two-sample tests for the equality of proportions. Three different thresholds, 0.1, 0.05, and 0.01, are considered for probability values and proportions of positive deviance differences. All calculations and graphics are implemented with the free software environment R.Table Detailed results for the first replicate from standard logistic regression and from the novel approach are also presented in Table Figure Figure Box plots of probability values and proportions of positive deviance differences in replicates 1 to 200 are shown in Figure Table Based on the integrated Bayesian/likelihood approach, the smallest median proportion of positive deviance differences (0.01) and the highest power (69% for a type I error rate of 10%) were also found for variants 7 and 13. Wilcoxon rank sum tests indicated that the distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift was variable (see Figure Genome-wide association studies usually rely on the common disease/common variant hypothesis: Polymorphisms are genotyped and their association with disease is investigated. The identified, most often indirect associations are assumed to reflect a shared inheritance of the genotyped markers and linked causal variants . The firThe investigated genotypes are usually ranked according to plausibility of causal association. Ranks are most often based on probability values, although probability values have important disadvantages compared to Bayes factors . ProbabiThe marked differences between the data in Table Our analyses have some limitations. We have considered one single gene and used a simplistic method to collapse rare variants; details on more sophisticated collapsing methods can be found in the overview by Dering et al. . The basThe authors declare that there are no competing interests.JLB conceived of the study, performed statistical analyses and drafted the manuscript. CF contributed to the study design and helped to draft the manuscript. LB extracted the necessary data for the subsequent statistical analysis. All authors read, commented on and approved the final version of the manuscript.


E. urophylla. We found high heritability for both Klason Lignin (hÂ²=0.85) and Syringyl to Guaiacyl ratio (S/G: hÂ²=0.62). Then, nucleotide and haplotype diversities were described for two linked genes involved in the lignification process (CCR and ROP1). High levels of nucleotide diversity and rapid decay of linkage disequilibrium were observed in a sample of 16 trees. A SNP by SNP association mapping was carried out in the 328 progenies using a mixed linear model (available in Tassel). A total of 4 SNP (3 in CCR and one in ROP1) were significantly associated with S/G ratio explaining between 1 and 1.8% of the trait variation. Finally, the additive effects of haplotypes  were estimated using a Bayesian approach of the mixed model including the pedigree (MCMCglmm package available in R software). This analysis confirmed the existence of two distinct additive effects for CCR and ROP1 on S/G ratio, suggesting the existence of two S/G QTLs for these two linked genes in E. urophylla.Lignin quantity and composition are major components of wood quality in eucalypts breeding programs. Optimizing these traits can have a huge economical impact for charcoal and pulp production. However, little is known about the genetic determinism of these traits. Our objective is to establish efficient early selection criteria to identify ideotypes for these traits using gene based markers. To reach this goal, we first estimated genetic parameters for lignin quantity and composition using a factorial design comprising 16 founders and 328 progenies of


The Portuguese Healthy Family study, were included in the analysis. Total daily energy expenditure was assessed using a 3 day physical activity diary. Six metabolic syndrome related traits, including waist circumference, systolic blood pressure, glucose, HDL cholesterol, total cholesterol and triglycerides, were measured and adjusted for age and sex. GxEE examination was performed on SOLAR 4.3.1. All metabolic syndrome indicators were significantly heritable. The GxEE interaction model fitted the data better than the polygenic model (p<0.001) for waist circumference, systolic blood pressure, glucose, total cholesterol and triglycerides. For waist circumference, glucose, total cholesterol and triglycerides, the significant GxEE interaction was due to rejection of the variance homogeneity hypothesis. For waist circumference and glucose, GxEE was also significant by the rejection of the genetic correlation hypothesis. The results showed that metabolic syndrome traits expression is significantly influenced by the interaction established between total daily energy expenditure and genotypes. Physical activity may be considered an environmental variable that promotes metabolic differences between individuals that are distinctively active.Moderate-to-high levels of physical activity are established as preventive factors in metabolic syndrome development. However, there is variability in the phenotypic expression of metabolic syndrome under distinct physical activity