Streblospio benedicti provides an opportunity to dissect a major transition in developmental mode using forward genetics. Females in this species produce either small eggs that develop into planktonic larvae or large eggs that develop into benthic juveniles. We identify large-effect loci that act maternally to influence larval size and independent, unlinked large-effect loci that act zygotically to affect discrete aspects of larval morphology. The likely fitness of zygotic alleles depends on their maternal background, creating a positive frequency-dependence that may homogenize local populations. Developmental and population genetics interact to shape larval evolution.Evolutionary transitions from indirect to direct development involve changes in both maternal and zygotic genetic factors, with distinctive population-genetic implications, but empirical data on the genetics of such transitions are lacking. The polychaete  Streblospio benedicti, a genetically tractable species that harbors both states as heritable variation  eggs that develop into obligately feeding larvae that spend weeks in the plankton. These larvae grow larva-specific swimming chaetae that are thought to deter predation   . In paraomosomes . Males aomosomes .2 larvae are intermediate in size and variable in morphology; 15% lack larval chaetae and 25% lack anal cirri. As expected for a maternal trait, G2 larval size showed no linkage to G2 genotype. Every other phenotype implicated a small number of large-effect loci  to planktotrophic females, thereby shifting the maternal background to assess the penetrance of the chaetae loci. We found that the zygotic-effect loci that affect chaetae length and number in an F1 maternal background also do so in the planktotroph maternal background , chaetae number, length of the longest chaeta, and presence/absence of pronounced anal cirri from images using ImageJ (NIH).We imaged G2 animals reached adulthood, fed with defaunated mud, we paired them randomly for crosses. Because offspring size is proportional to egg size and has a strong additive maternal basis  assembled with Platanus  males and females from the Bayonne and Long Beach populations. We also imaged meiotic diakinesis in males and zygotene in females. We used a karyotyping protocol modeled closely on 20 over one hour. The tissue was then fixed with freshly prepared 3:1 Methanol:Acetic Acid fixative, with three fixative changes over one hour. Next, tissue was macerated on a glass slide in 60% acetic acid, using glass needles. Finally, after the slides had dried, we stained them with 5% Giemsa stain in phosphate-buffered saline. We imaged the slides on a Zeiss Axio Imager M2 with 100x objective.We imaged giemsa-stained chromosomes from cells at mitotic metaphase from laboratory-raised inbred , the Y is larger than all other chromosomes . However, we only recoded multi-SNP contigs for which segregation in at least one G2 family was fully resolvable. Note that the assignment of genotypes to the maternal vs. paternal P0 is arbitrary because we lack genotypes for those individuals. To call a marker genotype for an individual worm, we required that all SNPs in the contig marker had to have genotype calls that conform to an intact P0 haplotype; this adds a layer of stringency in excluding genotypes that contain any erroneous SNP calls. Most multi-SNP contigs contain two or three SNPs, but one contained 16 SNPs spread across multiple sequencing reads. Because of missing data and genotype error, the requirement for intact P0 haplotypes across 16 SNPs is too stringent, and this contig was recoded into nine separate markers .We tested each SNP for sex linkage by applying Fisher’s exact test to genotype counts within each family and then combining the p-values using Fisher’s meta-analysis. For non-sex-linked contigs that contain multiple SNPs, we manually recoded each contig, when possible, to be a single intercross marker, with inferred P1 mother shown in their records but compatible with one of the other F1 mothers). These 21 strains were excluded from subsequent analysis, leaving a dataset of 292 worms distributed across five families , plus two for sites erroneously called as SNPs . For each SNP, we estimated likelihoods for each configuration for each family. We maximized the likelihood including a one-step error probability  allowed to range up to 10%. We next identified the segregation pattern (including the genotypes of the F1 parents of the G2 families) that maximizes the likelihood over the five families. This is a composite likelihood insofar as the genotype error probabilities are estimated for each family and the global pedigree likelihood derived from the appropriate combination of family likelihood estimates.We inferred segregation classes for each autosomal marker and imputed genotypes for the five parents of the GS. benedicti genetic map:We then used four steps to build the Construction of an autosomal map from markers segregating in an intercross pattern, using Rqtl .Construction of