Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population). Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset. Canine hip dysplasia results from malformation of the coxo-femoral joint, which leads to hip laxity and often results in hip joint degeneration, painful arthritis, and lameness   is from our previous research by Sánchez-Molano et al. , 2015 anThis study did not conduct new procedures for collecting genotype or phenotype information. For full discussion of data collection, we refer to the original studies of Sánchez-Molano et al. , 2015 anGenomic data from 1,179 Labradors Retrievers of both sexes were from Sánchez-Molano et al. , 2015; aGenotypes were obtained from buccal swabs and genotyped using the Illumina Canine High Density Beadchip. A total of 106,282 single nucleotide polymorphism (SNPs) remained after quality control procedures for low call rate, low reproducibility, low or confounded signal, significant deviations from Hardy-Weinberg equilibrium, and removal of SNPs with minor allele frequency (MAF) < 0.01  and 1.137 (sd: 0.101) for UK and Cornell dogs, respectively. The average off-diagonal value between UK dogs was 0.003 (sd: 0.077), between Cornell dogs 0.083 (sd: 0.107), and between UK and Cornell dogs −0.018 (sd: 0.050). The average FST between the two populations was estimated as 0.03 with PLINK v1.90b1g  model, as follows:y is the vector of Norberg angle scores, W is a matrix of covariates with the α vector of associated fixed effects including intercept, where t+1)(−θt) algorithm  and mean-squared-error (MSE) between the true Norberg angle scores and predicted scores, and the slope of the linear regression of the observed Norberg angle scores onto the predicted scores (bias or inflation). A good prediction has a large ρ, small MSE, and a slope close to 1. A slope closer to 1 indicates a smaller prediction bias, with slopes >1 (<1) indicating under- (over-) estimation.The heritability was calculated as with partial derivatives given as A 5-fold cross-validation scheme was used to estimate prediction accuracies. The UK dogs were randomly split into 5 groups (235–236 dogs per group), and the Cornell dogs were randomly split into 5 groups (48–49 dogs per group). Following this division, groups were defined as training (4 of the 5 groups) and validation (last of 5 groups) sets in three different ways: both UK and Cornell dogs as the training set Figures , only UKA principal component analysis (PCA) was conducted to evaluate how the population structure of the combined dataset could affect the prediction accuracy. A PCA of the joint dataset was performed using PLINK v1.90b1g  by ranking the dogs by their first principal component values. The Cornell dogs were divided into three groups; group “C” comprises 54 dogs that appear to be genetically similar to the UK dogs, while groups “D” and “E” comprise 128 and 60 dogs, respectively, that cluster together. The Norberg angle scores in each group are summarized in Supplementary Figure We defined 5 PCA groups based on the first and second principal components, highlighted in Figure The PCA groups defined by the principal component analysis were also used for defining training and validation sets, in this case with no cross-validation. Both single groups and different combinations of the PCA groups were used as training sets. Prediction accuracies were calculated separately for each remaining group not used in the training set.Prediction accuracies were estimated using subsets of SNPs, corresponding to 1, 5, 10, …, 98, 100% of all SNPs in the merged dataset. SNPs were selected either at random (“random SNPs”) or as the leading SNPs when ranked by the statistical significance of association in a genome-wide association (GWA) analysis (“pre-selected SNPs”). The GWA analyses were performed as in Sánchez-Molano et al. , using tBriefly, the GWA analysis was performed using the linear mixed model2 distributed with 1 degree of freedom. Based on this test, a p-value for each SNP-trait association was calculated.which is similar to the GBLUP, but with the addition of the estimation of each SNP effect p-values and prediction accuracies. The 5-fold cross-validation is as described above but with no replication. For each iteration of the cross-validation, the same training set was used for the GWA analysis to rank SNPs and to train the GBLUP model to avoid inflating the prediction accuracy  when calculated using only UK dogs, 0.73 (se: 0.21) when calculated using only Cornell dogs, and 0.28 (se: 0.14) when calculated using the joint training set. The heritabilities estimated as part of the 5-fold cross-validations had average values of 0.24 , 0.66 , and 0.28 . The genetic correlation of Norberg angle scores between the two populations was estimated as 0.52 (se: 0.28), as estimated using a bivariate GBLUP model.Using the joint training set with both UK and Cornell dogs, validation in the Cornell dogs achieved a higher correlation 0.26) than the UK dogs (0.19)  and substantially higher for the Cornell dogs than the UK dogs Table .The slope of a linear regression of the observed Norberg angle scores onto the predicted scores were all except one close to 1, i.e., there was little bias. Using Cornell dogs to predict UK dogs, the slope was 0.25 , increasing the number of dogs in the training set increased the correlations for both UK dogs Figure  and CornThe mean-squared-errors of the prediction Figures  were affFor the smaller training sets, the correlations for the UK dogs are close to zero. This is largely coupled to estimates of slopes of regressions that are either close to zero or are orders of magnitude >2, as may be expected with zero correlation. The slope estimates for the Cornell dogs as predicted by Cornell dogs also display some cases with very large estimates, but these are not coupled with correlations close to zero.Increasing the training set size by adding Cornell dogs to a training set of UK dogs , as is hip score  in predicting phenotypes of UK dogs by adding Cornell data may well have been due to chance. Within countries, larger training sets did provide more accurate predictions for dogs from their corresponding populations, which is in accord with current literature Goddard, , and clerLD = 0.97 compared to 0.86 in ours). It is possible that specific sub-populations within the Cornell and UK datasets were sufficiently distinct to be responsible for the failure of the across-country training sets, however there was no evidence that removing any of the subsets would dramatically change the predictive outcomes. In a related study using multi-breed dairy cattle , this is not very informative. The use of pre-selected SNPs that are enriched in causative variants or markers very tightly linked to causative variants may be useful for capturing shared variation across distant populations  and the relationship between Cornell dogs (0.083) was generally higher than among UK dogs (0.003). These values may also explain the higher prediction correlations among Cornell dogs, as the greater relationship represents more mutual information and hence greater predictive ability per dog in the training set. In comparison to similar studies in livestock . The “UK” dataset of 1,179 Labrador Retrievers is available from the corresponding author on reasonable request.The “Cornell” dataset analyzed during this study is available in the Dryad repository (SE and PW conceived and designed the study. SE performed the data analysis and drafted the manuscript. JW and PW oversaw the analysis and helped to interpret the results and refine the manuscript. JH and RT helped to interpret the results and refine the manuscript. SB, DC, and ES-M contributed to data collection and management. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Adaptation to local habitat conditions may lead to the natural divergence of populations in life-history traits such as body size, time of reproduction, mate signaling or dispersal capacity. Given enough time and strong enough selection pressures, populations may experience local genetic differentiation. The genetic basis of many life-history traits, and their evolution according to different environmental conditions remain however poorly understood.We conducted an association study on the Glanville fritillary butterfly, using material from five populations along a latitudinal gradient within the Baltic Sea region, which show different degrees of habitat fragmentation. We investigated variation in 10 principal components, cofounding in total 21 life-history traits, according to two environmental types, and 33 genetic SNP markers from 15 candidate genes.p-values under 0.001 before correction). These associations, yet non-significant after multiple test corrections, with a total number of 1,086 tests, were consistent across the study populations. Additionally, these nine genes also showed an allele frequency difference between the populations from the northern fragmented versus the southern continuous landscape.We found that nine SNPs from five genes showed strong trend for trait associations (Our study provides further support for previously described trait associations within the Glanville fritillary butterfly species across different spatial scales. Although our results alone are inconclusive, they are concordant with previous studies that identified these associations to be related to climatic changes or habitat fragmentation within the Åland population. Melitaea cinxia (L.), despite that the ecology of the species has been intensively investigated over the last 20 years in the Åland Islands, and the species is now considered as a model organism in the dynamics and evolution of natural (meta)population.Human land use has led to natural habitat loss and fragmentation such that numerous species and populations are now at risk of extinction . The impPhosphoglucose isomerase (Pgi) in butterflies but also in other insects , Pgi allozyme alleles are associated with survival in the field , a SNP in Pgi is associated with flight metabolic rate  and lifetime offspring production (h2 = 0.62)  . These sSdhd gene polymorphism , which is associated with a reduction in SDH enzyme activity in flight muscles and an increase of metabolic rate during flight in the Glanville fritillary  are consistent across the populations, and the two environments.Here, we aimed to test whether habitat fragmentation might drive genetic differentiation between five natural populations of the Glanville fritillary butterfly, using numerous SNP marker-phenotypic trait associations. We show evidence of associations at various loci, through a candidate gene study involving a total of 49 single nucleotide polymorphisms (SNPs) from 26 candidate genes (metabolic genes). All SNPs were picked mostly based on previous similar, but more limited studies in terms of total number of traits, populations, or markers included. We investigated 21 different life-history traits, selected from traits previously recorded by a study by We used the material reported in the phenotypic life-history study of Plantago lanceolata and Veronica spicata are the same in all five populations , with several individuals from the same family included. In contrast, for the experiment ran in the summer 2010, our samples originated from 50 larval family