Genetic diversity is important for crop improvement. An experiment was conducted during 2011 to study genetic variability, character association, and genetic diversity among 27 soybean mutants and four mother genotypes. Analysis of variance revealed significant differences among the mutants and mothers for nine morphological traits. Eighteen mutants performed superiorly to their mothers in respect to seed yield and some morphological traits including yield attributes. Narrow differences between phenotypic and genotypic coefficients of variation (PCV and GCV) for most of the characters revealed less environmental influence on their expression. High values of heritability and genetic advance with high GCV for branch number, plant height, pod number, and seed weight can be considered as favorable attributes for soybean improvement through phenotypic selection and high expected genetic gain can be achieved. Pod and seed number and maturity period appeared to be the first order traits for higher yield and priority should be given in selection due to their strong associations and high magnitudes of direct effects on yield. Cluster analysis grouped 31 genotypes into five groups at the coefficient value of 235. The mutants/genotypes from cluster I and cluster II could be used for hybridization program with the mutants of clusters IV and V in order to develop high yielding mutant-derived soybean varieties for further improvement. Glycine max (L.) Merr.], one of the major crops, is used for animal feed and human foods [Cultivated soybean [an foods . Unlike an foods . Due to an foods . Among tIn soybean, creation of genetic variation through hybridization is a tedious process due to small and fragile flowers, which make it very difficult to carry out the process of emasculation and injuring the parts of the flower and are prone to heavy flower shedding even under favorable conditions. These coupled with complete self-fertility impose limitations on the success of hybridization program . As a reKharkwal and Shu  reportedThe information as well as assessment of genetic variability in the existing germplasm of a particular crop is sought as prerequisite –25. FurtIncreased seed yield is the ultimate goal of the breeders. But seed yield itself is a product of interaction of many component traits which influence yield directly or indirectly. So, it is important to see the contribution of each of the traits in order to give more attention to those having the highest influence on yield. Moreover, understanding the relationship between yield and its component traits is of great importance to a breeder for making the best use of these relationships in selecting desirable genotypes for yield improvement programs , 29. As This study investigated the morphological variability among 27 soybean mutants along with four mother varieties using quantitative morphological traits including yield attributes. For an effective breeding program for crop variety development through hybridization, the analysis of genetic diversity is one of the useful tools and plays a fundamental role in identification of parents , 34. Mor60 gamma cell. Among those mutants, 18 promising mutants showed better performance in respect to seed yield per ha along with other morphological traits including important yield attributes than the mother varieties/line. In this research, we evaluated the performances of those mutants along with mothers from January to June 2011 regarding morphological parameters and yield traits through the studies of genotypic and phenotypic variability, character association, and genetic diversity among these mutants and mothers which have not yet been studied. Such information will serve as a useful tool for establishing suitable breeding program for further soybean improvement.From four mother genotypes , Bangladesh Institute of Nuclear Agriculture (BINA) developed 27 true breeding soybean mutants using gamma rays from the CoThe experiment was carried out at the experimental field of Bangladesh Institute of Nuclear Agriculture (BINA), Mymensingh, during January to June 2011. Geographically, the place is located at about 24°75′ north latitude and 90°50′ east longitude. The soil of the experimental site is sandy loam having 0.06% nitrogen, 1.05% organic matter, 18.5 ppm available phosphorus, 0.28 meq% exchangeable potassium, 18 ppm sulphur, and 6.8 pH.6 mutants and the other four were the mother genotypes, Sohag, Bangladesh Soybean-4 (BDS-4), BARI Soybean-5, and BAU-S/64, from which the mutants were evolved. The names of the 27 soybean mutants along with their respective mother genotype are listed in Thirty-one soybean genotypes were used as the experimental materials. Among the genotypes, 27 were the true breeding MThe experiment was laid out in a randomized complete block design with three replicates. Block-to-block and plot-to-plot distances were maintained as 1.25 and 0.75 m, respectively, with a plot size of 4.0 m × 3.6 m and line-to-line distance of 30 cm. Seeds were sown on 26 December 2010. Each entry was grown in 12 rows keeping plant-to-plant distance of 8–10 cm in rows.−1, respectively. Rhizobium inoculum for soybean was used at 25 g per kg seeds. Intercultural operations like weeding, thinning, application of pesticide, and so forth were done as recommended and when necessitated for proper growth and development of plants in each plot. Harvesting was done depending upon the maturity of the plants in each plot.Urea, triple super phosphate, muriate of potash, and gypsum were used as basal dose during final land preparation at 40, 150, 100, and 110 kg haData on plant height, number of primary branches and pods per plant, number of seeds per pod, and seed yield per plant were taken from 10 randomly selected competitive plants from each plot. Plants of each plot were harvested when the plants and pods of each plot turned into yellowish brown colour and almost all the leaves shed. Plot seed yield was taken from the eight middle rows avoiding border effects and plot seed yield was converted into kg per ha .σ2G (an estimate of genotypic variance) = (MSG − MSE)/r, where MSG is an estimate of mean square of tested accession, MSE is an estimate of mean square of error, and r refers to the number of replications;E is an estimate of σ2E;MSσ2P (an estimate of phenotypic variance) = σ2G (genotypic component of variance) + σ2E;σ2P is the phenotypic component of variance and PCV (phenotypic coefficient of variation) =σ2G is the genotypic component of variance and GCV (genotypic coefficient of variation) =h2B (an estimate of broad sense heritability) = σ2G/σ2p, where σ2G is the genotypic component of variance and σ2P is the phenotypic component of variance;GA (genetic advance) is taken as percent of the mean assuming selection of the superior 5% of the accessions;K (the standardized selection intensity) = 2.06 (at 5% selection intensity), σ2P is the phenotypic component of variance, h2B is the heritability in broad sense, and GA (as % of the mean) = Analysis of variance (ANOVA) and least significant difference (LSD) were computed for all traits using SAS 9.1 for identification of significant difference between progenies. Genetic parameters were estimated by the formula given by Burton , Burton Genotypic and phenotypic correlation coefficients for different characters were calculated in all possible combinations following the formula given by Miller et al. . Path coP ≤ 0.01) for all the nine characters like days to flowering and maturity, plant height, number of branches and pods per plant, seeds per pod, 100-seed weight, seed yield per plant, and seed yield per ha  on seed yield per plant followed by 100-seed weight (1.350), days to maturity (1.184), and pods per plant (0.659). Days to flowering, plant height, and branches per plant having significant positive correlation with yield  contributed mainly towards seed yield via days to maturity , pods per plant , and seeds per pod  with negative direct effects . Pods per plant and seeds per pod contributed negatively towards seed yield via 100-seed weight .Cluster analysis using all the nine morphological traits grouped the 31 accessions into five major groups at the genetic distance of 235.0 . It was A two-dimensional principal component analysis was performed using all the morphological traits. The cluster analysis was mostly confirmed by the PCA analysis. Two distant mutants such as SBM-27 and SBM-28formed their individual cluster/group alone both in cluster (clusters IV and V) and in PCA analyses (GIV and GV)  variations indicating the presence of sufficient amount of genetic variability among the mutants for all the studied traits. In soybean genotypes, significant variations have also been reported earlier by other researchers for various morphological traits [All the nine morphological traits showed highly significant  from Sohag and only three  from BARI Soybean-5 formed cluster with mother varieties Sohag and BARI Soybean-5, respectively, and others formed distinct clusters other than the mother genotypes. Similarly among four mutants from Bangladesh Soybean-4, only one (SBM-12) formed cluster with mother, and both mutants SBM-27 and SBM-28 from BAU-S/64 formed two individual clusters. Present results confirm that induced mutations are contributing significantly to creating genetic variations in crop plants. The first four principal components accounted for 99.999% of the total variation. Cluster analysis using dendrogram and PCA following two-dimensional method played complementary role to each other with little inconsistencies in respect of number of genotypes in cluster formation. To obtain greater heterosis, genotypes having distant clusters could be used as parents for hybridization program. Dendrogram and two-dimensional PCA graph clearly indicated that mutants SBM-27 and SBM-28 made two individual groups  and were far away from the other three clusters. Therefore, the mutants from cluster I and cluster II could be used for hybridization program with the mutants of clusters IV (SBM-27) and V (SBM-28) in order to develop high yielding mutant-derived soybean varieties.In plant breeding, generation of new genotypes from the existing ones with improvement in plant traits is the main objective. The present study revealed the presence of high levels of variations for nine different morphological traits including yield attributes and seed yield among the newly developed 27 mutants along with four mother genotypes of soybean. These mutants could be served as raw materials for further genetic improvement of different characters of the soybean. Among the nine traits, plant height, number of branches and pods per plant, and 100-seed weight exhibited high values of genotypic coefficient of variation, broad sense heritability, and genetic advance. Therefore, these traits can be considered as favorable attributes for soybean improvement through effective phenotypic selection and high expected genetic gain can be achieved for these characters. Most of the traits showed positive correlations between each other, which will assist in the combined improvement of these traits by selecting only highly heritable and easily measurable phenotypic traits. In addition, both the correlation and path coefficient analyses indicated that pod number per plant and seeds per pod and days to maturity appeared to be the first order traits for higher seed yield in soybean and priority should be given in selection due to strong associations as well as high magnitudes of direct effects on seed yield. Cluster analysis using all the nine different traits grouped 27 soybean mutants and four mother genotypes into five main clusters. These results also confirm that not only the geographical background, but also induced mutations significantly contribute to creating genetic variations. The first four principal components accounted for about 99.996% of total variation for all the morphological traits. This study indicated the presence of high levels of genetic diversity among the mutants for evaluated characters.


