In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits.Brachytic2 represses the Ubiquitin3 locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth.We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.The online version contains supplementary material available at 10.1186/s13059-021-02370-7. Crop breeding began with the initial domestication of wild ancestors to generate phenotypes suited for human use . Dozens Approximately three percent of all maize genes were subjected to selection during domestication and improvement , 9. The The improvement of quantitative traits such as flowering time, plant stature, grain yield, environmental adaptation, and biotic and abiotic stress resistance depends on the selection of biological interactions between multiple genes (polygenic interactions) . To achiZea mays) was one of the earliest crops to benefit from the power of heterosis by breeding filial one (F1) hybrids exhibiting superior vigor for plant growth and grain yield. The mystery of heterosis has been explored for over a century, but the underlying mechanism remains insufficiently understood [1s is the result of dominance complementation of many recessive, slightly deleterious alleles at different loci in the parental genomes [Maize . Additional limitations of bi-parental populations further restrict the effective detection of epistatic QTLs. For example, each segregating Fis plays , 33.1 populations. It was created by crossing inbred lines, developed as a synthetic population, with inbred lines typical of diverse heterotic groups from around the world. This design may allow the comprehensive dissection of heterotic QTLs and associated effects. Identification of heterosis-determining genes may refine our understanding of the mechanism behind heterosis formation. This new mechanistic knowledge may in turn accelerate the process of creating and fixing new heterotic patterns between different pools of germplasm, reducing genetic vulnerability and ultimately enhancing yield in maize improvement.Here, we present a new genetic design that overcomes these limitations and analyzes multiple linked F1 populations by crossing 1428 previously reported inbred lines from the CUBIC  synthetic population as a maternal pool [1 combinations uncovered the critical roles played by polygenic interactions and provided the framework to propose a theoretical model of the gene-regulation networks at work during floral transition. Based on this model, we used targeted genes and their associated effective variants to demonstrate the successful implementation of molecular design breeding (MDB) to facilitate selection of optimal genotypic combinations to fine-tune desired phenotypes. With the integration of G2P, GWAS and MDB on an actual breeding population, our work presents an exemplary solution to apply big data-driven decision-making strategies to target breeding for crop improvement , plant height (PH), and ear weight (EW)—on 17 individual plants for each hybrid. We also used the commercial hybrid ZhengDan958 as a control and planted it once for every 50 rows to adjust for phenotypic heterogeneity across fields. We then used the measured phenotypes from the 8652 F1s to train the G2P model and predict the phenotypes of the remaining 34,188 F1 hybrids.We employed the North Carolina II (NC-II) design to construct thirty inter-related Fpulation  with thisee the “” section1 populations consisting of 2856 hybrids between the 1428 maternal lines and the two paternal tester lines Zheng58 and Jing724, which have been widely used in Chinese commercial breeding programs. The remaining 28 F1 populations were represented by 5796 F1 hybrids between a subset of 207 maternal lines and the other 28 paternal testers  obtained from whole-genome resequencing of the 1458 parental lines . Among the 8652 F1 hybrids with measured phenotypes, 554 hybrids (or 6.4%) showed earlier DTT, shorter PH, and higher EW when compared to the commercial control variety ZhengDan958, illustrating the potential of our F1 hybrid population for the breeding of early-flowering, high-yielding, and compact cultivars.A comparison of parental and F1 combinations we did not phenotype in the field  for training purposes, while we used the remaining 207 samples  for testing. We repeated this procedure 28 times to derive predictability, measured as the Pearson correlation coefficient (r) between measured and predicted phenotypes for each F1 population. The average predictabilities of the 28 F1 hybrid populations for the three traits were 0.76 (DTT), 0.81 (PH), and 0.66 (EW); for the mid-parent heterosis, estimates were 0.61 (MPH.DTT), 0.805 (MPH.PH), and 0.89 (MPH.EW)  model to infer the phenotypes of the 34,188 Fsee the “” sectionThe second model testing method was designed to evaluate the predictability of the Zheng58 and Jing724 hybrids. We used the 6210  hybrids plus the 1221 Zheng58 hybrids as the training model to predict the performance of the 1221 Jing724 hybrids. We then repeated the same exercise, this time using the Jing724 hybrids alongside the 6210 hybrids in a new training set to predict the 1221 Zheng58 hybrids. When parental phenotypes were considered as fixed effects in the gBLUP model, predictabilities were 0.695 (DTT), 0.67 (PH), and 0.44 (EW) for Jing724 hybrids and 0.70 (DTT), 0.72 (PH), and 0.47 (EW) for Zheng58 hybrids  enlarged the effective population dataset fivefold, which may greatly enhance GWAS detection power. Indeed, GWAS signals significantly rose in GWAS analyses performed on the full dataset, which includes all 1428 maternal lines across the thirty F1 populations, when compared to the training set of 207 maternal lines  detected an additional peak on chromosome 8 at the ZEA CENTRORADIALIS 8 (ZCN8) locus  was weaker than when using measured DTT values  on the same samples, but was nevertheless one order of magnitude higher than that obtained with the subset of 207 samples. GWAS on both measured and predicted MPH.DTT detected a peak at the MADS15/31 locus, a pair of tandemly duplicated MADS-box transcription factor genes.The complete determination of 42,840 Fin maize : the detBrachytic2 (BR2) [We detected an unknown PH QTL on chromosome 6 in GWAS of the combined 1428 hybrids for both measured and predicted PH, although this peak was absent in the subset of 207 hybrids. We detected a peak at the c2 (BR2)  locus inWhen using predicted phenotypes for GWAS in the enlarged population containing 1428 samples, the risk of false discovery of excessive unrelated QTLs must be excluded. To quantitatively evaluate the GWAS accuracy, we performed a series of simulation analyses to infer the false discovery rate (FDR) and detection power (DP) under 36 circumstances of different prediction accuracy and population size. These analyses were based on simulating a polygenic trait determined by 20 spike-in QTNs (quantitative trait nucleotides) that contribute major, moderate, and minor effects to the phenotype see the “” sectionN1 is the number of false QTNs and N0 is the number of spike-in QTNs. When directly using the simulated observed phenotypes for GWAS without considering prediction error (r2 = 1.0), FDR still existed and increased with the increment of population size  were considered, FDR was not increased, rather decreased along with the decreased prediction accuracy. This pattern indicated that discovery of false-positive QTNs may always occur even if observed phenotypes were used for GWAS; on the contrary, inaccuracy in the predicted phenotypes may not cause the discovery of more false-positive QTNs compared to the use of observed phenotypes for GWAS. The possible explanation for this scenario is that inaccuracy in the predicted phenotypes may cause a dramatic decrease of the number of significant SNPs, which lowered the total amount of detected QTNs  to detect spike-in QTNs by shuffling the simulated observed phenotypes for 1000 times. The DP was defined as the proportion of successful detections out of the 500 times of GWAS for each QTN, and the averaged DP of all the 20 spike-in QTNs was computed to represent the overall DP under each circumstance of prediction accuracy and population size. DP of GWAS was determined by all of the three factors including population size, QTN effects and prediction accuracy  for GWAS. In this analysis, we used a permutation-based method to define a significance threshold , MADS1, MADS15/31, ZCN4, CCAAT-HAP3-transcription factor 32 , and myb74. Several genes showed weaker GWAS peaks when using the F1 phenotypes, for example, ca3p2, MADS15/31, and DTT-7 (an unknown peak on chromosome 7), although they rose above the significance threshold when using MPH.DTT for GWAS . Of the 47 QTLs (24 for DTT and 23 for MPH.DTT) in F1 hybrids, we detected MADS69 and ZCN8/RAP2.7 across all thirty populations, while the other QTLs peaks at the MADS15/31, ca3p2, MADS1, DTT-7, myb74, and ZCN4 loci were detected 29, 23, 22, 21, 9, and 8 times, respectively, with moderate signals. In a long genomic region spanning from ~ 75 to ~ 88 Mbp on chromosome 8 upstream the ZCN8/RAP2.7 QTLs (~ 123 Mbp), we identified a series of fragmented QTLs for MPH.DTT in 22 F1 populations, although this region does not contain previously reported flowering time genes , the causal SNP being a rare allele derived from inbred Zong3 [BRASSINOSTEROID-DEFICIENT DWARF 1 (BRD1) gene (peak SNP: chr1.s_248796560) [1 population, we observed an additional peak on chromosome 6 (peak SNP: chr6.s_95877243), overlapping with the ubiquitin3 (ubi3) gene previously mapped in GWAS studies for plant and ear height [BR2 peak associated with PH greatly diminished in the F1 population, but we did detect this QTL when using MPH.PH phenotypic values. The distinct GWAS signals for the BR2 and BRD1 genes for PH and MPH.PH suggests distinct genetic effects underlying heterosis of PH. Moreover, we identified 18 additional QTLs of moderate significance for PH and 14 for MPH.PH multiple times across the thirty F1 populations  , 38  detected in the maternal population but not in the F1 populations, that are caused by dominance complementation of the paternal allele against the recessive maternal allele in F1 hybrids. QTLs with complete dominance effects can be also detected by GWAS on the derived F1 MPH values, but additive QTLs are undetectable . Therefore, attributed to the unique design of the half-sibling F1 population in the present study, one of the genetic mechanisms of heterosis utilization is explained for single-cross breeding in modern maize industry. Although an advantageous QTL normally segregates in one heterotic group, the beneficial effect of the QTL is epistatically controlled by another QTL; this deleterious epistasis is diminished by the beneficial alleles contributed from another heterotic group, therefore resulting the expression of significant heterosis observed from either F1 phenotypes or MPH values  in the F1 population but not when using the derived MPH phenotypic values, while Gene D (dominant effect) is undetectable in the F1 population but is detected when using the derived MPH values , dominant (5.4%), and epistatic (89.2%) QTLs contributing to DTT, PH, and EW. In addition, these proportions differed in each F1 population. For example, while DTT showed relatively equal numbers of the three QTL classes, epistatic QTLs contributed much more to PH and EW  associated with the BRD1 peak. The tall PH group (215.3 ± 20.2 cm) included 548 lines bearing the chr1.s_248796560-CC genotype (BRD1-CC) and the short PH group (207.5 ± 20.0 cm) included 834 lines with the chr1.s_248796560-TT genotype (BRD1-TT).Photoperiodic adaptation to different environments involves the precise timing of the vegetative-to-reproductive transition to achieve stable yield production