groups from each population, widely distributed across the respective study areas, and for which the degree of relatedness was previously estimated to be low  and the post-diapause larval period . For each individual collected in the fall 2009, we recorded the sex and the weights of the fifth, sixth, and seventh instar larvae and pupae, the time period between successive molts, and the pupal period.T > 13 °C, We did not collect any adult traits from the 2007 pilot study. Twenty-four hours after eclosion, butterflies emerging from the larvae collected in the fall 2009, were marked with a unique number written on the hind wing and released into a large outdoor population cage (32 × 26 × 3 m) at the Lammi biological station in southern Finland . Data onP. lanceola and V. spicata each. Each egg clutch laid by a female was placed individually on a petri dish labelled with the female identity, the host plant species, and the date of oviposition, and incubated in the laboratory. The host plants were monitored for egg-laying females intensively, to record each egg clutch (P. lanceolate), and the egg hatch rate of each clutch (percentage of eggs that hatched).The cage had 200 potted host plants, 100 plants of g clutch . Eggs we1) included all the larval and pupal traits from both sexes using only those individuals for which information was available for all developmental traits (as described in 1 provided four PCs (PC1–1 to PC1–4). The second PCA (PCAM) included only male adult traits and produced three PCs (PCM1 to PCM3), while the third PCA (PCAF) included only female adult traits, and produced three PCs (PCF1 to PCF3) (see As many of the traits recorded were highly intercorrelated (2) was sampled from each butterfly using sterile biopsy punch . The biopsy does not impair the flight capacity of the butterfly  gene (Pgi pathway associated genes (upstream and downstream) (The candidate genes were selected based on a study by gi) gene , (ii) einstream) , and (iinstream) . Table Snstream) .Pgi SNPs genotyped did not reach our quality criteria, while for the samples collected in 2009, out of the 49 SNPs genotyped, 16 did not reach the quality criteria; they were hence excluded from subsequent analyses. The criteria included unclear heterozygote/homozygote cluster patterns and no heterozygote cluster. We also remove SNPs with significant deviations from the HWE or a MAF <0.05, or both in two or more of the populations. Similarly, if only one of the four populations did not comply with the HWE or the MAF at a certain SNP, we removed that population from the analyses , was assessed using an in-house quality control pipeline . False danalyses .Pgi SNP (Pgi:c.331A>C) on the pupal weight and post-diapause larval period. Sex, environment type, and genotype were used as explanatory variables. Population and family were added as a random factors nested within environment in the models.Subsequent analyses were carried out using R 3.1.3 software . For the1–1 to PC1–4) describing post-diapause larval and pupal development. Sex, environment type, genotype, and environment by genotype interaction were used as explanatory variables to those models, while population was included as a random factor nested within the environment type. Similar models, excluding the sex factor were used to test genetic associations to the three PCs from both PCAF and PCAM. Initial models with interaction term were subsequently simplified by the removal of non-significant terms to give a final minimal adequate model.For the samples collected in 2009, we used linear mixed models to separately test the effect of 33 SNPs on the four PCs  is currently estimated to reach a level of 0.45 at about 300 bp distance (r2 = 0.3), based on data from the Åland Island only). For simplicity of the analysis, we decided to investigate a 300 bp window from both ends of each gene with significant association with phenotypic trait(s), thus to identify neighboring genes and functional variants (N = 171) and the estimated effect size for either small or large effect , and for both additive and dominant models.In the Glanville fritillary, the linkage disequilibrium (LD) between genes . Let p21, p22 be the corresponding estimates in the southern continuous populations . For each locus, the test statistic was calculated as the vector of MAF differences: p11 − p21, p11 − p22, p12 − p21, and p12 − p22. Thus, signs and absolute values of the elements of the test statistic reflect collectively whether the MAFs are consistently smaller or greater in the northern fragmented environment, and how large the differences are. For every locus in which the differences were all consistently positive or negative, the random permutations were used to calculate the probability that at least as large a difference in the same direction was observed by chance, which resulted in the nominal empirical p-values for the locus. As the allele frequency differences between environments may be confounded by the distance separating the populations (isolation by distance), we measured pairwise genetic distances between populations  using the allelic profiles of the 33 SNPs that fulfilled our quality criteria, and the software GenoDive 2.0b27  were encoded into numerical form. We then implemented the “heatmap.2” function from the gplots R package  with theDryad under the provisional data identifier: doi 10.5061/dryad.81r20. Temperature data for the 1992–2001 period from Uppland coastal region, Öland and Gotland were extracted from the Metadata file ISO 19115:2003 ECDS (file identifier: 7085c74e-58a4-49f0-b8c0-b5a0eb3c74f9) publicly available from the Swedish Meteorological and Hydrological Institute (SMHI) website (http://www.smhi.se/en). The publicly available temperature data from Åland and Saaremaa were provided by the Finnish Meteorological Institute (http://en.ilmatieteenlaitos.fi/), and extracted from the RIMFROST database (http://www.rimfrost.no), respectively.All data are available in the public archive p > 0.12). Due to the large number of tests conducted and corrected for in this study, and to the relatively small sample size, our analyses might however have generated false negative results. Thus, we present below the results from the most significant associations before correction .After correction, no significant results were conserved , with females showing lowest PC1–1 and PC1–3 values. Sex had no effect on neither PC1–2 nor PC1–4 . Individuals with one or two copies of the C allele had higher values of PC1–3 than the AA homozygotes  showed significant interactions with environment type in affecting PC1–2 , but the individuals from the northern fragmented environments carrying at least one G allele had lower values of PC1–2 than the AA homozygotes  in the l instar .Hemolymph proteinase-5 gene (89 bp’s apart) on PC1–2  with mating success , thus male flying more mate more, despite starting to mate at older age. There were no direct effects of environment on any of the PCs.The male adult-traits were summarized by the first three PCs of PCAM  to refleM1 and the SNP c3917_est:386A>C in the Serine proteinase-like protein gene .We found an association between PCSgAbd-8 gene and PCM2 . Additionally, the direct effects of the two SNPs in the SgAbd-8 gene were coupled with the effect of environment by genotype interactions . Finally, there were significant associations between PCM3 and the SNP c50_est:824A>G in the Hemolymph proteinase-5 gene. Homozygote CC males show highest PCM3 values  of a female PCAF , suggesting that females laying small clutches produce less caterpillars. PCF2 shows positive correlation with first clutch size and hatch rate (ArcSin corrected), suggesting that larger clutches show higher hatch rate success. Finally, PCF3 is strongly positively correlated to female age at first oviposition, and to a lower degree to the size of the first clutch laid, suggesting that the first clutch of older females are larger. There were no effects of the environment nor the genotype on neither of the three PCs from PCAF.The female adult and oviposition related-traits were summarized by the first three PCs , Serine proteinase-like protein-1, Hemolymph proteinase-5, Vitellin-degrading protease precursor, Endocuticle structural glygoprotein (SgAbd-8), Cytochrome P450 (CYP337), Heat shock 70 kDa protein (Hsp70), and Glucose-6-phosphate-1-dehydrogenase (G6PD). Five of these genes also showed a significant association with larval development traits and/or male adult traits (as further described above), namely Pgi, Hemolymph proteinase-5, Vitellin-degrading protease precursor, HSP70, and SgAbd-8 (r2 = 0.45) window from spanning any of these eight genes, or any of the other genotyped SNPs. As there is no data available on the occurrence of epistasis in the Glanville fritillary butterfly, we also could not rule out the effect of hiding or masking gene(s) on the different associations tested in this study.There were 15 SNPs in eight genes with significant allele frequency differences between the northern fragmented and southern continuous environments, using directed permutation calculations. The eight genes are  SgAbd-8 . Additio SgAbd-8  revealedSerine proteinase-like protein-1 gene, the Hemolymph proteinase-5 gene, the CYP377 gene, the SgAbd-8 gene, the Pgi gene, and the Glucose-6-phosphate-1-dehydrogenase (G6PD) gene.The hierarchical clustering of samples used to generate the heatmaps supports the evidence of environment-specific variation in allele count for several SNPs with significant allele frequency differences between the northern fragmented and southern continuous environments, when using the directed permutation calculations described above. The environment-specific differentiations illustrated in the heatmaps  are espeWe highlight several associations between SNPs in candidate genes and some of the PCs from PCAs on various life-history traits in the Glanville fritillary butterfly , howeverThe material for the present study originates from five distinct populations, over two different collection years. Consistent results across this material can be considered to be more robust than would be comparable results for a single population in the Glanville fritillary butterfly. However, because the phylogenetic relatedness varies between the five populations (see 1.Phosphoglucose isomerase (Pgi) gene, for which genetic polymorphism has previously been linked to variation in a large number of traits (The glycolytic f traits  as furth2.Cytochrom p450 (CYP337) gene, which has been previously associated with life-time egg and larval production, and host plant preference in the Glanville fritillary  gene, for which a previous study shown that gene expression is up-regulated during thermal stress  or PCM3  and polymorphism in the Hemolymph protease-5 gene might suggest variation in the quality, through for example variation in immune functions, between individuals in the Glanville fritillary.The idoptera  and otheidoptera , and to 7.Endocuticle structural glycoprotein (SgAbd-8) gene, described as a structural gene associated with larval development in insects  gene, in which polymorphism has been associated with mating success in Colias butterflies , Pgi):c.331A>C are a case in point. We found significant associations with larval development. The AA homozygotes have heavier larvae following diapause, but smaller pupae than the other genotypes. These results were consistent across the four populations, even though there are great differences in the frequency of the C allele in the populations: the C allele is much more common in the two northern populations from continuous environment (around 50%) than in the southern populations from fragmented environment (around 20%).The results of the genetic association for the SNP (Pgi):c.331A>C AC heterozygotes have superior flight metabolic rate and dispersal rate in the field compared with the AA homozygotes :c.331A>C) lay larger clutches than AA homozygous females especially at low ambient temperatures , previous studies have shown that eratures . In Coliecundity . The CC Pgi):c.331A>C and life-history traits have found consistent results for several traits, including larval development. The individuals with the AA genotype do not appear to be as good dispersers as the heterozygotes . Active flight causes oxidative stress, which may become an important factor for dispersive butterflies. In summary, several studies on the Glanville fritillary examining associations between the SNP  by NordNordWest/Wikipedia. The original image was cropped using Adobe Photoshop CS6 .