Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn,  The ultimate goal of such programs is to achieve high-production efficiency through long-term genetic gain whilst successfully managing the rate of inbreeding using information on pedigree, genotypes, or haplotypes5. The application of genomic information in breeding design, i.e. genomic selection (GS), has been widely adopted for enhancing commercial traits in animal breeding6. In aquaculture for example, GS has been shown to predict breeding values (BV) more accurately for growth traits in Atlantic salmon7, common carp8, Nile tilapia9, channel catfish10, large yellow croaker11, yellowtail kingfish12, yellow drum13, Pacific oyster14, scallop16, whiteleg shrimp17 and banana shrimp18 compared to pedigree-based BV predictions and has recently been reviewed for applications in aquaculture by Zenger et al.19. GS uses the information obtained from genotypic markers to improve the accuracy of BVs. Estimated BVs inferred from molecular markers are termed genomic estimated breeding values (GEBVs), and can be used to accurately select the high-performing candidates for optimising breeding program21.Advanced animal breeding utilizes tools of reproductive biology, molecular genetics, statistics and computer programming in order to optimize the breeding design and enhance the desired commercial traits24. Different sequencing platforms are available for detecting and analysing SNPs. This includes (1) genotype-by-sequencing (GBS); (2) fixed high-density SNP arrays; and (3) low-density SNP panels. GBS does not necessarily provide genotypic data for all the detected SNPs in the population27. There are situations in quantitative genetics where the analysis requires reliable allelic information of the same loci across all the samples. For most applications, fixed arrays (> 40–50 K) are sufficient to capture genome-wide information. However, high-density SNP chips are generally expensive. This limits their use in routine agricultural applications. Hence, application of low-density SNP panels combined with imputation methods to generate higher density SNP genotypes, usually based on a reference panel25, can be a more cost-effective alternative. This approach has recently been extensively used in GS32.Among the many types of genomic markers, single nucleotide polymorphisms (SNPs) have primarily been used in GS due to four reasons, namely: (1) SNPs are relatively inexpensive to process as genetic markers; (2) SNPs are highly abundant and distributed across the genome; (3) they can capture a large proportion of the genetic variation through linkage disequilibrium (LD); and (4) their inheritance to the next generation is more stable than other markers, allowing multi-generation tracking34. In addition, errors in genotypic data could also be generated due to human mistake in the laboratory environment, e.g., contamination of DNA samples36. Some of the errors can be detected by analysing deviation from Hardy–Weinberg equilibrium (HWE)37, LD analysis within populations38, pedigree reconstruction40 and comparison with high-quality reference genotypes40. Once detected, erroneous genotypes could be filtered out or corrected. To correct the error, one solution could be re-genotyping of a sufficiently large number of individuals and compare it with the first set of genotyped samples, although this is a labour-intensive and expensive practice34. However, if the errors are known, imputation methods, based on, for example, application of maximum likelihood or Bayesian algorithms can be applied to estimate most probable genotypes41.Despite advancements in sequencing technologies, application of any of these three platforms could create errors in genotypic data. These errors occur mainly due to the structure of sequencing process and human–environmental factors. Genotypic errors might be inherent to the design of the study, e.g., failure of sequencing which can result in detection of null alleles or allelic dropout44, bias the estimation of frequency of haplotypes and genotypes45, degrade the accuracy of parentage assignment via false exclusion of parents from assignment47, return a false identification of individuals47, misrepresent the population structure47, underestimate the heterozygosity, departure from HWE and inbreeding coefficients47. To the best of our knowledge, the only relevant study in simulation breeding design using GS in aquaculture was conducted recently on the effect of genotypic error on the accuracy of genomic prediction48. Using population and genome structure from empirical breeding design of rainbow trout Oncorhynchus mykiss, they showed that implementing up to 10% error did not significantly impact the accuracy of genomic estimated breeding values (GEBV) across three heritabilities .Population genetic studies have shown that genotypic errors could reduce the power of gene mapping and association studiesPenaeus monodon.The objective of the current study was to evaluate the effect of genotypic error, family contribution, SNP density and heritability on the accuracy of genomic prediction and medium-term selection response. The range of these parameters investigated was chosen to mirror those observed in the black tiger prawn, 49 was used to simulate pedigree with its associated SNP genotypes and phenotypic values. Firstly, 400 historic generations with a constant population size of 1000 in each generation were simulated. In each historical generation, 500 males and 500 females were produced with random selection and random mating. From the last historic population, 50 males and 50 females were randomly selected to form a base population (G0). Then, these 50 males and 50 females of G0 were used as parents and mated randomly to generate 50 full-sib families in the first generation (G01). Within each generation, from G01 to G05, 50 sires and 50 dams across families with the highest estimated breeding values (EBVs), calculated within QMSim, were selected to generate 50 full-sib families in the next generation. Following this breeding design, two broad scenarios based on the size of families were considered:The QMSim softwareScenario 1 (S1): equal family contribution with 100 progeny per family where the probability of producing male and female progeny was 0.5. Fifty families with 100 progeny per family produced 5000 individuals per generation.Scenario 2 (S2): unequal family contribution was generated with family sizes of 5, 25, 50, 75, 100, 125, 150, 175 and 200 progeny with contribution probability of 5, 10, 12, 14, 18, 14, 12, 10 and 5%, respectively. This means that on average 5% of families were generated with 5 progeny, 10% with 25 progeny, and 12% with 50 progeny, etc. This was to keep the population size per generation as close as possible to number of individuals per generation in S1 (5000). In addition, the allocation of the number of progeny and their respective distribution was based on the study of maintaining the genetic diversity in P. monodon carried out by Foote, et al.50. They found that the highest contribution of a single family for P. monodon bred in captivity was 18%.Next, each scenario was divided in three datasets referring to the low, medium, and high heritability traits. In the first dataset, a trait was simulated with medium heritability (0.3), standardized mean of 0 and phenotypic variance of 1. Dominance and epistasis effects were considered absent. The phenotypic values were obtained by summing the random error, the polygenic effect, and the sum of the quantitative trait loci (QTL) effects generated by QMSim software. To allow both QTL and polygenic effects to contribute to variation of the trait, the combined effect of all QTLs were sampled from a normal distribution with mean (μ) of 0 and additive genetic variance  and ten independent replicates, has generated 60 independent datasets  is scored as genotype j , where the diagonal elements  52 and the predict function in ASReml-R53 were used to calculate genomic estimated breeding values (GEBVs) and EBVs, respectively. To calculate (G)EBVs, we considered the situation where traits cannot be measured on the selected animals. Consequently, the (G)EBVs of selected candidates were obtained from performance of their sibs. Then, the accuracy of (G)EBVs of candidates were calculated as the Pearson correlation of their (G)EBVs and TBVs. To do that, 30% of progeny per generation were randomly selected as the test set (selection candidates) and the remaining progeny in that generation as the training population (which includes sibs of selection candidates). Next, the phenotypic values of the test population were masked and GEBVs in the test set were obtained using the phenotypic values of their sibs and the GRM of all individuals within each generation.Following the simulation of populations and data preparation, true breeding values (TBV) were calculated by accumulating the QTL and polygenic effects for each individual. Then, the rrBLUP packagementclass2pt{minimn = 3); family type (n = 2); BV estimation method (n = 2); and within the BV = GEBV sets of simulations, genotypic errors (n = 4); and marker density (n = 4); multiple generations (n = 5). In addition, each of these scenarios was evaluated using ten replicates.There were six factors investigated in this simulation study, to assess their effect on accuracy as evaluated using Pearson correlations. These factors and their levels being heritability 56 between the off-diagonal