The method was used to simultaneously analyse the whole genome sequencing and genome-wide association studies data of Genetic Analysis Workshop 18 for detecting true genetic variants. The results show that goodness-of-fit test is comparable or better than the influential sequence kernel association test in many cases.Although many genetic factors have been successfully identified for human diseases in genome-wide association studies, genes discovered to date only account for a small proportion of overall genetic contributions to many complex traits. Association studies have difficulty in detecting the remaining true genetic variants that are either common variants with weak allelic effects, or rare variants that have strong allelic effects but are weakly associated at the population level. In this work, we applied a goodness-of-fit test for detecting sets of common and rare variants associated with quantitative or binary traits by using whole genome sequencing data. This test has been proved optimal for detecting weak and sparse signals in the literature, which fits the requirements for targeting the genetic components of missing heritability. Furthermore, this  According to the Catalog of Genome-Wide Association Studies updated by the National Human Genome Research Institute, approximately 7260 single-nucleotide polymorphisms (SNPs) have been identified for 770 traits in 1360 publications as of November 2012. However, researchers believe that a significant proportion of heritability of many complex traits is still missing [p values, it has the potential to be used as a meta-analysis for incorporating data from different sources. We apply GOFT to simultaneously analyse WGS data and genome-wide association studies (GWAS) data for detecting genetic loci associated with systolic blood pressure (SBP). The results show that even without the sophisticated weighting scheme, GOFT is comparable to, and sometimes better than, SKAT under its best weighting scheme. In addition, at small p value cutoffs, the GOFT meta-analysis provides higher power than that when only WGS data is used.To address this issue, we adopt a goodness-of-fit test (GOFT)  that hasSequence kernel association test (SKAT)  is a supp value from each genetic variant follows a uniform  distribution. So testing a group of variants can be considered as a GOFT that measures the consistency between the empirical distribution of the observed p values and the uniform distribution. Here we adopt a GOFT from Berk and Jones [p values from L individual variants and the trait. The GOFT statistic is The problem of determining the associations between a set of genetic variants and the trait can be viewed as a multiple hypotheses testing problem. Under the null hypothesis that there is no genetic association, the nd Jones , which wnd Jones . Let p1≤p value-based SNP-set testing methods that sum up all p values together in certain formulas [p value to the SNP set. At the same time, unlike the minimal p value method that fixes the smallest p value to represent the set, GOFT adapts to the signal pattern through the maximization procedure. Such adaptation is critical because the p value of a true association is not necessarily the minimal value, especially when true associations are sparse and weak [p values to work, so it can be flexibly applied to different genetic studies based on the corresponding appropriate p values, or to meta-analyses that incorporate various data sources.Comparing with many formulas , GOFT loand weak . Anothersth genome segment window from the original data and from the mth permutation of the genotype data, respectively. The empirical p value for the sth window is M = 1000 was used in the following data analysis.A permutation test can be applied to accommodate the various sizes of variant sets and the linkage disequilibrium structures among the variants. Specifically, let p values for associations of both collapsed rare variants and common variants are obtained and fed into GOFT test statistic in equation (1) to study the overall significance of variant groups.For the association study of complex diseases based on WGS data, a major challenge is to address rare variants that have weak statistical association as a result of small allele frequency. The GOFT is asymptotically optimal for weak and sparse signals, and is a right fit in this scenario. At the same time, because the effects of missense rare alleles are mostly in the same deleterious direction , collapsFor evaluating the above association tests, we used the WGS "dose" file of 1,215,399 SNVs and the GWAS file of 65,519 SNVs on chromosome 3 as the genotype data. The quantitative trait was the SBP for the 142 independent individuals who had no missing genotype. To assess how the SNV group's size may have affected the performance of these tests, we split chr3 into segments of fixed windows with 1 of 3 widths: 10 kilobase pairs (kbp), 100 kbp, and 500 kbp. Respectively at those 3 levels, the grouping strategy resulted in 19,472, 1950, and 391 windows, among which 87, 37, and 20 windows contained true SNVs that were either nonsynonymous or regulatory to SBP according to the GAW18 simulation . The trup values that are often used in practice. This is because large windows likely had more noise variants, which diluted the signals from true variants, and thus were harder to get very small p values. Meanwhile, rare variant collapsing did help to increase the power in general.For GOFT, we assessed its type I error rates as estimated based on the false-positive rate of the 19,385 false 10 kbp windows on chr3 over a sequence of cutoffs. The type I error rate was well controlled (results are available upon request). Figure p value cutoffs. The Beta and logistic weights performed better for small p value cutoffs. Figure p values. In fact, GOFTs had a larger area under the curve than SKATs when comparing their whole receiver operating characteristic (ROC) curves (results are available upon request).Under the window size of 10 kbp, we assessed SKAT with different strategies of weighting variants: flat weight, Beta, Beta, Beta1, 25), and logistic. Figure 5, and loleft panel), SKAT was better in 27 windows , and both were similar. Figure p value = 0.10). The type I error rate was well controlled here (results available upon request).To study the performance in detection of various patterns of genetic effects, we compared the power of GOFT and logistic-weight SKAT in detecting each of all 87 true 10-kbp windows on chr3. The power was estimated by the true-positive rate of a true window among 200 replicates. There are 3 patterns of comparisons: GOFT was better in 42 windows . Figure left panel) and that adding GWAS data helped to improve the power of GOFT in detecting true 10-kbp windows on chr3 in general.Because GOFT only requires We attempt to address the low power issue of association tests for WGS data from 2 aspects. First, we prefer to use tests specially designed for detecting weak and sparse genetic effects. For this purpose, the GOFT is asymptotically optimal in the sense that its asymptotic detection boundary is one of the lowest boundaries among all statistical methods. If signals are weaker or sparser than this boundary, no statistical methods would work well. Second, we try to borrow information from other data sets through meta-analysis. Although there has been some debate on how much of total heritability could be explained by GWAS data , the comAt the same time, several future works could be considered based on the limitations of the current study. First, the sample size is likely still small for either verifying asymptotical results or larger power of detecting weak genetic effects simulated in the data. It would be nice to further confirm the patterns of comparisons among these association methods by simulated and real data with much larger sample size. Second, gene-based collapsing can be applied as an alternative to the window scheme used here. Third, we have applied a simple rare variant collapsing process by direct summing the genotypes. This collapsing strategy is less sophisticated than the weighting strategy of SKAT. In fact, GOFT can further incorporate more successful collapsing strategies to improve its power, for example, by weighting the SNPs, like what SKAT and other methods have adopted -17. LastWe adopted a GOFT to WGS data analysis for detecting disease-associated genomic segments, and compared it to the SKAT by using the GAW18 simulation data with SBP_1 as response. Even without a sophisticated weighting scheme, in many cases, GOFT is comparable to or better than SKAT with the best weighting scheme. GOFT can be applied to a combination of GWAS and WGS data. Our results show that such meta-analysis has the potential to provide higher power over WGS data analysis only. In all cases, the power is still low for detecting overall heritability under the sample size of 142 independent individuals for genetic association study.The authors declare that they have no competing interests.ZW designed the overall study. LY, JX, and ZW conducted statistical analyses. LY and ZW drafted the manuscript. All authors read and approved the final manuscript.