under each day-length regime . Genes i oxidase , was detBRD1-CC and BRD1-TT groups . Without the overwhelming masking effect of BRD1, we detected additional peaks within the two groups, including peaks at the BR2 and ca3p2 loci in the tall PH group, and peaks at the MADS69 and ZCN8 loci in the short PH group  to compare their PH and DTT phenotypes. We observed different DTT and PH phenotypes in the subgroups for each of the four genes in the different BRD1 backgrounds, suggesting that BRD1 may genetically interact with these  genes to affect PH and DTT . Since myb74-TT caused significantly earlier DTT  without affecting PH or EW, we also included this gene as an additional target for MDB.The fact that we identified different sets of DTT- and PH-related genes by conditional GWAS in contrasting BRD1 genotypes showed no discernible differences in PH for any of the early DTT genotypes of the three flowering time genes  showed the earliest DTT and shortest PH, EW also dropped to the lowest weight compared to other combinations, probably due to a strong negative effect of ZCN8-TT on EW  . Of all maternal lines, only 11 shared these alleles, but they displayed intermediate DTT, reduced PH and, importantly, unaffected EW , even if they are beneficial to traits and normally segregate in one population, their advantageous effects may be epistatically repressed by another QTLs (Gene X). When the population is hybridized with inbred lines from a complementing group that may generate excellent heterotic effects, such deleterious epistasis is diminished, resulting in expression of superior agronomic traits in F1 hybrids. We believe this is a common mechanism of heterosis utilization for single-cross breeding in modern maize industry.Heterosis has been reported to result from dominant and epistatic effects involving many genes with complex allelic, intra-genomic, and inter-genomic interactions . The uni1 hybrids may release its repressive influence on the detection of downstream QTLs, that were defined as the epistatically controlled QTL. The prevalence of epistatically controlled QTL in our data may support the finding that dominance complementation is crucial for heterosis and may help explaining the co-contribution of dominant QTLs and epistatically controlled QTLs to hybrid vigor.We uncovered three forms of genetic effects underlying heterosis, the most prevalent of which was due to epistatically controlled QTLs (89.2%), with a small proportion of dominant QTLs (~ 5.4%). We have several explanations to the detection of dominant and epistatically controlled QTLs for heterosis. During the process of constructing the 1404 CUBIC lines in six generations of open pollinations and six generations of self-pollinations, the deleterious alleles with large effects negative to fitness might have been eliminated, resulting in the accumulation of many slightly deleterious mutations maintained. On the other hand, given that deleterious allele in maize are often rare , the numBR2 and ubi3 loci, in which dominance complementation may disrupt the epistatic repression of advantageous alleles to recover superior F1 phenotypes  subunit of the CCAAT-box-binding transcription factor belonging to the Nuclear Factor Y (NF-Y) family. It was therefore previously called ZmNF-YB3 [A role for all the abovementioned genes has been previously described in the control of vegetative growth and floral transition. The developmental process of vegetative-to-reproductive transition is one of the most critical stages in crop breeding, and breeders must select the optimal regulatory pattern to balance flowering time and plant stature. One selection target for this balance has been the ZmNF-YB3 .NF-YB3 gene is homologous to the rice (Oryza sativa) gene DAYS TO HEADING 8 . This gene encodes a protein with a HAP3h domain that physically interacts with the CCT domain-containing proteins HEADING DATE 1 (OsHD1) and OsGhd7 to suppress flowering under long day conditions through the downregulation of EARLY HEADING DATE (OsEhd1) and OsHd3a transcription [OsDTH8 also exhibits pleiotropic effects on plant height and grain yield, presumably through its indirect interaction with phytohormone signaling pathways [NF-YB3 gene thus most likely executes a similar function as OsDTH8 in suppressing flowering, independently of the MADS69-RAP2.7-ZCN8 module. In addition, the concurrent detection of ZmNF-YB3 with the BR2 and BRD1 QTLs suggests the involvement of the brassinosteroid (BR) and auxin phytohormones in coordinating floral transition. This hypothesis is consistent with previous evidence pointing to the importance of BR signaling during floral transition, perhaps involving direct communication between BR and auxin [ZmNF-YB3 and integrates BR and auxin signaling in the maize flowering pathways , Shenyang , Beijing , Baoding , and Xinxiang  in Northern China. In 2015, we planted the 30 paternal testers and 6210 F1 hybrids resulting from the crossing of 207 randomly selected maternal lines with the 30 paternal testers in the same five locations to collect phenotypic data. We also included the hybrid cultivar ZhengDan958 as a control once every 50 rows.Crossing the 1428 maternal lines and 30 paternal testers will theoretically generate 42,840 F1 hybrids in the field following a completely randomized design, in which 17 individual plants per inbred line or F1 hybrid were planted as a row. In each location, we planted the maternal line Chang7-2 once every 30 rows as control within the plots of parental lines, and the hybrid cultivar ZhengDan958 once every 50 rows as control within the plots of F1 hybrids. We later used the phenotypes measured for Chang7-2 and ZhengDan958 to correct for spatial heterogeneity in the field. We collected phenotypic data for three representative agronomic traits: days to tasseling (DTT), measured as the interval from sowing date to the date when tassels appeared on at least ten individual plants per line; plant height (PH), measured as the vertical height from the ground to the top of the tassel; and ear weight (EW), measured as the average weight of five fully formed ears in the middle of each row to avoid edge effects within the plots.We planted the 237 parental lines and 8652 F1 hybrid and each parental line for the phenotypic data in the five locations and 2 years, using a mixed linear model in the R package “lme4” [yh is the hybrid phenotypic values and ym and yp are the phenotypes from the maternal and paternal line, respectively. Because phenotypic variation caused by population stratification may introduce systematic bias in genomic selection (GS) prediction, we normalized BLUP values calculated for the F1 hybrids within each set of F1 populations to relative values using the z-score algorithm: yi is the phenotype of the F1 hybrid or parental line i, 1 hybrid or parental line and sd(y.) is the standard deviation of the phenotype in the F1 hybrid or parental line. The z-scores were subsequently used in GS predictions.To reduce the influence from the environment (years and locations), we computed the best linear unbiased prediction (BLUP) value of each Fe “lme4” . BLUP va1 hybrids, we performed a series of SNP filtering steps using the following criteria: (1) if a SNP was heterozygous in a parent, the SNP in the hybrid was annotated as missing; (2) SNPs showing a heterozygous rate ≥ 10% in the maternal population and heterozygous rate ≥ 1/30 in the paternal population were removed; (3) SNPs showing no polymorphism in either the maternal or paternal populations were removed; (4) SNPs showing minor allele frequency (MAF) < 0.02 in parental lines were removed; (5) SNPs showing minor homozygous genotype < 0.5% (210 lines) out of the 42,840 F1 hybrids were removed. SNP filtering resulted in 4,549,828 high-quality and informative SNPs remaining for downstream analysis. We then inferred the genotypes of all 42,840 F1 hybrids from the genotypes of their maternal and paternal parents for these 4,549,828 SNPs. Missing genotypes of the 42,840 F1 hybrids were imputed using the software Beagle version 4.0 [We called 14.8 million single nucleotide polymorphisms (SNPs) from whole-genome resequencing of all 1428 maternal lines and 30 paternal testers by using the same pipeline described in the previously reported analysis of the CUBIC population . Before sion 4.0 . We perfsion 4.0 , and consion 4.0 .1 hybrids  to train the model to predict the phenotypes of the remaining 34,188 F1 hybrids . The 1:4 sample division is a commonly used ratio in the seed industry to perform G2P-assisted breeding, considering the balance between phenotyping cost and prediction precision [1 hybrid populations using the “sommer” package in R [y is a vector of phenotypes, X is a designated matrix for the fixed effects, β is a vector of fixed effects, Z is a designated matrix for random effects, u is a vector of additive genetic effects for an individual with variance K is the centered genomic relationship matrix (cGRM) deduced by the GEMMA (Genome-wide Efficient Mixed Model Association) software [ε is a vector of residual errors with variance We used data from the 8652 Frecision . We utilage in R . The modsoftware  based onz-score-transformed phenotypes of training samples in order to avoid the systemic bias across the thirty sets of F1 populations. When prediction was accomplished, the z-score form of predicted phenotypes were converted back to absolute values using the following algorithm applied within each F1 population: zi is the z-score form of the predicted phenotypes of F1 hybrid i in the predicting population; sd(y) are the mean and standard deviation of the phenotypes of training samples, respectively. To evaluate model precision, we computed the Pearson correlation coefficient (r) between predicted phenotype and observed phenotype for the samples with known phenotypes.The G2P model took the βi = 0.96i where i = 1, 2, 3, …, 20 [To quantitatively evaluate GWAS accuracy in terms of both false discovery rate (FDR) and detection power (DP), a series of simulation analyses were conducted. Based on the actual genotypes from the 4.5 million SNPs in the CUBIC population, we randomly selected a set of 100,000 SNPs for simulations. Assuming a polygenic trait is determined by 20 QTNs (quantitative trait nucleotides) contributing effects following an exponential distribution, the 20 QTN effects were expressed as 3, …, 20 . From 10xi and βi are the numeric genotypic values and simulated QTN effects at the ith QTN, respectively. Contribution of each QTN was further represented by a value of PVE (phenotypic variance explained) expressed as PVE = βf(1 − f) where β is the QTN effect and f is allelic frequency in the 1428 samples. Based on PVEs, the 20 QTNs were categorized into three classes of QTNs with major , moderate , and minor  contributing effects.We first simulated the observed phenotype of one sample by summing the 20 QTNs’ contribution expressed as y′ = y + ε where y is the simulated observed phenotype and ε is the residual representing the fluctuated prediction bias assumed following a normal distribution expressed as r2 is the squared correlation coefficient between the observed and predicted phenotypes. Thus, quantitative evaluation of FDR and DP of GWAS under six levels of prediction accuracy  may be carried out. Because GWAS power is also influenced by population size, each simulation was performed for six times, using five randomized subsets  out of the 1428 samples as well as the complete set. Then, 36 combinations of population size and prediction accuracy were set for one round of simulation and each round was repeated for 500 times to obtain averaged results of FDR and DP. During each time of repeat, a new set of 20 QTNs and predicted phenotypes