conditions. In the present study we applied a Genotype X Environment interaction method to examine the presence of GxEE interaction in the phenotypic expression of metabolic syndrome. A total of 958 subjects, from 294 families of  The understanding of the wide range of physical activity (PA) and total daily energy expenditure (TDEE) levels in different populations has been of utmost concern in epidemiological research, because of its relationship with health in general individual has their own array of disease susceptibility genes that will interact with physical inactivity to produce maladaptive changes in gene-expression that often passes a clinical threshold into a chronic disease phenotype” (pg. 148). Data supporting this evidence in humans is scarce. Recently, in a 2004 statement by NIH Phenotypic variation in different MetS indicators at the population level has been studied using different approaches Contemporary technological advances and new statistical models allow geneticists to study EE and PA and its impact on phenotypic variance in different MetS traits using an escalating wealth of complex genetic models Under the VC model approach, we can formally test for genotype x environment (GxE) interaction The main purposes of the present study, using a nuclear family design, are to estimate the magnitude of genetic factors responsible for the architecture of MetS traits, and to study potential GxEE interaction.Estudo de Famílias Saudáveis Portuguesas (FAMS), investigates the relationship among MetS traits, PA, physical fitness and body composition in nuclear families. Children and adolescents aged 10 to 18 years were recruited in schools from the north and central regions of mainland Portugal, and were approached to freely participate in the study with their siblings and parents. The ethics committee of the Faculty of Sport, University of Porto, approved the study, and written informed consent, and assent, was obtained from the parents (or guardians). Given that families with 3 or more children are scarce in the Portuguese population The Portuguese Healthy Family study, from the Portuguese Using a 3 day physical activity diary Blood samples were collected after an overnight fast of at least 10-12 h. Glucose (GLU), total cholesterol, HDL-cholesterol (HDL), and triglycerides (TG) were analyzed with an LDX point of care analyzer Resting systolic blood pressure (SBP) was measured with an Omron Model M6 (HEM-7001-E) device according to The International Protocol of the European Society of Hypertension Waist circumference (WC) was measured with a Holtain flexible tape at the level of the smallest waist perimeter, with the subject standing erect with relaxed abdominal muscles and at the end of normal expiration.2) were computed using a maximum likelihood approach to estimate variance components under the standard polygenic model as implemented in SOLAR v.4.3.1 software Univariate quantitative genetic procedures as implemented in SOLAR To test for GxEE interaction, basic initial hypotheses were formulated regarding the variance/covariance relationship of a MetS indicator between family members with different levels of TDEE. As regards GxEE interaction, the fundamental null hypothesis is that the expression of a polygenotype  is independent of TDEE level. It can be shown from first principles that if there is no GxEE interaction, the same MetS indicator measured in subjects with different levels of TDEE will have a genetic correlation of 1.0 and the genetic variance will be homogeneous across all levels of TDEE i and j. Here we also have an elegant reexpression of the interaction null hypothesis, in this case regarding the genetic correlation, in that a genetic correlation equal to 1 is equivalent to The foregoing requires that we model the variance and correlation as functions of TDEE levels. For the genetic variance function , we modeled the variance using an exponential function to ensure positivity, which is required since any variance is a squared term For reasons detailed in Diego et al. 2 estimates ranging from 0.21 (TG) to 0.59 (HDL) (All MetS indicators showed highly significant h59 (HDL) , meaningTo test for the influence of TDEE and PA on the expression of MS indicators, the polygenic model was compared to the GxEE model by means of a log-likelihood ratio test see . The GxEHowever, to verify if there is GxEE interaction, the full model was compared to its constrained alternatives  was the most heritable of the MetS traits which is consistent with results from elsewhere ranging from 0.46 in Tehran Lipid and Glucose Study (TLGS), and 0.63 from the Family Heart Study (FHS) population. On the other hand, TG (h2 = 0.21), in disagreement with the data from TLGS (h2 = 0.36) and FHS (h2 = 0.48) The present findings confirm that MetS traits are highly heritable in agreement with previous results It is well established that PA has a preventive effect on a variety of morbidities associated with cardiovascular diseases GNPDA2 and FTO genes, and in Hispanic-American at LZTR2/SEC16B. In 2011, a robust meta-analysis of 218,166 adults and 19,268 children found