elements of GRMs was also considered as the measurement of reliability. Since Lin’s correlation takes into the account both correlation and correspondence, it is a more reliable estimate for expressing the impact of genotypic errors on GRM. Consequently, the aggregate function was used to summarize the average results over trait heritabilities, replicates and family contributions. All the analysis was carried out using R statistical package54.The effect of genotypic error on the GRM was investigated using correlation of off-diagonal elements of the GRM without error with off-diagonal elements of matrices obtained from different SNP densities and genotypic error rates. The consideration of off-diagonal elements was due to the fact that they show the genetic relationship between each pairs of individuals. The GRM was constructed according to method of Yang et al.The detailed results of accuracy of genomic predictions, as measured by the correlation between TBVs and EBVs/GEBVs, for different generations, replicates, family type, SNP density and genotypic error rates are provided in Supplementary Table Accuracy of (genomic) estimated breeding values using pedigree-BLUP (BLUP) and genomic-BLUP (GBLUP) for equal versus unequal family contribution are provided in Fig. Accuracies of EBVs and GEBVs using BLUP and GBLUP, respectively, for equal versus unequal family contribution are given in Fig. Accuracies of EBV and GEBV for heritability of 0.05, 0.3 and 0.5 over five consecutive generation are illustrated in Fig. Variation of GEBV accuracies over different genotypic error rates for equal and unequal family contributions is provided in Fig. Comparison of accuracy of GEBVs for SNP densities of 0.5 K, 3 K, 10 K and 20 K over equal and unequal family contributions is presented in Fig. The variation in accuracy of GEBVs with genotypic error rates of 0, 1, 5 and 10% over the heritabilities of 0.05, 0.3 and 0.5 is depicted in Fig. Comparison of GEBV accuracies for SNP densities of 0.5, 3, 10 and 20 K over trait heritabilities of 0.05, 0.3 and 0.5 is depicted in Fig. Changes in the accuracy of GEBV for SNP densities of 0.5, 3, 10 and 20 K over genotypic error rates of 0, 1, 5 and 10% are illustrated in Fig. Changes in the accuracy of GEBV over five consecutive generations for SNP densities of 0.5, 3, 10 and 20 K are illustrated in Fig. A complete list of Pearson and Lin’s correlation coefficients between off-diagonal elements of the GRM without error and those with error rates of 1, 5 and 10% is provided in Supplementary Table 51 and population structure of P. monodon under captivity as described by Foote et al.50. We explored the effects of SNP density, heritability, and family type on accuracy of GEBVs. In addition, the effects of family type and heritability on accuracy of EBVs was also studied, as well as comparisons of accuracies between EBVs and GEBVs. Accuracy of predictions were investigated across five consecutive generations and ten independent replicates in a simulated breeding design. The previously published simulation study on the effect of genotypic error on accuracy of genomic predictions based on genome and population structure of rainbow trout48 showed that 10% error had no effect on the accuracy of genomic predictions. However, the current breeding design of black tiger prawns in Australia is different from rainbow trout breeding. Whilst breeding of rainbow trout is based on GS around the globe, in black tiger prawn, it is currently based on mass spawning in the communal rearing environment which would result in presence of unequal contribution of families59. The existence of unequal family contributions was recently demonstrated using an experiment on the captive breeding of P. monodon in Australia50. In addition, the genome structure of black tiger prawn is also different from rainbow trout, e.g. the number of chromosomes, making the re-evaluation of study performed by Dufflocq et al.48 necessary for black tiger prawn breeding.The current study evaluated the effect of genotypic error on accuracy of genomic prediction and estimation of GRM, and the study was designed to mirror the genome structure of the black tiger prawn reported by Wilson et al.The summary analysis over different scenarios in our study showed that the accuracy of GEBVs was on average higher than EBV accuracy when two-way descriptive analysis was performed for family types-generations Fig. , family 48. The simulation study on the rainbow trout showed that GBLUP accuracies were normally 8% higher than their corresponding BLUP-based accuracies across heritabilities of 0.1, 0.2 and 0.4 and SNP densities of 0.5 K, 3 K, 7 K and 42 K. Comparing accuracy of prediction using traditional and genomic-based BLUP in several simulations64 and empirical studies in aquaculture72 has also demonstrated the higher accuracy values for GEBVs over EBVs. The reason for obtaining higher GEBV compared to EBV accuracies using 0.5 K SNP density can be perhaps due to different experimental design and parameters in other studies as compared to this study. For example, Dufflocq et al.48 used full-sib families with a family size of 32 whilst in this study the family size of full-sib families was 100. This allowed more accurate prediction of EBVs in the test population based on the presence of a large number of full-sibs in the training set resulting in more variable EBV estimates compared to GEBVs using 0.5 K SNP density.Obtaining higher EBV compared to GEBV accuracy using any SNP density or genotypic error rate e.g. from 0.5 K SNP density across different trait heritabilities were in contrast to the results from the simulation study performed on a rainbow trout breeding program73.This study was unable to find any substantial differences between equal and unequal family contributions as presented in Figs. 62. However, this pattern was not repeated across different SNP densities as presented in Supplementary Table The accuracy of prediction was generally increased at higher trait heritabilities as presented in Fig. 63 and Dufflocq et al.48. The main difference between our study and the two others; in addition to size of full-sib families, was the mating ratio. Whilst in the current study a mating ratio of 1:1 was implemented, resulting in 50 full-sib families, Dufflocq, et al.48 used a 1:3 mating ratio, leading to the production of 120 half-sib families. This has presumably led to a higher chance of better-performing animals to be selected for the next generation. Consequently, this combination of better-performing animals and more families has probably resulted in lower inbreeding, higher additive genetic variation, and better accuracy of prediction across generations64. Another explanation would be the effect of selection method on accuracies; called the Bulmer effect, and/or the choice of selection method, e.g., selection based on EBVs versus GS75. The choice of selection can change the extent of LD or unintentionally create low LD, which in turn can change/reduce the accuracy of genomic prediction over generations76. Otherwise, the difference between our study and the others could be simply due to the extent of relationships between the training and test population. As such, the higher relationship between the two sets can result in higher accuracy of prediction and vice versa79.Our results showed that both EBV and GEBV accuracies have decreased over the period of five generations except for GEBV estimates for medium heritability trait (0.3) in which accuracy slightly increased as depicted in Fig. 48 and results of empirical studies on accuracy of prediction for skin and fillet colour80 and, disease resistance68 in Atlantic salmon. In addition, our results showed that accuracy of GEBVs did not increase significantly beyond 10 K SNP density. The outcome of this study was also in agreement with results of an empirical study on accuracy of disease resistance in rainbow trout71 and Atlantic salmon67 which showed that SNP densities of more than 10 K did not have a meaningful effect on increasing the accuracy of genomic predictions. However, the effect of higher marker densities, e.g., 50 or 100 K, on accuracy of genomic predictions was not evaluated against 10 or 20 K SNP densities. In addition, in some scenarios especially for medium (0.3) and high (0.5) trait heritabilities, even 3 K SNP density efficiently estimated the accuracies. Consequently, it can only be concluded that there was a marginal difference between accuracy of prediction using 3 K and 20 K SNP densities within this study especially when the genotypic errors was relatively low (< 5%).The outcome of this study in Supplementary Table 48 where no substantial difference between accuracy of genomic predictions in the presence of 0% and 10% genotypic errors across different marker densities was reported.The descriptive summary analysis results showed that when the genotypic error increased from 0 to 10%, accuracy of GEBVs decreased by approximately 6% and 7% across different heritabilities , there was little effect of reducing the accuracy, and the correlation between off-diagonal of GRMs either using Pearson or Lin’s correlations remained high, even using 0.5 K SNP density. As mentioned, random errors can be captured using LD or HWE analysis, pedigree reconstruction, comparison with high-quality reference genotypes, etc., as well as additional analysis such as quality control checking for Mendelian inheritanceSupplementary Information.