with randomized residuals were generated. Finally, the whole simulation process generated a total of 18,000 sets of GWAS results for computing averaged FDR and DP under the six different circumstances of population size and prediction accuracy.Then, we simulated the predicted phenotypes with six levels of prediction accuracy, so that the correlation between GWAS and G2P accuracies may be established. Simulation of predicted phenotype was based on the equation of To demonstrate that any enhancement of GWAS detection power using 1428 samples can be attributed to the enlarged population size compared to that of 207 samples, we conducted a simulation analysis to determine the correlation between population size and GWAS detection power. The GWAS simulation was conducted following a previously published procedure .1 hybrids, in which 19 gradients of QTN effects ranging from 0.1 to 1.9 with a step of 0.1 were used for each round of simulation. Based on the genotypes  of the chosen SNP among Zheng58 F1 hybrids, the original phenotype was transformed to a simulated phenotype, calculated using the original phenotype of the AA hybrids + (QTN effect × SD) and AG hybrids − (QTN effect × SD). Under each QTN effect gradient, we randomly picked 100 SNPs to generate 100 simulated phenotypic distributions for the Zheng58 F1 hybrids. Next, we performed GWAS 100 times using the 100 sets of simulated phenotypes for the Zheng58 F1 hybrids. Using a p-value = 2.2 × 1e− 7 as the significance cutoff, we counted the number of significant SNPs (n) out of the 100 SNPs passing the cutoff and used the corresponding percentage (n/100) to represent the GWAS detection power. For each trait, we repeated the above procedure 19 times using the 19 QTN effect gradients to generate GWAS detection power predictions on the 207 and 1428 samples for a comparison. Finally, we conducted a simulation for effect and sample sizes that impact on the GWAS detection power under the genetic architectures of three real traits: DTT, PH, and EW in both measured and predicted phenotypic variations. With this simulation analysis, we deduced that a significant QTN with an effect of 0.4 would be detected using 1428 samples, while using 207 samples the QTN effect would have to be over 1.2 to be detected.In this procedure, one SNP (0.25 ≤ MAF ≤ 0.35) was randomly picked from the ~ 4.5 million SNPs, and a spike-in QTN (quantitative trait nucleotide) effect was assigned to this SNP; The QTN effect is a designated coefficient of the standard deviation (SD) of the actual phenotypic distribution of Zheng58 F1 populations using the GEMMA software. The pairwise kinship matrix was calculated for each set of 1428 F1 hybrids using the centered genomic relationship matrix (GRM) algorithm in GEMMA based on the ~ 4.5 million SNPs. We then loaded the GRM with the model as a polygenic random effect. We performed a total of 183 rounds of GWAS on 1428 maternal inbred lines, for the three traits and their associated MPH values for each of the 30 F1 populations.To limit the influence of population stratification caused by the 30 paternal testers from different heterotic groups, we performed GWAS analysis within each set of F1 populations as follows. We grouped significant SNPs  that were spaced less than 800 kbp apart into one locus, 800 kbp being the length of linkage disequilibrium (LD) decay at r2 = 0.1 [r2 > 0.1 and the two peaks were spaced < 5 Mbp apart, the two adjacent loci were further merged as a single locus. This process was performed until no more loci could be merged. Next, if a locus contained ≥ 10 significant SNPs, the locus was defined as a QTL in which the most significant SNP was chosen as the peak SNP. If a locus contained < 10 significant SNPs, an extended region of 400 kbp  was searched for SNPs exceeding p-value = 1e− 4, until the locus contained ≥ 10 SNPs at this suggestive level, at which point it was called a QTL with the most significant SNP chosen as the peak SNP. After summarizing QTLs for each trait or MPH associated with each trait within each F1 population, we generated an overall QTL summary by combining the QTLs over the 30 F1 populations. This was done by merging the overlapping QTLs from within any two F1 populations. Therefore, for each trait, three sets of merged QTL maps were generated, including QTLs identified in the maternal population; and QTLs of the trait and of the MPH.Trait in hybrid populations.To better interpret the genetic architecture and identify key genes for the six traits, we summarized all QTLs for DTT, MPH.DTT, PH, MPH.PH, EW, and MPH.EW from the 183 sets of GWAS results in the maternal population and the 30 Fr2 = 0.1 . We calcAdditional file 1. Heterotic group classification of the 30 paternal testers.Additional file 2. Figures S1-S11.Additional file 3. Prediction precision of the thirty F1 populations.Additional file 4. Manhattan plots for GWAS of three traits across all populations.Additional file 5. QTL summary for three traits detected by GWAS across the maternal and 30 F1 populations.Additional file 6. The merged QTLs and genetic effects detected by the three traits.Additional file 7. Review history.


Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the US maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color quantitative trait loci ranging from 2.4% to 17.5%. These quantitative trait loci were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids. Zea mays ssp. mays L.). Maize is a cereal crop ranking globally among the most important sources of daily calories, and is estimated to provide 38% of the food supply in Africa  pathway produces isopentenyl pyrophosphate (IPP), a precursor from which carotenoids and other plastidic isoprenoid compounds are derived , which adds the ε-ring to lycopene and represents a key branchpoint of the carotenoid pathway, and beta carotene hydroxylase 1 (crtRB1), which converts β-carotene to β-cryptoxanthin and then zeaxanthin (Two carotenoid biosynthetic genes in particular have been utilized in maize provitamin A biofortification efforts thus far: axanthin . Increasaxanthin . AMD is While biofortification of maize with provitamin A and other priority carotenoids (such as lutein and zeaxanthin) is a promising opportunity to address deficiency and reduce global disease burdens, it has also been critical to consider consumer preference with regards to kernel color. In parts of eastern and southern Africa, white maize is preferred for human consumption, whereas yellow maize is dispreferred, including due to historical issues associated with long storage periods for shipments of yellow maize  were a subset of those analyzed in Ordinal scores for kernel color, on a scale of 1 (yellow) to 12 (deep orange), were as evaluated for these 10 families in Using the phenotypic data from the 10 families, best linear unbiased estimators (BLUEs) were generated from the P-value  corresponding to a Type I error rate of α = 0.05. Exit thresholds were set to equal twice the entry threshold, so that entry and exit of a given marker could not take place in the same step. One pair of multicollinear SNPs (defined as magnitude of Pearson’s correlation of SNP genotype state scores being greater than 0.8) was identified for lutein. The SNP having the lower sum of squares was removed from the JL model, and the model was fit again with re-scan in the vicinity of the remaining peak markers in the model. Namely, if another marker in the respective support interval now exhibited a larger sum of squares than the originally identified peak marker, it would be included in the model instead and the support interval recalculated, until a local maximum in the sum of squares was identified. Once the final model was fit, the allelic effect estimates were calculated, nested within family, as described in Joint-linkage (JL) analysis was conducted in TASSEL 5 as described in First, 1,000 permutations were generated, using the residuals generated for each trait during joint-linkage analysis. From the permutation results, a significance threshold was identified for each trait corresponding to a genome-wide association study (GWAS) false discovery rate of 0.05. The GWAS was run in TASSEL 4 using custom scripts (available in the script repository for this publication). This method utilized a bootstrap resampling method in which 100 iterations of GWAS were conducted, with 80% of the RILs sampled with replacement in a given iteration. The markers tested in GWAS were the ∼26.9 million SNPs and short indels (up to 15 bp in length) of the HapMap v1 and v2 projects , with thα = 0.05 using FDR-corrected P-values that were generated via the Benjamini–Hochberg method. These correlations were examined both within a single JL-QTL interval and at the genome-wide level, and were visualized using the network package in R  was examined within the same HapMap v1 and v2 input data set used for GWAS. Specifically, LD was examined for markers exhibiting one or more significant associations in GWAS  as in Linear models were produced using the lm function from the lme4 package in R . Each liR2) from sklearn.metrics. Baseline Error is defined as the absolute value of the predicted kernel color transformed BLUE minus the known test value of the kernel color transformed BLUE. Percent Accuracy is defined as 100 times the Baseline Error, divided by the test-set values of kernel color, subtracted from 100%. To add certainty that results were not affected by the randomness of the train-test-split, and to enhance reproducibility of results, the process was repeated 30 times using 30 different random seeds, for the linear model. The model metrics were averaged over these 30 runs before being reported in Supplementary Table 8. Variable importance was extracted from the random forest model, and plotted using pyplot from matplotplib (Random forest models were produced using SciKit-Learn in Python version 3.8.5 . The traplotplib . Featurea priori evidence of involvement in biosynthesis of IPP or biosynthesis and/or retention of carotenoids based on prior studies in plant systems (Supplementary Table 10).The carotenoid and kernel color traits analyzed in this study exhibited natural variation in the 10 families examined herein, with moderate to high line-mean heritabilities . A totalD-xylulose 5-phosphate synthase (DXS), which catalyzes the first and committed step of the MEP pathway. dxs2 was identified with PVEs of 1.0% to 5.2% for eight of the nine traits analyzed herein, including 2.4% for kernel color , which catalyzes the fifth step in the MEP pathway. mecs1 was identified with PVEs of 0.8% to 3.6% for five traits  catalyzes the synthesis of phytoene from the condensation of two 20-carbon geranylgeranyl diphosphate (GGDP) molecules. A null allele at psy1 conditions negligible carotenoid levels in the maize endosperm (psy1 was identified for six traits: phytofluene (12.2% PVE), β-cryptoxanthin (4.7%), lutein (5.4%), zeaxanthin (7.3%), total carotenoids (21.4%), and kernel color (12.5%) (lycopene epsilon cyclase (lcyE), which is the committed step in α-carotene biosynthesis (lcyE had the largest PVEs (15.3% to 53.2%) observed for seven of the nine traits analyzed in this study, and affected kernel color (with PVE of 17.5%) but not total carotenoids.As the committed step in carotenoid biosynthesis, phytoene synthase ( 12.5%)  and 2. T.5%  and ynthesis . lcyE halut1 encodes CYP97C, an ε-ring hydroxylase that catalyzes the conversion of α-carotene to zeinoxanthin and further hydroxylation of zeinoxanthin to yield lutein  was identified with PVEs of 1.6% for β-cryptoxanthin and 1.8% for zeaxanthin. Zeaxanthin epoxidase (encoded by zep1) converts zeaxanthin to violaxanthin, with antheraxanthin as an intermediate. zep1 was identified with PVEs