that, in adults, PA attenuated the effect  of the minor (A2) allele of rs9939609 on obesity. Even though this interaction failed to be statistically significant in children and adolescents, in adults the minor allele of rs9939609 increased the odds of being obese less in the physically active group  than in the inactive group . These results seem to suggest that there are genes associated with increased MetS risk factors, such as obesity, that actually interact with different EE levels. Further, that we observed a genetic correlation function different from 1 across TDEE differences is consistent with the activation of genes involved in pro- and anti-inflammatory processes at relatively low and high PA levels, respectively Our results showed that there is additive genetic variance heterogeneity for GLU, TC, TG, and WC across TDEE levels, and that for WC and GLU, the genetic correlation between distinct levels of TDEE was different from 1. These results may be related to work demonstrating that physical activity and inactivity have anti- and pro-inflammatory effects, respectively We speculate that potential epigenetic changes driven by EE might be linked to a major epigenetic modification - DNA methylation - that suppresses gene expression by modulating the access of the transcription machinery to the chromatin or by recruiting methyl binding proteins As mentioned earlier we were unable to find similar papers to which we could compare our results and assess the suitability of our interpretations. It is quite interesting that despite the great wealth of data on PA and MetS, be it from descriptive and/or interactions studies, results are still lacking on the complex and divergent effects of PA on MetS traits given individualś genotypes. Perhaps this may be due to the complex nature of MetS, the statistical challenges that GxE interaction poses, and the necessity of having large samples of families. Moreover, with the development of DNA sequence analysis, and the possibility of running genome wide associations scans (GWAs), many investigators have devoted their attention to finding specific loci associated with the phenotypic variability of MetS traits Notwithstanding the importance of the present results, the fact that this study is based on a free health check-up, that may not be representative of the general Portuguese population, is a limitation as well as the relatively young and healthy sample of families may limit the generalizability of the results to older individuals who are more prone to develop MetS. Also, the lack of information about resting metabolic rate may influence the PAEE results, although previous work In conclusion, the present results demonstrate that MetS trait expression is significantly influenced by the way in which the genotype “deals” with distinct TDEE levels. As such, PA may be considered an environmental variable that promotes metabolic differences between individuals that are distinctively active. This is valuable information for health practitioners. More efforts should be devoted to identify specific loci that control MetS traits and to test if those loci are regulated or not by TDEE and/or PA.


Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components . MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F We look at gene-by-diet and gene-by-sex interactions underlying natural variation in multiple metabolic traits mapping to the same regions of the genome in a mouse model. We find that the underlying genetic architecture of these traits is different in different sex and diet contexts. We further use expression data and whole-genome polymorphism data to identify compelling candidates for experimental follow-up. We use these results to examine theoretical evolutionary predictions about how variation in populations can be maintained. There has been much discussion of late on how to use evolutionary theory to inform medical genomics. Mouse models may be especially appropriate for bridging the divide between evolutionary and biomedical research, because they allow the study of the effects of natural alleles on normal variation and because human-mouse homology is well defined. Our study is unique in examining quantitative trait loci from both evolutionary and biomedical perspectives, and we highlight the complex connections of the traits comprising the metabolic syndrome and the evolutionary implications of their underlying genetic architecture. This is important for understanding disease etiology and is relevant to personalized medicine. Metabolic syndrome (MetS) is an array of co-occurring disorders including dyslipidemia, high blood pressure, impaired glucose tolerance, and obesity. Individuals diagnosed with MetS have increased risk of developing cardiovascular disease (CVD) and type-2 diabetes (T2D) per se is not well defined in mice because the physiological features of individual components vary between mice and humans, reflecting 65–85 million years of divergent evolution ob/ob mice) and transgenic models. Further understanding of MetS will come from interrogating genes with small allelic effects on physiological