Pinus sylvestris L.) trees, was undertaken for growth and wood quality traits, using 6,344 single nucleotide polymorphism markers (SNPs) generated by genotyping-by-sequencing (GBS). Genomic marker-based relationship matrices offer more effective modeling of nonadditive genetic effects than pedigree-based models, thus increasing the knowledge on the relevance of dominance and epistatic variation in forest tree breeding. Genomic marker-based models were compared with pedigree-based models showing a considerable dominance and epistatic variation for growth traits. Nonadditive genetic variation of epistatic nature (additive × additive) was detected for growth traits, wood density (DEN), and modulus of elasticity (MOEd) representing between 2.27 and 34.5% of the total phenotypic variance. Including dominance variance in pedigree-based Best Linear Unbiased Prediction (PBLUP) and epistatic variance in genomic-based Best Linear Unbiased Prediction (GBLUP) resulted in decreased narrow-sense heritability and increased broad-sense heritability for growth traits, DEN and MOEd. Higher genetic gains were reached with early GS based on total genetic values, than with conventional pedigree selection for a selection intensity of 1%. This study indicates that nonadditive genetic variance may have a significant role in the variation of selection traits of Scots pine, thus clonal deployment could be an attractive alternative for the species. Additionally, confidence in the role of nonadditive genetic effects in this breeding program should be pursued in the future, using GS.Genomic selection study (GS) focusing on nonadditive genetic effects of dominance and the first order of epistatic effects, in a full-sib family population of 695 Scots pine ( Pinus sylvestris L. (Scots pine) breeding strategy uses full-sib mating with forward selection and progeny testing, where clonal testing is of great interest because it enables more efficient selection using clonal cuttings as propagation method  study, the diameter at breast height at age five showed a large amount of epistatic variance    Voss (white spruce)   is expected to accelerate the rate of genetic improvement of Norway spruce and Scots pine in Sweden, one of the largest forest producer of the world . In addition, the response to the selection of three GBLUP and two PBLUP models used was also evaluated, and genetic gains of early GS and conventional pedigree selection compared.In this study, a Scots pine full-sib progeny trial was used (named as F261-Grundjärn). The trial was established in 1971 for the Swedish tree improvement program at Skogforsk (the Forestry Research Institute of Sweden) and was described in Fries . In brieAn exhaustive description of the GBS library preparation, SNP markers filtering, and calling was already described in Calleja-Rodríguez et al. . In summA baseline imputation of missing genotypes was first performed through the LD K-nearest neighbor method  or additive and dominance (PBLUP-AD and GBLUP-AD) genetic effects were used. In addition, for GBLUP, a third model including additive, dominance, and epistatic genetic effects (GBLUP-ADE) was used.A) and the dominance numerator genetic relationship matrix (D) were estimated according to Lynch and Walsh , called realized relationship matrix was estimated as, Z is rescaled genotype matrix following M−P; where M is the genotype matrix with genotypes coded as 0, 1, and 2 according to the number of alternative alleles and with dimensions number of individuals (n) by number of loci (m); P is the matrix of locus scores 2pi, with pi being the ith allele frequency and DG) was estimated following Vitezica et al.  , a is the vector of additive genetic effects, and is assumed to follow a normal distribution with expectations of A and AG were described above; I denotes the identity matrix and X and AZ are the incidence matrices for β and a, respectively.where For the PBLUP-AD and GBLUP-AD models:d is the vector of random dominance effects which follows a normal distribution with D and DG were described above; dZ is the incidence matrix for d.where For the model including all effects (GBLUP-ADE):eaa, ead, and edd are the vectors of random additive × additive, additive × dominance, and dominance × dominance epistatic effects; which are assumed to follow normal distributions with expectations e1Z, e2Z, and e3Z are the incidence matrices for eaa, ead, and edd, respectively.where yfull), i.e., t-test. Additionally, standard error of the predictions (SEPs) was compared to evaluate the precision of the predicted estimated total genetic values (EGVs) for models containing nonadditive effects  or estimated breeding values (EBVs) for models with only additive effects .The five models were compared based on the Akaike information criterion (AIC) for each trait. Based on the full dataset and depending on the model used, goodness-of-fit was evaluated by calculating the correlation between estimated total genetic or additive genetic values  was defined as the Pearson product-moment correlation between the cross-validated estimated total genetic (y), i.e., r1 was estimated separately for The predictive ability (r2) was estimated according to Legarra et al.  and traditional phenotypic selection (RPS) were calculated as percentage of the average population following Resende et al. ,where Since one of the greatest advantages of GS in conifer breeding is the possibility to shorten the breeding cycle length, we compared the RGS per year (RGS/year) of GBLUP-A, GBLUP-AD, and GBLUP-ADE models, with the response of conventional phenotypic selection per year (RPS/year) of PBLUP-A and PBLUP-AD models, by assuming a reduction of 50% breeding cycle (11 years) in the GS scenario compared with the full cycle length (23 years) in the PS scenario.G) were estimated for two types of selection and deployment strategies, as a percentage of the overall mean .where Generally, the lowest AICs were observed for PBLUP-A and PBLUP-AD models for almost all traits, but no considerable differences between the AIC values were obtained .yfull).The highest goodness-of-fit was between total genetic values from the GBLUP-ADE model and adjusted phenotypes for all traits except Ht1, which had its highest goodness-of-fit for the total genetic values estimated from PBLUP-AD . All modThe comparison between SEPs for all models are presented in Estimates of P models . UsuallyBased on nonadditive models, The PBLUP-AD model estimated a considerable amount of dominance variance particularly for Ht1 and DBH2, accounting, respectively, for 28.16 and 14.69% of the total variance . Neverthr1 than growth traits, and they were similar among all models, whatsoever genetic effects were considered in each model, apart from Ht2 and DBH1 for which r1 was slightly higher for both PBLUP models , being even higher between both PBLUP models (0.998–1.000) and between the three GBLUP models  was compared with the response to phenotypic selection per year (RPS/year), by assuming that the Scots pine breeding cycle could be shortened by 50% (from 23 to 11 years) by reducing field test periods and by stimulating early female flowering. To estimate RGS and RPS per year, different proportions of individuals were selected . The resExpected genetic gains were estimated for traits that showed substantial nonadditive genetic effects in the GBLUP model, i.e., growth traits, DEN, and MOEd. For the first deployment strategy based on conventional testing, results from the PBLUP model with additive effects were used, whereas for the second strategy, i.e., early GS, results from GBLUP-ADE model were used to account for the total genetic effects. A common selection intensity of 1% was used for both strategies. Expected genetic gains  effects more precisely without performing specific mating designs to generate full-sib families for clonal selection . Similar results were described in eucalypt hybrids  Voss × Picea engelmannii Parry ex Engelm.,  in Norway spruce  in radiata pine  in Eucalyptus nitens  in Pseudotsuga menziesii (Mirb.) Franco  resulted in the overestimation of additive genetic and dominance variance and larger residual error variances for most of the growth traits, wood density, and dynamic modulus of elasticity, compared to genomic-based models (GBLUP). For the GBLUP model with epistatic effects, only type additive × additive variance was noticed for growth traits, wood density, and dynamic modulus of elasticity. This partition of variance components decreased estimates of narrow-sense heritability, but higher broad-sense heritabilities were reached compared to the models with dominance effects. Dominance variation was not observed for wood quality traits. These results indicate that nonadditive genetic effects may have an important role in the variation of objective traits in the Swedish Scots pine breeding program and that the GS model would be more able to detect nonadditive genetic variance. GBLUP model with the epistatic effects exhibited the higher predictive ability of additive values and outperformed other models in terms of correlations between phenotypes and total estimated genetic values, which was especially noticeable for height at age 30, DBH at age 36, wood density, and modulus of elasticity. Future studies with more extensive phenotypic data and further developed genotyping platform would extend our knowledge on the role of nonadditive genetic effects in the breeding of Scots pine.The data presented in the study are deposited in the NCBI SRA Bioproject repository, accession number PRJNA732895, and in the article AC-R performed the data analysis, drafted the manuscript, participated in field sampling, performed DNA extractions, and obtained funding for open-access publishing. ZC participated in field sampling, performed the imputation of marker data, and assisted with script model development and editing of the final manuscript. MS assisted in drafting the manuscript and discussed the analysis. JP performed the SNP filtering and calling. HW designed the study, discussed the analyses, assisted in editing the final manuscript, and obtained funding. All authors contributed to the article and approved the submitted version.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.