of 20.3% for zeaxanthin, 7.1% for total carotenoids, 2.2% for β-cryptoxanthin, and 1.7% for phytofluene. Finally, the White Cap locus represents a macrotransposon insertion containing some number of tandem copies of ccd1, which encodes a carotenoid cleavage deoxygenase (whitecap1 locus (QTL24) was identified with PVEs of 5.6% for lutein and 6.9% for total carotenoids, respectively. Overall, three of the identified genes were detected for kernel color: lcyE (17.5% PVE), psy1 (12.5%), and dxs2 (2.4%).Within the α-branch of the carotenoid pathway, ectively  and 2. Wr; Pairwise correlations between untransformed BLUEs for each trait were generally moderately to strongly positive for compounds within the same pathway branch, and near-zero to negative across branches  was observed for kernel color and carotenoid traits at certain of the identified genes . dxs2 exβ-branch .R2 = 0.727; Supplementary Table 7). Models that included this family term, which contains a unique identifier (or factor level) for each of the 10 families included in this study, consistently performed better than those with the same grain carotenoid traits as predictors that omitted the family term (Supplementary Table 7). The second-best performing model contained only lutein, zeaxanthin, total carotenoids, and the family term . The random forest performed almost identically to a linear model fit to the same training and prediction sets . The feature with highest importance in the random forest model was zeaxanthin (0.27), followed by total carotenoids (0.19) and the other two β-branch carotenoids: β-carotene and β-cryptoxanthin . Together, these results suggest that zeaxanthin and total carotenoids are among the top predictors of kernel color.In modeling of kernel color as the response variable with grain carotenoid traits as predictors, the top-performing linear model was a full model that included all eight carotenoid traits analyzed in this study and a family term   was also identified in this study for five carotenoid traits. MECS catalyzes the fifth step in the MEP pathway, which provides precursors for biosynthesis of carotenoids, tocochromanols , and other plastidic isoprenoids. Markers proximal to mecs2—another homolog encoding MECS in maize—were significantly associated with maize grain tocopherol traits in a pathway-level analysis   encodes the first and committed step of carotenoid biosynthesis, and is a major controller of natural variation in grain endosperm carotenoid levels . lcyE, which encodes an enzyme acting at the main branchpoint of the carotenoid pathway, is a major point of genetic control for levels of compounds residing in the α- or β-pathway branches  and lutein (the product of that enzyme), which is consistent with previous findings . lut1 was the identified gene that was most specific to α-branch compounds, without significantly affecting β-branch carotenoids, total carotenoids, or kernel color. Within the β-branch, crtRB5 (encoding β-carotene hydroxylase) was identified for β-cryptoxanthin and zeaxanthin. While crtRB1 has been the homolog more frequently identified and characterized in previous studies, and with larger effect , psy1 (12.5%), and dxs2 (2.4%) . lcyE was found to exhibit negative pleiotropy between kernel color and α-branch carotenoids (and positive between kernel color and β-branch carotenoids). Thus, if directing flux toward the β-branch of the carotenoid pathway is desirable, selecting on lcyE for kernel color could be advantageous. Alternatively, if increased concentrations of certain α-branch compounds, such as lutein, are also desired, it appears that dxs2 and psy1 could be used to improve kernel color (and lutein) without imposing such tradeoffs. In that case, the allele to be selected upon at lcyE could then be determined based primarily on the balance of α- and β-branch compounds that is optimal for human nutrition in the desired use case. Notably, negative pleiotropy was also observed within the β-branch for lcyE, which was not observed in the 25 NAM families . Such tradeoffs could potentially be mitigated through selection at other genes exhibiting large PVEs for β-branch carotenoids   and 2. Ois study . psy1 exlcyE, psy1, and dxs2—were also identified in lcyE for hue angle (representative of perceived color), psy1 for a* (greenness to redness), and dxs2 for both a* and hue angle. The fourth gene identified at the genome-wide level in a*, was zep1, which was identified for three traits herein but not for kernel color. While there were GWAS variants for kernel color for which zep1 was in the ±250 kb search space (Supplementary Table 9), an individual-trait JL-QTL interval was not identified for kernel color in the vicinity of zep1 in RefGen_v4 coordinates (Supplementary Table 6). Intervals were identified for kernel color in RefGen_v2 coordinates that contained zep1 (PVE 3.0%) as well as dxs3 (PVE 4.3%), but one support interval bound for each of these intervals uplifted to a contig rather than to the same chromosome (Supplementary Table 6), such that those intervals were not included in downstream analyses. Future studies involving these two genes could likely still benefit from continued monitoring of potential impacts on kernel color.The three genes identified for kernel color in the present study—lcyE (PVE of 38.3%) and psy1 (20.7%) as well as zep1 (6.6%) and ccd1 (8.7%). A JL-QTL support interval was detected in the present study for kernel color (PVE 2.9%) which contained the ccd1-r progenitor locus. However, there were not significant marker-trait associations for kernel color within that interval , such that the interval was not resolved to the gene level. The macrotransposon insertion site containing a tandem number of copies of ccd1, located 1.9 Mb upstream of the ccd1-r progenitor site  with total carotenoids as observed in the present study. Stronger relationships between kernel color and α-branch carotenoids than those observed in this study could likely be expected if white-endosperm lines were also to be included in the populations under examination.It is thus not surprising that kernel color was more strongly correlated with β-branch carotenoids in these populations with yellow to orange grain. The results of this study indicate that zeaxanthin was the primary driver of this relationship, given its feature importance in the random forest model , and its higher abundance compared to—and positive correlations with—these two compounds. Negligible correlations between kernel color and α-branch carotenoids were less expected. At least weak correlations could have been feasible, given the abundance of lutein in grain of diverse maize accessions  levels were also found to have a deeper orange perceived kernel color  were detected as modifiers in that background, as they conferred altered endosperm color and kernel vitreousness  has also been found to affect perceived kernel color  in future studies would partially remove this potential confound arising from physical/structural relationships and could be of relevance to additional use cases for maize grain. When improving grain carotenoid traits and whole-grain kernel color in tandem, including through selection at one or more of the genes identified in this study, it would be helpful to monitor kernel hardness, both to ensure maintenance of this important trait and to further improve our understanding of relationships between the abundance/composition of grain carotenoid traits and final perceived kernel color, in breeding populations and developed maize varieties.Finally, kernel hardness  is an important agronomic and processing trait, and is also a relevant consideration with regards to carotenoids and kernel color. A nonfunctional allele of el color . Linear This study simultaneously investigated the genetic basis of grain carotenoid traits and visually scored kernel color, in the same experimental framework (field environments and populations). Nine genes were identified for these traits, which encode activities in the precursor and core carotenoid pathways as well as carotenoid degradation. The genetic and phenotypic relationships dissected herein provide additional refinement—including key genes, relevant synergies and tradeoffs, and other potential look-out points—for breeding of orange, carotenoid-dense maize, including from germplasm pools having yellow to orange grain.a priori candidate genes. Scripts used in this study are available in Zenodo at https://doi.org/10.5281/zenodo.5774515. Supplementary material is available at figshare: https://doi.org/10.25387/g3.17298446.The data underlying this study are available as supplementary material. Specifically, Supplementary Table 2 contains the transformed and untransformed best linear unbiased estimators (BLUEs); Supplementary Table 3 contains the 0.1 cM consensus genetic linkage map used in JL analysis; Supplementary Table 4 reports the JL-QTL allelic effect estimates nested within family; Supplementary Tables 5 and 6 report the JL results; Supplementary Table 9 reports the GWAS results; and Supplementary Table 10 contains the list of


Genetic Influence in Exercise Performance, includes five original investigations and three reviews that, we think, contribute to building evidence for the potential influence—or actual lack of effect—of some genetic factors on athletic performance.Numerous studies in the last two decades have analyzed the association between genetic variants and athletic performance, or other related traits . However, the practical applicability of most findings in the field is still limited, with several caveats to consider. Notably, a deeper understanding is needed of potential gene–gene and gene–environment interactions. Future studies should also assess larger samples than in the past, as well as replication cohorts. This Special Issue, entitled PPARGC1A) and nuclear respiratory factor 1 (NRF1) genes  on the muscle fiber composition of healthy individuals. PPARGC1A (commonly known as PGC1α) is a central inducer of mitochondrial biogenesis and function [vastus lateralis of healthy men. Yet, in women, rs8192678 A/A and rs6949152 A/A genotypes showed a link—individually or in combination—with a higher and lower of proportion of I and IIx isoforms, respectively. This investigation suggests that the potential influence of both PPARGC1A and NRF1 genes on endurance performance might be mediated by an effect on muscle fiber differentiation, at least in women. Yvert and colleagues  investigfunction ,4,5,6. ICYP1A2) rs762551 and adenosine A2A receptor (ADORA2A) rs5751876 polymorphisms on the ergogenic effect of acute caffeine intake in professional handball players. The CYP1A2 enzyme is responsible for ~95% of caffeine metabolism, and individuals with the rs762551 A/A genotype are categorized as “fast metabolizers” because they usually show a higher rate of caffeine metabolization into paraxanthine than their referents with the C-allele [ADORA2A gene because a major mechanism explaining the potential ergogenic effect of caffeine at the brain level is a blockade of the receptors for an inhibitory neurotransmitter, adenosine, thereby increasing arousal [ADORA2A T/T are potentially “high-responders” to caffeine compared to those with the C-allele [CYPA1A2 A/A [ADORA2A T/T [CYPA1A2 and ADORA2A genotype. Thus, this investigation contributes to the body of knowledge on the ergogenic effect of caffeine on sports performance in real sport scenarios, and it does not support a role for CYPA1A2 or ADORA2A genetic variants. In this regard, we suggest that, although they might appear less attractive or even less publishable, “negative” findings are as important as positive ones in order to build a balanced perspective of the scientific evidence. If only “positive” findings were to be published, we would only see the tip of the iceberg in the nurture vs. nature debate. Muñoz and colleagues  study th arousal . In thisC-allele . AlthougA1A2 A/A  and ADORRA2A T/T  individuRA2A T/T ,15,16,17MMP1 rs1799750, MMP10 (rs486055) and MMP12 (rs2276109)) and the risk of an important injury in the context of sports—non-contact anterior cruciate ligament (ACL) rupture. These authors address a relevant question. Indeed, the prevention of ACL injuries is essential, given the long time lost for competition, the long-term consequences on articular cartilage, even in surgically treated injuries, and the resulting high economic costs [MPP variants with non-contact ACL rupture when comparing genotype distributions in individuals with primary non-contact ACL rupture vs. their referents without any history of this condition. Thus, their findings do not support the use of genetic screening among athletes to prevent ACL rupture , which is in line with recent research [Lulińska and colleagues  study thic costs . MMPs plic costs , as thesic costs , with ge MMP12 rs76109) anic costs ,24. Howeresearch . AMPD1) on the effects of a 12-week aerobic training program in obesity related parameters among healthy women. This enzyme plays an important regulatory role in muscle metabolism by catalyzing the deamination of adenosine monophosphate (AMP) to inosine monophosphate (IMP) and shifting the reaction towards ATP resynthesis in muscles. In fact, the aforementioned polymorphism has been previously linked with endurance performance, with the C/C genotype associated with a higher likelihood of being an elite endurance athlete vs. carriage of the mutant T-allele [2peak) after three months of training compared to their C/C counterparts [Leońska-Duniec and colleagues  investigT-allele ,27,28. Aterparts , which iterparts . Interesterparts . In the terparts , a 12-we2peak is of medical relevance given the strong, independent, and predictive value of this variable with regard to cardiovascular risk and mortality. A seminal study by Claude Bouchard and colleagues found comparable VO2peak values in brothers of the same sibship, and the similarities in VO2peak were even greater in dizygotic and monozygotic twins [2peak reached ~40%. In this issue of the journal, Gaowa and colleagues [2peak among ~1000 untrained Han Chinese people, with a similar representation of both sexes. When setting a stringent threshold p-value of 0.0004, only one single nucleotide polymorphism (rs4295), located in the gene encoding angiotensin-converting enzyme, was associated with VO2peak. Furthermore, from a list of 125 candidate variants showing previous evidence for an association with exercise or cardiovascular traits, only three  accounted together for just 2.2% (p = 0.0007) of the interindividual variance in VO2peak. The authors conclude that a strong independent health indicator, such as cardiorespiratory fitness, might be influenced by several polymorphic variations in candidate genes (at least in the pre-trained state), but these polymorphisms represent a minor portion of the variance, suggesting a more important role for environmental factors. An important novelty of this study is the ethnic origin of the population, as the bulk of the evidence on genetics and exercise-related traits comes from research conducted in Caucasian people, and these findings might not be extrapolated to other ethnicities.Determining the potential influence of genetic factors on VOic twins , with thlleagues  study thFEV) in the serotonergic system, D2 receptor (DRD2) and catechol-O-methyltransferase (COMT) in the dopaminergic system, and potentially transcranial direct current stimulation tDCS-responsive genes in the GABAergic system). Future research ideas are also discussed. On the other hand, Hall et al., [There are three high-quality reviews in this Special Issue. Kitazawa and coworkers  discuss  et al.,  propose  et al.,  discuss Although the field of sports genetics is fascinating, the task of identifying “the champions’ genes” is a very difficult one. Much more research is needed before genetic screening becomes a useful tool in the context of personalized exercise training for disease treatment or prevention.


Genetic differences between individuals underlie susceptibility to many diseases. Genome-wide association studies (GWAS) have discovered many susceptibility genes but were often limited to cohorts of predominantly European ancestry. Genetic diversity between individuals due to different ancestries and evolutionary histories shows that this approach has limitations. In order to gain a better understanding of the associated genetic variation, we need a more global genomics approach including a greater diversity. Here, we introduce the Healthy Life in an Urban Setting (HELIUS) cohort. The HELIUS cohort consists of participants living in Amsterdam, with a level of diversity that reflects the Dutch colonial and recent migration past. The current study includes 10,283 participants with genetic data available from seven groups of inhabitants, namely, Dutch, African Surinamese, South-Asian Surinamese, Turkish, Moroccan, Ghanaian, and Javanese Surinamese. First, we describe the genetic variation and admixture within the HELIUS cohort. Second, we show the challenges during imputation when having a genetically diverse cohort. Third, we conduct a body mass index (BMI) and height GWAS where we investigate the effects of a joint analysis of the entire cohort and a meta-analysis approach for the different subgroups. Finally, we construct polygenic scores for BMI and height and compare their predictive power across the different ethnic groups. Overall, we give a comprehensive overview of a genetically diverse cohort from Amsterdam. Our study emphasizes the importance of a less biased and more realistic representation of urban populations for mapping genetic associations with complex traits and disease risk for all. The city of Amsterdam is a modern urban society with a vibrant composition of different ethnicities. It is one of the most diverse cities in Europe with ethnic minorities making up more than half of its population. The history of the Netherlands is well represented in the diversity of the communities that live there. Several communities are from the Dutch former colony the Republic of Suriname. During the colonial period, plantation labor was carried out by African slaves and after the abolition of slavery by indentured laborers from Asia. The history of forced or voluntary migration of ethnic groups from diverse continents is reflected in the current Surinamese population. Three of the main Surinamese ethnicities living in Amsterdam are the Afro-Caribbean, hereafter named African Surinamese; the South-Asian; and the Javanese Surinamese. Besides Surinamese, there are Moroccan, Turkish, and Ghanaian communities from recent migrations living in Amsterdam. This diversity has consequences for the city’s health-care system because the ethnic background can be associated with disease susceptibility, progression, and medication response . DiffereThe genome-wide association study (GWAS) approach has proven to be successful in identifying genomic regions associated with diseases and in studying how and to what extent genetic variation contributes to disease susceptibility . In the An important step toward global genomics is to compose cohorts from the modern ethnic urban landscapes and a trans-ethnic inclusion of participants. This more diverse composition of cohort sampling likely ensures a more representative genomic disease prediction and optimized treatment decisions. The Healthy Life in an Urban Setting (HELIUS) study was initiated to address multiethnic representation in life science research. HELIUS is a prospective cohort study characterized by several large ethnic groups living in Amsterdam . A crossHealthy Life in an Urban Setting (HELIUS) is a prospective cohort study executed in Amsterdam, characterized by ethnic diversity . HELIUS A cross-selection of 10,283 HELIUS participants was made for genotyping. Whole blood for DNA isolation was collected in EDTA tubes and stored at −80°C in the AMC Biobank. DNA was isolated using the Gentra Puregene Isolation Kit (Qiagen), and quality control procedures were performed to determine the DNA yield and purity.DNA was shipped to the Erasmus MC Human Genomic Facility where genotyping was performed. For genotyping, the Illumina Global Screening Array 24v1-0 designed for the multiethnic genome-wide content purpose was used. An in-house protocol of the Human Genomic Facility, with Illumina’s GenomeStudio software, was used to perform the initial genotyping of the array. Subsequently, a second quality control (QC) was performed for removing the individuals with discordant gender information and when more than 5% called data on markers per individual were missing.p ≤ 10−5), and heterozygosity deviations from a mean larger than ± 3 SD  of <1%, violation of the Hardy–Weinberg equilibrium (HWE)  or INFO scores <0.8 (1000G Sanger imputation). Height phenotypes were measured as individual’s length in centimeters. For the height analysis, gender, age –based tool fastGWA was used for the GWAS . Height p-value ≤1.0e-5 , random-effects (SE), and the Han and Eskin’s random effects model (SE2) using Metasoft . Beside  ≤1.0e-5 . The speHeritability estimation for height and BMI was calculated using GCTA-GREML . In summPolygenic risk scores were calculated using the protocol described in p-value thresholds 0.001, 0.05, 0.1, 0.2, 0.3, 0.4, and 0.5. Regression in R, with correction for the first 10 PCs, was used to determine the “best-fit” PRS for each ethnicity, explaining the highest phenotypic variance. Bootstrapping was used to calculate the CI for each “best-fit.” Results were plotted with the European ancestry proportion based on the highest K in European ethnicities of the ADMIXTURE K = 3 prediction.PLINK was used for clumping and calculating the polygenic scores with the score function. PRS was calculated for Materials and Methods section), principal component analysis (PCA) of the genotypes was used to check congruence between genetic ancestry and the HELIUS database reported ethnicity. PCA was performed after merging the HELIUS cohort with the 1000 Genomes (1000G) cohort to evaluate the clustering with known reference populations  was used for genotyping the HELIUS cohort . After gulations . Based oulations . All cohp-value = 0.002 for C1 and p-value = 0.003 for C2) and other African Caribbean’s , possibly indicating different degrees of ancestry variance. The genetic background of the HELIUS cohort demonstrates the complexity of the diversity in individuals with widely separated geographic ancestry and admixture.Because of the admixture within the different ancestries from the diverse continents within the Surinamese, we used the program ADMIXTURE to detect the substructure within the three Surinamese ethnic groups. The HELIUS cohort was merged with the 1000G cohort to infer these substructures and ancestral admixture. For the admixture method, it was necessary to make assumptions on the number of ancestral source populations . AssuminImputation reliability is highly dependent on the use of the correct reference populations containing adequate haplotype diversity compared to the imputed cohort. Imputation reference panels from TopMed, Michigan, and Sanger imputation servers using removed GSA array markers for the HELIUS ethnicities were used to get an indication of imputation accuracy. The Sanger imputation server was used with the 1000G and Haplotype reference panel (HRC) . For thePopulation stratification can lead to false-positive or false-negative signals in a genome-wide association