Blood pressure (BP) is a complex trait, with a heritability of 30 to 40%. Several genome wide associated BP loci explain only a small fraction of the phenotypic variation. Family studies can provide an important tool for gene discovery by utilizing trait and genetic transmission information among relative-pairs. We have previously described a quantitative trait locus at chromosome 17q25.3 influencing systolic BP in American Indians of the Strong Heart Family Study (SHFS). This locus has been reported to associate with variation in BP traits in family studies of Europeans, African Americans and Hispanics.To follow-up persuasive linkage findings at this locus, we performed comprehensive genotyping in the 1-LOD unit support interval region surrounding this QTL using a multi-step strategy. We first genotyped 1,334 single nucleotide polymorphisms (SNPs) in 928 individuals from families that showed evidence of linkage for BP. We then genotyped a second panel of 306 SNPs in all SHFS participants  for genes that displayed the strongest evidence of association in the region, and, in a third step, included additional genotyping to better cover the genes of interest and to interrogate plausible candidate genes in the region.TBC1D16, HRNBP3 and AZI1). In BQTN analysis, used to estimate the posterior probability that any variant in each gene had an effect on the phenotype, AZI1 showed the most prominent findings (posterior probability of 0.66). Importantly, upon correction for multiple testing, none of our study findings could be distinguished from chance.Three genes had multiple SNPs marginally associated with systolic BP  contains supplementary material, which is available to authorized users. Blood pressure (BP) is a complex trait and genetic factors account for 30 to 40% of the blood pressure variation in a population . Recent Family studies can provide an important tool for gene/loci discovery by utilizing trait and genetic transmission information among relative-pairs. Several genome scans of BP and hypertension have been published but few overlapping regions have been identified . MaWe have previously described a QTL-specific genotype-by-sex interaction for systolic BP on chromosome 17q25.3 in American Indians participants of the Strong Heart Family Study (SHFS) . This isWe used data from the SHFS, a large family-based genetic component of the Strong Heart Study (SHS), a population-based study of cardiovascular disease and its risk factors in American Indians 45 years or older recruited from tribes in Arizona (AZ), Oklahoma (OK) and North and South Dakota (DK). The SHFS began as a pilot study in 1998 when ~900 members of extended families of the SHS cohort were examined. Additional family members were recruited from 2001 to 2003 for a total of 3,807 individuals in 94 multigenerational families . The SHFS protocols were approved by the Indian Health Services (IHS) Institutional Review Board, by Institutional Review Boards of all Institutions, and by the Indian tribes , 30. All2). BP was measured using a standard protocol across the three recruiting centers [Baseline socio-demographic, medical history, lifestyle and behaviors  and medications were obtained through an interview using standardized questionnaires. Physical exams collected data on height, weight, systolic and diastolic BPs. Body mass index (BMI) was estimated using height and weight  and minor allele frequency (MAF) (SNVs with a MAF <0.001 in the HapMap data were removed) to select SNVs for genotyping. We also included 2 variants located in miRNA. A total of 1,536 SNVs were genotyped in 933 SHFS participants who are members of families showing evidence of linkage for systolic BP. Of the 1,536 SNVs on chromosome 17 that were included, 1334 SNVs were heterozygous, 18 SNVs were not polymorphic, and 184 SNVs failed genotyping. Five individuals had call rates < 95% and were removed from further analyses. Therefore, 1,334 heterozygous SNVs in 920 individuals were available for analyses.SNVs were selected within the 1-LOD unit drop support interval of the chromosome 17 QTL from 69,509,00 to 77,946,426 bp (genome build 35). We identified all polymorphic variants in HapMap CEU and JPN/HCB. We used linkage disequilibrium (LD) metrics , for a total of 980 SNVs.To provide evidence of replication for the genes on chromosome 17 that show the highest association, we genotyped 639 of the most significant SNVs of the Panel 1 analysis in all SHFS members  and included additional SNVs in regions where we had relatively low coverage in Panel 1 (n = 30 SNVs). We also genotyped additional SNVs in these genes and in several other candidate genes in the region . Briefly, genomic DNA (250 ng) was activated with biotin, hybridized to a pool of locus-specific oligos. PCR amplified using fluorescent-labeled primers and hybridized to the Sentrix Array Matrix, and then fluorescence intensities were analyzed using the Illumina BeadArray Reader and BeadStudio software. Cluster calls were checked for accuracy and genotypes were exported as text files for further use in association analysis. Additional samples were typed in replica as controls for genotyping and allele calling consistencies.QTN) analysis.The SHFS has existing genotypic data on ~400 microsatellite markers . MAFs we2, and BMI and stratified by study center.We evaluated quantitative variation in systolic BP. To account for the use of anti-hypertensive medications, we added a constant to treated measures of systolic BP (10 mm Hg). Systolic BP was log-transformed due to non-normality of the data. Models also adjusted for age, sex, age−5).We implemented a single marker test for each SNV. To evaluate the association of SNVs with BP traits among family members, we fitted linear mixed effects models to account for within pedigree correlations  . Genotypet al.[The SHFS does not have ancestry informative markers to adjust for population stratification. Therefore, we tested for the evidence of population stratification for each variant using the quantitative transmission disequilibrium test (QTDT)  and a teet al., both imBQTN to estimate the probability that each SNV is functional [BQTN method is designed to separate potentially functional variants from neutral variants in LD with them based on a displacement in the observed phenotype values and it incorporates each variant one by one, evaluating the likelihood of a model in which the trait mean varies by genotype. SNVs having a LD r2 higher than 0.90 are treated as in one group and only one SNV in each group will be used in the analysis. Bayesian model averaging/model selection was used based on additive QTN effects, for which there are 2m possible models, where m is the number of QTNs considered (m was restricted to ≤ 15 SNVs). The approach evaluates all such models and utilizes Bayesian methods to estimate the posterior probability that each SNV is functional. It then evaluates models with all possible combinations of two variants, three variants, and so on. Each model will also have the effect size estimate and its standard error for each SNV appearing in that model, the averaged effect size and standard error for that SNV in that gene and the sum of posterior model probabilities across all models containing the SNV.For genes showing the most significant associations with systolic blood pressure, we used the nctional . The BQTTBC1D16, HRNBP3 and AZI1) and low evidence for heterogeneity across centers  but no strong probabilities of effects for any of the typed SNVs. BQTN analyses of Panel 2 SNVs did not reveal any SNV with a notable posterior probability of effect (data not shown).We used the es Table . This anTBC1D16, HRNBP3 and AZI1) in single test analyses. Importantly, upon Bonferroni correction for multiple testing, none of these study findings can be distinguished from chance. Using the BQTN method to estimate the posterior probability that any SNV in each gene had an effect on the systolic BP, the AZI1 rs12939525 SNV showed the most prominent findings (posterior probability of 0.66).To follow up persuasive evidence of genome wide linkage findings for systolic BP in American Indians, we performed a comprehensive fine mapping of a chromosome 17 genomic region. We identified evidence for locus heterogeneity in association analyses, with suggestive  associations of SNVs in three genes  and cohort (SHS) data.(PDF 4 MB)Additional file 1:


Glycine max L. Merr.). The identification of these signatures of selection is meaningful from the perspective of evolutionary biology and for uncovering the genetic architecture of agronomic traits.Crop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures of selection within the genome of modern soybean , and systematically phenotyped for 9 agronomic traits. A cross-population composite likelihood ratio (XP-CLR) method was used to screen the signals of selective sweeps. A total of 125 candidate selection regions were identified, many of which harbored genes potentially involved in crop improvement. To further investigate whether these candidate regions were in fact enriched for genes affected by selection, genome-wide association studies (GWAS) were conducted on 7 selection traits targeted in soybean breeding  and 2 non-selection traits (pubescence and flower color). Major genomic regions associated with selection traits overlapped with candidate selection regions, whereas no overlap of this kind occurred for the non-selection traits, suggesting that the selection sweeps identified are associated with traits of agronomic importance. Multiple novel loci and refined map locations of known loci related to these traits were also identified.These findings illustrate that comparative genomic analyses, especially when combined with GWAS, are a promising approach to dissect the genetic architecture of complex traits.The online version of this article (doi:10.1186/s12864-015-1872-y) contains supplementary material, which is available to authorized users. Glycine max (L.) Merr., was domesticated in China from its wild ancestor, G. soja Sieb. et Zucc., which has a wide geographic distribution in East Asia. Although the exact series of steps by which soybean was domesticated is still unknown, the divergence between G. max and G. soja likely happened ~0.8 million years ago based on inter-genomic comparison analysis [Glycine max landraces [, there are 45,000 accessions of G. max in ex situ collections around the world [G. max, just 346 (0.77 %) of those landraces account for 76.29 % of the nuclear contribution of 1300 Chinese soybean cultivars released between 1923 and 2005 based on pedigree analysis [The cultivated soybean, analysis . The lonandraces , which ahe world . SubsequHow can one detect this selected class of genes that contributes to the variation of agronomic traits? Historically, quantitative trait locus (QTL) mapping has been used to localize genomic regions underlying phenotypic variation. Since only small numbers of recombination events can be accumulated over the few generations during the development of a recombinant inbred line mapping population, this approach has rarely led to candidate gene isolation . AssociaG. soja. [G. soja and G. max accessions identified 682 genes showing signatures of positive selection including some lineage-specific genes and genes with copy number variation [Apart from the above mentioned QTL and association analysis methods, selective sweep analysis is another approach that can be used to detect loci of potential agronomic importance. A selective sweep alters the allele frequencies of single nucleotide polymorphisms (SNPs) in the vicinity of the selected allele, and causes (i) reduced local variability, (ii) a distorted pattern of genetic variation, (iii) increased linkage disequilibrium (LD) and (iv) extended haplotype structure , 14. TheG. soja. –19. Receariation , 19, weaariation  or limitariation . Many sei) the extent to which the genetic diversity throughout the genome has been impacted by selection, (ii) the regions of the genome that have been affected by selection during soybean improvement, and (iii) whether our candidate regions are truly enriched for genes affected by selection of traits targeted by breeding.In this study, a high-density customized oligonucleotide array  was used to genotype 342 traditional landraces and 1062 improved soybean lines. On the basis of quantified variation in nucleotide diversity, linkage disequilibrium and population structure, XP-CLR statistics  were useTo better understand the patterns of genomic modification imposed by selection, profiles of 52,041 SNPs were characterized in 342 soybean landraces and 1062 improved lines. After quality control, a high-density haplotype map, comprised of genotypes for 35,708 SNPs, was generated for all sampled accessions. Phylogenetic relationships among these accessions were determined using the genetic distances calculated from these SNPs. The resulting neighbor-joining (NJ) tree showed two divergent groups belonging to the landraces and improved lines, except for a few admixed genotypes between the two groups Fig. . This reAi) and diversity index (Hi) (Table Hi) of the diversity present in the landraces. This is not only close to previous observations [Although our estimates of genetic diversity in improved lines may be inflated by the larger sample size, the soybean landraces were still more diverse than the improved lines in terms of both genetic richness (i) Table . A signii) Table . Note thrvations , but alsrvations . An examSince increased LD is another hallmark of genetic bottlenecks, we compared the haplotype block size and LD decay rate of the soybean landraces with that of the