(A) Map of the Baltic Sea region with five populations of the Glanville fritillary: the Uppland coastal region, and Öland, Gotland, Saaremaa and Åland Islands. (B) Average monthly temperatures (full lines) and average monthly maximum and minimum temperatures (dashed) from 1992 to 2001 in Åland (orange), Uppland (purple), Öland (green), Gotland (red) and Saaremaa (blue). The map was created by A. Duplouy using a modified version of the image “Location map of the Baltic Sea” . The SNPs appear on the X-axis. (a) The heatmap includes 33 SNPs. The red colour at the top coloured panel highlights the15 SNPs with significant allele frequencies differences between landscape type . (b) The heatmap includes only the 15 SNPs with allele frequency differences between landscape types. Only samples collected in 2009 are included in this analysis.Click here for additional data file.10.7717/peerj.3371/supp-3Supplemental Information 3(A) The 5th instar larval weight (mg) for the different genotypes of samples collected in 2009. (B) The pupal weight (mg) for the different genotypes of samples collected in 2006. Sample size is given by the number above the bar. Heavy horizontal lines represent median values, boxes give interquartile ranges, whiskers and dots give the minimum and maximum, and outlier values, respectively.Click here for additional data file.10.7717/peerj.3371/supp-4Supplemental Information 4Sample size for each category is given by the number at the top of the graph. Heavy horizontal lines represent median values, boxes give interquartile ranges, whiskers give minimum and maximum values, dots represent outliers.Click here for additional data file.10.7717/peerj.3371/supp-5Supplemental Information 5Sample size for each category is given by the number at the top of the graph. Heavy horizontal lines represent median values, boxes give interquartile ranges, whiskers give minimum and maximum values, dots represent outliers.Click here for additional data file.10.7717/peerj.3371/supp-6Supplemental Information 6Sample size for each category is given by the number at the top of the graph. Heavy horizontal lines represent median values, boxes give interquartile ranges, whiskers give minimum and maximum values, dots represent outliers.Click here for additional data file.10.7717/peerj.3371/supp-7Supplemental Information 7Sample size for each category is given by the number at the top of the graph. Heavy horizontal lines represent median values, boxes give interquartile ranges, whiskers give minimum and maximum values, dots represent outliers.Click here for additional data file.10.7717/peerj.3371/supp-8Supplemental Information 8M  and PCAF .In the main experiment (2009) and the pilot experiment (2007), and number of individuals included in each PCA1 , PCAClick here for additional data file.10.7717/peerj.3371/supp-9Supplemental Information 9R) and asterisks denote significance at the 0.05 (*), 0.01 (**) and 0.001 (***) levels. The Bonferroni alpha values were corrected for 27, 15 and 91 correlations, respectively.Values are Pearson correlative values ” failed our quality criteria, while SNPs tagged with “(*)” were genotyped for the 2007 pilot study. HWE, Hardy Weinberg Equilibrium; MAF, Minimum Allele Frequency; Gen, Genotyping; Homoz, Homozygote. Aland (AL) and Uppland (UP) populations evolve in northern fragmented environments, while Gotland (GO), Oland (OL) and Saaremaa (SA) populations evolve in southern continuous environments.Each Click here for additional data file.10.7717/peerj.3371/supp-14Supplemental Information 14Models of inheritance are given within brackets as Dominant, Recessive or Additive. Both main effects and interactions are reported. “NS” stands for non-significant.Click here for additional data file.10.7717/peerj.3371/supp-15Supplemental Information 15p-values give the posterior probability of the hypothesis that there is no difference between the environment types.The minimum allele frequency for each SNP is shown in bold to ease visualization of the results. Allele frequencies differences between environments were calculated using directed permutation tests (1e+6 random permutations). The Click here for additional data file.10.7717/peerj.3371/supp-16Supplemental Information 16Pgi locus was genotyped twice. HW, Hardy Weinberg equilibrium; MAF, Minimum Allele Frequency; Gen, Genotyping; Homoz, Homozygote.Each Click here for additional data file.


Musa spp.) is an important crop in the African Great Lakes region in terms of income and food security, with the highest per capita consumption worldwide. Pests, diseases and climate change hamper sustainable production of bananas. New breeding tools with increased crossbreeding efficiency are being investigated to breed for resistant, high yielding hybrids of East African Highland banana (EAHB). These include genomic selection (GS), which will benefit breeding through increased genetic gain per unit time. Understanding trait variation and the correlation among economically important traits is an essential first step in the development and selection of suitable GS models for banana. In this study, we tested the hypothesis that trait variations in bananas are not affected by cross combination, cycle, field management and their interaction with genotype. A training population created using EAHB breeding material and its progeny was phenotyped in two contrasting conditions. A high level of correlation among vegetative and yield related traits was observed. Therefore, genomic selection models could be developed for traits that are easily measured. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to less difficult traits they are highly correlated with. Genotype response to cycle and field management practices varied greatly with respect to traits. Yield related traits accounted for 31–35% of principal component variation under low and high input field management conditions. Resistance to Black Sigatoka was stable across cycles but varied under different field management depending on the genotype. The best cross combination was 1201K-1xSH3217 based on selection response (R) of hybrids. Genotyping using simple sequence repeat (SSR) markers revealed that the training population was genetically diverse, reflecting a complex pedigree background, which was mostly influenced by the male parents.Banana ( Radopholus similis and banana weevil (Cosmopolites sordidus). Black Leaf Streak (Black Sigatoka), a fungal disease caused by Mycosphaerella fijiensis reduces the photosynthetic area of the plant, which decreases yield. Banana bacterial wilt caused by Xanthomonas campestris pv. musacearum causes 100% yield loss when the banana is attacked [East Africa is considered a secondary center of banana genetic diversity. Uganda in particular is a home to over eighty cultivars of East African Highland banana (EAHB) commonly divided into cooking and beer types . The croattacked –8. Variaattacked , 10 makiIn order to meet the food demand for the growing population, breeding for resistance and high yielding varieties is considered to be the most sustainable solution to address banana production constraints , 12. UnlMarker assisted selection (MAS) has been implemented in many animal and crop breeding programs. The success of MAS greatly depends on the genetic architecture of traits being improved. To date MAS has not been effectively deployed in banana breeding. The possible reasons are polyploidy, important economic and agronomic traits may be controlled by many quantitative trait loci (QTL), each with a small additive effect, and the lack of saturated linkage maps for QTL mapping. It is believed that the application of genomic selection (GS) will improve the efficiency of crossbreeding programs especially for crops with long breeding and selection cycle , 18 likeGS studies have reported varying accuracies in prediction (predictive ability of GS models) and this has been attributed to differences in trait heritability, number of markers, training population size and genotype x environment interaction . BananasData were collected at the International Institute of Tropical Agriculture, Uganda from a banana genomic selection (GS) training population between 2013 and 2016. The institute is located at Namulonge research station, 0.53° N 32.58° E, 1150 m above sea level with rainfall of about 1200 mm/y split into two rainy seasons, March-June and September-December and an average annual temperature of 22°C. The GS population consisted of 307 genotypes that included diploid (11%), triploid (85%) and tetraploid (4%) plants . The ploThe yield-related traits scored included: days to fruit maturity (DFM) that is, days between flowering and harvesting, bunch weight at full maturity (BWT), number of hands (cluster) (NH) and number of fruit fingers (NF), fruit length (FL), fruit circumference (FC), fruit diameter (FRD), pulp diameter (PLD) and peel thickness (PED), where PED = (FRD—PLD)/2. The vegetative (growth) traits included: number of standing leaves at flowering (NSLF), youngest leaf spotted with Black Sigatoka at flowering (YLSF), index of non-spotted leaves at flowering (INSL), height of tallest sucker at harvesting (HTSH), plant height at flowering (PHF), plant girth at 100 cm from soil surface (PG), height of tallest sucker at flowering (HTSF), total number of suckers at flowering (TS), number of standing leaves at harvesting (NSLH) and youngest leaf spotted with black sigatoka at harvesting (YLSH).Total number of suckers (TS) was recorded at flowering in cycle 1 only after which each mat was left with a maximum of three plants and these included the flowered plant, follower sucker and the sucker produced by follower sucker if present. A Vernier caliper was used to measure FRD and PLD. Fruit related traits such as FL, FC, FRD and PLD were recorded from the middle finger of the second hand on the bunch. Measurements for FC, FRD and PLD were recorded midway the length of the finger. However, to measure FRD and PLD, a cross-section of the fruit was made to expose the pulp. The INSL was