study when samples with different ancestries are analyzed simultaneously. Some methods have been developed that take population structure and relationships between samples into account, such as linear mixed-effects regression models (LMM) . GWAS dap-value of <5 × 10e−8. Height’s strongest association with both the entire cohort and ethnic-specific meta-analysis was the GDF5-BFZB locus on chromosome 20 known to be involved in the alterations in bone growth and development , 8 , and 11  did not reach genome-wide significance with the Sanger 1000G imputation. The signals on chromosomes 9 and 14 were only found when using the TopMed imputations. For markers in these signals, a two-fold higher frequency was observed in African populations. In line with the higher frequency of these markers in African populations, we found that the TopMed signal of the chromosome 7 marker was within the IGFBP3/TNS3 genomic area previously reported in a height GWAS in individuals of African ancestry (The entire cohort and ethnic-specific height and BMI GWAS results were compared with the results of previously published GWAS on those traits . Due to elopment  (Sanna eelopment . With thelopment . Three oancestry . For theFor BMI it showed that particularly for the entire cohort analysis, despite not being genome-wide significant, the strongest associations were for known BMI-associated gene regions MC4R, SEC16B, and, to a lesser extent, the FTO gene  Dina et. Two sigSNP-based heritability for BMI and height was determined for the entire cohort and all HELIUS ethnicities separately . The SNPPRS results for the genetic complex height and BMI phenotypes were substantially different between ethnicities in our cohort. Especially the European ancestry of the cohort was correlated with the degree of explained phenotype-variance by the PRS . The lowThe Amsterdam Urban genetic map shows the diverse composition of the city and the challenges of urban sampling. Besides the clustering of all participants with their ethnic substructure, substantial admixture can be observed . NotableWithin the genotyped HELIUS participants, a large proportion of individuals had Surinamese descent with three distinct ethnic groups, namely, the African Surinamese, South-Asian Surinamese, and Javanese Surinamese. African Surinamese descended from the transatlantic slave trade to the Americas having roots in Western Africa . South-AThe effect ethnicity has on the imputation results revealed that both the imputation server and specific imputation reference cohort have an impact on the results. Our re-imputation data suggest that a close match of the genetic background from the imported individuals with the reference panel was important. This was also emphasized by studies of human genetic variation that discover new common genetic variation through the inclusion of new populations . It alsoBesides imputation, another challenge is performing GWAS with ethnically diverse cohorts. Despite the fact that the HELIUS cohort is smaller in size than cohorts of previous height and BMI studies, few of the reported associations were replicated. For the strongest associations, no big differences between the used imputation reference panels were observed. GWAS using the TopMed reference panel did however found more genome-wide associations. Better imputation of the HELIUS cohort with the TopMed reference panel could be the explanation for this. When using the entire cohort, the risk is that ethnic-specific variations will be lost during QC and not analyzed. This can be overcome by splitting the cohort into ethnic groups on which the QC and association testing are performed. The disadvantage of splitting the cohort into ethnic groups for the meta-analysis is even smaller groups were created, which is likely the reason that the replicated associations were weaker . Also etHeight and BMI were specifically chosen as a trait because of their high heritability. Compared to BMI, height showed a higher degree of heritability across all groups. This higher heritability of height than BMI is in line with previous findings reporting the heritability of these complex traits . EstimatAltogether, the HELIUS cohort with its Amsterdam urban roots reflects the current genetic diversity of the Dutch metropolitan city. The uniqueness of the dataset lies in the considerable genetic diversity of the participants that all live in close geographic proximity with each other and that therefore all are exposed to similar urban environmental influences. Its diverse composition covers a substantial portion of the human population genomic diversity of today. The consequence of the genetic diversity and admixture within the population will be that the analytic methods must be carefully considered. An important point is the choice of the imputation reference cohort. With the genetic diversity of the HELIUS population, the most diverse reference panel is preferred. When comparing the BMI and height associations of the HELIUS cohort with the ethnic-specific meta-analysis, the entire cohort analysis showed stronger associations signals. The effectiveness of polygenic risk prediction is dependent on the ancestral background of the discovery GWAS, confirming the need for large-scale non-European GWAS efforts for a wider range of complex traits and diseases, to which the HELIUS cohort can potentially contribute. Shifting toward more diverse cohorts in genetic research has the potential to help health research improve for larger portions of our increasingly globalizing and ethnically diverse societies.


Phaseolus vulgaris) production in the United States and around the world. Genetic resistance is needed to effectively control root rot disease because cultural control methods are ineffective, and the pathogen will be present at the end of one season of production on previously clean land. A diversity panel of 149 snap bean pure lines was evaluated for resistance to Fusarium root rot in Oregon. Morphological traits potentially associated with root rot resistance, such as aboveground biomass, adventitious roots, taproot diameter, basal root diameter, deepest root angle, shallowest root angle, root angle average, root angle difference, and root angle geometric mean were evaluated and correlated to disease severity. A genome wide association study (GWAS) using the Fixed and random model Circulating Probability Unification (FarmCPU) statistical method, identified five associated single nucleotide polymorphisms (SNPs) for disease severity and two SNPs for biomass. The SNPs were found on Pv03, Pv07, Pv08, Pv10, and Pv11. One candidate gene for disease reaction near a SNP on Pv03 codes for a peroxidase, and two candidates associated with biomass SNPs were a 2-alkenal reductase gene cluster on Pv10 and a Pentatricopeptide repeat domain on Pv11. Bean lines utilized in the study were ranked by genomic estimated breeding values (GEBV) for disease severity, biomass, and the root architecture traits, and the observed and predicted values had high to moderate correlations. Cross validation of genomic predictions showed slightly lower correlational accuracy. Bean lines with the highest GEBV were among the most resistant, but did not necessarily rank at the very top numerically. This study provides information on the relationship of root architecture traits to root rot disease reaction. Snap bean lines with genetic merit for genomic selection were identified and may be utilized in future breeding efforts.Root rot is a major constraint to snap bean ( Phaseolus vulgaris) wherever they are grown. It has been and continues to be a primary yield limitation in both snap and dry bean production. Root rot is a broad term that can refer to infection by a variety of pathogens or complexes thereof , where resistance is conditioned by major genes. Some studies on F. solani resistance indicate that the markers that were discovered may be useful in breeding for resistance. However, there is little evidence of their application in breeding programs. The underlying reason for this is probably the polygenic nature of F. solani resistance. MAS has not proven to be very effective for such traits. Genomic selection (GS) is emerging in common bean as a technique that allows selection of quantitative traits without the labor-intensive approach that traditional MAS would require (Marker assisted selection (MAS) have been most successfully applied to traits conditioned by major genes, or in some cases, major QTL  and spec require . GS mode require  as well  require , cooking require , and nem require  to discoDiffering models for genomic selection are similar in their predictive accuracy. One study of maize traits found that rrBLUP had a slightly higher predictive accuracy in comparison to four other genomic prediction models . Other rF. solani sp. phaseoli in snap beans under field conditions typically found in a major snap bean growing region of the United States. As the genetic background of snap beans is unique, this is an important gap that needs investigation separate from previous dry bean studies of Fusarium root rot genetic architecture. To achieve this goal, three research focus areas were identified: (1) Analysis of root and plant morphological traits in a diversity panel of snap beans as related to root rot resistance or susceptibility, (2) GWAS on root rot resistance in a diversity panel of snap beans, and (3) Genomic prediction of cultivars to identify lines with superior breeding potential based on the totality of all marker effects in order to better capture minor allelic effects that may be missed by GWAS.The purpose of this research was to improve the understanding of the genetics underlying resistance to F. solani in Oregon. This diversity panel contains pure line examples of both centers of domestication with a representative cross section of historical and contemporary snap beans, but no wild materials. About 83% of the lines in the SBDP are of Andean center of domestication with the remainder being of Mesoamerican derivation  Snap Bean Diversity Panel  were evaluated for resistance to root rot, which primarily consists of rivation . They carivation , more thF. solani root rot resistance  and strongly resistant  cultivars were included. The OSU cultivars included in the panel were bred and selected on the research farm under constant root rot pressure, and as a result, have high levels of resistance, and consistently grouped with the most resistant lines in the diversity panel. Additionally, the panel included ‘FR-266’, an experimental snap bean line bred in the Pacific Northwest for sistance . This lisistance . It has The trials were planted with a modified randomized complete block design with the field divided into four replicated blocks on a north-south axis. This method of blocking was chosen as the size of this experiment exceeded previous years’ plantings and extended into soil that may have had a lower level of disease pressure. Due to their unique characteristics and need for a trellis system, the pole beans were planted in a separate four block randomization at the west end of the field. The plots were 3.0 m long, planted in a single row at a density of 50 seeds per plot. Rows were spaced 75 cm apart. A border row of OSU5446, a root rot susceptible experimental line, was planted on the north and south edges of the field, as well as 1.5 m end plots on the east and west ends of each row to minimize edge effects. Planting dates were 10 June in 2014 and 21 May in 2015. The seed was treated with captan pre-emergent fungicide (Bonide Products Inc.) prior to planting to improve germination and emergence uniformity and reduce differences in stand among lines.F. solani, nearly all researchers have used 1–5 or 1–9 visual rating scales . Each plot was evaluated at this uniform phenological stage. A Shovelomics protocol , 2013 wag scales . Taprootg scales .Root angle difference, root angle average, and root angle geometric mean were calculated from deepest and shallowest root measurements. Root angle difference was the shallowest root angle subtracted from the deepest root angle. This conveys the span of the soil profile accessed by the plant. Root angle average is the mean of the deepest and shallowest root angles and expresses the general orientation of the roots, from zero to 90°. Root angle geometric mean is the geometric mean of the root angle average and the root angle difference and was formulated to provide a single value that integrated soil profile span and root orientation.