improved lines. In general, both landraces and improved lines exhibited low LD decay rates Fig. . In compGiven that our average inter-marker distance (density) is 35 kb, we expect to have reasonable power to identify common variants of large effect associated with agronomic traits in association mapping. However, the low rate of LD decay in soybean also may lead to resolution limitations for the association mapping.FST, averaged 0.139 among the subgroups of improved lines  [FST among the subgroups of landraces was estimated at 0.10 on average  [To understand the geographic structure of genetic diversity and population stratification, NJ tree plots and principal components analysis (PCA) were applied to determine the relatedness among the sampled accessions. The resulting NJ trees and PCA plots showed that the landraces had 6 subgroups, whereas the improved lines had 8 Fig. . The mea = 0.14) . The FST = 0.12) .Fig. 2Pop < 0.0001) between the two grouping factors. Furthermore, significant associations (p < 0.0001) were also observed between the 8 SNP-data-based subgroups and maturity groups in the improved lines  were selected, a total of 472 20 kb-windows exceeded the cutoff value. Adjacent windows within the same LD block were grouped into ‘features’, with each likely representing the effect of a single selective sweep. After the joining of adjacent 20 kb windows, 125 features were identified . The correlation between feature width and XP-CLR is larger than that found in improvement scans in maize . The reason for this may be attributed to the observation that LD is more extensive in self-pollinated species than in cross-pollinated species. Moreover, average significant XP-CLR values (13.3) from our improvement scan were substantially lower than those observed for maize improvement (XP-CLR = 19.1) [Additionally, high correlation between XP-CLR scores and the width of candidate features was found (R = 19.1) . One pos = 19.1) , yield c = 19.1)  and dise = 19.1) .Glyma.14 g201100 has protein homology to OsNAC9, which affects grain yield and drought resistance in rice [Glyma.02 g138100, highly expressed in soybean seed, is orthologous to maize SAD that is involved in converting stearic acid to oleic acid [Glyma.18 g022400 (an amino acid transmembrane transporter) is one of the three genes known to contribute to SCN resistance [Glyma.04 g101500 (GmCRY1) is a major regulator of photoperiodic response in soybean and correlates with latitudinal distribution of soybean [Glyma.08 g109300  is one of the genes which controls the distribution of seed-coat color by inhibiting coloration over the entire seed coat [We assigned the gene closest to the 20-kb window with the maximum XP-CLR score in each LD block as the most likely candidate , they should at least partially overlap with QTL regions associated with these traits, whereas no overlap of this kind should be observed for non-selection-target traits (such as pubescence and flower color). To validate this hypothesis and dissect genetic architecture of agronomic traits, GWAS was performed on 9 agronomic traits. To increase the reliability of agronomic trait data, especially for quantitative traits, phenotyping was conducted at 7 different locations over a 6-year period. With the exception of lodging, we observed abundant phenotypic variation and normal distribution for grain yield, plant height, maturity date and protein and oil content in the tested accessions , which did not include population structure (Q) and familial relatedness (K), departed from the expected distribution under a model of no association with significant inflation of nominal P-values. The mixed linear model (MLM) model that includes Q and K allowed us to compress the excess of low P-values for these traits  plots  on Chromosome 8, inhibiting coloration distribution, has been found to be associated with seed coat color [P-value of 1.3 × 10-19, was located within the complex cluster of the 5 genes and explained 23 % of phenotypic variation , a gene that codes for flavonoid 3’5’-hydroxylase [In contrast with seed coat color, there is no preferred color (tawny or gray) for pubescence in any soybean breeding program, so this trait can be taken as an example of a non-selection-target trait. A total of 46 SNPs were found to be significantly associated with pubescence color. Most of these SNPs, spanning a region of approximately 2 Mb (between 17-19 Mb on Chromosome 6), overlapped the ce color   or GWAS signals, a total of 10 loci found in this study fall within such intervals or adjacent to previous GWAS signals  is adjacent to a cluster of genes that are homologous to Arabidopsis APETALA2 genes (homeotic regulator) known to influence seed weight and size [We further conducted GWAS for six quantitative traits including grain yield, plant height, lodging, maturity date, and protein and oil content. For grain yield, a total of 15 loci were identified in the elite cultivars and explained 36.6 % of the phenotypic variation. Since yield is a very complex trait and no candidate gene has been identified as a functional gene underlying soybean yield or yield component traits, it is difficult to assess the exhaustiveness and accuracy for these QTLs. However, when compared with chromosomal regions previously reported to contain yield QTLs (and size .www.soybase.org), a total of 12 loci found in this study fall within such intervals or adjacent to previous GWAS signals  of selective sweeps Table . FailureOur results showed that modern breeding has introduced detectable genetic changes to the soybean genome. A genome-wide screen for artificial selection identified 125 genomic regions of potential agronomic importance. By means of association mapping, a set of new loci as well as refined map locations of known loci were found to contribute to the phenotypic variance of 9 agronomic traits, which will be attractive candidates for further investigation. Major genomic regions, associated with selection traits, overlap with candidate selection region, whereas no overlap of this kind occurred for the non-selection traits. This indicates the potential for using comparative genetic techniques to identify genomic regions relating to phenotypes of importance to soybean breeders. Ultimately, uncovering the genetic architecture of agronomic traits will provide the basis for improving yield, quality and sustainability of soybean.The plant materials included genotypes from two soybean populations. The first population consisted of 342 traditional landraces from multiple geographic origins including China, Japan, Korea, Kyrgyzstan and Russia. The soybean landrace was defined as a locally adapted, traditional variety of a domesticated soybean that has developed over time, through adaptation to its natural and cultural environment. There are 21 accessions belong to 80 ancestral soybean lines listed by Gizlice et al. . To maxiSoybean genomic DNA was extracted from young leaf tissue following the standard CTAB method . All theAi) and gene diversity index  were calculated using TASSEL4.0 [r2) for estimating the degree of LD between pair-wise SNPs was calculated using the software TASSEL4.0. The extent of LD decay was measured as the chromosomal distance at which the average pairwise correlation coefficient (r2) dropped to half its maximum value.Summary statistics were computed for the polymorphic SNP data sets in both landraces and improveed lines. The statistics, including the number of alleles , were ci,, ref. . Principi,, ref.  based onASSEL4.0  to deterr2 > 0.75) were down-weighted to minimize the effect of dependence on the composite likelihood score. Likelihood ratio (XP-CLR) was estimated and assigned to each 20-kb window [define blocks function of Haploview 4.2 software [Evidence for selection across the genome during improvement was evaluated between improved lines and landraces. A cross-population composite likelihood ratio test (XP-CLR) was used to perform the genome scan for selection . A 0.05-b window . We detesoftware .http://www.css.msu.edu/varietytrials/soybean/Soybean_Home_Page.htm. Since the improved lines were phenotyped in multiple environments, the best linear unbiased predictors (BLUPs) were used for the overall association analysis of the soybean improved lines. Analysis of variance (ANOVA) for the phenotypic data was performed with the R package, lm(stats) and anova.lm(stats).For each soybean landrace accession, pure line seeds of all accessions were obtained from the U.S. Department of Agriculture Soybean Germplasm Collection . The landrace accessions were planted in single row plots, 6 m long with 0.75 m row spacing, at the Agronomy Farm of Michigan State University. Three Mendelian traits, flower color, seed coat color and pubescence color were investigated. Measurement methodology of each trait was described in Additional file y = Xα + e and y = Xα + Pβ + Kμ + e, respectively, where y is the vector of phenotypic observations, α is the vector of SNP effects; β is the vector of population structure effects; μ is the vector of kinship background effects; e is the vector of residual effects; P is the PCA matrix relating y to β; X and K are incidence matrices of 1 s and 0 s relating y to α and μ, respectively [P matrix for population structure correction in the two panels. Analyses were performed by the software TASSEL 4.0, which implemented the EMMA and P3D algorithms to reduce computing time [Two different models, general linear model (GLM) and mixed linear model (MLM), were used to test the associations between the SNPs (MAF > 5 %) and phenotypic variations. The GLM and MLM can be expressed as ectively . The toping time . False dThe data sets supporting the results of this article are included within this article and its additional files.


Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed Following this assumption, we investigate the type I error and the power of two proposed collapsing methods  using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful. Studiesews, see -5).In this study, we compare two collapsing methods that use the genetic information in different ways. Specifically, we consider the combined multivariate and collapsing (CMC) method  and funcet al [f of a linear combination of the genotype data and a normalized weight function is constructed. To capture the genetic variations in the genotype function, the weight function is chosen to maximize the variance of f. This setting results in an optimization problem that can be transformed to a PCA or an eigenfunction problem. Therefore, the solution delivers not only the optimal weight functions but also principal component functions for the genotype data of the considered ROI. Because the optimization problem consists of integral functions and is difficult to solve in closed form, a solution is derived by discretizing the continuous eigenanalysis problem. Finally, principal component scores are constructed using the derived principal component functions and the genotype data. These then form the basis of the final FPCA test statistic, which considers the mean squared distance of averages of these principal components scores in cases and controls.Luo et al  use the et al  and prin2 test or Fisher's product method, is applied for the analysis of all groups of variants together. In this analysis, Fisher's product method was used.The CMC method combines collapsing with a multivariate test . The groWe applied both methods to case-control data provided for GAW18. Genotypes were provided for odd-numbered autosomes, but we dropped chromosome 5 data because of quality issues. We considered the simulated dichotomous phenotype of hypertension (HTN) in the sample of unrelated individuals and defined those individuals as cases who were defined as affected at least once at any time point of investigation. Controls were defined as the complement set of the cases. In the original data set, there were 157 unrelated individuals. However, only data from 142 of these individuals were used by the GAW18 organizers to create the 200-replicate data set. Because of the definition of case and control status with longitudinal data, the total numbers of cases and controls differed for each replicate, but in median, there was a ratio of cases to controls of about 0.84 over all replicates. Analyses were restricted to minor allele counts, so dosage files were used.Gene information data was obtained by merging single-nucleotide polymorphism (SNP) data with the ENSEMBL database . In totaet al [For our analyses, we used a modified version of the FPCA function provided by Luo et al  in R. Alet al . For CMCFigure KRT23, FPCA had its highest power in gene SAT2 with 0.30. The second highest power of FPCA is in gene DBP with 0.075. Table SAT2. The power of FPCA to detect this gene was 0.3; for CMC, it was 0.33.Figure KRT23. Table In this study, we compared two different collapsing approaches using the GAW18 data. We considered the simulated dichotomous phenotype HTN among the unrelated individuals without any restriction to MAF or covariates. The proportion of unassociated genes exceeding a given type I error of 0.08 for FPCA was moderate, but CMC's corresponding proportion of 0.75 was highly unacceptable. The power of both methods was too low for identifying most of the truly associated genes. Because CMC had a high false-positive rate, it cannot be used reliably for judging power. Its greatest power of 0.66 was observed for the gene The authors declare that they have no competing interests.AZ designed the overall study, CD and AS conducted statistical analyses and CD and IRK drafted the manuscript. All authors read and approved the final manuscript.