Dense single nucleotide polymorphism (SNP) panels are widely used for genome-wide association studies (GWAS). In these panels, SNPs within a genomic segment tend to be highly correlated. Thus, association studies based on testing the significance of single SNPs are not very effective, and genomic-window based tests have been proposed to address this problem. However, when the SNP density on the genotype panel is not homogeneous, genomic-window based tests can lead to the detection of spurious associations by declaring effects of genomic windows that explain a large proportion of genetic variance as significant. We propose two methods to solve this problem. Genome-wide association studies (GWAS) have been widely used to locate quantitative trait loci (QTL) for traits of interests –3. TypicMany approaches have been developed to make inferences of associations based on genomic windows. Chen et al.  generalimentclasspt{minimaThe objectives of this study are to show that genomic-window based tests can lead to the detection of spurious associations when the SNP density on the genotype panel is not homogeneous and to propose two methods to solve this problem. The article is composed of three parts. First, we will demonstrate the problem of spurious associations in analyzing a real data set. Second, two methods to overcome this problem are described and compared with the original method. Finally, the validity of our methods is examined through simulated data sets.i from n genotyped individuals can be written as:i, j for individual i , j, and i. We assume that For simplicity of the description, all analyses in this study were based on a single-trait regression model with a general mean as the only fixed effect. The model for individual cument}π , a Bayesw is calculated as:w. Then, the genetic variance explained by window w is calculated as:w for individual i and n is the total number of individuals. Similarly, the total genetic variance, i. Finally, the proportion of the genetic variance explained by genomic window w is:T. A constant value of N is the total number of genomic windows [Inferences of associations based on genomic windows were made using the method described by Fernando et al. . The posndows in , and oth windows .Oryza sativa data set [Oryza sativa individual records of genotypes and the trait of flowering time in Arkansas was used to demonstrate the problem of spurious associations. After removing the records with missing data for the trait and genotypes with a minor allele frequency less than 0.05, 374 individuals and 33,701 SNPs were included in the analysis. The data are publicly available at the Rice Diversity Panel (http://www.ricediversity.org). Inferences of association based on genomic windows using Bayesian regression models were conducted using non-overlapping windows of size 1 Mb. The genome of the real data was divided into 378 non-overlapping genomic windows of size of 1 Mb, and the value of T was set to N = 378. The lower, median and upper quartiles for the number of SNPs per window (An data set  with 413T to T for a window size of 1 Mb (1 MbTw) is proposed as the second method. Here, instead of a constant T, a set of window-specific w and p is the total number of SNPs.The results of the analysis with real data showed false positives on dense genomic windows, which will be demonstrated in the "The Rice Diversity Panel data were reanalyzed using the two methods proposed above. In order to examine the properties of the solutions, the three methods, 1 MbT, 100 T, and 1 MbTw, were also compared through a simulation study. Phenotypes were simulated based on the genotypic data from the Rice Diversity Panel. Since the number of QTL (The GWAS performance of the three methods was compared using the area under receiver operating characteristic (AUC), which was calculated through the R package ROCR . In ordeWe also conducted the GWAS based on EMMAX using a window size of 1 Mb  denoted All analyses based on Bayesian regression methods were performed using the JWAS package , which iIn the GWAS result based on 1 MbT, the correlations of s BayesB , 20 or Bs BayesB , would rT from a constant to a window-specific value (1 MbTw), the correlation between T does not change the proportion of genetic variance explained by each window, but it changes the WPPA through different values for T.After changing the value of ic value  MbTw, thT value to calculate WPPA, while the correlation significantly decreased when using a window-specific T value . The correlation between the negative logarithm of p-values and the number of SNPs in each window was low (− 0.11) for 1 MbFre in the real data analysis, and none of the dense windows was significant (Fig. The pAUC5 values of 1 MbT, 1 MbTw and 100 T are shown in Table 055 SNPs . In the T value, so that false positives caused by window density may be avoided.When SNP densities on the genotype panel are heterogeneous, genomic-window based tests can lead to the detection of spurious associations by declaring effects of genomic windows that explain a large proportion of genetic variance as significant. Two straightforward solutions are offered in this paper. One is to make each window contain an equal numbers of SNPs, and the other one is to use a window-specific


Crassostrea virginica) is an important aquaculture species along the Atlantic and Gulf Coasts of the United States, but increases in heavy rainfall events expose oysters to acute low salinity conditions, which negatively impact production. Low salinity survival is known to be a moderately heritable trait, but the genetic architecture underlying this trait is still poorly understood. In this study, we used ddRAD sequencing to generate genome-wide single-nucleotide polymorphism (SNP) data for four F2 families to investigate the genomic regions associated with survival in extreme low salinity (<3). SNP data were also used to assess the feasibility of genomic selection (GS) for improving this trait. Quantitative trait locus (QTL) mapping and combined linkage disequilibrium analysis revealed significant QTL on eastern oyster chromosomes 1 and 7 underlying both survival and day to death in a 36-day experimental challenge. Significant QTL were located in genes related to DNA/RNA function and repair, ion binding and membrane transport, and general response to stress. GS was investigated using Bayesian linear regression models and prediction accuracies ranged from 0.48 to 0.57. Genomic prediction accuracies were largest using the BayesB prior and prediction accuracies did not substantially decrease when SNPs located within the QTL region on Chr1 were removed, suggesting that this trait is controlled by many genes of small effect. Our results suggest that GS will likely be a viable option for improvement of survival in extreme low salinity.As the global demand for seafood increases, research into the genetic basis of traits that can increase aquaculture production is critical. The eastern oyster ( Globally, aquaculture has out-produced capture fisheries for a decade (Crassostrea virginica) production from aquaculture  is a distinct trait from survival in low salinity (∼6–15) . Tissue was collected from all individuals, both dead and alive, and genome-wide single-nucleotide polymorphisms (SNPs) generated with ddRADseq  analyses in four F1 hybrid families were generated by the Aquaculture Genetics and Breeding Technology Center (ABC) at the Virginia Institute of Marine Science from crosses between individuals from the low salinity and high salinity family breeding lines  were randomly divided into equal-sized replicate plastic baskets depending on total number of oysters for each family. Replicate baskets were secured to the bottom of custom-made Taylor floats and submerged in 6-ft diameter tanks (∼1800 L) located indoors at the Horn Point Laboratory in Cambridge, Maryland, USA. Oysters were exposed to acute low salinity (<3) following a very similar experimental design to ®  at 1.5% total dry tissue biomass and with 3 L of live, cultured phytoplankton from the Horn Point Laboratory Oyster Hatchery. Individual mortality was assessed daily by checking for gaping individuals . A total of 132, 114, 111, and 106 individuals were analyzed for families 11, 43, 22, and 65, respectively. DNA was extracted from a total of 471 tissue samples, 463 samples from the challenge individuals plus the dams and sires of the four F2 families, using the E.Z.N.A Tissue Extraction Kit  following the protocol for preserved animal tissue. Following extraction, DNA concentration was quantified for each sample using a Qubit Fluorometer . Genome-wide SNPs were generated using double digest restriction association DNA (ddRAD) sequencing techniques outlined by 2 family, 5% of the individuals were duplicated to calculate genotype error rate at each locus. GeneWiz demultiplexed libraries based on Illumina indexes, and libraries were further demultiplexed into individual barcoded libraries and renamed using process_radtags from the Ddocent pipeline  were chosen for downstream analysis because they had the largest sample size per family and had the largest range in mortality across the 36-day challenge period  had substantial missing genotype data, so the initial individual missingness filter threshold was relaxed for this family only . All subsequent filtering steps were identical between family 22 and the other families . The combined LD analysis  was performed on the dataset with initial missingness set at <50% (see below). Genotype error rate (%) was calculated for 21 DNA samples with duplicate RAD library preps (same DNA different barcode) as the cumulative number of mismatches between duplicate genotypes at each SNP divided by the total number of genotypes tested, not including SNPs that had missing data  for either duplicate.Reads were trimmed and aligned to the eastern oyster reference genome C_virginica-3.0  and because an excessive number of markers are not needed given the architecture of the F2 families . Markers were then ordered sequentially according to their location in the genome and phase information was generated using the “map” function (https://github.com/lexymccarty/OneMap-to-Rqtl-4waycross).Linkage maps were created for each of the four FP < 0.001] were also removed. Conditional genotype probabilities were calculated  for each family and a two-part single-QTL model (model=“2part”) was used for phenotype day to death for families 11, 22, and 43 since the phenotype spikes at day 50, representing individuals that survived the low salinity challenge . If the individual has the nonzero value , the value is assumed to be normally distributed with mean day to death (µg) and standard deviation (σ)  . TherefoLD analysis was performed on the filtered SNPs from a total of 372 individuals across the four families in TASSEL  in TASSEL with the four generated files: the phenotype file filtered for the trait of interest , the combined filtered genotype table, the first 10 components of the PCA , and the kinship matrix (K)  to account for any LD between SNPs  of the individual, u is the average population phenotype, iZ is the marker-derived matrix of