calculated from the formula, INSL = 100*(YLSF-1)/NSLF . This fowww.r-project.org. A combination of Shapiro-Wilk test, boxplots, standard deviations and histograms were used to check for normality and outliers in the data and where necessary the outliers were removed before further analysis. Total number of suckers and bunch weight were transformed by square root. Using the aggregate function from library (plyr), trait means were calculated for every genotype and cross combination (family) in every cycle, and field and these were used in correlation analysis and principal component analysis (PCA).All analyses were performed in R, open source statistical software from 2) was calculated as a square of correlation coefficient between cycle 1 and 2 data. To understand the structure of the population and how different traits influenced that structure, principal component analysis was done using PCA function provided in the library (FactoMineR). Traits (dependent variable), cross combinations and individual genotypes were projected on the first two components (Dim1 and Dim2).Correlation analysis and test of significance for the correlations between traits were done using library (Hmisc) and Student’s t-test based on cycle 2 data for cross combinations. Coefficient of determination  based on cycle 1 and 2 data. Linear models were constructed for each trait in respect to each cycle, field management practice and their interaction with genotype as model_fit = lm where lm = linear model function. Type III SS ANOVA tables were generated using Anova function provided in the library(car) as result = Anova. In cases where no significant interactions were observed between two independent variables and where one explanatory variable was not significant, then type II or type I SS ANOVA was used for further investigation.Selection differential (S) and response to selection (R) were used to compare performance of parental cross combinations . S and RMusa SSR primers were used to genotype the GS training population. The list of primers used, polymerase chain reaction (PCR) conditions, and fragment analysis procedure were adopted from Christelová et al. [Genetic diversity of the training population was assessed using simple sequence repeat (SSR) markers. Cigar leaf samples were collected from the training population in Uganda and shipped to the Institute of Experimental Botany, Olomouc, Czech Republic under cold chain. Samples were lyophilized prior to DNA extraction. DNA from lyophilized samples was extracted using NucleoSpin Plant II kit, Macherey-Nagel, Germany, following the manufacturer’s instructions. The concentration and quality of DNA was assessed by NanoDrop ND-1000 spectrophotometer. Nineteen informative á et al. .Two independent rounds of PCR were performed on each sample. The concordance of alleles from each sample were inspected and scored manually in GeneMarker v1.75 . A third round of PCR was performed only for samples that showed incongruity with the two reactions. Alleles were scored as dominant markers for presence and absence (1/0). Data were imported in R and squared Euclidean distances were generated using the function dist provided in the library(ape). Clustering was done with function hclust based on ward.D method , 34. PolXanthomonas wilt before full maturity, plants that snapped due to weevil damage and premature breaking of the peduncle due to windstorm.During data analysis, some genotypes were excluded for some traits due to missing data or extreme outliers. The outliers were mainly recorded on plants that were infected with banana Significant correlations were observed among and between growth and yield traits Tables  and 2. PPrincipal component analysis showed that in both fields, the yield (fruit) traits contributed to the first component (Dim 1) while the vegetative (growth) traits contributed to the second component (Dim 2) . Among tFor individual genotypes, a similar trend was observed with Dim 1 and Dim 2 accounting for 31.43% and 19.11% of total trait variation, respectively . ProjectBased on Black Sigatoka resistance and fruit filling (indicated by FRD), four main groups were represented in the population: (i) genotypes with high INSL and good fruit filling, (ii) high INSL with poor fruit filling, (iii) low INSL with good fruit filling and (iv) low INSL with poor fruit filling. On average the observed INSL and FRD for the genotypes in the four groups were as follows: (i) 78.1% and 3.0cm, (ii) 80.1% and 1.4cm, (iii) 66.8% and 3.1cm, and (iv) 67.1% and 1.4cm, respectively. Genotypes projected on Dim 2 had high average scores on NSLF, YLSH, INSL, and NSLH and in contrast they had the lowest average scores on BWT, FL, FC, FRD, and PLD and the reverse was true for those projected on Dim 1.Visual inspection of boxplots for various traits indicated a cycle effect on data distribution of some traits while others were not affected by cycle. For example, Plant height increased at cycle 2 while index of non-spotted leaves did not increase  and thisCoefficients of determination showed that under low input, cycle had less effect on NSLF, YLSF, INSL, TS, HTSF and PED across genotype cross combinations . The StuWhen generating ANOVA models, genotype (clone) was assumed to be the main source of variation. In addition to genotype the effect of cycle, field and their interaction with genotype was investigated. In all models for all traits, genotype had significant effect on trait variation with P < 0.001 . Traits Whereas there were significant interactions between genotype and field management, there was no significant main effect of field on NSLF, YLSF, HTSF, INSL, TS, NSLH, YLSH, HTSH, NH, NF and PED. Similarly, in the presence of significant interaction between genotype and cycle, there was no main effect of cycle on INSL, HTSF, HTSH, FC, PLD and PED . When thThe GS training population consisted of 77 different cross combinations representing about two decades of banana breeding activities by IITA and NARO Uganda. Some of these cross combinations gave rise to the tetraploids and improved diploids that are part of the core breeding lines in the program. Tetraploids and triploids were predominantly used as female parents while the diploids provided the pollen source but in some instances 2x by 2x or 4x by 4x crosses were made. The majority of the cross combinations were excluded for this analysis in this work because they had less than five hybrids in the population. However, crosses between different EAHB with Calcutta 4 were treated as one cross because the EAHB represent a clone set with very low genetic diversity . In totaThe best cross in terms of yield and fruit size was C10 (1201K-1xSH3217). Many hybrids from this cross had the highest bunch weight (R = 3.8) characterized by longer fruit fingers, big fruit circumference and the highest pulp content. However, the plants were very tall with big girth. Their maturity period was shorter (about 4.5 months on average) and comparable to hybrids from EAHBxCalcutta 4. Generally, crosses involving SH3217, SH3362 and 9128–3 as male parents produced hybrids that had good fruit filling characteristics although they varied in Black Sigatoka resistance and suckering behavior. For example, crosses involving 9128–3 generated hybrids that had the lowest INSL.Hybrids from a cross between 5610S-1 and 2180K-6 produced the highest number of leaves scored at flowering (R = 2.1). They had the highest resistance to Black Sigatoka as reflected by INSL (R = 7.2%) despite the parents being susceptible. They were the shortest (R = -62.3 cm) with smaller plant girth. Their average maturity period was almost two months more than the average of all parental lines (R = 54.6 days) and the longest of all other hybrids. Due to long maturity period the number of standing leaves at harvest was very low because of normal leaf senescence. Despite producing many fruit fingers and slightly more hands per bunch, their average yield and size of fruits were lower than those of the parents. However, some exceptional lines such as 25031S-7 (diploid) had sizable bunch with relatively big fruits.M. acuminata ssp. malaccensis 250 as male parent produced hybrids that were tall, slender, with bunches that had many fruit fingers poorly filled with pulp but some individual genotype exceptions were observed. The hybrids were resistant to Black Sigatoka and had the highest number of functional leaves at harvesting. Hybrids from cv. Rose were slender and shorter and were the highest in sucker production while other traits varied considerably.Crosses involving Hybrids from different cross combinations had longer maturity period (128–185 days) than EAHB. On average EAHB mature within 90 days after flowering while the average maturity period for all parental lines was 130 days.Out of the nineteen SSR markers, eighteen were used to delineate the structure of the study population, because marker mMaCIR164 produced ambiguous allele profiles across samples. From 18 loci, 195 alleles were scored and the number of alleles per locus ranged between 4 and 18 with an average of 10.8. Polymorphism information content (PIC) of the markers was high with an average of 0.87 (0.53–0.95) while the major allele frequency was on average 0.22 (0.1–0.45).M. acuminata ssp. malaccensis 250 formed their own cluster but were closely linked to that of EAHB, thus supporting the hypothesis that the tetraploids were formed after fusion of unreduced gametes from triploid EAHB and haploid gametes from diploid cv. ‘Calcutta 4’ and M. acuminata ssp. malaccensis 250. The within cluster dispersion was rather homogenous and not highly diverse for the tetraploid hybrids probably due high allele dosage from EAHB. SSR data suggested that the tetraploid presumed to be hybrids of cv. Enzirabahima by M. a malaccensis 250 , were in fact admixtures from pollination of EAHB with cv. ‘Calcutta 4’. These tetraploid inherited 17 alleles specific for cv. ‘Calcutta 4’ and none of ssp. malaccensis 250 specific alleles across the 18 SSR markers used.Despite the complex pedigree background of the GS population, SSR markers were informative enough to delineate the structure of the population . Hierarcmalaccensis 250 were more distinct from the rest of the population and formed their own cluster. Four hybrids  presumed to be progeny of 2180K-6, cv. ‘Calcutta 4’ and cv. ‘Rose’ as male parents clustered together with ssp. malaccensis 250 hybrids. SSR genotype profiles suggested that those four hybrids were misidentified because they had ssp. malaccensis 250 specific alleles. The highest genetic diversity was observed in the diploid parents and between families. Diploids that were linked by pedigree clustered together but the within cluster differences were high compared to EAHB and tetraploids. Diploids such as cv. ‘Calcutta 4’, 861S-1, 5610S-1, 2180K-1, Kokopo, and cv. ‘Rose’ clustered with their hybrids. Hybrids derived from 5610S-1 x 2180K-1 were all diploids and closely related to cv. ‘Calcutta 4’ and 861S-1 and formed a separate cluster. Although the pedigree of 2180K-1 could not be traced, there is a possibility that one of its parents was cv. ‘Calcutta 4’. Hybrids from cv. ‘Long Tavoy’ and cv. ‘Calcutta 4’ were not easily delineated because of the close resemblance of these genotypes. One cluster (J) comprising