processes, the source of variation in complex traits relevant to evolution and biomedicine, rather than from single genes with large defects.Mouse models have made major contributions to our understanding of complex disease etiology, including hypertension, obesity, and T2D 16 generation of an Advanced Intercross Line (AIL) formed from the LG/J and SM/J inbred mouse strains . Variation in complex traits in LG/J x SM/J is due to many genes of small effect interacting with each other and with the environment. Quantitative trait loci (QTL) have previously been mapped for obesity, serum chemistries and growth-related phenotypes in crosses of these strains We present results of a study of loci associated with normal variation in multiple MetS components: obesity (fatpad and organ weights), serum lipid levels , and diabetes  in an FWe report additive and dominance genotypic effects in addition to parent-of-origin genomic imprinting effects. Parent-of-origin imprinting is defined as the unequal expression of maternally and paternally derived copies of an allele, and has been shown to affect variation in metabolic traits Another mechanism that can maintain genetic variation in a population is balancing selection at pleiotropic loci, those associated with variation in multiple phenotypes, with different dominance relations for the different traits, so-called differential dominance foraging gene in Drosophila melanogasterWe examine context-dependent genetic effects and differential dominance at pleiotropic loci associated with MetS components. Patterns of pleiotropy are thought to reflect functional and developmental relationships among traits et al. 2005 We identify 23 pleiotropic QTL associated with normal variation in two or more MetS components. Of these 23 loci, 12 pass genome-wide significance while 11 pass chromosome-wise significance. The average locus is associated with variation in 4 traits. The traits examined here show moderate to high genetic correlations among each other and are reported with their respective heritabilities in Ehrich et al. (2008) Additive effects are found at 20 loci (87%), and dominance and imprinting effects are found at 21 loci (91%). On average, in cohorts showing additive effects, LL homozygotes have higher serum lipid levels  and heavier weights (fatpad and/or organ weights) but respond better to a glucose challenge  than SS homozygotes. In cohorts showing dominance effects, the L allele is dominant to the S allele 52% of the time. In cohorts with dominance effects and no additivity, we find overdominance  60% of the time and underdominance  40% of the time. In cohorts showing parent-of-origin imprinting effects, 15% show maternal expression imprinting, 10% show paternal expression imprinting, 21% show polar dominance imprinting (no additive effects), and 54% show bipolar dominance imprinting (no additive or dominance effects) . DescripDMetS1b, associated with variation in both serum lipid levels and obesity. This region overlaps QTL previously associated with high-density lipoprotein cholesterol (HDL) levels in studies using multiple crosses of mouse The genotypic effects at this QTL are complex . For choF11r, Fcgr2b and Nr1i3, are associated with variation in MetS components  are significantly differentially expressed between the two strains. Three of these 9 genes, mponents  [49]–[51ndidates  [47], , dominance [d  =  ((GLS+GSL)−(GLL-GSS))/2] and imprinting [i  =  (GLS−GSL)/2] genotypic scores, where G refers to the mean phenotype of all individuals sharing the subscripted genotype, and their interactions with sex (s) and/or with diet (d). Family and its interactions with sex and diet, including the three-way interaction, were included as random effects in the model. The −2 ln(likelihood) of this model was compared to a null model including only sex, diet and sex-by-diet interaction terms using a chi-square test with 12 df. Probabilities were transformed into LOD  =  −log10(Pr).Single locus analyses were performed using maximum likelihood in the Mixed Procedure in SAS 9.2. Our full mapping included: sex, diet, sex-by-diet interaction, the direct effects of the genomic locations  as well as separately for each autosome  ≤ 0.05) indicates there is most likely more than one QTL in the region (hence rejecting the null hypothesis of pleiotropy). Support intervals were determined using a standard one-LOD drop from the highest LOD at the combined peak of the QTL QTL for separately analyzed traits related to two or more metabolic syndrome (MetS) components mapping to the same cM position are considered pleiotropic QTL. When QTL support intervals for separately analyzed MetS component traits overlapped, but the separate trait peaks did not map to the same cM position, a formal test of pleiotropy was performed as described by Cheverud kl is the trait value for the lth individual of the kth cohort. Xk and SDk are the mean and standard deviation of the kth cohort, and SDpooled is the average SD over all cohorts. This standardization removes the effect of differential dispersion of cohorts' means, hence eliminating the differences in the scale of