genetic relatedness between individuals (GRM), βi is the vector of SNP effects, and ie is a vector of residual error. Both trait models were fit using a Bayesian Reproducing Kernel Hilbert Spaces Regression (RKHS), but survival was fit with a logit link function because it is a binary trait. Models were assessed by creating five random 20%/80% testing/training validation sets, which was repeated 10 times. Narrow-sense heritability (h2) was estimated as the additive genetic variance from the GRM over the total phenotypic variance , as follows:a is the additive genetic variance from the GRM, and σp is the total phenotypic variance, which is the sum of the additive (σ2a) and residual (σ2e) variance. For survival, the residual variance is fixed at 1, so the heritability becomes:Genomic estimated breeding values (GEBVs) for both traits were estimated in BGLR following the model below:Five independent models were run to estimate heritability for each trait, and the average of the five estimates is reported. Number of iterations, burn in, and thinning parameters were determined by assessing the convergence and autocorrelation for all models using the “coda” package in R  occurred from days 9 to 21 of the challenge, with peak mortality occurring on day 11 with 53 total dead oysters  induced mortality in all four F oysters . Peak mo2 families. The total number of SNPs was significantly reduced after filtering, and the majority of SNPs were removed when filtering for SNPs present in 90% of individuals, a minimum quality score of 30, minor allele count of 3, MAF of 0.05, and a minimum average depth of 20 reads. A total of 28,638 SNPs across 399 individuals remained after applying filters. Mean read depth per site, after accounting for the number of individuals in each group, was 73 and average missingness for each individual was 3.4%. Duplicated samples had an average genotype error rate of 1.97% across all individuals  and within-family error rates ranged from 1.07% (family 22) to 2.74% (family 11).All libraries yielded high-quality read data, with >93%, >70%, and >94% of raw reads retained after demultiplexing library 1, 2, and 3, respectively, which resulted in discovery of 4,092,824 SNPs for 489 individuals from the four FFinal linkage maps were created from 123, 100, 91, and 95 individuals using a total of 380, 288, 370, and 400 genotyped markers (after thinning to 50–100 SNPs per chromosome), for family 11, 43, 22, and 65, respectively. A significant QTL on chromosome 1 was identified by the 2-part model  for family 11 and 43 . All sigFor family 43, the QTL region on chromosome 1 was significant for day to death conditional on survival (red line), the probability of survival (black line), and mean day to death  were incorporated to account for population structure. When looking at the scree plot of variance explained for each principal component, there was a severe drop after PCA component 3 . Four di 6 and 7 . The scr−6 after correcting for the number of effective tests , The combined LD analysis on a total of 28,502 SNPs identified regions on chromosomes 1 and 7 significantly associated with both survival and day to death. There were a total of 87 and 46 SNPs for survival and day to death, respectively, that were above the significance threshold of 1.95 × 10R2 value of 0.128  for survival and day to death, respectively , significant SNPs were located in a total of 16 characterized genes, seven of which were shared between the two traits . For theectively . When grectively .Realized prediction accuracies including all SNPs ranged from 0.489 to 0.547 and 0.507 to 0.57 for day to death and survival, respectively . RealizeRealized prediction accuracies decreased when the number of SNP markers used was reduced (thinned) to below 250. For example, realized accuracy dropped to 0.43 and 0.39 for RKHS and BayesB, respectively, when models were run with 100 markers .Narrow-sense heritability estimates were moderate to high for both traits. The heritability estimate for day to death was 0.406 : 0.231–0.595), which is slightly lower than the estimate for survival, 0.539 (CI: 0.326–0.750). The genetic correlation between the two low salinity challenge traits, survival and day to death, was large and significant, 0.867 ± 0.027.2 eastern oyster families to examine the genetic basis of extreme low salinity survival. QTL mapping and combined LD analysis using genome-wide SNPs revealed significant QTL on chromosomes 1 and 7 for both traits, survival and day to death. Genes within, or proximal to, identified QTL had functions related to DNA/RNA function and repair, ion binding and membrane transport, and in the response to stress. Genomic prediction accuracies (0.48–0.57) suggest that GS is a viable option for improving survival in acute low salinity for the eastern oyster, at least based on this dataset. However, future studies with a more appropriate experimental design are necessary. Furthermore, a larger genomic prediction accuracy of the BayesB regression model, along with the lack of substantial decrease in prediction accuracy when removing SNPs within the significant QTL region on chromosome 1, suggest that survival in extreme low salinity may be controlled by many genes of small and potentially unequal effect, as opposed to being controlled by relatively few major-effect QTL.An experimental challenge at extreme low salinity  was conducted with four FQTL mapping and combined LD analysis revealed similar QTL on chromosome 1 related to both survival and day to death. The four significant QTL  were loc2 families tested. Our sample size was relatively small (<400) and further analyses should be conducted on a larger sample size with individuals from many populations. A sample size larger than 1000 is recommended for higher resolution when detecting QTL  identified for multiple traits in salmonids have been proposed for incorporation into MAS programs  suggest that similar QTL were identified when analyzing either trait independently. Similarly, the same major QTL region on chromosome 1 was also detected in the combined LD analysis for both traits, and 85 significant SNPs were shared within the same 6.8 million base pair region . A high genetic correlation was detected between the two traits (0.867 ± 0.027), and large genetic correlations (0.95) have previously been detected for disease-related survival traits  in both salmon and trout . Combined LD analysis was able to detect not only the significant region on chromosome 1, but also an additional significant region on chromosome 7. Suggestive peaks for the combined LD analysis were present just below the significance threshold on chromosomes 5, 7, 8, and 9, and may contribute to the overall variation in this trait . In contFor both survival and day to death, the four major QTL and 133 significant SNPs were located within, or proximal to, a total of 16 annotated genes. These genes have functions belonging to three major categories: DNA/RNA function and repair, ion binding and membrane transport, and the response to stress. Our results build upon previous transcriptomic studies of oysters and highlight potential genes and physiological processes underlying survival in extreme low salinity (<3).Aplysia californica) rho gene reduced β-adrenoceptor-induced apoptosis  have functions related to RNA binding and gene transcription. Previous work examining the transcriptomic response of eastern oysters to a salinity of 8 and Olympia oysters to a salinity of 5, revealed the strongest enrichment for genes related to DNA replication and transcription . Rho pro+, Ca2+, and Mg2+) and free amino acids within their cellular fluid to maintain osmotic balance and conform to the salinity of their surrounding environment (The majority of genes (7/16) proximal to or underlying QTL were related to membrane transport and ion binding. Oysters are osmoconformers that regulate the concentration of inorganic ions  in the Pacific oyster . Predicte.g., greater than 500 individuals from at least 23 full or half-sibling families  used and the relatedness between training and testing sets (only 4 full-sibling families). The prediction accuracies estimated for low salinity survival in this study are most similar to those reported for growth-related traits in Yesso scallops  have also reported rather subtle decreases in prediction accuracy (∼0.1) when sampling down to hundreds of markers  in marker number (100 markers) was required to observe a noticeable reduction in prediction accuracy . This result is likely a consequence of the F markers . Overalli.e., GBLUP, BayesA, and BayesC) to ensure we have the model that best reflects the distribution of marker effects on our trait. While a Bayesian model  may fit our trait best, the animal models (GBLUP and RHKS) are easier to implement with faster run times, and differences between GBLUP and Bayesian approaches have proven to be unsubstantial  between individuals approximated from genetic effects (marker genotypes), as opposed to a traditional GBLUP where the genetic signal is equal to the marker genotypes . In RKHSh2 = 0.539) and day to death (h2 = 0.406) were very similar to previously reported values using a pedigree-derived relationship matrix between half-sibling families  under project name (PRJNA756884). Phenotypic information for all individuals, raw and curated markers (SNPs) used in the combined LD analyses and GS models, and genotype files used in Onemap and R/qtl are available in Figshare (https://doi.org/10.6084/m9.figshare.c.5577813).All raw sequence data are deposited in NCBIs SRA (