of triploid hybrids showed high within cluster diversity. Majority of advanced hybrids especially NARITA hybrids comprising of potential candidate varieties are found in this cluster. The ssp. zebrina accessions included in the analysis clustered within the main clusters suggesting their genetic relatedness with other acuminata genotypes. In the population, some genotypes were duplicates. The duplicates identified included 28465S-2 (A&B), 26337S-11 (A&B) and 26337S-22 (A&B) while 27524S-12 (A&B) that were assumed to be duplicates were clarified to be genetically different although both were progeny of ssp. malaccensis 250. Other supposed unique genotypes were identified as likely clonal pairs, such as 24948S-9 and 24948S-10, 24948S-22 and 24948S-27, 25623S-11 and 25628S-11, 24948S-12 and 24948S-21, 12479S-1 and 12479S-13, 25737S-1 and 25356S-1, and 25066S-1 and 25066S-2.Hierarchical clustering of hybrids was much influenced by male parents used in the cross. The biggest percentage of hybrids was produced from crosses involving tetraploids derived from EAHB and cv. ‘Calcutta 4’. Hybrids from ssp. Bananas express many traits that are used to evaluate hybrids in breeding programs. These traits can be broadly classified as vegetative/agronomic (growth) traits, or yield and consumer appeal (fruit) traits. Growth and yield related traits are used to assess the level of introgression of resistance genes and this is done in the early evaluation trial. The index of non-spotted leaves (INSL) is a measure of resistance to Black Sigatoka, a fungal disease that causes rapid drying of leaves hence reducing the photosynthetic area . ResultsCorrelation analysis showed a positive correlation between INSL, NSLF and YLSF. However, these three had low but significant negative correlations with yield-related traits under low input field management conditions. These results suggest that whereas some Black Sigatoka resistant genotypes give good yield, others produce inferior fruits. Reduction in functional leaves and photosynthetic area has been shown to impact banana yield potential . TushemeMusa triploids. This phenomenon could be attributed to differences in carbon source to sink capacities.The present study shows that based on yield and growth traits, four groups of bananas existed in the training population that is, genotypes with high INSL and good fruit filling, high INSL with poor fruit filling, low INSL with good fruit filling and low INSL with poor fruit filling representing the four planes of the two components. However, PCA could not resolve the population structure into clear-cut clusters due to complex pedigrees, although Osuji et al.  used thiPlant physiological studies have shown that the balance between source and sink translocation of photosynthetic assimilates is key to plant productivity . In banaThe training population consisted of poor and good fruit filling genotypes based on FL, FC, FRD and PLD. This characteristic was consistent across cycles and field management, with two overlapping peaks in a binary pattern . HoweverMusa.This study did not find sufficient evidence to show that absolute apical dominance existed in our training population. Different levels of sucker regulation (1–25 suckers) were observed in different cross combinations. This result is in agreement with the observation made by Ortiz and Vuylsteke  that nonThe effects of cycle and field input management on the genotype and how the genotype interacted with these two aspects of the environment were evaluated. The effect of cross combination was also assessed. Based on coefficients of determination and analysis of variance, genotype, cycle, field and their interactions had different levels of effect on trait variation among cross combinations and individual genotypes. While PHF and PG significantly increased at cycle 2, field management did not have a significant effect on these traits. This could be attributed to the fact that the suckers used were at different physiological maturity. Yield traits were also affected by cycle but the bi-modal distribution was maintained. When bananas are planted in the field they first undergo an establishment phase and build reserves that can accelerate growth of the daughter plants. Therefore, cycle 2 is best to compare genotypes especially with regard to yield traits. Tushemereirwe et al.  reportedThe present results show that different banana traits may have different genetic architecture with some traits influenced by GxE. In marker assisted selection this can hamper deployment of classical marker technologies that rely on identifying QTLs. Approaches such as genomic selection that utilize genome-wide markers in complex populations such as in this study provide an opportunity to dissect such traits and could be exploited by banana breeders to increase genetic gain per unit time. Genotype by environment interaction has been shown to affect the accuracy of genomic selection models , 44. TheMusa reference genome [Many hybrids generated from crossbreeding usually have inferior fruit size irrespective of the ploidy level. Such inferiority has been attributed to linkage drag from wild diploids . Bananase genome , 47 and e genome . This prThe true breeding value of a genotype is determined by the quality of hybrids produced when it is involved in a cross. By comparing the responses to selection (R) and selection differentials (S) of sixteen cross combinations it was concluded that no single cross combination presented all the good qualities targeted by the breeders in hybrids. This further explains the complex trait variation observed within study population. No attempt was made to determine heritability of the traits because of unbalanced design and the possibility of confounding from heterosis . Some hyTenkouano et al.  reportedWhereas principal component analysis on cross combinations and individual genotypes showed that high genetic diversity existed in the training population, its power to resolve the structure of the population into clear-cut clusters that make biological sense was limited. This was attributed to complex pedigrees in the population with 77 cross combinations represented. The half-sib families were closely related to one another with which they shared a common parent. The population was interconnected due to shared parents in their pedigree. Use of SSR markers proved valuable in delineating the population structure that could be easily interpreted. The set of markers used was reported to be informative and has been used on genotyping the banana collection from the International Transit Center . The polM. acuminata ssp. malaccensis 250 formed a distinct cluster. Three tetraploids presumed to be arising from a cross of EAHB with ssp. malaccensis 250 grouped together with those derived from EAHB by cv. ‘Calcutta 4’. The presence of Calcutta 4-specific alleles in these tetraploids and the absence of ssp. malaccensis 250 specific alleles suggest that these hybrids are progeny of EAHB by cv. ‘Calcutta 4’ hence the high genetic relationship with the rest of the tetraploids. Nevertheless, these tetraploids should be tested as parents to determine their breeding values so that the breeding genetic pool is expanded.Triploid EAHB and tetraploids derived from them by crossing with cv. ‘Calcutta 4’ formed two distinct but closely related clusters, supporting the hypothesis of production of unreduced 3n and reduced n gametes during meiotic events in the tetraploid progenitors . DespiteThe SSR markers proved useful in identifying duplicates and closely related genotypes based on pedigree background. A combination of highly polymorphic SSR markers and the power of Ward’s clustering method that minimizes the within-group dispersion  in the EApart from obtaining important data on the banana GS training population, important lessons were learned during the course of this work. Dedicated efforts are required to understand the genome organization of bananas through cytological approaches. Ploidy analysis should be routinely employed in breeding programs to differentiate ploidy levels so that different selection criteria are used to select hybrids intended for the breeding pipeline from those eligible for variety release. Despite a majority of the improved hybrids being triploids, their fertility should be tested so that further improvements can be made on them as a way to achieve gene pyramiding while minimizing inbreeding.The response of genotype trait expression to cycle and field management practices varied greatly. The largest proportion of genetic variation was due to the greater genetic diversity of male parents used in crosses since the tetraploids used in the majority of crosses as female parents were genetically related. Yield traits accounted for 31–35% of the total principal component variation observed in the population and were loaded on the first component while vegetative traits contributed to the second component with 15–22%. A high level of correlation within vegetative- and yield-related traits was observed but correlation between vegetative and yield traits was low and depended on the interaction with field management practices. Therefore, genomic selection models could be developed for traits that are easy to measure. It is likely that the predictive ability of traits that are difficult to phenotype will be similar to traits easily measured but highly correlated. The study population was observed to be genetically diverse with complex pedigree structure. Yield-related traits showed a bi-modal distribution, which was not influenced by cycle or field management. Resistance to Black Sigatoka was also stable across cycles but varied under different field management depending on the genotype. Principal component analysis could not delineate this complex population structure but the application of SSR markers in combination with Ward’s hierarchical clustering proved powerful and resolved the structure into biologically meaningful groups.S1 Fig(A) is a histogram showing the bimodal distribution of fruit circumference (FC), (B) cross sections of poor filling fruits, (C) good filling fruits with fruit diameter (FRD) and pulp diameter (PLD) values in cm, and (D) poor filling and good filling banana fruits.