the genotypic effects and leaving only the changes in the order of the genotypic means as the source of the interaction The genotypic values for each cohort were obtained at QTL showing significant additive*sex, additive*diet, and/or additive*sex*diet interactions. When the genotypic values for individuals with LL and SS genotypes were of opposite ranks between two cohorts showing significant additive effects, a formal test for crossing interactions was run. The effect of differential dispersion of trait means in different cohorts was removed by standardizing the variance in trait means for each cohort by the following equation:At necropsy, liver and gonadal fat pads (white fat) were collected from 4 males and 4 females representing each strain and diet. Tissue was immediately frozen in liquid nitrogen and stored at −80°C until extraction. RNA was extracted using RNeasy 96 Universal Tissue extraction kits , and quantified using a Nanodrop 2000 . Samples were submitted to the Washington University Microarray Core Facility, where quality was assessed using a 2100 Bioanalyzer . RNA was reverse transcribed and amplified using an Illumina TotalPrep amplification kit  and then hybridized onto Illumina WG-6 v.2 BeadChips . Arrays were scanned using the Illumina Beadstation 500, and images processed using Illumina BeadScan software. Intensity values were analyzed using Illumina BeadStudio.Illumina raw data from 45,281 unique probes were examined using LUMI Whole-genome sequencing for the LG/J (≈20X haploid coverage) and the SM/J (≈14X haploid coverage) inbred mouse strains was completed by the Washington University School of Medicine Genome Sequencing Center using Illumina sequencing in two steps as described in Mardis et al. http://samtools.sourceforge.net/) High-quality SNPs for each strain were called using SamTools  male mice Nr1i3 is involved in lipid homeostasis Expression differences in white fat between LG/J and SM/J for positional candidate genes in (TIF)Click here for additional data file.Figure S2DMetS1b. Five of 10 genes that are significantly differentially expressed between LG/J and SM/J in liver are associated with MetS components. F11r is also differentially expressed in white fat, and in both tissues the LG/J strain shows the highest levels of expression. Nr1i3 is differentially expressed in a strain-by-dietary context in white fat, and shows significantly higher expression levels in LG/J in liver. Apoa2 affects cholesterol levels Hsd17b7 is involved in fetal cholesterol synthesis Usf1 is associated with blood serum lipid levels and MetS Expression differences in liver between LG/J and SM/J for positional candidate genes in (TIF)Click here for additional data file.Table S1Pleiotropic QTL affecting MetS components, their interactions and MetS positional candidate genes.(DOC)Click here for additional data file.Table S2Differential expression in MetS QTL among genes that are expressed in LG/J and SM/J strains in white fat tissue.(DOC)Click here for additional data file.Table S3Differential expression in MetS QTL among genes that are expressed in LG/J and SM/J strains in liver tissue.(DOC)Click here for additional data file.Table S4Number of SNPs between LG/J and SM/J in both coding and non-coding sequence in MetS QTL.(DOC)Click here for additional data file.Table S5Markers used for QTL mapping in the F16 LG/J x SM/J Advanced Intercross.(DOC)Click here for additional data file.Table S6Chromosome-wise and genome-wide significance thresholds.(DOC)Click here for additional data file.Table S7Significant crossing interactions at MetS QTL.(DOC)Click here for additional data file.


Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum in four separate environments. The FHB scores based on FHB incidence (Type I resistance)×FHB severity (Type II resistance) indicated a wide phenotypic variation of the varieties with BLUE (best linear unbiased estimation) values ranging from 0.07 to 33.67. Genotyping with 732 microsatellite markers resulted in 782 loci of which 620 were placed on the ITMI map. The resulting average marker distance of 6.8 cM allowed genome wide association mapping employing a mixed model. Though no clear population structure was discovered, a kinship matrix was used for stratification. A total of 794 significant (−log10(p)-value≥3.0) associations between SSR-loci and environment-specific FHB scores or BLUE values were detected, which included 323 SSR alleles. For FHB incidence and FHB severity a total of 861 and 877 individual marker-trait associations (MTA) were detected, respectively. Associations for both traits co-located with FHB score in most cases. Consistent associations detected in three or more environments were found on all chromosomes except chromosome 6B, and with the highest number of MTA on chromosome 5B. The dependence of the number of favourable and unfavourable alleles within a variety to the respective FHB scores indicated an additive effect of favourable and unfavourable alleles, i.e. genotypes with more favourable or less unfavourable alleles tended to show greater resistance to FHB. Assessment of a marker specific for the