To identify genetic associations of quantitative metabolic syndrome (MetS) traits and characterize heterogeneity across ethnic groups.Data was collected from GENetics of Noninsulin dependent Diabetes Mellitus (GENNID), a multiethnic resource of Type 2 diabetic families and included 1520 subjects in 259 African-American, European-American, Japanese-Americans, and Mexican-American families. We focused on eight MetS traits: weight, waist circumference, systolic and diastolic blood pressure, high-density lipoprotein, triglycerides, fasting glucose, and insulin. Using genotyped and imputed data from Illumina’s Multiethnic array, we conducted genome-wide association analyses with linear mixed models for all ethnicities, except for the smaller Japanese-American group, where we used additive genetic models with gene-dropping.GLI2-TFCP2L1 associations with weight, and (b) one chromosome 11 variant near CADM1-LINC00900 with pleiotropic blood pressure effects.Findings included ethnic-specific genetic associations and heterogeneity across ethnicities. Most significant associations were outside our candidate linkage regions and were coincident within a gene or intergenic region, with two exceptions in European-American families: (a) within previously identified linkage region on chromosome 2, two significant This multiethnic family study found genetic heterogeneity and coincident associations (with one case of pleiotropy), highlighting the importance of including diverse populations in genetic research and illustrating the complex genetic architecture underlying MetS.The online version contains supplementary material available at 10.1186/s13098-021-00670-3. Metabolic syndrome (MetS) is a common, complex condition characterized by hyperlipidemia, hypertension, hyperglycemia, and excess abdominal fat –3. The NMIP1, MC4R, and PRKD1, yet when these same traits are combined with hypertension, they are associated with FTO and TMEM18 . Moreover, the I2 metric [I2 was calculated as the percentage of variance that is due to heterogeneity of effect size (β) estimates across ethnic groups. I2 values of 0 indicate no heterogeneity of effect sizes across ethnic groups; values over 75% and up to 100% indicate considerable heterogeneity [I2 values were not calculated for QTNs present in only one ethnic group . The R program [We performed genome-wide association testing across all imputed and genotyped QTNs. Quantitative traits with non-normal distributions were transformed in order to satisfy normality assumptions. The skewed distributions of HDL, TG, waist, and insulin measures were log-transformed, whereas a rank-based inverse normal transformation resulted in approximately normal distributions for weight, DBP, SBP, and fasting glucose. For AAs, EAs, and MAs, linear mixed models were used in Genome-wide Complex Trait Analysis (GCTA) software  with the2 metric  was used2 metric , I2 was ogeneity . I2 valu program  was also program .2 > 0.80) with QTNs significantly associated with MetS traits. These QTNs in LD were considered to be functional if they were exonic or had a RegulomeDB [Finally, evidence of biological function was characterized by annotating significant QTNs and any QTNs in linkage disequilibrium (LD). Specifically, we used ANNOtate VARiation (ANNOVAR) software  to annotgulomeDB  rank  using circular Manhattan plots in Fig. –8) results for each ethnic group including two variants with suggestive evidence (5 × 10–8 < P ≤ 10–6) of an association with MetS traits for EA: (a) rs1260326, a nonsynonymous GCKR variant with possible functional importance and (b) rs186742063, a possible pleiotropic variant with evidence of association with SBP and DBP traits. Specifically, there was a suggestive association of log(TG) and a non-synonymous QTN  in the GCKR gene among the EAs. Additionally, in the EA group, on chromosome 11 at 115,495,297 bp (hg19/GRCh37) between CADM1 and LINC00900, there was a pleiotropic QTN, rs186742063, with a significant association with DBP (P = 4.42 × 10–8) and a suggestive association with SBP (P = 9.92 × 10–8), respectively.We evaluated genetic association results for traits with at least one significant QTN P ≤ 5 × 100–8 using–9). Specifically, on chromosome 4, a rare, intronic PCDH7 variant (rs568152609 with MAF = 0.7% in the AFR population of gnomAD) was strongly associated with log(TG) in AA. Additionally, in MA on chromosome 15, an intergenic variant (rs4522365) between FAM189A1 and BC043570 was also significantly associated with log(TG) (P = 2.0 × 10–9). All suggestive results (5 × 10–8 < P ≤ 10–6) are included in Additional file Moreover, two variants were still significant after using a more stringent Bonferroni correction for analyzing 8 traits  and within the KIF6 gene on chromosome 6. However, in MA families, log(TG) and fasting glucose levels were significantly associated with chromosomes 15q13.1 and 4q31, respectively. These regions were not associated with MetS quantitative traits in EA or JA groups (P > 10–6).Although there were significant genetic associations with both fasting glucose and TG in both AA and MA Fig. , the locQRFPR on chromosome 4. Moreover, in JA families, log-transformed waist circumference was significantly associated with QTNs on chromosomes 11 (STIM1) and 15 , whereas in EA there were only suggestive associations with log(waist) .In addition, although in different physical locations, both EA and JA had significant genetic associations with weight Fig. . For EA I2 ≥ 75% in Table I2 = 0). On the other hand, other variants besides the chromosome 4 QRFPR intronic variants associated with weight in JA families in Table I2 ≥ 75%) across ethnic groups or were unique to a single ethnic group  [Thus, there was no overlap of significant or suggestive associations among ethnic groups. Moreover, there was evidence of heterogeneity between genetic effects across ethnic groups (culated) .GLI2 and TFC2L1) within 7.3 Mbp of the candidate linkage region (2q12.1–13)  between the genes CNTN6 and CNTN4 at 3p26.3–p26.2 with log(HDL); both QTNs had genetic effects that were heterogeneous and differed across ethnic groups . Suggestive evidence for association with log(TG) and a non-coding RNA intron within a pseudo-gene, AK126539, (nominated by QTN at chr16:11562798) was identified within our linkage candidate region of 16p13.2–16p12.1 and was unique to AA families. Additional suggestive associations in AA with log(TG), specifically, three QTNs unique to AA and one QTN, rs78637841 in WWOX, having a high level of heterogeneity with I2 = 85%, and weight  were also found on 16q13.13 and 16q23.1, respectively . The genetic effect of this QTN is not unique to JA and was comparable across ethnic groups (I2 = 0).Some significant and suggestive associations overlapped or were near previously nominated candidate linkage regions found on chromosomes 1, 2, 3, 5, and 16 , 62–65  significantly associated with waist circumference also had potential damaging effects as indicated by both a CADD score of 10.86 and EIGEN score of 0.29. On the other hand, in MAs, only one intergenic variant (rs76209611) had weak evidence of being functional with an EIGEN score near zero at 0.08.Additionally, in Table TMEM132D intronic variant (rs14606502) was in LD with QTNs (rs142863227 and rs116163662) that had annotations of regulatory effects based on: transcription factor (TF) binding, having a DNase peak (indicating DNase I hypersensitivity sites), and/or being in any motif that could be functional. Notably, in the EAs, rs1260326 was in LD with another GCKR intronic variant, rs780094, that had evidence of regulatory effects including: TF binding, matched TF motif, and having a DNase peak. The five intronic QRFPR variants significantly associated with weight among the JAs were all in LD with rs55975435, an exonic but synonymous variant.Furthermore, we determined if the significant QTNs located in intronic and intergenic regions were in LD with nearby, functional QTNs. Table This study identified several suggestive and significant associations within previously defined candidate linkage regions. Multiple significant associations were also identified outside candidate regions, nominating other putative genes associated with MetS traits. We showed substantial heterogeneity as evidenced by trait-to-genotype associations that were unique to each ethnic group, a lack of sharing significant genetic associations between ethnic groups, and differences in genetic effects across ethnic groups. Interestingly, only one of these QTN associations (rs186742063) appeared to have pleiotropic effects only in the EA families. A large percentage of our findings were in intronic and intergenic regions, which are consistent with results of the ENCODE project .GLI2-TFCP2L1 variants (rs139940998 and rs144756634) that were associated with weight but did not lie within the previously identified candidate linkage region. In addition, the suggestively associated, non-synonymous QTN (rs1260326) within the GCKR gene in EA was found to be significant in both our trans-ethnic meta-analysis [GCKR gene is located within a previous microsatellite linkage region nominated for harboring QTNs influencing the variation of waist circumference among GENNID AAs. Two suggestive associations on chromosome 3q26–27 region near the CNTN4 gene were found to be unique to the MA sample. The CNTN4 gene has been implicated with MetS traits [ADIPOQ gene was nominated as a candidate gene in our linkage region on 3p26 [ADIPOQ region with any of the univariate MetS traits, despite previous reports of ADIPOQ being associated with multiple underlying MetS conditions [STIM1 variants (rs146792726 and rs147605117) on chromosome 11 that were significantly associated with waist circumference (with the latter variant having evidence of function based on CADD and EIGEN scores). However, both variants were in linkage equilibrium (uncorrelated) with another STIM1 variant rs12290747 (r2 = 0.004) that was found to be significantly associated with urolithiasis in a recent, large-scale Japanese population GWAS [There were several associations that were not within candidate linkage regions/regions. The only significant association findings on chromosome 1 was within 1q42.2, outside our candidate linkage region of 1q12–1q21.1 . Among tanalysis , 68 and S traits , 63 and S traits . The ADITN rs12606 within nditions  and Nortnditions . In the ion GWAS .GLI2 and TFCP2L1, which is expressed in the kidneys [QRFPR on chromosome 4, and there were associations between waist circumference and QTNs on chromosomes 11 (STIM1) and 15 (between WDR72 and UNC13C). These two regions were not nominated in the MA and AA groups. Similarly, in MA families, a significant genetic association was demonstrated between log(TG) and 15q13.1, but in AA families, log(TG) was significantly associated with multiple QTNs in 1q42.2, PCDH7 on chromosome 4, the intergenic region between DG599799 and BC04811 on chromosome 7, and in TMEM132D with possible regulatory effects  are located in between  kidneys  and may  kidneys . Moreovefamilies . This phFurthermore, the use of family-based analyses enabled us to use a sample size smaller than what is needed for a traditional case–control GWAS to detect rare QTN associations . AlthougGLI2-TFCP2L1 associations with weight were found; also, there was one chromosome 11 variant (rs186742063) with pleiotropic blood pressure effects found in the EAs. The results of this project provide new insights into the complexity and genetic architecture of MetS and highlight the utility of family-based studies and the importance of including diverse populations in genomic research.Our study associations of MetS traits across a diverse group of multiethnic Americans. We built on our previous linkage analysis using highly informative microsatellites and utilized the power of genome-wide QTN association testing in families to refine and extend our previous work examining evidence for heterogeneity and pleiotropy. In summary, heterogeneity across ethnic groups was evident in not only the genetic location of the QTN, but with different associated traits and genetic effects. There was some evidence of intergenic and intronic variants having functional properties based on annotation scoring. Most significant associations were outside our candidate linkage regions and were coincident. However, in EA families and within the chromosome 2 candidate region, two significant Additional file 1.Table S1: Suggestive MetS association results (5 × 10−8 < p < 10−6) by ethnic group. Summary Table of suggestive results.Additional file 2.Table S2: Association within or nearby linkage regiona (P< 10−6 and LOD ≤ 1.9). Summary Table of association results within or nearby candidate linkage regions.