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits.GPC5 , which has not been reported in previous large-scale GWAS of lipid traits.We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements “reverse regression” methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach. In the past decade, genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have proven to be successful in identifying loci contributing genetic effects to a wide range of complex human traits, including susceptibility to diseases . InteresThe observation of multiple phenotype association at the same locus can occur as a result of pleiotropy . BiologiThe traditional approach to the analysis of GWAS is to consider each phenotype separately (i.e. univariate), despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. However, under these circumstances, there may be increased power to detect novel loci associated with multiple phenotypes through multivariate analyses . A wide https://cran.r-project.org/web/packages/MultiPhen/index.html.One of the most flexible multivariate methods for multiple phenotype analysis uses “reverse regression” techniques. With this approach, phenotypes are used as predictors of genotype at a SNP in an ordinal regression model . Unlike Here we implement a reverse regression model for multiple correlated phenotypes in SCOPA  that has a number of key advantages over MultiPhen. First, the software can accommodate directly typed and imputed SNPs (under an additive dosage model), appropriately accounting for uncertainty in the imputation in the downstream association analysis. Second, dissection of multivariate association signals is achieved through model selection to determine which phenotypes are jointly associated with the SNP. Third, SCOPA association summary statistics can also be aggregated across GWAS through fixed-effects meta-analysis, implemented in META-SCOPA, enabling application of reverse regression in large-scale international consortia efforts where individual-level genotype are phenotype data cannot be shared between studies.To demonstrate the power and utility of this approach, we apply the software to two GWAS of high— and low-density lipoprotein (HDL and LDL) cholesterol, triglycerides (TG) and body mass index (BMI), and evaluate association signals in established lipid and obesity loci.J phenotypes denoted by y1, y2, …, yJ. At a SNP, we denote the genotype of the ith individual by Gi, coded under an additive model in the number of minor alleles (dosage after imputation). Under linear reverse regression, we model the genotype as a function of the observed phenotypes, such thatConsider a sample of unrelated individuals with βj denotes the effect of the jth phenotype on genotype at the SNP, and ϵi ~ N, where σ2 is the residual variance. A joint test of association of the SNP with the phenotypes, with J degrees of freedom is constructed by comparing the maximised log-likelihood of the unconstrained model (1), with that obtained under the null model, for which β = 0. The maximum likelihood estimate, jth phenotype is adjusted for all other traits included in the reverse regression model, and thus implicitly accounts for the correlation between them.In this expression, It is important to account for potential confounding, for example arising as a result of population structure. We therefore recommend that phenotypes are replaced by residuals after adjustment for “general” confounders, such as age, sex and principal components to account for population structure, as covariates in a generalised linear modelling framework. However, where a potential confounder might share genetic effects with the phenotypes under investigation, such as body-mass index in the analysis of waist-hip ratio, we would recommend including this as an additional variable in the reverse regression model.p <5×10−8) with the phenotypes, it may be of interest to further dissect the signal through model selection. We obtain a maximised log-likelihood of the model (1) for each possible subset of phenotypes (so that βj = 0 if the jth phenotype is excluded from the model). We then determine the “best” subset of phenotypes associated with the SNP as the model with minimum Bayesian information criterion (BIC).For SNPs attaining genome-wide significant evidence of association  and after meta-analysis (denoted λMA), enabling “double” genomic control correction. Elements of the variance-covariance matrix of the kth study, Vk, are inflated by λk, unless λk <1. Similarly, elements of the variance-covariance matrix after meta-analysis are inflated by λMA, unless λMA <1.To correct for residual population structure within and between GWAS, which is not accounted for in study-level association analyses, we calculate the genomic control inflation factor, henotype . The infGenome-wide study-level multiple phenotype analysis, including dissection of association signals, has been implemented in SCOPA. The software requires specification of input genotype and sample files, and a list of phenotypes to be included in the analysis. SCOPA includes options to enable filtering on the basis of imputation quality (info score) , to outpGenome-wide meta-analysis has then been implemented in META-SCOPA. The software requires specification of a list of SCOPA output files representing studies to be included in the meta-analysis. META-SCOPA includes options to enable genomic control correction , and filtering of SNPs on the basis of minor allele frequency (MAF) and imputation quality.http://www.stats.ox.ac.uk/~marchini/software/gwas/file_format.html. Conversion to GEN/SAMPLE files from other formats for genotype/phenotype data can be performed using GTOOL: http://www.well.ox.ac.uk/~cfreeman/software/gwas/gtool.html.SCOPA requires genotype and phenotype data in GEN/SAMPLE file format utilised by IMPUTE and SNPTEST –17. Thisp <10−6), and MAF <1%. Principal components were derived from a genetic related matrix in each study to account for population structure in downstream association analyses [We considered two GWAS of LDL cholesterol, HDL cholesterol, TG and BMI from the Estonian Biobank at the Estonian Genome Center, University of Tartu . Individanalyses . The genanalyses . The phaanalyses , separatanalyses , 16. SNP2 [In both studies, HDL cholesterol, LDL cholesterol and TG were measured from serum extracted from whole blood. Lipid measurements deviating more than 5 standard deviations from the mean were set to missing. Individuals were excluded if they received lipid-lowering medication at sample collection. The four phenotypes were adjusted for age, ageλOMNI = 1.001 and λ370 = 0.999 for EGCUT-OMNI and EGCUT-370, respectively, at the study level, and λMA = 1.003 after meta-analysis.We applied SCOPA to the four phenotypes in each GWAS, and aggregated association summary statistics across studies using META-SCOPA. There was no evidence for residual population structure within and between GWAS that was not accounted for in the association analysis: p <5×10−8) with lipids and BMI ; CETP ; GPC5 ; and LIPC . For comparison, we also performed univariate tests of association in SCOPA for each phenotype, separately, within each GWAS, and aggregated summary statistics across studies through fixed-effects meta-analysis (inverse-variance weighting of effect sizes) using GWAMA [Our META-SCOPA analysis revealed four loci attaining genome-wide significant evidence of association p <5×0−8 with MI Figs. , Table 1APOE locus, rs7412, has been previously reported, at genome-wide significance, in univariate GWAS meta-analysis of lipid traits [APOE ε2/ε3/ε4 alleles [CETP and LIPC has also been previously implicated in univariate GWAS meta-analysis of lipid traits, where the primary associations are with HDL cholesterol [r2 = 0.971 between rs56156922 and rs17231506 at CETP; r2 = 0.849 between rs2043085 and rs261291 at LIPC), suggesting that they represent the same underlying association signals. The APOE locus has also formerly been associated with BMI, at genome-wide significance, in univariate GWAS meta-analysis [r2 = 0.013 between rs7412 and rs2075650), suggesting that this signal is distinct from that identified for LDL cholesterol.The lead SNP at the d traits , where t alleles . Geneticlesterol , 24. Ourlesterol  , a gene that plays a role in the control of cell division and growth regulation. The gene is involved in retinoid and carbohydrate metabolic processes, making it a highly plausible candidate gene for lipid metabolism, although further replication of the association signal in additional studies is required.Genetic variation at the CETP and LIPC, the phenotype subset with minimum BIC for the lead SNPs included only HDL cholesterol. This model is consistent with previous reports [GPC5, the phenotype subset with minimum BIC for the lead SNP included only TG, suggesting that the primary association signal at this locus is driven by this specific serum lipid trait. Finally, at APOE, the phenotype subset with minimum BIC for the lead SNP included HDL cholesterol, LDL cholesterol and TG. Previous reports have highlighted association signals with multiple lipid traits at this locus [We dissected multiple phenotype association signals for the lead SNPs at the four loci attaining genome-wide significance after meta-analysis. We determined the best subset of phenotypes according to the BIC across studies, which represents a trade off in overall model fit with the number of parameters required Table . At CETP reports , 23 thatis locus . Our anaGPC5 , which has not been reported in previous GWAS of lipid traits. Dissection of the APOE locus highlighted associations with LDL and HDL cholesterol and TG, and suggested biological pleiotropy as a likely driving mechanism for this multiple lipid signal.The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach. Application of the software to two GWAS of HDL and LDL cholesterol, TG and BMI highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The meta-analysis also revealed a novel signal of association for triglycerides mapping to Project name: SCOPA.http://www.geenivaramu.ee/en/tools/scopa.Availability: the SCOPA and META-SCOPA software, documentation and tutorial can be found at: Operating system(s): Linux.Programming language: C++ .Any restrictions on use by academics: none.


MLK), fat (FAT), and protein (PRT) in 305 days, from 2,590 Holstein sires in Japan were analyzed. All sires were genotyped with 40,646 single-nucleotide polymorphism (SNP) markers. A genome-wide significant region (P < 0.01) common to all three traits was identified by regional genomic mapping on chromosome (BTA) 14. In contrast, single SNP analysis identified significant SNPs only for MLK and FAT (P < 0.01), but not PRT in the same region. Regional genomic mapping revealed an additional significant region (P < 0.01) for FAT on BTA5 that was not identified by single SNP analysis. The additive whole-genomic effects estimated in the regional genomic mapping analysis for the three traits were positively correlated with one another (0.830–0.924). However, the regional genomic effects obtained by using a window size of 20 SNPs for FAT on BTA14 were negatively correlated (P < 0.01) with the regional genomic effect for MLK (–0.940) and PRT (–0.878). The BTA14 regional effect for FAT also showed significant negative correlations (P < 0.01) with the whole genomic effects for MLK (–0.153), FAT (–0.172), and PRT (–0.181). These negative genomic correlations between loci are consistent with the negative linkage disequilibrium expected for traits under directional selection. Such antagonistic correlations may hamper the fixation of the FAT increasing alleles on BTA14. In summary, regional genomic mapping found more regions associated with milk production traits than did single SNP analysis. In addition, the existence of non-zero covariances between regional and whole genomic effects may influence the detection of regional effects, and antagonistic correlations could hamper the fixation of major genes under intensive selection.Genome-wide association studies can be applied to identify useful SNPs associated with complex traits. Furthermore, regional genomic mapping can be used to estimate regional variance and clarify the genomic relationships within and outside regions but has not previously been applied to milk traits in cattle. We applied both single