an autosomal map using the refined intercross marker dataset from step 1 and hundreds of additional non-intercross autosomal markers, using lep-MAP .Addition of a sex-linked linkage-group.Assignment of founder origin to each autosomal haplotype.1s are heterozygous and G2s segregate 1:2:1, implying that the parental genotypes are homozygous for alternate alleles). These SNPs include 448 from multi-SNP contigs that are recoded into 193 intercross markers, plus 101 individual SNPs identified as intercross by likelihood. We excluded 17 markers that were missing data for more than 10% of individuals and three other markers that exhibited strong departures from Mendelian proportions (p<10−10). From the resulting dataset of 274 markers and 245 G2 individuals, we constructed an autosomal genetic map in Rqtl (v.1.40 – 8) following the protocol of Broman , estimated the genotyping error rate as 0.013 by maximum likelihood, and reordered the markers with another 20 cycles of orderMarkers with error.prob = 0.013.The first strategy uses data from 549 non-sex-linked SNPs that individually or in contig-groups segregate in an intercross pattern  genotype calls to missing data. We then reestimated the maximum likelihood error rate as 0.004 for the remaining data and reordered the markers with 50 cycles of 1 parents by likelihood, as described, and we used lepmap2 (1s and 241 G2s in four families) and 1,111 markers. Processing steps included ParentCallGrandparents with XLimit = 8 and ZLimit = 8, Filtering with epsilon = 0.02 and dataTolerance = 0.0001, SeparateChromosomes with lodLimit = 15, and JoinSingles with lodLimit = 10 and lodDifference = 5. This analysis assigned 676 markers to 10 autosomes and 97 markers to two sex-linked chromosomes . For the remaining non-intercross SNPs, we imputed the genotypes of the five F lepmap2   for that run.To order the autosomal markers, we used  lepmap3  . These are uninformative for the genetic map.For each of the 1,389 SNPs in the original dataset, we estimated the likelihood of each possible autosomal or sex-linked segregation pattern. We identified 32 SNPs with clean X-linked segregation (AAxBY or BBxAY) and none with Z-linkage. The X-linked SNPs mapped to one major and one minor sex-linked linkage group in the lepmap analysis at step two above. The other major lepmap-defined sex-linked linkage group contained only SNPs that segregate as fixed differences between X and Y chromosomes . These markers were then ordered in Rqtl as in step 1. A group of three markers, which form a separate, minor sex-linked linkage group in our lepmap analysis at step 2, here form a distantly linked piece of the X chromosome . The final map, incorporating 702 markers, is Sb702.The maps produced at each of the three steps are provided as Rqtl cross objects in 1 worms that contributed to the G2 generation, and we therefore lack information about which autosomal alleles derive from which founder. To assign parental origins to genotypes, we tested the two possible orientations using population-genomic data. We showed previously . Population allele frequencies were estimated from read counts in the pooled population sequence data. To account for the presence of zero counts of reads of some alleles in either population, which can be due to true absence or to low read coverage, we added one read to every allele count and then used these modified count proportions as population frequency estimates. In total, we used data from 110 SNPs (4 to 27 per chromosome).1):For the hypothesis that founder genotype one derives from Bayonne and founder genotype two from Long Beach, we calculate the likelihood as L * P,2). We report the log10 likelihood ratio as a statistic.assuming Hardy-Weinberg equilibrium within each population. We then calculated the likelihood of the alternative assignment  G2 families , presence of anal cirri (p=0.70), or G3 offspring size (p=0.80). We identified family effects for the length of the larval chaetae (p=0.0003) and G2 size (p=10−7).Our linkage mapping approach tests for loci that differ between the founding Long Beach and Bayonne parents of the cross, and QTL that are heterozygous in the P2 families may be due to one or more of (1) environmental maternal effects, including familial experimental counfounders such as the exact age of full-sib larvae at the time they were measured; (2) maternal or zygotic genetic effects that segregate among the F2 families due to heterozygosity in the P0 founders; and (3) genetic effects that differ among F2 families due to differential segregation distortion or chance allele frequency variation but not due to heterozygosity in the P0s. Because G2 size is a maternal-effect trait, the family effect for this phenotype can only be due to environmental maternal effects among the F1 mothers or segregating maternal effect factors that were heterozygous in the P0s. However, in the latter case, we expect the family effect to recur as variation among the G2 families in G3 offspring size. We observe no such