∗Year with Year treated as a random effect and the Covtest option to test for homogeneity of variances. Variances from 2014 and 2015 demonstrated homogeneity, and both years of data were combined into a single analysis. Second, normality by year was examined using PROC GLM with the model [Trait] = Rep Variety. Third, a mixed model analysis of variables with years combined was performed using PROC GLM with the model [Trait] = Variety Year Rep(Year) Year∗Variety with Year, Rep(Year) and Year∗Variety treated as random effects. As the two individual plants measured from each plot were intended to capture information on a plot-mean basis rather than an individual plant basis, mean scores for each plot were used.To characterize the variation observed in the 2014 and 2015 trials, the following statistical approach was used. First, homogeneity of variances across years was examined using PROC GLIMMIX  using the model [Trait] = Variety Rep(Year) Year VarietyTo evaluate whether root morphological traits and disease severity were positively or negatively associated, a Pearson’s correlation coefficient analysis was performed in SAS 9.3 on the least square means of the phenotypic data for disease severity, aboveground biomass, adventitious roots, basal root diameter, taproot diameter, shallowest root angle, deepest root angle, root angle difference, root angle average, and root angle geometric mean. Least square means were generated from combined data from the 2014 and 2015 trials when ANOVAs were conducted as described above. Correlations were generated for all pairwise combinations of traits.The genotypic dataset consisted of 10,607 SNPs generated by using two Illumina iSelect 6K Gene Chip sets (BARCBEAN6K_1 and BARCBEAN6K_2) . These BNarrow sense and broad sense heritability are essentially equivalent in a highly inbred crop such as common bean. With complete homozygosity, it can be assumed that there are no dominance effects present. In the absence of dominance effects, variance among inbred lines, or Var(G), provides an estimate of additive genetic variance or Var(A), rendering the two equations equivalent . Additive is the number of environments, and r is the number of replications per environment. Heritability for each trait was calculated using SAS code developed by was used to determine heritability, where The entire SNP dataset was utilized for GWAS analysis. The phenotypic data used for GWAS was a single value for each trait, averaged across four reps and two years. Due to the incongruity of a pole bean plant architecture for biomass measurements, pole type beans were removed from the biomass analysis leaving 139 genotypes (lines) for this analysis. All other traits were measured with the full set of genotypes.R2 values, FarmCPU was run within GAPIT (version 3) with the added code, Random.model = TRUE. The SNP data was formatted in Microsoft Excel and was filtered for a minor allele frequency (MAF) of 0.05 within R.The FarmCPU statistical method was performed in version 4.0.2 of the R software environment . To deri1 Principal components one to five accounted for 22, 33, 41, 48, and 52% of the variation, respectively. Based on the widely accepted criterion of principal components accounting for between 25 and 50% of the variation  was conducted in TASSEL, version 5.2.73.ariation , the choariation . LinkageTwo different thresholds were examined for a cutoff of significance in the Manhattan plots. The more conservative threshold was a Bonferroni cutoff that utilized the effective marker number of 2,411 as determined by the SimpleM method . This geP. vulgaris genome, version 2.1). Using conservative estimates of linkage disequilibrium in common bean  was determined. Correlations between observed and predicted values using the entire population (100%) in both the rrBLUP calculations and the cross validated rrBLUP calculations were determined in R using a Pearson correlation coefficient.To evaluate the predictive power of the rrBLUP calculations, cross validation was performed by randomly splitting all the genotypes within this study into a training set and validation set. The models evaluated used ratios of training set to validation set of 60:40, 70:30, 80:20, and 90:10%. Random partitioning into training and validation sets with the training set used in rrBLUP to predict the phenotype of the validation set was iterated 10,000 times  with 10 repetitions at each level for each trait from which the mean predictive accuracy  were created. The full SNP sets had 7,082 for biomass and 8,032 for all other traits (number of SNPs retained after filtering for MAF < 0.05). These were reduced in an exponential manner  to create the subsets. Each subset contained the most highly significant SNPs identified by GWAS. For each subset, the correlation of observed with predicted values was computed in rrBLUP.Associated SNPs from the GWAS analysis were not added to the rrBLUP model as fixed effects  because Means and standard errors for the traits measured in the BeanCAP SBDP are shown in Disease severity was negatively correlated with aboveground biomass, basal root diameter, and taproot diameter , and posh2 = 0.75 and 0.74, respectively. The root angle traits had the lowest heritability, ranging from h2 = 0.32 for root angle difference to h2 = 0.41 for root angle average.A range of heritabilities was observed for the different traits measured . AbovegrGWAS assumes normality . The disA biplot of the first two PC axes  revealedR2) explained by SNPs indicated a low contribution to disease resistance by each SNP ranging in value from 0.9 to 10.8% with the highest value for ss715647578 on Pv03 and the lowest value for ss715646526 on Pv07. The effect of allelic substitution was negative for three SNPs and positive for two  for disease severity, biomass, and the five root architecture traits , biomass (P ≤ 0.001), adventitious roots (P ≤ 0.001), basal root diameter (P ≤ 0.05), deepest root angle (P ≤ 0.05), and root angle geometric mean (P ≤ 0.05), but no statistical significance for the remaining traits. The significant interactions for disease severity and biomass appeared to be due to differences in magnitude rather than changes in rank years as shown by moderate but highly significant correlations between years based on Spearman rank correlation (data not shown). The pattern exhibited by the replicates for disease score differed in 2014 and 2015, most likely due to differences in order of evaluation. In 2014, lines in all reps were evaluated when reaching the desired physiological stage but in 2015, reps were evaluated sequentially. In 2014, spatial variation in reps was important with the two inner reps showing more disease than the outer reps. In 2015, disease severity increased over time. Coefficient of variation (CV) was relatively low at 15 and 23 for disease severity and biomass, respectively, with other traits similar to biomass, except shallow root angle, which was had a CV of 68. The high disease pressure and consistent watering likely contributed to this uniformity across years and a low CV. Although our study could not exclude every environmental factor present in an outdoor field, these environmental factors may be both confounding but also offer the possibility of capturing complex interactions between genes and the environment that could be important to disease manifestation in a grower’s field.Our ANOVA results showed significant year x line interaction for disease severity  domain candidate gene was found in the vicinity of SNP ss715645486. PPR domains have been implicated in an increase of biomass in a study of ia trees , and areia trees . The thria trees .Fusarium resistance and abiotic stress tolerances? Fusarium root rot with various cultural practices that can affect the development of disease. Their findings were that anything that constricts the root system (such as cold soils and compaction) will exacerbate disease development. Intermittent drought stress combined with these factors restricting root growth will further increase disease pressure. Excess soil moisture even if it is intermittent and of short duration will prevent oxygen diffusion to the roots and further inhibits root growth. High population densities also tend to increase root rot. Previously bred Fusarium root rot resistant dry bean cultivars tended to tolerate cold soils, drought and compaction better than susceptible cultivars, but in waterlogged soils, resistance was defeated. In the present study, there does not appear to be a tradeoff among these traits with one exception: the correlation of shallow root angle with disease resistance, which might lead to less drought tolerant plants. Correlation is not causation so this supposition would need to be tested and could be carried out by subjecting the snap bean diversity panel to drought as well as other forms of abiotic stress. On the other hand, nutrient use efficiency, especially for phosphorous (P), is associated with shallow root systems  with relatively few (126–221) SNPs. In contrast, root traits were best modeled with one-half to one-quarter of the full SNP data set. Other studies have shown that genomic prediction models that incorporate GWAS can improve accuracy in breeding programs . Disease severity and GEBV for disease severity were negatively correlated with TPC . One piece of the missing heritability may be conditioned by genetic variability in the phenolic/flavonoid biosynthetic pathway. Flavonoids and phenolics have been shown to possess antimicrobial properties which have been associated with resistance to root rots . One linTo achieve acceptable processing quality, most contemporary snap bean cultivars are white-seeded, which eliminates anthocyanins and flavonols from the pods. If we had found a strong relationship between TPC and disease severity, those associations with pigment production would not be useful in a breeding program. Although lines varied for total TPC, all but one was white-seeded (preventing anthocyanin accumulation in the pods) and thus do not present barriers to use in a breeding program for root rot resistance.In common bean, geographic origin and population structure have been shown to be an important influence on genetic variation in wild and landrace beans . With thFusarium root rot resistance in common bean and will give snap bean breeders additional tools to dissect and manipulate resistance to Fusarium root rot in snap beans. The heritabilities give information on the expected gain from selection that could be achieved. The correlations among disease and root traits provide valuable information on the root architecture necessary to develop resistant lines. The GWAS analysis provides additional markers to a growing number associated with resistance. The genomic predictions identify individual lines with genetic merit worth pursuing by utilizing the totality of marker effects. Future research could include a more detailed investigation root trait associations with biotic and abiotic stress tolerance, combine snap bean data with dry bean for a meta-GWAS, and development of a MAGIC population  can be found below: ScholarsArchive@OSU: JM conceived of the field study. AH designed and collected all the data on the measured traits and contributed to significant portions of the writing through her master’s thesis. AH and JM performed the analysis in SAS for the ANOVA, correlations, and heritabilities. LW condensed and edited the manuscript of AH, contributed significantly to the writing, performed the GWAS, rrBLUP, ANOVA, and QQ plots of residuals in R, and created LD heat maps and PCA in TASSEL. JM and LW performed genomic prediction analyses, and edited and contributed to all parts of the writing and analysis. 