SNP analysis and regional genomic mapping to investigate SNPs or regions associated with milk yield traits in dairy cattle. The de-regressed breeding values of three traits, total yield (kg) of milk ( The information provided by the numerous SNPs mapped on the whole genome gives us a powerful tool that can be used in breeding to improve complex traits in livestock populations. Genome-wide association studies (GWAS) can be applied to identify useful SNPs associated with complex traits , 2. FurtDGAT1, for example, is a well-studied gene with substantial effects on dairy traits in cattle [DGAT1 are segregating rather than being fixed for the single most beneficial genotype, their segregation being the reason why we can detect their effect and position. It is interesting to consider why alleles of such large effect can remain segregating, even after long and intense selection [Recent progress in GWAS has led to the discovery of individual genes that exert substantial effects on traits of economic importance in domestic animals. n cattle , 15. Allelection , 15, 16.Our objectives here were to use both single SNP analysis and the regional genomic mapping method to investigate regions with large effects on milk production traits. The ability to evaluate the relationships between whole and regional genomic effects within and between traits might reveal why genes with large effects remain segregating in livestock even after intense selection. As far as we know, this study is the first to have investigated the genomic relationships within and outside regions with large effects on the traits under selection.P value lower than 10−6. Finally, 40646 SNP markers  of milk (MLK), fat (FAT), and protein (PRT) in 305 days—from 2590 Holstein sires in Japan. Average phenotypic values of daughter cows were approximately 9226 kg for MLK, 364 kg for FAT, and 301 kg for PRT. The sires had been evaluated by using the milking records of more than 50 daughters each, and the results of these evaluations were obtained from the National Dairy Herd Improvement Program of Japan. DNA samples isolated from semen were used for genotyping all sires with the Illumina Bovine 50K beadchip  SNP array. This array was constructed with 54001 SNPs distributed across the whole genome with an average SNP spacing of 51.5 kb and a median spacing of 37.3kb. All procedures, for example amplifying and hybridizing, were carried out according to the Illumina Infinium assay protocol  and have been detailed previously . We used markers  were useStandard practices of animal care according to the notification of the Japanese Ministry of Environment  were applied to the animals used. The sires, and their daughters from which we collected the milking records, were not subject to any experimental procedures, and all data were collected with the owners’ permission as part of normal husbandry practices and according to Japanese regulations; hence ethics committee approval was not necessary for this study. The sires were owned and managed by four government-approved insemination organizations in Japan. The SNP data were collected from semen straws provided as part of the regular semen collection process by staff of the insemination stations.ij between individuals i and j, using identity by state (IBS), was used, such that:ik (gjk) is the genotype of the i-th (j-th) individual at the k-th SNP  [ij is for the major allele and n is the number of SNPs. For relationship matrices in mixed model equations, 2fij is used for the off-diagonal elements between individuals, and the diagonal elements are 1 plus the inbreeding coefficient [We used a model in which the additive genomic effect was divided into two parts, namely regional genomic and whole genomic effects. The whole genomic polygenic value was estimated by using all SNPs to construct the whole genomic relationship matrix. The regional genomic polygenic value was obtained from a regional genomic relationship matrix that used 100 (or 20 or 10) adjacent SNPs from each region. The genomic kinship fctively) , 19. Thefficient . A regiofficient  in whichy is a vector that represents the de-regressed breeding values, and Z is the design matrix for random effects. The remaining vectors are u, which is the additive whole genomic effect; w, the additive regional genomic effect, and e, the residual. G and I are whole genomic relationship matrices using all SNPs for the whole genomic value and an identity matrix for residuals, respectively. Q is the regional genomic relationship matrix obtained by using 100 or fewer SNPs for the regional genomic value. Whole genomic values, regional genomic values, and residuals are random effects, and their variances are σu2, σw2, and σe2, respectively. The details of the regional genomic method have been described previously [We followed the two-step method described by George et al.  for varieviously .0/L1) was calculated, where L0 and L1 represent the respective likelihood values under the hypothesis of either the absence (H0) or presence (H1) of regional variance. The LRT is assumed to be drawn from a distribution under the null hypothesis that is a mixture of chi-square distribution with 1 degree of freedom and zero  at a test region (window), the likelihood ratio (LR) test statistic LRT = –2ln(LVisscher ) with BoWe applied the concept of GRAMMAR (Genome-wide Rapid Association using Mixed Models and Regression) by Aulchenko et al.  for sing10P-values were 27.9  for MLK and 5.0  for PRT by using window size 10; for FAT the value was 43.8  by using window size 20. A significant region common to all three traits was found only on BTA14; however, BTA 5 had a significant region with LRT 23.3 (P < 0.01) for FAT when the window size was 100. This value increased to LRT 23.7 with a window size of 20. In addition, BTA18 had a relatively large LRT 13.7 for PRT using window size 100, and it increased to 15.6 at a window size 20. The genomic variances for the three traits from whole genomic values and regional genomic values  on BTA14 are given in We estimated regional genomic variance by using 100 or fewer SNPs to obtain likelihood ratio test values (LRT). The first window (size 100) on BTA14 had the largest LRT for MLK (116.8), FAT (181.3), and PRT (25.2) . FocusinP < 0.01 and 3 SNPs with P < 0.05) and for FAT (11 SNPs with P < 0.01 and 4 SNPs with P < 0.05) but no significant SNPs for PRT  with the regional genomic effect for MLK (–0.940) and PRT (–0.878), and regional effects for FAT also showed negative correlations (P < 0.01) with the whole genomic effect for MLK (–0.153), FAT (–0.172), or PRT (–0.181). The regional genomic effect for FAT (LRT 23.7) at window size 20 on BTA 5 showed almost no correlation (0.07) with the whole genomic effect for FAT. PRT (LRT 15.6) on BTA18 had a moderately positive correlation, 0.29, with the whole genomic effect for PRT.Correlations between whole and regional genomic effects by using the first window (size 20) on BTA14 were calculated . All corWe categorized individual bulls with three genotypes that had the most significant SNP common to the three traits (ARS-BFGL-NGS-4939) from the first window (size 20) on BTA14 and plotted their regional genomic effects to display the correlations between the three traits . IndividP < 0.01) on BTA 14 for all three traits  and on BTA 5 for FAT, and a relatively large LRT (P < 0.1) on BTA 18 for PRT. SNPs from some of these regions have been reported in previous studies using single SNP association approaches . Given the previous findings, DGAT1 affects not only FAT and MLK but also PRT [By using a regional genomic mapping approach we identified significant regions , 24P < 0s  on BTA 5. The position (92.6 Mb) of the middle of this region is very close to a previously reported SNP at 92.1 Mb for MLK . Wang et0 on BTA DGAT1 on milk yield traits, they reported an extremely large proportion of the variance due to the QTL on BTA14 in the total genetic variance. Using a fixed regression model for the substitution effect of two alleles, they reported that the QTL explained more than 27% (= QTL 18% / (QLT 18% + polygene 49%)) of the total genetic variance for MLK and 21% (= QTL 15% / (QTL 15% + polygene 55%)) of that for FAT. However, our regional genomic mapping from the first window (size 20) on BTA14, including the DGAT1 region, estimated 8.1% for FAT, 4.6% for MLK, and 1.5% for PRT (DGAT1 had two alleles, but they did not exclude the existence of other, as yet uncharacterized, DGAT1 alleles that might contribute to genetic variance in milk traits. Regional genomic mapping using simultaneous estimation of both whole and regional genomic variances in mixed models that did not assume a particular number of alleles might have yielded less bias in variance estimation. We also reported that the most notable feature of the regional genomic approach was that, in a simulation study, its power was affected little by the number of SNPs [When Grisart et al.  describe for PRT . The regWe might estimate more accurate variances and regions by using a regional mapping approach and use these results for genomic selection; however, fixing preferred genes in these regions may be difficult. Correlations between the regional genomic effect for FAT on BTA14 and either the whole genomic effect for FAT or the whole or regional genomic effects of MLK and PRT were negative .DGAT1 for fat yield had negative effects on milk and protein yield. Therefore, these previous results are in agreement with ours. However, a negative correlation between the whole genomic effect and the regional genomic effect for FAT has not yet been reported.Grisart et al. , 23 and Although it is not possible to reach a definitive conclusion, it is known that the Bulmer  effect cIn addition, relationships between a region and the whole genome might cause bias in the estimation of both regional and whole genome variances. If we consider only the whole genomic effect without regional or SNP effects in a model, for example, then negative correlation reduces the apparent whole genomic variance and heritability. In contrast, a positive relationship between a region and whole genome leads to increased genetic variance and heritability.Considering only the regional effect or SNP effect in a model, a negative correlation with the whole genomic effect can reduce the regional variance and hamper the detection of the effective regions or SNPs. If correlations prove to be a widespread phenomenon across populations and species, this method might produce the interesting consequence that the summed effect of the region and the whole genome differ from the total genetic variance due to the contribution of the covariance. A positive covariance might inflate the total heritability in comparison to the sum of its components, resulting in an apparent “missing heritability”  due to tHere, we revealed the relationships between the whole and regional genomic effects for some important dairy traits and provided a possible explanation for why genes with large effects remain segregating in livestock even after intense selection. Applying these approaches to the analysis of larger single-locus and regional effects in other livestock likely would be informative. Such analyses may shed light on both trait genetic architecture and selection in these populations.