Feather pecking is a major welfare issue in laying hen industry that leads to mortality. Due to a ban on conventional cages in the EU and on beak trimming in some countries of the EU, feather pecking will become an even bigger problem. Its severity depends both on the victim receiving pecking and on its group mates inflicting pecking (indirect effects), which together determine plumage condition of the victim. Plumage condition may depend, therefore, on both the direct genetic effect of an individual itself and on the indirect genetic effects of its group mates. Here, we present estimated genetic parameters for direct and indirect effects on plumage condition of different body regions in two purebred layer lines, and estimates of genetic correlations between body regions.Feather condition scores (FCS) were recorded at 40 weeks of age for neck, back, rump and belly and these four scores were added-up into a total FCS. A classical animal model and a direct–indirect effects model were used to estimate genetic parameters for FCS. In addition, a bivariate model with mortality (0/1) was used to account for mortality before recording FCS. Due to mortality during the first 23 weeks of laying, 5363 (for W1) and 5089 (for WB) FCS records were available.Total heritable variance for FCS ranged from 1.5% to 9.8% and from 9.8% to 53.6% when estimated respectively with the classical animal and the direct–indirect effects model. The direct–indirect effects model had a significantly higher likelihood. In both lines, 70% to 94% of the estimated total heritable variation in FCS was due to indirect effects. Using bivariate analysis of FCS and mortality did not affect estimates of genetic parameters. Genetic correlations were high between adjacent regions for FCS on neck, back, and rump but moderate to low for belly with other regions.Our results show that 70% to 94% of the heritable variation in FCS relates to indirect effects, indicating that methods of genetic selection that include indirect genetic effects offer perspectives to improve plumage condition in laying hens. This, in turn could reduce a major welfare problem. Feather pecking (FP) is a major welfare issue in commercial laying hens. Depending on the severity of the pecking it can result in feather loss or damage, or skin or muscle injuries . If the FP is a multi-factorial problem caused by both animal-related and environmental factors . A commoFP is a social interaction phenomenon, that involves both the victim that receives the pecking and its group mates that inflict the pecking -18. WhenTo measure FP, behavioural observations are needed. However, several authors have used a feather condition score (FCS) to assess plumage condition as an alternative to behavioural observations ,27,28. DSo far, most studies that focus on the plumage condition of laying hens have ignored the effect of group mates. However, it is expected that IGE contribute significantly to the heritable variation of plumage condition in laying hens, especially in the case of damaging behaviour such as FP. To improve plumage condition in laying hens, knowledge of the genetic parameters for both direct and indirect effects is required.In the present study, genetic parameters of plumage condition in laying hens were estimated using a classical animal model and a model that combined both direct and indirect genetic effects. Plumage condition was measured on four body regions; neck, back, rump and belly using the FCS. In addition, genetic correlations between the different body regions were estimated.For this study, data from the experiment that is described in Ellen et al.  were useData were collected under control of Institut de Sélection Animale B.V., the layer breeding division of Hendrix Genetics. Hendrix Genetics complies with the Dutch law on animal wellbeing. Data on two purebred White Leghorn layer lines were provided by the Institut de Sélection Animale B.V.. The two lines were coded W1 and WB. Data from line WF were not used in this study because fewer observations were available for this line and its mortality due to cannibalism was low (in comparison with the other two lines), which would lead to inaccurate genetic parameters for direct and indirect effects of survival time .Within a line, sires and dams were mated at random. Matings were done in two batches with a six-month time period between the two batches. Sires used for both batches were largely the same (89% for line W1 and 94% for line WB), while dams were all different. For each batch, sires (36 of line W1 and 35 of line WB) were mated to approximately eight dams each, and each dam contributed on average 12.3 female offspring. For both lines, observations from a single generation were used. Chickens of both lines were hatched in two batches and each batch consisted of four consecutive age groups that differed by a two-week period each. All 12 192 chickens had intact beaks.ad libitum.When the hens were on average 17 weeks old, they were transported to two laying houses with traditional four-bird-battery cages. The two batches were each placed in separate laying houses, termed 1 and 2. In both laying houses, the 17-week-old hens were randomly allocated to laying cages, with four birds of the same line and age in a cage. The individuals making up a cage were combined at random, without taking size of the hens into account. In both laying houses, cages were grouped into eight double rows. Each row consisted of three levels . A feeding trough was in front of the cages and each pair of back-to-back cages shared two drinking nipples. A standard commercial layer diet and water were provided In both laying houses, the hens started with a light period of 9 hours/day. Every week the light period was increased by 1 hour until 16 hours/day, when the hens were on average 26 weeks of age.Plumage condition was measured at 40 weeks of age on all hens alive. It was measured at eight time points, each separated by a two-week period, starting with the oldest hens, so that all birds were measured at the same age. To quantify plumage condition, the feather condition score (FCS) described in Bilčik and Keeling  was usedAll hens were observed daily. Dead hens were removed, and wing band number, cage number, and cause of death were recorded. Cause of death was determined subjectively without dissection. Thus for all hens in the dataset, their status alive (0) or dead (1) was known at the time of feather scoring. A total of 12 192 hens were present in the dataset composed of 5920 hens of line W1 and 6272 hens of line WB. Due to mortality during the first 23 weeks of laying, FCS were unavailable for 9.4% of the W1 hens and for 18.9% of the WB hens, which resulted in 5363 FCS records for line W1 and 5089 for line WB.A preliminary data analysis was performed using the SAS statistical program . The GLMA linear animal model was used in ASReml to estimate genetic parameters for FCS . First, y is a vector of observed FCS; b is a vector of fixed effects, with incidence matrix X linking observations to the fixed effects; a is a vector of the breeding values, with incidence matrix Z linking the observations on individuals to their breeding value; c is a vector of independent random cage effects, with incidence matrix V linking the observations to the random cage effect; and e is a vector of random residuals. The variance structure of the model terms are: A is the matrix of additive genetic relationships between individuals based on five generations of pedigree, I an identity matrix, n = 63). The observations on these hens were included in the analysis to better estimate fixed effects.where Second, genetic parameters were estimated for both the direct and indirect genetic effects using a direct–indirect effects model ,24,aD is a vector of direct breeding values, with incidence matrix ZD linking observations on individuals to their direct breeding value, aS is a vector of indirect breeding values, with incidence matrix ZS linking observations on individuals to the indirect breeding values of their group mates . The coThe classical animal model (Equation 1) and the direct–indirect effects model (Equation 2) were statistically compared using a log-likelihood ratio test. The classical animal model was compared with a model without random effects (null model) and a model with only a random cage effect to test additive genetic variance. The direct–indirect effects model was compared with the classical animal model to test the indirect genetic (co)variance.In the classical animal model, the heritability is the ratio of heritable variance  in both expressions refers to the  n - 1 group mates with which the individual interacts. For socially affected traits, the ratio of total heritable variance and phenotypic variance (T2) is:In the direct–indirect effects model, the total heritable variance . In laying house 2, back and rump had the lowest FCS, whereas in laying house 1 neck had the lowest FCS. Line WB had the worst plumage condition in both laying houses. Furthermore, significant differences in FCS between the three levels of each row and between the corridors were found (Table 5. In layTable h2) for FCS of the different regions and for total FCS were low, ranging from 1.5 to 9.8%. In previous studies, h2 estimates for total FCS ranged from 22 to 54% [h2 estimates for separate body regions have been reported in the literature.Table 2 to 54% -36. To oh2 of FP behaviour (inflicting and receiving FP) increased when hens grew older (69 weeks compared to 38 weeks). Using younger birds could explain the lower h2.There are several reasons that could explain the difference in heritabilities between the present and previous studies. In the present study, FCS were recorded when the hens were 40 weeks of age, whereas in previous studies, they were recorded when hens were between 51 and 56 weeks of age ,36. Kjaeh2 observed in this study relates to the use of individual records vs. records pooled by cage. We used individual records on four random hens of the same line kept in one cage, while Craig and Muir [h2 found by Craig and Muir [A second reason for the lower and Muir  used thes et al. , the anaand Muir  refers tand Muir .Statistical comparison of the direct–indirect effects model Equation 2) and the classical animal model (Equation 1), showed a significant improvement of the goodness of fit for both lines and for all body regions  was 1.3 to 4.0-fold larger than σTBV. For the body regions with significant IGE, non-direct genetic effects explained 70 (neck line W1) to 94% (rump line WB) of the total heritable variation in FCS (Table T2) ranged from 9.8 to 53.6%  at 40 weeks of age, did not result in significant changes of the estimated genetic parameters compared to the univariate analyses Table . CompariEstimates of genetic correlations from the direct–indirect effects model are in Table otal FCS -36. In total FCS , whereasotal FCS . In addiotal FCS . Furtherotal FCS  showed tFCS was recorded by four persons, which could introduce bias due to incomplete correlation of FCS scores. The phenotypic correlation between observers was greater than 0.84 for neck, back, and rump, but was as low as 0.64 for belly (data not shown). The largest difference in means of the persons recording FCS was found for neck  heterogeneity of residual variance was evaluated by fitting a separate residual variance for person and (2) genetic correlations between persons were estimated using the classical animal model (data not shown). Accounting for heterogeneous residual variances did not change the estimated genetic parameters. Therefore, homogeneous residual variance was assumed. Genetic correlations of total FCS between persons were greater than 0.80 for both lines, which indicates only minor differences in FCS between persons at the genetic level.In this study, we showed that a substantial part of the total genetic variation in plumage condition of different body regions in two purebred layer lines is due to IGE. Accounting for IGE in the genetic analysis showed that total heritable variation was up to 9-fold greater than suggested by results of the classical animal model. Thus, using breeding programs that exploit the heritable variation due to IGE can considerably accelerate response to selection on FCS. In previous studies, FP was significantly related with lower FCS ,28. SincMeasuring the plumage condition by recording FCS is time-consuming. Moreover, laying hens need to be at least 40 weeks before FCS can be recorded, and FCS measured on selection candidates is not very useful, because selection candidates are housed individually. Hence, selection would have to be based on sib or progeny information. These obstacles can be overcome by using genomic selection ,42. Our T2) ranges from 9.8 (rump line W1) to 53.6% . A substantial part, 70 to 94%, of the total heritable variation relates to IGE. Thus, it is expected that including both direct and indirect effects in a genetic selection program will contribute to a reduction in FP behaviour, one of the major welfare issues in the laying hen industry.Social interactions have a substantial effect on plumage condition in laying hens. This study shows that, for the different body regions , the total heritable variance of FCS, expressed as the proportion of the phenotypic variance . Table S3. Title: Estimates of genetic correlations between the four body regions from the direct–indirect effects model. Description: Estimates are shown for the direct genetic correlations .Title: Estimates of genetic parameters from the univariate classical animal model and corresponding SE of FCS. Description: Data represents estimates of genetic parameters from the univariate classical animal model and corresponding SE of FCS for each body region and the total FCS in the two lines . Click here for file