dwarfing gene Rht-D1 resulted in strong effects. The results provide a prerequisite for designing genome wide breeding strategies for FHB resistance.A total of 358 recent European winter wheat varieties plus 14 spring wheat varieties were evaluated for resistance to  Fusarium head blight, caused by the fungal pathogens Fusarium graminearum and Fusarium culmorum is one of the most important fungal diseases of wheat and other cereals in the world. In addition to causing severe yield losses, F. graminearum is known to produce two important mycotoxins, deoxynivalenol (DON) and zearalenone, which can contaminate the diseased grains (http://www.wheatscab.psu.edu/PDF/Fusarium_Head_Blight_.pdf). The development and use of resistant cultivars is considered an important strategy for avoiding Fusarium infection. Resistance to Fusarium is quantitatively inherited. Therefore during the last decade a number of quantitative trait loci (QTL) mapping studies using molecular markers were conducted. The first mapped QTL originated from the Chinese spring wheat accession ‘Sumai 3’ or its derivatives Fusarium resistance and mycotoxin accumulation in a number of bi-parental winter wheat populations Fusarium head blight resistance became evident Rht-D1b and Rht-B1b on susceptibility to Fusarium head blight were studied Fusarium head blight resistance was studied using an association genetics approach in barley Genome-wide association studies (GWAS), originally developed in human genetics, are nowadays employed in numerous studies involving model and crop plants and are showing there much greater success with fewer resources than in human genetics (for review Fusarium head blight in a set of 358 recent European winter wheat varieties and 14 spring wheat varieties as outgroup in four year/location combinations ( = environments) in the field, (2) to determine the marker-trait associations for resistance to Fusarium based on genome wide coverage using 732 SSR markers plus markers for candidate genes, and (3) to compare the obtained results with QTL reported in the literature.The goal of this study was (1) to assess the resistance to FHB rating was based on four separate environments and BLUEs (best linear unbiased estimations) values were calculated across all environments . For theHighly significant correlations between the environments indicated a good reproducibility of experiments . The ANOThe 372 varieties were genotyped with 732 microsatellite markers, of which 48 amplified more than one locus, resulting in a total of 782 loci spread across the 21 chromosomes. Heterozygote genotype calls were found for 2.6% of the data. With 4.8% missing data points, 276844 datapoints (95%) were available for the association analysis. For the construction of a genetic map using the ITMI  recombinant inbred mapping population, a LOD score of 3.0 was taken as the threshold to declare linkage. A total of 620 markers were placed on this map, with 19 SSR markers mapping to more than one position in the genome, resulting in a map of 4470 cM length. The remaining markers could not be mapped onto this population due to lack of data or polymorphism. Altogether, this resulted in an average marker distance of 7.2 cM (ranging from 4.6 cM on chromosome 4B to 10.2 cM on chromosome 6A). The genome was evenly covered, with the D-genome being slightly overrepresented (38.5% of the markers) and homoeologous group 6 being slightly underrepresented (9.8% of markers).2. Of the 8146 marker pairs, which returned r2 values, 62.9% (5121) showed statistically significant (p<0.05) LD as determined via 10,000 permutations. The average r2 for loci on different chromosomes (unlinked loci) was 0.031. R2 was in general low, being higher between neighbouring marker pairs (0.03 on chromosome 6A to 0.14 on chromosome 4B) than between all marker pairs on the same chromosome  and reached its lowest value for marker distances above 50 cM with 0.01 between all marker pairs on the same chromosome. For distances below 10 cM it was slightly higher  than for distances between 10 to 20 cM . The critical value for LD was 0.176. Above this value LD was considered to be due to genetic linkage. Only 207 marker pairs were in LD because of genetic linkage since their r2 was higher than 0.176 (Linkage disequilibrium (LD) is described with the parameter ran 0.176 .Population structure was assessed via principal coordinate analysis employing Rogers' modified distance . No distth edition a total of 794 individual significant (−log10(p)-value≥3.0) associations between SSR-loci and FHB scores were detected using a mixed linear model with kinship matrix  on chromosome 4D significant associations were observed in all environments (Ppd-D1 (formerly Ppd1) on chromosome arm 2DS, the photoperiod insensitive Ppd-D1a mutant had a positive additive effect, increasing FHB severity and decreasing resistance, as compared to the photoperiod sensitive wild type with negative additive effect, increasing resistance for FHB severity  and 209 alleles with unfavourable (resistance decreasing) effects were detected for FHB score . The num2 = 0.493 and Y = 21.9–0.31 X. For