Cultivars with efficient root systems play a major role in enhancing resource use efficiency, particularly water absorption, and thus in drought tolerance. In this study, a diverse wheat association panel of 136 wheat accessions including mini core subset was genotyped using Axiom 35k Breeders’ Array to identify genomic regions associated with seedling stage root architecture and shoot traits using multi-locus genome-wide association studies (ML-GWAS). The association panel revealed a wide variation of 1.5- to 50-fold and were grouped into six clusters based on 15 traits. Six different ML-GWAS models revealed 456 significant quantitative trait nucleotides (QTNs) for various traits with phenotypic variance in the range of 0.12–38.60%. Of these, 87 QTNs were repeatedly detected by two or more models and were considered reliable genomic regions for the respective traits. Among these QTNs, eleven were associated with average diameter and nine each for second order lateral root number (SOLRN), root volume (RV) and root length density (RLD). A total of eleven genomic regions were pleiotropic and each controlled two or three traits. Some important candidate genes such as Formin homology 1, Ubiquitin-like domain superfamily and ATP-dependent 6-phosphofructokinase were identified from the associated genomic regions. The genomic regions/genes identified in this study could potentially be targeted for improving root traits and drought tolerance in wheat. Triticum aestivum L.) is one of the most important cultivated food grain crops with an area and production of 218.54 million hectares and 771.71 million tons, respectively [Oryza sativa L.) as a major staple food crop in India with an acreage of 30.55 million hectares, and total production and productivity of 107.18 million tons and 3508 kg/ha, respectively [Globally, wheat , and rice [Hordeum vulgare L.), maize and soybean (Glycine max L. Merril) [The knowledge on extent of genetic variability in wheat germplasm collections could pave the way for successful conservation and utilization in crop improvement. In recent years, cost effective advanced high throughput genotyping approaches, such as single nucleotide polymorphism (SNP) arrays and sequencing based genotyping methods such as genotyping by sequencing (GBS), restricted site associated DNA sequencing (RAD-seq) and whole genome re-sequencing, have greatly accelerated genomic level characterization of genetic diversity of crop germplasm resources in many species. Availability of these techniques has made it feasible to conduct whole genome scan studies, such as association mapping (AM) and genomic selection in a variety of crops including wheat, maize  ,25,26,27 Merril) ,23.Exploring genetic variability of root traits in a diverse germplasm collection could assist in development of varieties with desired root features for drought tolerance or target environments. To date, only a few reports are available on genetic variability and QTL mapping of root traits in wheat ,29,30. Tp ≤ 0.001) was observed among the genotypes for each phenotypic trait except for lateral root density, LRD  was performed for 15 RSA and shoot traits to determine their contribution to overall variability. The first four principal components (PCs) in the PCA analysis with eigenvalues >1 contributed a cumulative variance of 79.24% among the genotypes . The scrvariance A. Based variance . Length variance B which aThe diverse association panel of the wheat mini core subset  was divided into six clusters  when sub2 of allele pairs between two loci. Among these, a total of 19,528 SNP pairs were in perfect LD (r2 = 1), with B sub-genome having a maximum (7287) number followed by D (7100) and A (5141) sub-genomes. Overall, in the current association panel, low mean LD was observed across the 21 chromosomes with values ranging from 0.087 (4D) to 0.298 (1D). The average LD (r2) per chromosome ranged from 0.087 for 4D to 0.298 for 1D. The measures of LD decay for all 21 chromosomes are summarized in 2 = 0.157 (background LD). LD decayed the fastest in sub-genome A followed by sub-genome D and B. In sub-genome A, LD decayed at 3 Mb compared to 3.4 Mb in sub-genome B and 4.2 Mb in sub-genome D  ≥ 10. The SNP markers were proportionally distributed across the 21 chromosomes and their number ranged from 287 for Chr4D to 1179 for Chr2B, and the average SNP markers per chromosome was 791. The polymorphic SNPs covered 14066.28 Mb of wheat genome. The sub-genome A included 5355 SNPs, the sub-genome B had 6343 SNPs and 4918 SNPs were located on the sub-genome D. On sub-genome A, maximum SNPs were found on 2A (952) followed by 1A (911). Further, on sub-genome B, maximum SNPs were found on Chr2B (1178), followed by Chr5B (1125). Chr2D of Sub-genome D had maximum SNPs (1037) followed by Chr1D (895) . The SNPgenome D .Population analysis of the association panel was performed using both STRUCTURE and PCA which indicated optimal number of population (K) as two . Of the 2 = 1.8% for QTN-SOLRN at Chr 6A- AX-95244609 to R2 = 38.6% for QTN-SOLRN at Chr 3B- AX-95150666. The highest number of QTNs was identified in chromosome 7B (44) followed by 6A (34), 3B (33), 7A (31), 1D (31), 2B (29), 1B (29) and 6B (27). Further, among the total 456 QTNs, 87 QTNs and 27 QTNs were repeatedly detected by two, three or more ML-GWAS models, respectively. These were considered reliable QTNs for the respective traits. The reliable QTN that explained phenotypic variation ≥ 10% for the trait in at least one of the GWAS models was considered a major QTN. Among these 87 reliable QTNs, 11 QTNs were found to be associated with AD and 9 each for SOLRN, RV and RL, respectively. Further, a total of 12 QTNs for traits including SOLRN: AX-95077960, AX-94448890, AX-95150666 and AX-95244609; LRS: AX-95123855; AD: AX-95244609; RV: AX-94439232 and AX-94664277; SRN: AX-94551988; SDW: AX-94470023, RL: AX-94502864 and SRL: AX-94457792 were detected in four or five models (Q.SDW-4AL) and RL (Q.RL-6BL) by five ML-GWAS methods are presented in Marker-trait association analyses using six different ML-GWAS models  revealed 456 significant quantitative trait nucleotides (QTNs) for 15 seedling stage root architecture and shoot traits at a logarithm of the odd (LOD) score ≥3 with phenotypic variance in the range of 0.12–38.60%. Among these, 18, 59, 93, 92 and 194 significant QTNs were detected using FASTmrEMMA, mrMLM, FASTmrMLM, ISIS EM-BLASSO and pLARmEB, respectively . In thise models . ManhattDifferent root traits are mostly correlated and complex biological mechanisms might be involved in coordination for their expression. The pleiotropic action of genetic loci on different traits or their tight linkage results in a correlation between traits. A total of 11 QTNs were found to be pleiotropic markers . A locusp ≤ 0.01). Among these seven QTNs, three showed a significant effect on SOLRN  and one each on LRS (Q.LRS-7BS: AX-95123855), RV (Q.RV-5BL: AX-94439232), SDW (Q.SDW-4AL: AX-94470023) and AD (Q.AD-6AL: AX-95244609). The marker, AX-95244609 at Chr6AL was pleiotropic for SOLRN, LRS and AD. These significant associations suggest their plausible role in determining root architecture traits.Seven major loci detected in four or more ML-GWAS methods were analyzed to determine the range of phenotypic variations for all traits . AssociaTraesCS2B02G406800 and is predicted to encode Formin-like protein which regulates root-hair elongation in rice [Out of the total SNPs associated with various root and shoot traits, 52 SNP markers were found to be located within genes . Some im in rice . This lo in rice . The oth in rice . Most QTRoot traits play a major role in resource uptake, particularly with respect to water access and uptake in the context of drought tolerance. In this way, root traits help maintain crop yield under limited water environments. To realize maximum yield in wheat, a well-developed root system is warranted ,35. The H2) of the trait is a critical parameter determining their utility in breeding. We observed high heritability for traits such as SOLRN, SDW, RDW, SL, RL, AD, RV and moderate heritability for traits such as, LRS, RLD, SRN, RSDWR, SRL, TRS , and lowest AD (0.31 mm), thus its root distribution occupied a larger area of soil. Further, the genotype IC539574 ranked one with the value of 240.66 for FOLRN whereas the genotype IC82425A recorded the highest mean value of 490.33 for SOLRN. Similarly, top ranking genotypes for RLD, LRS, LRD and AD were IC542076 (0.75 cm−2), IC82425A (0.86), IC29008 (5.89 cm−1) and EC187159 (0.49 mm), respectively. These genotypes could serve as donors of these traits for crop improvement to combat drought stress. The genotypes with the highest AD are considered best for drought stress tolerance due to large xylem vessels which can extract more water and nutrients from deep soil layers [For acquisition of water and nutrients from the deep zone of soil, RV with a greater number of root tips is essentially required ,10. Furtl layers ,38. We rl layers . Howeverl layers  reportedLinum usitatissimum L.) and Arabidopsis thaliana [Correlations between root and shoot traits shows the balance between the organs of roots and shoots and resource partitioning between the above ground and below ground plant parts ,41. In tthaliana ,43. Thisthaliana ,10. A hithaliana . One of Based on PCA, RV, RDW, SOLRN, RLD, TRS, RL, LRS, FOLRN, SRN and RSDWR were major contributors to PC1 and most significantly associated with genotypic variations. Likewise, SDW and SL are major contributors to PC2. Hence these traits need to be focused on root studies. RV indicates the proliferation of roots which is essential for plants to uptake more water and nutrients from the soil, whereas linear root elongation may be quite useful in extracting water from deeper soil layers in the event of drought. However, RDW varies among genotypes with varying RL. Therefore, it would be desirable to have genotypes with high RL as well as RDW for a more efficient root system. Higher root density is found to be useful for the plants to uptake more nutrients and therefore the plants with a deeper root system would be more advantageous for drought tolerant genotypes ,46. Clus2) for the traits ranged from 0.12% to 38.67%, indicating that RSA traits are controlled by multiple loci with small to moderate effects. This also revealed the complex genetic control of these traits at an early stage of crop growth. Among the six