Currently, the majority of genetic association studies on chronic obstructive pulmonary disease (COPD) risk focused on identifying the individual effects of single nucleotide polymorphisms (SNPs) as well as their interaction effects on the disease. However, conventional genetic studies often use binary disease status as the primary phenotype, but for COPD, many quantitative traits have the potential correlation with the disease status and closely reflect pathological changes.Here, we genotyped 44 SNPs from four genes  in 310 patients and 203 controls which belonged to the Chinese Han population to test the two-way and three-way genetic interactions with COPD-related quantitative traits using recently developed generalized multifactor dimensionality reduction (GMDR) and quantitative multifactor dimensionality reduction (QMDR) algorithms.1%pre, the forced vital capacity (FVC), and FEV1/FVC, the best interactions were seen from SERPINE2 and TGFB1.Based on the 310 patients and the whole samples of 513 subjects, the best gene-gene interactions models were detected for four lung-function-related quantitative traits. For the forced expiratory volume in 1 s (FEV1), the best interaction was seen from EPHX1, SERPINE2, and GSTP1. For FEVThe results of this study provide further evidence for the genotype combinations at risk of developing COPD in Chinese Han population and improve the understanding on the genetic etiology of COPD and COPD-related quantitative traits.The online version of this article (doi:10.1186/s40246-016-0076-0) contains supplementary material, which is available to authorized users. Chronic obstructive pulmonary disease (COPD) is defined as airflow limitation that is not fully reversible . Cigaret1) and forced vital capacity (FVC) has emphasized the importance of the functional assessment of usually progressive, non-fully reversible airflow limitation [1 in reflecting the severity of COPD and effective in predicting the mortality in patients with COPD [Recent advances in genetic studies have implicated that COPD represents a complex disease with genetics contributions from multiple genes. For example, although many studies did not find the association between EPHX1 and the susceptibility to COPD or disease severity , 6, Vibhmitation . The BODith COPD . TherefoRecently, some extension algorithms for detecting and characterizing epistatic interactions in the context of quantitative outcomes named as generalized multifactor dimensionality reduction (GMDR)  and quanP5–P95, 0–4). This MMRC distribution indicates that the degrees of various physical activities that precipitate dyspnea of the COPD patients in our study are mild. The distribution of BODE index is as follows: 9 patients (2.9 %) BODE 0, 56 (18.1 %) BODE 1, 93 (30.0 %) BODE 2, 71 (22.9 %) BODE 3, 36 (11.6 %) BODE 4, 26 (8.4 %) BODE 5, 11 (3.5 %) BODE 6, 4 (1.3 %) BODE 7, 3 (1.0 %) BODE 9, and 1 (0.3 %) BODE 10, with a median of 2 . The BODE distribution also indicates that the severity of our COPD patients is mild. In addition, in terms of the effect of a single SNP, except rs41266229 (EPHX1) and rs729631 (SERPINE2) which display the significant difference between the COPD patients and normal controls after passing the multiple testing corrections [The general characteristics of COPD-related quantitative traits for patients are shown in Additional file rections , no signDE 0, 56 8.1 % BODIn the current study, we performed the analysis of single marker effect for seven COPD-related quantitative traits. We included covariates age, sex, and pack-years of smoking in the model. For the seven COPD-related quantitative traits, except rs729631 (SERPINE2) which showed a strong association with FVC, no single marker effects showed a significant association after being corrected by Bonferroni procedure  and GSTP1 (rs4147581). The 1000 permutation testing revealed a significant P value of 0.019. The average maximum of FEV1 (1.82) was seen from rs2292568 (CC)*rs4147581 (GG), whereas the average minimum of FEV1 (1.08) was seen from rs2292568 (CT)*rs4147581 (GG)  and rs6957 (TGFB1). The 1000 permutation testing also revealed a significant P value of 0.042. The average maximum of FEV1%pre (59.92) was seen from rs1051741 (CT)*rs6957 (AA), whereas the average minimum of FEV1%pre (35.57) was seen from rs1051741 (CT)*rs6957 (AG)  was seen from rs7583463 (AA)*rs2241713 (CG), whereas the average minimum of FVC (2.40) was seen from rs7583463 (CC)*rs2241713 (GG) . For FEV1/FVC, 6-min walk test (6MWT), BODE, and MMRC, the best interactions are rs17196253 (SERPINE2) and rs6748795 (SERPINE2)  Fig. . This inAG) Fig. . For FVCGG) Fig. . HoweverE2) Fig. , rs75834E2) Fig. , rs46748E2) Fig. , and rs2E2) Fig. , respect1, FEV1%pre, FVC, and FEV1/FVC), we further detected the two-way gene-gene interaction using GMDR, QMDR, and traditional QTL based on the whole samples (310 patients and 203 controls). GMDR, QMDR, and traditional QTL all found that the best interaction model was EPHX1 and GSTP1 for FEV1, SERPINE2 and TGFB1 for FVC, and FEV1/FVC , we also used a web-based tool GeneMANIA  to find 1, the best interaction was seen from EPHX1, SERPINE2, and GSTP1. For FEV1%pre, FVC, and FEV1/FVC, the best interaction models were seen from SERPINE2 and TGFB1. Interestingly, we found EPHX1 interact with GSTP1 directly for FEV1 trait for COPD patients, which is consistent with the interaction identified by GeneMANIA. In previous studies, Lakhdara et al. have suggested that combined EPHX1, GSTP1, GSTM1 and GSTT1 genetic polymorphisms may play a significant role in the development of COPD, emphysema and decline of the lung function based on the analysis for Tunisian population [Up to now, although there are many different candidate genes which have been investigated for their potential roles in lung function impairment in smokers , 18, fewpulation . Su et apulation . Therefopulation .In addition, we know that the advantage of MDR is not only that it can deal with sparse and high-dimension data and therefore might uncover non-linear SNP-SNP interactions that are missed by QTL but also it can detect high-order interactions between genes which cannot be performed by QTL as the model complexity increases with the order of interactions. In the present study, we used GMDR and QMDR to detect the three-way interactions between genes and found that the best interactions were seen from some of these four genes. Furthermore, we have determined that the genes more frequently detected by GMDR, QMDR, and QTL were more likely to be functional, such as SERPINE2 . This suMoreover, we know that the most common variable selection strategy for interaction studies often select SNPs with main effects to test for the interactions. However, such an approach might cause the miss of some of the true interactions between genes. It has been reported that the false positive report probability (FPRP)  depends Furthermore, we must point out the limitations of the present study. Firstly, we limited our studies to interactions within four COPD-related genes, but interactions between genes based on the other priori biology evidences or on pathways may be related to disease risk as well and warrant further exploration. Secondly, a newly developed multivariate quantitative multifactor dimensionality reduction (Multi-QMDR) algorithm is approved to have better performance than QMDR and GMDR when multiple quantitative phenotypes are available . This me1/FVC of less than 0.7 and FEV1%pre of less than 0.8 predicted and no evidence of primary asthma or other respiratory diseases. The control group comprised of 203 subjects with the same age range as the case group. They have no history of respiratory symptoms and exhibit normal pulmonary function of FEV1/FVC of more than 0.7 and FEV1%pre of more than 0.8 predicted. Written informed consent was obtained from every participating subject, and the study protocol was approved by the research ethics boards of all participating hospitals. The complete name of the ethics committee is Institutional Review Board of Beijing Chaoyang Hospital who approved this study. The detailed entry criteria and the baseline characteristics of study subjects were seen from our previous study [We recruited 310 unrelated COPD patients aged 40–75 years from respiratory outpatient clinics at 12 hospitals in Beijing from October 2007 to March 2009. The cases are physician-diagnosed COPD; the pulmonary function test shows FEVus study , 27.r2 threshold of 0.8. The detailed description was seen from our previous study [Genomic DNA was isolated from whole blood leukocytes by the conventional phenol-chloroform method. SNPs were genotyped using Illumina VeraCode technology performed on BeadXpress genotyping platform . Forty-four tagging SNPs (MAF > 0.05) were genotyped to capture the common variants of the four genes  under pairwise mode with us study , 27.FEV1and FEV1%pre: FEV1 is called the forced expiratory volume in one second, indicating the volume in a 1-s forced exhalation. It is then converted to a percentage of predicted based on your height, weight, and race normal named as FEV1%pre. FEV1%pre is a key value to be known by smokers and COPD patients in order to assess the severity of the disease [1%pre of COPD patients is less than 0.8. disease . In the FVC is called the forced vital capacity, indicating the amount of air exhaled forcefully and quickly after maximum inspiration [piration .FEV1/FVC is a calculated ratio used in the diagnosis of obstructive and restrictive lung disease. It represents the proportion of a person’s vital capacity that they are able to expire in the first second of expiration [1/FVC of COPD cases is less than 70 %.piration . In the 6MWT is the 6-min walk test which is the most commonly used exercise test in pulmonary rehabilitation. In the current study, the 6MWT was carried out according to the ATS guidelines [idelines . Each paMMRC is the modified Medical Research Council dyspnea scale which uses a simple grading system to assess a patient’s level of dyspnea. The MMRC dyspnea scale classified the breathless into six grades (0 to 5) according to self-perceived breathlessness during daily activities [tivities . Grade 5BODE index is a multidimensional index comprising the BMI, degree of airway obstruction (FEV1%pre), functional dyspnea , and exercise capacity (6MWT). For the calculation of the BODE index, we used an empirical model as previously described [escribed . For theescribed .In the present study, for COPD patients, we focused on seven COPD-related quantitative traits to perform our analysis. These traits were described simply as follows:1, FEV1%pre, FVC, and FEV1/FVC, which were subjected to our further analysis.Considering that 6MWT, BODE, and MMRC are measure indexes only appropriate for COPD patients, therefore the quantitative traits measured for normal controls are four lung-function-related traits: FEVP value of <0.05 is considered statistically significant. All statistical analysis was completed using SPSS version 19.0 .The general characteristics of COPD-related traits for patients are described, and the comparison among multiple genotype combination is performed using the analysis of variance (ANOVA) or Kruskal-Wallis test. A http://pngu.mgh.harvard.edu/~purcell/plink/), and we used an additive model for gene effects. We included covariates age, sex, and pack-years of smoking in the model. A correction for multiple comparisons was performed using the Bonferroni procedure [For seven COPD-related quantitative traits for patients, we used the QTL method  to perform single marker analysis using the PLINK software . Considering that the covariates like age, sex, and smoking status might have a strong main effect and may potentially interfere with the ability of MDR and QTL to achieve their goal [In the present study, we used recently developed GMDR and QMDR algorithms to test for the two-way and three-way gene-gene interactions with COPD-related quantitative traits based on the COPD patients and the whole samples, respectively. GMDR uses score-related statistics derived from a generalized linear regression model and allows covariates to be adjusted . QMDR exst model . In addieir goal , we therTo extend/explore the potential joint genetic effects of these four genes  with other genes, we also used a web-based tool GeneMANIA  to find 1, FEV1%pre and FVC. The MDR approaches used in this paper that have the potential in the identification of complex biological links contribute to COPD development processes. Our study provides further evidence for the genotype combinations at risk of developing COPD in Chinese Han population and improves understanding on the genetic etiology of COPD.In conclusion, our study suggests the potential interactions between EPHX1, SERPINE2, GSTP1, and TGFB1 contributing to COPD-related quantitative traits, such as FEV