unfavourable alleles, the same type of analysis resulted in r2 = 0.485 and Y = 2.8+0.12 X. This indicated a certain additive effects for favourable as well as unfavourable FHB-alleles  were significant in at least three environments and BLUEs. While no meta-QTL for this region were described, marker BARC45, mapping in a similar region, was found significant in an association analysis study The MTA discovered on chromosome arm 3AS by four linked markers  co-located with a meta-QTL for resistance to spread linked to BARC12 On chromosome arm 3BS, the well-known QTL derived from ‘Sumai 3’ is located The MTA detected by marker CFD71 on chromosome arm 4AS in several environments mapped closely to marker GWM165, which is linked to a major gene controlling resistance to Fusarium head blight type I resistance Rht-D1. Three of these markers were in highly significant LD with Rht-D1 . We did not include Rht-D1 in our reference map, because no mapping data were available, however mapping positions are indicated elsewhere Chromosome 4D is characterized by a highly significant linkage block of four markers , which are located in the neighbourhood of A QTL linked to markers GWM129 and GWM304 was described for ‘Sumai 3’ derived material Chromosome 5B was the chromosome, where the highest number of individual MTA was detected, and it was characterized by two highly significant linkage blocks of four markers each . While several QTL regions were described for chromosome arm 5BL Homoeologous group 6 was the most sparsely covered group with significant associations in our study. While for chromosome 6B a ‘Sumai 3’ derived QTL On the end of chromosome 7AS, six linked markers  showed MTA in multiple environments. A QTL for chromosome arm 7AS was described for cultivar ‘Frontana’ Rht-D1, with the mutant allele decreasing the resistance. A general correlation was detected between plant height and FHB resistance  rather than the type II resistance (spread of pathogen). However, in our dataset we detected significant MTA of Rht-D1 for FHB score, as well as FHB incidence and FHB severity. We did not find any effects for Rht-B1 (former Rht1) on chromosome 4BS. The reason may be, that this dwarfing gene was present in only few varieties. The two most resistant varieties ‘Stava’ and ‘Kosack’ carried the wild type alleles for Rht-D1 as well as for Rht-B1. In terms of plant height they ranked on position 227 and position 112 out of 372 varieties, which represented the medium range of plant height. This indicates that besides the absolute plant height other factors influenced by Rht-D1 may affect the resistance behaviour.We found strong effects for the dwarfing gene sistance . The samPpd-D1a mutant showed decreased resistance. Since the inoculation process was repeated three times during the flowering of the tested varieties, we assume that this procedure compensated for variation in the effectiveness of inoculation caused by variation in the flowering time of the varieties. The spring varieties flowered on average two days earlier than the winter varieties, however in both types of varieties there were early and later varieties. While early flowering varieties may have experienced more than one inoculation event, also the weather conditions at the time of flowering at the respective trial sites play a role and can influence the spread of disease. The high correlations of FHB score between independent trial sites indicated that the procedure used is reliable and reproducible concerning the ranking of varieties.Early flowering varieties and varieties carrying the photoperiod insensitive Ppd-D1 alleles based on FHB severity. Opposite effects were reported Ppd-D1a mutant allele was mainly observed in French varieties, since it has most impact in southern latitudes of Europe. On the other hand this germplasm often had higher susceptibility to FHB.A significant marker-trait association was detected for the The use of genome wide marker association provides a comprehensive analysis of the FHB resistance QTL present in Central European germplasm. As phenotypic data we used a combination of type I (incidence) and type II  resistance, which provided a good estimation of practical field resistance. The overall resistance of a variety seems to be controlled by a relatively high number of loci with resistance decreasing or increasing effects. Nevertheless, several of the tested varieties showed a good general resistance to FHB in various environments. The observed additive effects of favourable and unfavourable alleles encourage strategies of QTL-pyramiding. The avoidance of susceptibility loci may also increase the general FHB resistance. Due to the high number of loci involved, breeding for quantitative FHB resistance will require strategies of genomic selection A total of 358 European winter wheat varieties and 14 spring wheat varieties were evaluated in this study . Spring Fusarium graminearum and Fusarium culmorum isolates (1/3 F.g.