Triticum aestivum L.) grains using a European wheat diversity panel of 353 varieties [339 winter wheat (WW) plus 14 of spring wheat (SW)] and phenotypic data based on two field seasons. High genotyping densities of single-nucleotide polymorphism (SNP) markers were obtained from the application of the 90k iSELECT ILLUMINA chip and a 35k Affymetrix chip. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to measure the calcium concentrations of the wheat grains. Best linear unbiased estimates (BLUEs) for calcium were calculated across the seasons and ranged from 288.20 to 647.50 among the varieties (μg g-1 DW) with a mean equaling 438.102 (μg g-1 DW), and the heritability was 0.73. A total of 485 SNP marker–trait associations (MTAs) were detected in data obtained from grains cultivated in both of the two seasons and BLUE values by considering associations with a -log10  ≥3.0. Among these SNP markers, we detected 276 markers with a positive allele effect and 209 markers with a negative allele effect. These MTAs were found on all chromosomes except chromosomes 3D, 4B, and 4D. The most significant association was located on chromosome 5A (114.5 cM) and was linked to a gene encoding cation/sugar symporter activity as a potential candidate gene. Additionally, a number of candidate genes for the uptake or transport of calcium were located near significantly associated SNPs. This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally variable traits in genetically highly diverse variety panels. The research demonstrates the feasibility of the GWAS approach for illuminating the genetic architecture of calcium-concentration in wheat grains and for identifying putative candidate genes underlying this trait.Mineral concentrations in cereals are important for human health, especially for people who depend mainly on consuming cereal diet. In this study, we carried out a genome-wide association study (GWAS) of calcium concentrations in wheat ( Triticum aestivum L.) is one of the most essential and widely planted crops worldwide with its products feeding most of the global population 1. Many people relying strongly on wheat-based food stuff suffer from nutrient deficiencies, especially of Fe, Zn, Ca, and Mg  for calcium in grains of wheat, rice, sorghum, barley, maize, pearl millet, or beans . In a te content .1; thus we chose a panel of recent European wheat varieties to explore the genetic variation of calcium in 353 varieties [339 winter wheat (WW) and 14 spring wheat (SW) varieties], and to identify QTLs associated with this trait by using a genome-wide association study (GWAS) in order to detect potential candidate genes.European countries are among the top wheat producers and exporters in the world A European wheat panel consisting of 353 varieties mainly coming from Germany and France was used in this study. This panel included 339 WW and 14 varieties of SW. Field experiments were carried out at the Leibniz Institute of Plant Genetics and Crop Plant Research in Gatersleben, Germany , during two consecutive seasons (2014/2015 and 2015/2016). The individual plot size was 1 m × 1.5 m with four rows spaced 0.20 m apart. All varieties were sown in autumn and subjected to standard agronomic wheat management practices.TM PLUS rapid sample introduction system and a CETAC autosampler . Fifty micrograms of dried and ground samples from each variety were wet digested in 2 ml nitric acid  using a high-performance microwave reactor . Digested samples were filled up to 15 ml final volume with de-ionized distilled water . Element standards were prepared from Bernd Kraft multi-element standard solution (Germany). Calcium as an external standard and Y  were used as internal standards for matrix correction.Phenotypic analysis was conducted for the whole set of wheat varieties in each season. For each sample, 50 kernels were counted using a digital seed analyzer/counter Marvin  and the thousand-grain weight (TGW) was estimated. The samples were milled using a Retsch mill  and the milled samples were dried overnight at 40°C. Calcium concentrations were measured by inductively coupled plasma optical emission spectrometry  combined with a CETAC ASXPRESSThe resulting calcium values for wheat grains of each variety and environment were used to calculate the best linear unbiased estimates (BLUEs), by applying the residual maximum likelihood (REML) algorithm with mixed linear models (MLMs) function  and consThe broad sense heritability of Ca was calculated using the equation:where Analyses of variance (ANOVA) and Pearson’s correlation coefficient were calculated for the calcium trait across the two environments with SigmaPlot package 13.2 using a new 90k iSELECT Infinium array (® Wheat Breeder’s Genotyping Array3) which contained 7762 mapped polymorphic SNPs. For the reference map, the ITMI-DH population . Association analysis was performed using the “Single trait association analysis” function with Kinship matrix (K) as a relationship model to control for population structure by GenStat v16, though no obvious population structure was observed in the described population .P-value of -log10 (P) ≥3. When Bonferroni correction with P < 0.05 was applied the resulting -log10 (P) threshold rose to 5.49. The proportion of the phenotypic variation (R2) was calculated using the software package TASSEL 5.0. Marker effects (positive/negative) were estimated by GenStat v16 based on the effect of specific allele in the varieties.To declare the significant marker–traits associations (MTAs), we considered a threshold BLUEs of the trait, each variety and across the seasons (2015 and 2016) were calculated by applying the “mixed models REML” module with the “linear mixed models” of GenStat v16.r2) and were considered to be in significant LD when r2 ≥0.2. LD plots were performed by GenStat (v16) to examine the average LD decay within each chromosome.Linkage disequilibrium (LD) which is the non-random association between pairs of loci was studied in the whole panel, observed by using squared allele frequency correlation and calculated within each chromosome. Loci in the LD region were determined according to the squared allele frequency correlations . Related regions for these significant associations were anchored using the wheat sequence assembly. For the resulting gene IDs the Human-Readable Descriptions were selected to define annotated gene functions by ftp://ftpmips.helmholtz-muenchen.de/plants/wheat/IWGSC/genePrediction_v2.2. The whole set of marker sequences was blasted using the software package Geneious 105 and the most significant hit was selected . In the season 2015, the highest measured grain Ca concentration was 797 μg g-1 DW, while in 2016 the highest Ca value was around 647 μg g-1 DW . Estimated BLUEs ranged from 288.2 to 647.5 μg g-1 DW with a mean of 438.1 μg g-1 DW . Based on BLUEs the highest scored value for calcium in the whole set of wheat varieties was 647.5 μg g-1 DW for the variety Nirvana from France . The ANOVA showed significant effects of the genotype and the environment on calcium concentrations in the grain . The highest correlation was between season 2015 and BLUEs , while the lowest but still significant correlation was between seasons 2015 and 2016 . The broad sense heritability equaled 0.73 across the two environments for 353 varieties indicating that the phenotypic values in the two years are relatively stable for the different varieties.Calcium measurements were performed for the whole set of European wheat varieties  grown in two seasons (2015 and 2016)  ≥3] association signals for both environments and BLUEs . The most significant association was detected on chromosome 5A. On the other hand, most of the significant MTAs for grain calcium were identified on chromosome 2A (111 MTAs) and chromosome 5B (127 MTAs). On chromosome 2A, most of the MTAs were located in the genomic region of 64.3–67.4 cM. Based on the analysis, we found 31 consistent associations, which were present in both environments plus BLUEs and 20 consistent associations are above the Bonferroni correction threshold which equals 5.49 (Table 1). The explained phenotypic variances (R2) ranged from 0.81 to 11.27%.GWAS analysis was performed using a MLM with 90k and 35k SNP markers for the calcium data from the two growing seasons 2015 and 2016. Additionally the MTAs for the BLUEs from both years were calculated. Our analysis detected 485 significant [-logFigure 4. Two significant markers [RAC875_c8642_231 marker (M2) (MAF = 0.09) and wsnp_Ex_c17575_26299925 marker (M5) (MAF = 0.16)] had positive allele effects. Another three significant markers  showed negative effects. Marker RAC875_c8642_231 (M2) had a highly significant positive effect (P < 0.001) with 43.3 μg g-1 DW .The additive effects of five representative significant markers based on BLUEs are depicted in 10  ≥3], together with other markers in LD (r2 ≥0.2), were used to query the available wheat genome sequence data in the POPSEQ assembly  related to divalent metal cation transport together with two further genes (Traes_5AL_6C9A5537F and Traes_5AL_6C8BD96CB) related to heavy metal transport/detoxification. Along this chromosome we were able to find loci associated with Ca-permeable ion channels, such as Traes_5AL_E1F7DD9EA and Traes_5AL_C89AC9640 coding for cyclic nucleotide-gated channels (CNGCs) or Traes_5AL_F1522B81F and Traes_5AL_98814295D encoding mechanosensitive ion channels. However, these genes were not closely located to the significant markers  in our analysis was located on chromosome 5A (114.5 cM). The gene underlying this marker encodes a cation/sugar symporter, while the second significant locus (wsnp_Ex_c20899_30011827) on the same chromosome (117.7 cM) carries a gene that encodes an AP2-type transcription factor. We further detected two genes, which may be related to calcium transport near this significant region (114.5–117.7 cM): one gene (Traes_5AL_898DAA873) is related to plasma membrane ATPases while another gene (Traes_5AL_637EB761F) encodes an H+ antiporter), and Traes_2AL_CF9F964E6 underlying a calcium-transporting ATPase. The most significant association on chromosome 5B was the locus Traes_5BL_DF8D1B819, which is related to an ammonium transporter. Other closely linked genes are Traes_5BS_4AEE5C2AE encoding a mechanosensitive ion channel family protein and Traes_5BS_98C73F5CA, Traes_5BS_06F7D0060, Traes_5BS_272FDBF9D, Traes_5BL_4AACBDDAA, and Traes_5BL_E4BE45756, all related to cation/H+ antiporters. Near to the highly significant markers of chromosome 5B , Traes_2AL_ D33454518 (cation/H-1 DW. The heritability was high (0.73) indicating that the major part of the variability was due to genotypic effects, which is in agreement with previous studies (P < 0.01). Marker effects (R2) which explained the proportion of phenotypic variance for consistently significant markers (appearing significant in both seasons and BLUEs) contributed a modest proportion ranging from 0.81 to 11.27%. The ANOVA results indicated that genotypes and environmental factors have a significant effect on calcium concentration in wheat grains. A similar conclusion was reached by  are conferred mostly by genome A , while one locus was related to the B genome (chromosome 5B) and another one related to the D genome (chromosome 5D). A mapping study of a RIL population in tetraploid wheat detected significant QTL for calcium concentration on chromosomes 1A, 4A, 6A, 2B, 4B, 5B, 6B, and 7B  and autoinhibited Ca2+-ATPase (ACA/IIB-type)], (ii) divalent cation–H+ antiporters/exchangers , and (iii) Ca-permeable ion channels that include mechanosensitive calcium-permeable channels (MSCCs), glutamate receptors (GLRs), CNGCs and two-pore channels (TPC)  Cals (TPC) . The higR2 value equaling to 11.27% and a favorable additive effect, is related to Traes_5AL_F49663738 gene encoding a putative cation/sugar symporter. The second significant gene (Traes_5AL_19637DE03) with R2 equaling 7.83 encodes an AP-2 complex subunit alpha-2-like protein that is possibly related to calcium transport function . These include 8 Ca/proton exchangers, 4 Ca-ATPases, and 31 channels in addition to other genes that are putatively related to calcium transport, such as Traes_5AL_6C9A5537F which is annotated as heavy metal transporter (Supplementary Table function . Anotherfunction . All of Apart from focusing on the concentrations of iron and zinc in wheat, which has taken much attention in previous studies, only few genetic studies are available on calcium concentrations in wheat grains are available. Improving levels of grain calcium concentration in hexaploid wheat remains one of the most important breeding objectives for the nutritional security of the whole population and especially for the poor from the nations where wheat is the main source of calories. Overall, through measurable phenotypic and genotypic variation for grain calcium concentrations as well as by considering the detected favorable QTLs distributed across various chromosomes and potentially responsible genes in the current research, we aimed to deepen the understanding of the genetic basis of calcium accumulation in wheat grains and to open the door to more efficient ways to increase calcium concentration in the grain and thereby overall wheat quality.DA performed the data analysis including genome-wide association scan and related analyses. KE and NvW participated in calcium concentration measurements. KP and MR designed the experiment. MR conceived the idea and participated in the interpretation of results. DA and MR wrote the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.