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mycobacterium tuberculosis, is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect  whilst correcting for local ancestry for Bantu-speaking African ancestry . Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.Pulmonary tuberculosis (TB), caused by  It is opathogen . Accordipathogen . TB therpathogen , but witpathogen . The varpathogen .Previous investigations into southern African history and population structure elucidated indigenous KhoeSan ancestry in the region, in addition to populations being multi-way admixed due to multiple inter-and intra-continental migrations . This poPrevious admixture mapping and association studies investigating TB susceptibility loci in South Africa were restricted by a low number of controls, small reference population sample size and low SNP density , 2014a. M.tb, and could therefore be regarded as latently infected  and this study (S20/02/041) were both approved by the Health Research Ethics Committee of the Faculty of Health Sciences (HREC), Stellenbosch University. The research was conducted according to the principles expressed in the Declaration of Helsinki (2013).https://imputation.sanger.ac.uk) and the African Genome Resource (AGR) reference panel (https://www.cog-genomics.org/plink/2.0/)  multi-ethnic genotyping array (MEGA) comprising ∼1.7 million markers . The Sance panel  was utilce panel  using PLnk/2.0/)  in orderAfter merging of admixed and source ancestral populations, all individuals missing more than 10% genotypes were removed, SNPs with more than 3% missing data were excluded and a Hardy-Weinberg equilibrium (HWE) filter was used in controls (threshold < 0.01). The data was screened for relatedness using the software KING  and indik ancestral populations and corresponding ancestral genotype frequencies through cross validation. For the purpose of computational efficiency, redundant single-nucleotide polymorphisms (SNPs) were removed and only tagging SNPs representative of the genetic haplotype blocks remained. Therefore, each SNP that has a linkage disequilibrium (LD) r2 of >0.1 within a 50-SNP sliding window (advanced by 10 SNPs at a time) was removed. A total of 261,694 autosomal markers after LD pruning and 820 individuals (413 cases and 407 controls) were used to infer ancestry in an unsupervised manner for k = 3–10 (5 iterations). All 820 individuals were grouped into running groups of equal size together with 289 ancestral populations whilst inferring global ancestry proportions. Related individuals were included in separate running groups. Running groups were created to ensure an equal number of reference populations and admixed populations whilst removing relatedness as a confounding factor during global ancestry assignment. After determining the correct k number of contributing ancestries through cross validation, the software RFMix was used to infer global ancestry proportions for downstream statistical analysis, since ADMIXTURE is not as accurate as haplotype-based analyses  model, first described by 1. Global ancestry proportions were included as covariates and thus represents the null model. This test is regularly used in GWAS to investigate whether an additive allelic dose affect exists on the phenotype, not considering local ancestry .2. Local ancestry expressed in terms of the number of copies of a specific ancestry  at a locus were included as covariates. This model is often utilised to conduct admixture mapping studies to elucidate ancestry effects of variants which showcases frequency disparities across ancestral populations .3. Minor allelic effects were used in an additive manner and were included as covariates whilst still adjusting for local ancestry. Therefore, jointly testing for model 1 + 2.4. This model utilises the ancestry-specific minor alleles at a locus, thus the minor alleles together with the corresponding ancestry of the minor allele were included as covariates . This model is an extension to the allelic (3) and local ancestry (2) model by modelling the combination of the minor allele present at a specific locus and the ancestry of the specific allele at that genomic locus. .The following four regression models were tested simultaneously to detect the source  of the association signals observed:Since the true underlying causal variants as well as the LD between the marker under study are unknown, modelling all three terms simultaneously is the most effective approach to elucidate causal variants in an admixed cohort with minimal power loss . TherefoThe development of power and sample size analysis tools for mapping ancestry-specific effects are lacking. The power to detect significant associations depends greatly on the proportion of admixture, differences in effect sizes between diverse ancestries and differences in the allele risk frequencies among ancestral populations. It is noteworthy to highlight that this information will vary for each admixture scenario. Nonetheless, it remains critical to conduct some sort of power calculation to ensure the reliability of elucidating ancestry-specific genomic regions amongst admixed individuals. Hence, we conducted a priori power analysis in order to ensure the reliability of results given our samples size using G*Power , 2009.STEAM (Significance Threshold Estimation for Admixture Mapping)   was usedk number of contributing ancestries was determined to be k = 5, since this was the lowest k-value through cross validation  were included in logistic regression models to assess whether any loci were significantly associated with TB status . More information regarding the distribution of age, gender and ancestry proportions of the cohort can be found in the p-value < 1.078 × 10−6) due to an allelic SNP effect  whilst adjusting for Bantu-speaking African local ancestry on chromosome 4  (FSTL5), which is a protein coding gene involved in calcium ion binding. No restrictions on the analysis or inflation of results were observed as indicated by the Quantile-Quantile plot  were identified. Furthermore, it is clear from our results that multiple distinct lead variants were identified for each ancestry.One variant (rs28647531) was significantly associated with TB status ( × 10−7) . This vaile plot . AlthougCUL2 (Cullin-2), located on chromosome 10. The lead variants identified by conducting admixture mapping (model 2), are summarised in The lead variants identified using only the global ancestry as covariates (model 1), are summarised in We conducted local ancestry allelic adjusted association analysis in a multi-way admixed South African (SA) population to investigate whether ancestry-specific genetic regions are associated with TB susceptibility. Multi-way admixed populations allow the opportunity to simultaneously assess the association of TB status in multiple continental populations and elucidate possible ancestry-specific effects on TB susceptibility. Previous studies were confounded by the limited number of representative reference populations available to infer local ancestry and the use of the low-density Affymetrix gene chip array (∼500k markers) in the analyses. New, more representative ancestral populations and an increase in accuracy of several software tools facilitated the novel findings presented here.CUL2. Although the function of CUL2 on M.tb clearance is still uncertain, CUL2 forms an important part of the cullin-RING-based E3 ubiquitin-protein ligase complex and subsequently targets the ubiquitination of target proteins  gene  was found to be associated with an increased risk for an Asian subgroup in a meta-analysis investigating TLR variants and susceptibility to TB  gene  and is iresponse . This acresponse . Polymorpulation , as wellpulation . This suty to TB . Additioty to TB . If the FSTL5 gene, which has not been associated with TB susceptibility previously. This gene is a coding protein and was previously associated with colorectal cancer and acute myeloid leukaemia (WT1 gene located on chromosome 11p13 and locus 18q12 and polymorphisms in the TLR8 genes (DSEL-AS1) was identified to be in LD with a leading SNP located on chromosome 18q12 for Bantu-speaking African ancestry  passed the significance threshold and is located on chromosome 4q22 using the allelic model adjusting for Bantu-speaking African local ancestry . This vaeukaemia . Previoueukaemia . LikewisR8 genes . Althougancestry . DSEL-ASe levels , but no TRPM3), located on chromosome 9, is a protein coding gene which belongs to the family of transient receptor potential (TRP) channels. TRPM3 is a permeable non-selective cation gene channel , is involved in calcium signalling and was amongst the lead variants identified for KhoeSan ancestry located on chromosome 2q14 by the LAAA model. This gene forms part of the cyclic nucleotide phosphodiesterases, which plays a role in signal transduction by regulating intracellular cyclic nucleotide concentrations through hydrolysis of cAMP and/or cGMP to their respective nucleoside 5-prime monophosphates. Therefore, this gene is important for calmodulin binding and cGMP binding, as well as associated with urate measurement and glomerular filtration rate , we have 95% chance to correctly rejecting the null hypothesis for large (>0.5) and medium effect sizes (>0.3). We do however lose power if the effect size is small  and any reported associations with a smaller effect size should therefore be interpreted with caution . Furtherin silico and in vitro validation. Moreover, progression to active TB might be explained by numerous variants having a small effect on disease outcome, or exceptionally rare variants (M.tb strain should also be included in association analysis, if possible, since it appears that M.tb co-evolved with humans (M.tb lineage affects TB severity (M.tb lineages, i.e., the genotype and lineage that co-existed historically, had the lowest average TB score (Future studies should also include variants . Varianth humans  and thatseverity . The comTB score . AccordiTB score . Thus, tTB score . Thus, tIn conclusion, this is the first study to apply the LAAA model to a complex five-way admixed population from South Africa which exhibits extensive genetic heterogeneity. This was enabled by newly developed algorithms for local ancestry inference, updated reference panels to represent contributing ancestral populations and a more suitable genotyping platform for diverse populations worldwide. We have demonstrated that the LAAA model robustly captured the source of association signals in highly complex admixed individuals. The true underlying architecture at each locus is unknown for most southern African populations, indicating that careful consideration of both global-and local ancestry is required for successful complex-trait mapping. Furthermore, local ancestry information across the genome is likely to become relevant to determine whether a genetic variant is expected to be useful in precision medicine, specifically in admixed populations.