effect, implying that the G2 families do not differ in their genotype distributions at maternal-effect offspring-size loci. For larval chaetae length, we cannot exclude the possibility that some of the variation among G2 families is due to QTL that were heterozygous in P0s. In a model including the significant QTL (detailed below), G2 family explains 5.7% of total variance in the larval chaetae length phenotype, while the two significant QTL explain 47.0%.The observed differences among Gfitqtl function, we tested for pairwise interactions between detected QTL across linkage groups and for effects of G2 family. In each case we compared a model of additive and dominant within-locus effects to models that incorporated the additional terms, and we used likelihood ratio tests to determine the statistical significance of improvements in model fit. We estimate the effect size for QTL using the best-fitting model, as described in 2 family. For G3 offspring area, the model includes an interaction between two QTL. Effect estimates are reported below in To model the genetics of each trait using the Rqtl r2 = 0.22, p<0.001). The two traits share a QTL on LG3 that may act pleiotropically. However, when variation due to the LG3 QTL is removed, there is still a strong correlation between the trait residuals .We find that chaetae number and length are correlated, as in 2 females for which all three traits were scored .We analyzed the three continuous traits – mean offspring size, chaetae number, and chaetae length – in a fully multivariate QTL scan . The appY = XB + e, where Y is the 149 × 3 matrix of phenotypes, X is a matrix of fixed effects, B is the vector of fixed-effect coefficients to be estimated, and e is normally-distributed residual error. The fixed effects are the intercept, G2 family identity, and two vectors coding the additive and dominance variables for a single genetic marker. At each marker, we fit this model and compared it to a model with intercept and G2 family as the only fixed effects, calculating a p-value from the Pillai-Bartlett statistic. We estimated thresholds for genome-wide significance by repeating the analysis on 1000 datasets in which phenotype vectors were permuted among individuals within G2 families. R code that reproduces the analysis is included in We fit the following model at each marker: Five QTL exceeded the genome-wide threshold for significance (p≤0.001 in each case), coinciding with the five QTL detected for these traits by univariate analysis. In a model incorporating additive and dominance effects for each QTL, five additive and two dominance effects (for the QTL on LG3 and LG6) were retained as significant p<0.01; . We then2 male genotype on the distribution of chaetal phenotypes in their backcross progeny. We used a linear mixed-effect model to account for the relatedness of larvae within a brood and then compared models with and without the loci implicated in each trait in the G2 generation .Thank you for submitting your article "Decoupled maternal and zygotic genetic effects shape the evolution of development" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This paper investigates the genetics of a major life-history dimorphism – development via a morphologically distinct larval form vs. direct development – in a marine worm. Rather than "super-genes" determining life-history strategy, separate genetic loci with different modes of inheritance were found to influence egg size and larval morphology.This is a substantial and well-written contribution addressing a biological question of broad interest.Essential revisions:There were two major issues that could lead to over-interpretation of the results. First, fitness effects are implied but not measured, and this is not always clear from the writing. A hypothetical fitness landscape is shown in Figure 1B, and it is clearly stated in the figure legend that this landscape is hypothetical. However, at several other points in the text, sentences are worded in a way that implies that fitness may have actually been measured, rather than inferred based on direction of phenotypic effects. This could be fixed by more cautious phrasing. For example:In Abstract, original text: "The fitness of zygotic alleles depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."More conservative wording: "The likely fitness effects of zygotic alleles depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."Even more conservative wording: "The phenotypic effects of zygotic alleles on a hypothetical fitness landscape depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."S. benedicti populations can segregate 729 possible genotypic combinations, most of which are maladaptive."In the Discussion, original text: "We identified six chromosomes with large effects for four traits, implying that S. benedicti populations can segregate 729 possible genotypic combinations, most of which would produce combinations of intermediate phenotypes that are likely to be maladaptive."More conservative wording: "We