ML-GWAS models used in our analysis, the pLARmEB model was the most powerful, which revealed the maximum number of associations (194) whereas the FASTmrEMMA was the least powerful as it detected the lowest number of MTAs (18). These findings were consistent with the observation of the Safdar et al. [Identification of a novel source of genes and genomic regions associated with RSA traits at the seedling stage is expected to boost development of high yielding drought tolerant varieties. In this context, historical wheat germplasm collections maintained in gene banks could prove valuable genetic resource for searching genes/genomic regions using association genetics approach. In this study, we used a diverse panel of 136 wheat genotypes including landraces, varieties, local collection, synthetic germplasm and elite breeding line for mapping RSA and shoot traits. For conducting GWAS, ML-GWAS models were used as these are considered superior to SL-GWAS models for mapping complex traits due to the fact that in ML-GWAS models, all-marker effects are simultaneously estimated. Moreover, unlike SL-GWAS models, these do not require testing of identified associations using stringent multiple testing corrections that generally result in rejection of significant associations . Using sr et al.  who usedQ.RL-6BL was consistently detected in five models. Further, this marker was pleiotropic and was associated with RV and RLD which were positively associated traits. Two reliable QTNs for RL (Q.RL-6BL and Q.RL-7AL) were not documented in earlier studies. There, they are considered novel QTNs, whereas other QTNs were reported to be associated with wheat seedling root development under abiotic stresses and phosphorus starvation [TraesCS7B02G086500) that encodes Calcineurin which was reported to control inward ion flux in the root, a process essential for plant survival and growth [Among the root traits, TRS was found to be associated with drought tolerance in wheat due to spreading of roots in the soil and its effect on the resource uptake . The SNParvation ,52. For d growth .Q.SRL-2BL) was associated with a gene encoding for Formin-like protein, a family of actin organizing protein involved in root hair development [Q.AD-1BL (AX-94620468) and Q.AD-6AL (AX-95244609) were detected in three or more models and could represent important genomic regions controlling this trait. Moreover, we found that marker AX-95244609 for QTN Q.AD-6AL was associated with other traits such as SOLRN and LRS, and thus could be a potential target for improvement of more than one RSA traits.The SRL is a widely used morphological parameter for root trait which characterizes economic aspects of root system, demonstrating the role of root mass for nutrient acquisition. Genotypes with large SRL have thin roots that increase the surface area per unit root volume enhancing water and nutrient uptake efficiencies . Furtherelopment . Furtherelopment ,56,57,58Q.SRN-2DL: AX-94551988) having consistency in four methods. We found that SNP marker AX-94551988 associated with Q.SRN-2DL was located within gene TraesCS2D02G478700 encoding for histone-lysine N-methyltransferase. In a previous study on Arabidopsis, histone-lysine N-methyltransferase gene was reported to inhibit lateral root development [Another important trait is SRN, which emerges first from coleorhiza of seed embryo. The gravitropic response of wheat seminal roots was heritable and suggested to be under control of a single dominant gene . Sorianoelopment . Based oQ.RDW-7AS) explained maximum percentage of phenotypic variance (18.35–18.60%) and coincided with a meta-QTL, root_MQTL_68 [Moreover, root biomass accumulation at the seedling stage is beneficial for drought escape as it could enable efficient uptake of water and nutrient from soil. In previous studies, RDW QTLs have been identified under both normal and drought stress conditions . Among t18.35–18.% and coi_MQTL_68 . Studies18.35–18.% and coiQ.SDW-4AL which explained phenotypic variance in the range of 8.09–19.05%, and was found reliable, as detected by using all five models. The SNP marker associated with this QTN was located within a gene TraesCS4B02G095100 that encodes for an F-box-like domain superfamily. F-box family proteins play a diverse role in plant growth and development and could be critical for shoot growth as their expression has also been detected in the shoot tissues of plant species [The underground root traits significantly affect development of above ground traits including shoot traits such as, SDW and SL, and thus grain yield. We could identify six and five reliable QTNs for SDW and SL, respectively. The strongest QTN for SDW was identified on 4A,  species .Applications of root phenotype-genotype association through GWAS has enabled the identification of important QTNs for root traits that impact shoot traits including yield ,64. MarkPhenotyping of 140 wheat germplasm of diverse panel including mini core subset was carried out at the Indian Council of Agricultural Research—National Institute of Plant Biotechnology (NIPB), New Delhi (India) as pot screening in growth chambers under controlled environmental conditions during Kharif season (June–September), 2019. Detailed information about the origin and pedigree of the material is given in v/v). Germinated seeds that were two days old with primary roots of 1 cm in length were placed inside the pot with the root radical facing down at equal depth. The Murashige–skoog (MS) liquid media were supplemented with 8 mM NO3− as nutrient media at uniform intervals during the entire growth period. The calcium concentration in all media was kept constant by adjusting the amount of CaNO3. Murashige–skoog (MS) N+ nutrient solution was prepared as per the given composition and stored in 4 °C. For one liter of working solution preparation, 100 mL of MS media, 4 mL of CaNO3 and the remaining 896 mL of distilled water constituted 1 L of solution, which were added in equal proportion to all the genotypes grown in controlled conditions at equal intervals. In the growth chamber, 150–200 μmol photon/m2/s light intensity, 10/14 dark/light hours, 70% relative humidity (RH) and at 22 ± 1 °C conditions were maintained as described by Sinha et al. [In this experiment, five germinated wheat seeds were sown initially in pots (4-inch diameter) containing perlite and vermiculite  as per the manufacturer’s guidelines. The root images from the scanner were analyzed with customized software WinRHIZO™  [After completing 15 days of growth in the controlled growth conditions, the seedlings were harvested from the pots by removing the perlite + vermiculite mixture and plants were separated carefully without damaging the roots and shoots. The plants were immediately placed on a tray to wash out the adhering perlite + vermiculite particles by running tap water with the utmost care so that to avoid breakage of any roots. The entire seedling was carefully spread on blotting paper and maximum root length and shoot length were measured with the help of a length measuring scale. The root was then extracted intact by cutting at the collar region using a sharp blade and the roots were placed in a tray containing distilled water. The roots were then individually scanned in an Epson Perfection V 700 Photo Canada) . Various Canada) ,67,68,692) was estimated from the analysis of variance.Mean data across the replications were used as the input data for statistical analyses and GWAS analysis. Descriptive statistics and frequency distribution were analyzed to check range of variability among the traits. Pearson’s correlation coefficient, cluster analysis and PCA was performed using SAS software version 9.3 (JMP) program . Heritability . Samples with good quality DNA were genotyped using Axiom® Wheat Breeders’ Array  according to the procedure described by Affymetrix . SNP markers with >10% missing data and <10% MAF were excluded. Identified SNPs were localized on a wheat genome assembly International Wheat Genome Sequencing Consortium (IWGSC) RefSeq version 1.0 using BLASTn program with default parameters.Genomic DNA was extracted from 15-day-old wheat seedlings using CTAB methods . DNA qua2) between SNP markers was used to estimate LD across sub-genome A, B, and D using TASSEL v5.0  [2 reduced to half of the maximum LD value.Population structure was estimated with 525 unlinked SNP markers nearly uniformly distributed across the wheat genome using the Bayesian model-based approach implemented in STRUCTURE program version 2.2  . A burn-NY, USA) . LD decahttps://www.wur.nl/en/show/Mapchart.htm accessed on 8 June 2021) [The use of multi-locus methods that capture small effect loci in complex polygenic traits such as in plant roots and shoots have recently become a popular and feasible approach. To benefit the algorithmic merits of different models and support results of one by another, it is also advantageous to apply multiple methods . GWAS anne 2021) . The favTriticum aestivum genome assembly IWGSC-refseq version1.0 in online web resource Ensemble plants  using BLASTn with default parameters to identify potential candidate genes. BLAST2GO tool was used to get annotation of expressed transcripts [SNPs (probe sequences) that were significantly associated with root architecture traits were searched against the nscripts .This study revealed wide variability for RSA and shoot traits at the seedling stage in the studied association panel. Ten top and bottom performing lines for 15 traits were identified for use in genetics, development of mapping population and introgression studies. Based on PCA, several traits such as RV, RDW, SOLRN, RLD, TRS, RL, LRS, FOLRN, SRN and RSDWR, were the most influential traits for phenotypic variations. The GWAS analysis enabled genetic detection of RSA traits and revealed 11 pleotropic loci associated with correlated traits. Further, putative candidate genes were identified from the associated genomic region that could be validated using a functional genomics approach. Development of wheat cultivars possessing superior root traits will play an important role in enhancing drought tolerance under water stress conditions. The large variability found in Indian germplasm for RSA traits and the novel genomic regions regulating them makes this germplasm a valuable source for improving root architecture, which plays a significant role in absorption and uptake of water and nutrients and increases crop productivity.