Genomics promises exciting advances towards the important conservation goal of maximizing evolutionary potential, notwithstanding associated challenges. Here, we explore some of the complexity of adaptation genetics and discuss the strengths and limitations of genomics as a tool for characterizing evolutionary potential in the context of conservation management. Many traits are polygenic and can be strongly influenced by minor differences in regulatory networks and by epigenetic variation not visible in DNA sequence. Much of this critical complexity is difficult to detect using methods commonly used to identify adaptive variation, and this needs appropriate consideration when planning genomic screens, and when basing management decisions on genomic data. When the genomic basis of adaptation and future threats are well understood, it may be appropriate to focus management on particular adaptive traits. For more typical conservations scenarios, we argue that screening genome-wide variation should be a sensible approach that may provide a generalized measure of evolutionary potential that accounts for the contributions of small-effect loci and cryptic variation and is robust to uncertainty about future change and required adaptive response(s). The best conservation outcomes should be achieved when genomic estimates of evolutionary potential are used within an adaptive management framework. The rapid pace of human-driven global environmental change is a well-recognized threat to global biodiversity  to track suitable environments. However, whilst tracking environments spatiotemporally can be an important response to environmental change, concomitant factors (e.g. habitat loss and fragmentation) can limit the effectiveness of the response  or over multiple generations through evolutionary adaptation. There is increasing recognition of, and experimental evidence for, the importance of plastic responses to climate change  have not proven good predictors of genome-wide diversity and/or evolutionary potential  outline challenges associated with estimating evolutionary potential in wild populations, (ii) summarize knowledge of the genetic and epigenetic components of evolutionary potential, (iii) review current methods and techniques for characterizing adaptively important variation, (iv) develop recommendations for estimating evolutionary potential in a management context, and (v) outline future research directions. Whilst our primary focus is restricted to genomics as a tool for estimating evolutionary potential in wild populations, we would also direct readers to exciting advances in the parallel field of phenomics which, through use of high-dimensional phenotype data, offers an alternative, nongenetic approach to estimating evolutionary potential  and environmental components. To allow comparison across traits and populations, measures of additive genetic variance must be standardized Houle . In quanEven with recent genomic advances , a quantitative genetics approach to measuring evolutionary potential in natural populations has some limitations   Hill , genomicow) Hill . There aow) Hill .The eukaryote genome is broadly comprised of exons , introns (noncoding sequence in a gene) and intergenic regions (noncoding sequence between genes). In the human genome, only ∼1.5% of sequence codes for amino acids, but around 10–15% of sequence is estimated to be functionally constrained, highlighting the evolutionary significance of noncoding sequence (Ponting and Hardison cis-regulatory elements in the upstream 5' untranslated region (UTR), the downstream 3'UTR, the promoter region and within the coding sequence), they can also occur at greater distances (e.g. trans-regulatory elements); enhancers often lie 10–100 kb from their target genes , populations containing large amounts of cryptic variation adapted six times faster to a new chemical environment than did populations with little cryptic variation , releases a variety of different phenotypes that are not expressed under benign conditions (Chen and Wagner Drosophila melanogaster (Takahashi A large amount of genetic variation that is present but invisible at the phenotypic level (i.e. cryptic variation) enables populations to adapt faster to environmental change  is a contagious cancer that has devastated Tasmanian devil Arabidopsis: Atwell et al. Drosophila: Burke et al. It is now understood that many – if not most – complex phenotypic traits are polygenic: they are controlled by a large number of interacting alleles of individual small phenotypic effect  have revealed many novel variants associated with complex traits, but have not proven a panacea; significantly associated variants are collectively still unable to explain a substantial part of heritable variation in complex traits  in humans  could not be attributed to individual SNPs, but could be explained by positive covariance in allelic effects. Similarly, for another well-studied tree species, the loblolly pine Pinus taeda, Eckert et al.  , gene expression studies were unleashed for use in nonmodel organisms by the advent of high-throughput sequencing of RNA (RNA-seq). Many such studies begin with a Expression of many genes is highly tissue-specific , or not currently adaptive, may still contribute to a population's evolutionary potential. Much of this complexity is difficult to detect using standard population genomic methods, and limitations of current methods need to be considered when designing the screening approach, and when basing management decisions on genomic data (e.g. by screening coding and noncoding parts of the genome and not routinely biasing management decisions to large-effect loci). Embracing recent advances in other fields  and factoring in the contributions of polygenic adaptation, as well as cryptic and epigenetic variation, should result in better-informed applications of conservation genomics and better estimates of evolutionary potential than have been previously possible for most wild organisms.sensu Frankham et al. As we are unlikely to know in advance the best course of conservation action, a risk-management approach is appropriate for making initial decisions . There are well-known examples of traits known to be controlled by a small number of large-effect QTLs, including armour plating associated with marine-freshwater divergence in sticklebacks, , anonymous large-effect loci associated with the adaptive divergence can be detected using standard genome-scan methods  that provides resistance to disease epizootics in the wild could be used to inform selection of resistant or immune individuals for captive breeding, reintroductions and other forms of population genetic augmentation. Key examples might include resistance to chytrid fungus in frogs (Savage and Zamudio Bufo marinus), which cause almost certain, rapid death in susceptible individuals, leading to massive population declines and local extinctions  that will be required. An extreme illustration is provided by toxic invasive cane toads variances, genetic correlations and genetic architecture) will be limited, and/or the predictability of environmental change and the required adaptive response(s) will be low. Under this scenario, screening genome-wide variation should be a sensible approach that may provide a generalized measure of evolutionary potential that accounts for the contributions of small-effect loci and cryptic variation and is robust to uncertainty about future change and required adaptive response(s). We use the term ‘genome-wide variation’ in a broad sense to reflect variation sampled representatively across the genome, which could encompass a range of metrics, including allelic diversity, heterozygosity and weighted measures that place greater importance on specific loci of interest within the context of genome-wide variation.FST-outlier loci. Second, and more significantly, for highly polygenic traits, genome-wide variation currently provides the best predictions of phenotypes  were derived using the relatedness matrix and then validated using extensive simulations and known pedigree information.In some conservation situations , which may often be challenging in wild populations , additive genetic (co)variances and genetic correlations could be estimated in natural populations using large-scale genomic data without the need for detailed pedigrees is an exciting one and would open up a raft of possibilities for exploring trait architecture and evolutionary potential in nonmodel systems . The authors were able to identify major QTL influencing the thermal sensitivity of flowering time and to build a genetic model that was able to successfully predict flowering time of given genotypes in their future climate scenarios. Approaches that link genotypes to their environments within an experimental evolution framework can identify the loci that may promote adaptive responses to environmental change. Although there are obvious limitations to applying these experimental approaches directly to many wild populations, there is good scope for application to commercially valuable crop and animal species and potential for some information to transfer to related wild taxa  under future environments  have looked for correlations between traditional measures of standing genetic diversity (e.g. based on a handful of microsatellite markers) and either fitness and/or evolutionary potential in response to environmental stressors (Reed and Frankham A feasible approach might be to calibrate genomic estimates of evolutionary potential in nonmodel organisms with estimates from model populations where evolution has been more rigorously measured. Past empirical studies of model organisms (e.g. Here, we have outlined some of the complexity regarding adaptation genetics and explored limitations and recent advances in the application of genomic tools to conservation. Recent advances in the fields of quantitative and human genetics have revealed that adaptively important variation is dispersed throughout the genome, with regulatory variants and epigenetic mechanisms playing an important role in shaping gene expression and rapid adaptive evolution. Polygenic adaptation is widespread and even cryptic genetic variation that is hidden at the phenotypic level may make an important contribution to evolutionary potential. The complexity of adaptation genetics needs to be appropriately considered when planning genomic screens and subsequently when basing management decisions on genomic data. When the genomic basis of adaptation and future threats are well understood, it may be appropriate to focus management on particular adaptive traits that are likely to be of importance. For more typical conservation situations, we argue that screening genome-wide variation may provide bet-hedging estimates of evolutionary potential that account for small-effect and cryptic variants and are relatively robust to uncertainty about future environments and required adaptive change. Mining such data to understand adaptation within an adaptive management framework of conservation actions, integrating genomic advances into a quantitative genetics framework, empirically testing the effectiveness of genomic measures of evolutionary potential relevant to population persistence, and exploration of the relative contributions of epigenetic and genetic mechanisms to evolutionary potential are exciting fields in the development of conservation genomics.