∶2/3 F.c.) using a water volume of 600 L/ha. Each wheat variety was inoculated on three dates at intervals of 3 days with the spore suspension. It was started at the beginning of flowering (at stage BBCH 61) and finished at the end of flowering (at stage BBCH 65/69). Since not all varieties flowered simultaneously, some experimental variation between early and later flowering varieties may exist. The experiment was designed so that each variety received at least one inoculation during its flowering time. To determine the variety resistance against Fusarium head blight (FHB) the parameters incidence and severity were recorded. FHB incidence (Type I resistance) was visually rated as the percentage of infected spikes from 50 observed spikes per plot. Severity (Type II resistance) was rated as the percentage of infected area per spike of the infected spikes Spray inoculations were performed with 50000 spores per ml of The varieties were also evaluated for heading date and plant height in a companion study in eight different locations over the same two seasons.http://wheat.pw.usda.gov/GG2/index.shtml) and from own formerly performed mapping endeavours Rht-B1 and Rht-D1Ppd-D1a allele of the photoperiod response locus Ppd-D1DNA extraction and PCR were performed as described by Population structure was inferred with a principal coordinate analysis based on modified Rogers’ distance 2 calculated with the programme TASSEL v. 2.1 2. The LD decay was estimated at the point where a second degree Loess curve, which was calculated with the function loess.smooth in R, intersects the threshold of the critical LD. Critical LD was evaluated following 2 values for all available marker pairs on different chromosomes were square root transformed and the 95th percentile was chosen as the threshold above which LD is likely due to genetic linkage.Linkage disequilibrium was measured with the parameter rth edition  using the “Mixed Models REML” module and the “Linear Mixed Models” function.Each year-location combination was considered as an environment in our study. For each environment, the FHB incidence value (Type I resistance), the FHB severity value (Type II resistance) and FHB score value (incidence*severity)/100 were calculated separately for every genotype. For each genotype, the arithmetic mean for phenotypic observations in three replications and three assessments per genotype were generated and results were used as the phenotypic data for association analysis. In addition, best linear unbiased estimators (BLUEs) across all four environments were estimated by assuming fixed genotypic effects. Since the datasets for all environments were complete, the BLUEs, in fact, equalled the arithmetic means across environments. The calculation was performed by GenStat 14For estimating correlations, Spearman rank correlations were calculated with the software package SigmaPlot 11.0.th edition. In GenStat, the “QTL analysis” module and the “Single trait association analysis” function was utilized, and the kinship matrix was chosen as the relationship model.For the calculation of genotype-phenotype associations, microsatellite data were converted into a bi-allelic data format, resembling SNP data. Each allele was considered as a single marker and genotypes were coded as ‘A’, when the variety possessed the allele and ‘C’ otherwise. A minor allele frequency threshold of 3%  was set and alleles with a lower frequency were excluded from the analysis. After filtering process, 3176 alleles remained and were employed for the association mapping approach. The FHB incidence value, FHB severity value and FHB scores value were used as trait values. Marker-trait associations were calculated separately for the four environments and the BLUEs across all environments. Calculations were performed with the software packages GenStat 14Figure S1Chromosomal location of marker-trait associations.(PDF)Click here for additional data file.Figure S2Analysis of linkage disequilibrium (LD) in the region of Rht-D1 on chromosome 4D.(PDF)Click here for additional data file.Table S1Fusarium head blight scores of 372 varieties. FHB scores (FHB incidence×FHB severity) were assessed in four environments. The BLUEs were calculated across all environments using restricted maximum likelihood (REML). A low FHB score relates to high resistance, while a high FHB score relates to high susceptibility to FHB.(XLSX)Click here for additional data file.Table S2Spearman rank order correlations of FHB score of 372 varieties among four environments and BLUEs. *** P<0.001.(DOCX)Click here for additional data file.Table S3Analysis of variance of FHB score in 372 varieties in four environments.(DOCX)Click here for additional data file.Table S4Spearman rank order correlations of FHB-BLUEs of 372 varieties with HD-BLUEs (heading date) and PH-BLUEs (plant height). P<0.001.(DOCX)Click here for additional data file.Table S510(p)>3.0) marker-trait associations. Columns G to J describe the additive effect for each environment. A positive additive effect means that the resistance to FHB is decreased, a negative additive effect means that the resistance to FHB is increased.List of significant (−log(XLSX)Click here for additional data file.Table S6Information about varieties.(XLSX)Click here for additional data file.