Brassica napus, (ii) describe the relationships between the shoot ionome and seedling development, and (iii) identify genetic regions associated with variation of the shoot ionome. The plant material under study was a germplasm set consisting of 509 inbred lines that was genotyped by a 6K single nucleotide polymorphism (SNP) array and phenotyped by analyzing the concentrations of eleven mineral nutrients in the shoots of 30 days old seedlings. Among mineral concentrations, positive correlations were found, whereas mineral concentrations were mainly negatively correlated with seedling development traits from earlier studies. In a genome-wide association mapping approach, altogether 29 significantly associated loci were identified across seven traits after correcting for multiple testing. The associations included a locus with effects on the concentrations of Cu, Mn, and Zn on chromosome C3, and a genetic region with multiple associations for Na concentration on chromosome A9. This region was situated within an association hotspot close to SOS1, a key gene for Na tolerance in plants.Knowing the genetic basis of the plant ionome is essential for understanding the control of nutrient transport and accumulation. The aim of this research was to (i) study mineral nutrient concentrations in a large and diverse set of  Plant cells depend on the presence of nutritional elements, which fulfill a variety of functions, where elements act, amongst others, as regulators, cofactors, or structural components Baxter, . The comArabidopsis thaliana and analyzed the mutation frequency based on the elemental profiles. Their findings led to the conclusion that about 2–4% of the genome is involved in the regulation of the nutrient and trace element content in the species. Another extensive study of the ionome of A. thaliana under different iron and phosphorus conditions showed through logistic regression modeling that there is a relationship between the ionome and specific physiological responses, e.g., to Fe or P deficiency  examine mineral nutrient concentrations and their interactions in a large and diverse set of B. napus, (ii) study the relationships between the shoot ionome and seedling development traits, and (iii) identify genetic regions associated with variation of the shoot ionome.With regard to the aforementioned studies, association mapping (AM) based on linkage disequilibrium (LD) is a complementary approach for the detection of associations between quantitative traits and variation on the molecular level in crops. AM allows to discover the genetic bases for traits under certain scenarios, including when contributing alleles are at moderate frequencies within the population  markers as described by Bus et al. (B. napus ssp. napobrassica), 7 semi-winter OSR, 204 spring OSR, 4 spring fodder, 10 vegetable, and 6 unspecified inbred lines.We investigated a species-wide diversity set of s et al. . The divhttp://rsb.info.nih.gov/ij/)  and k  were calculated from LA08–LA16 by non-linear regression. Furthermore, data on the relative leaf chlorophyll content as measured by Minolta company-defined SPAD  values (SPD), fresh mass (FHM), dry mass (DYM), and H2O content (H2O) of the plants were collected after a growing period of 30 days after sowing. Plant material for ionome analysis was harvested from 30 days old seedlings, oven-dried, and lyophilized.For ionome and seedling development analysis, the plant material was grown in a greenhouse experiment that comprised six replicates which were performed in a row within a period of 6 months, where each replicate lasted 30 days  Table . Other s/) Table . Growth The entirety of all investigated mineral concentrations is herein referred to as the shoot ionome. The concentrations of altogether eleven plant nutritional elements, B, Ca, Cu, Fe, K, Mg, Mn, Na, P, S, and Zn Table , were anhttp://aafc-aac.usask.ca/ASSYST/). Only SNPs with a minor allele frequency larger than 5% were included in the association analysis. Of all of the SNPs from the array, altogether 3910 SNPs were used, and SNP data were available for 505 inbreds of the germplasm set.Plant genomic DNA for SNP genotyping was extracted from fresh or lyophilized leaf material using the BioSprint 96 DNA Plant Kit and the BioSprint 96 robotic workstation (Qiagen), following the manufacturer's protocol. Genotyping of the SNPs was performed at Agriculture and Agri-Food Canada using a custom Illumina Infinium 6K array (Adjusted entry means (AEM) were calculated for each genotype-mineral concentration combination using the following mixed model:ikmy was the observation of the ith genotype in the mth block of the kth replicate, μ the general mean, ig the effect of the ith genotype, kr the effect of the kth replicate, kmb the effect of the mth block in the kth replicate, and ikme the residual error. Outliers in residual plots were discarded. For the calculation of AEM, we considered g as fixed and all other effects as random. For estimating the genotypic variance (σ2g) and the error variance (σ2e), all effects were considered as random. The heritabilities h2 of each mineral concentration on an entry mean basis were calculated as follows:where b was the number of replicates. Variance components and AEM were determined by the restricted maximum likelihood (REML) method. The mixed model analyses were performed with ASREML release 2.0  was to be identified, μ the intercept term, v the number of selected variables, pb the regression coefficient of the pth mineral nutrient, pix the AEM of the pth mineral nutrient for the ith genotype, and ie the residual.where The mixed model according to the PK method  0, 0.025, …, 0.975, K matrices between all inbreds were calculated. Negative kinship values between inbreds were set to 0. The optimum T value (showing the minimum likelihood) was calculated according to Stich et al.  of less than 0.05 was required. The association analysis of the mineral concentrations with the polymorphisms was carried out using the R package EMMA . For each SNP, the BLAST hit with the highest score was selected. If the two highest scores were identical or very similar, both positions were selected. SNPs that could not be assigned to a B. napus chromosome were excluded from further searches. For each locus an interval of 700 kb upstream and downstream of the defined position was screened for annotated genes. Predicted genes' sequences were annotated using Blast2GO  to 0.86 (S) with a mean of 0.74 (Table r2 ranged from 0.01 (P) to 5.46% (Na), with a mean of 1.46%  of the seedling development traits that could be explained by a linear combination of a selected set of the mineral concentrations in the germplasm set ranged from 0.042 (PRK) to 0.570 (DYM)  associations of 27 unique SNPs with Ca (3 associations), Cu (4 associations), Mg (2 associations), Mn (2 associations), Na (8 associations), S (2 associations), and Zn (8 associations) Figure . The pheLinked SNPs from marker-trait associations that were in significant LD with each other were combined to represent genetic regions associated with the respective traits. For Ca, Mg, Na, and Zn, two  and four (Na) linked loci associated with the respective trait were in significant LD  .Based on the highest scores of the BLAST results, the chromosome information derived from the r2 was nevertheless low across the shoot ionome  as well as Mg and P (0.04 vs. 0.86 in year 1 and 0.39 in year 2), and Mn and Zn, although differences were only obvious for year 1 (0.61 vs. −0.08 in year 1 and 0.53 in year 2). These differences might be due to the measurement of elements in different tissues, as the ion balance is partly tissue-specific , the mean adj. R2 was 13%. We can therefore conclude that mineral concentrations are more suitable as predictors for seedling development traits than seedling development traits as predictors for agronomic and seed quality traits. This also implies that ionome data from field trials have the potential to predict agronomic data, but this will require further research.The identified relationships between the shoot ionome and seedling development raised the question whether one trait category might be predicted by the other. Multiple linear regression models were used to assess mineral concentrations as predictors for seedling development traits. The mean adj. r et al.  fitted lB. oleracea, which was confirmed also for B. napus by the results from our study  and P (0.43) and high for all other traits  to the association hotspot is known to be an Na+/H+ antiporter that controls the uptake and efflux of Na, a mechanism which is crucial for plants under Na-rich conditions  in the category biological process for Na was found for the term “sodium ion transport.” Furthermore, the second smallest P value (6.46 × 10−6) in the category molecular function for Na was found for “sodium ion transmembrane transporter activity,” and the GO slim term “transporter activity” was overrepresented for Na  for biological process in Ca. A relationship between Ca and phototropism is expected because Ca controls the auxin-induced elongation of cells, and it has been described before in maize seedlings (Gehring et al., Associated with the candidate genes within 700 kb upstream or downstream the significant SNPs, we identified a number of enriched GO terms that are functionally associated with the traits under study (Supplementary Table With the association of the SNP Bn-ctg7180014703649-p1976 with Cu, Mn, and Zn Table , 3A suggUsing SNPs from arrays in AM may cause ascertainment bias which brings forward an oversampling of mutations at intermediate frequencies, these again cause amounts of LD that are lower than those present in completely randomly selected SNP sets (Ingvarsson and Street, The significant associations found in this study explained between 3.02 and 9.73% of the phenotypic variation in single marker analyses Table . By way B. napus reference sequence. Jinquan Li supported the AM analysis. Anja Bus and Benjamin Stich wrote the manuscript. Benjamin Stich designed and supervised the study.Anja Bus analyzed the data. Niklas Körber contributed the data on seedling development. Isobel A. P. Parkin provided the 6K array data. Birgit Samans and Rod J. Snowdon carried out the candidate gene and GO term enrichment analysis on the pre-published 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.