identified six chromosomes with large effects for four traits, implying that In the Discussion, original text: "Finally, we found that the evolutionary transition to lecithotropic development requires the interaction of two genotypes, the mother and offspring. The fitness of a zygotic allele depends on the probability it occurs in a favorable maternal genetic background"More conservative: "Finally, we found that the evolutionary transition to lecithotropic development requires the interaction of two genotypes, the mother and offspring. Because of this dual control of larval phenotype, the likely fitness effects of a zygotic allele depends on whether it occurs in a favorable maternal genetic background." etc.The second point is more difficult to address. Due to failed genotyping, the genotypes of the P0 grandparents of the existing cross, or the F1 parents used to make F2s, are not known. Thus the direction of the allelic effects in the cross is actually not known, although the paper is generally written as if they went in the expected direction . This is not a trivial point, and it affects the conclusions of the paper. For example, the difficulty contrasting alleles would have invading different genetic backgrounds is less of an issue if existing planktotrophic and lecithotropic populations already harbor lots of alleles with non-concordant phenotypic effects.This issue could be addressed by carrying out additional genotyping to try to confirm whether the planktotrophic and lecithotropic alleles segregating in the cross do in fact come from either the planktotropic and lecithotropic grand parental populations. Note that the additional genotype calls would only be in the regions harboring key QTLs, rather than on a whole genome basis. With a focused genotyping effort, it may be possible to determine these key genotypes on limited or poor quality material still available from the original grandparents. Alternatively, it may be possible to infer the likely grandparental genotypes in the key QTL regions, based on the differential frequency of linked markers found in additional individuals sampled from the starting populations.If you cannot produce these data, a thorough rewrite will be required to make sure all claims are supported, and the manuscript will be reviewed again.Less substantially, please clarify in what sense Figure 4 shows maternal "masking", and in general improve description, i.e. clearly label what exactly is the offspring of one cross, how many individuals were scored for each chaetae number.… Essential revisions:There were two major issues that could lead to over-interpretation of the results. First, fitness effects are implied but not measured, and this is not always clear from the writing. A hypothetical fitness landscape is shown in Figure 1B, and it is clearly stated in the figure legend that this landscape is hypothetical. However, at several other points in the text, sentences are worded in a way that implies that fitness may have actually been measured, rather than inferred based on direction of phenotypic effects. This could be fixed by more cautious phrasing. For example:In Abstract, original text: "The fitness of zygotic alleles depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."More conservative wording: "The likely fitness effects of zygotic alleles depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."Even more conservative wording: "The phenotypic effects of zygotic alleles on a hypothetical fitness landscape depends upon their maternal background, creating a positive frequency-dependence that may homogenize local populations."In the Discussion, original text: "We identified six chromosomes with large effects for four traits, implying that S. benedicti populations can segregate 729 possible genotypic combinations, most of which are maladaptive."More conservative wording: "We identified six chromosomes with large effects for four traits, implying that S. benedicti populations can segregate 729 possible genotypic combinations, most of which would produce combinations of intermediate phenotypes that are likely to be maladaptive."In the Discussion, original text: "Finally, we found that the evolutionary transition to lecithotropic development requires the interaction of two genotypes, the mother and offspring. The fitness of a zygotic allele depends on the probability it occurs in a favorable maternal genetic background"More conservative: "Finally, we found that the evolutionary transition to lecithotropic development requires the interaction of two genotypes, the mother and offspring. Because of this dual control of larval phenotype, the likely fitness effects of a zygotic allele depends on whether it occurs in a favorable maternal genetic background." etc.We have made all of the wording changes that the reviewers suggest, and we have modified other references to fitness to make clear that we expect intermediate phenotypes to be suboptimal based on theory but that we have not measured fitness.The second point is more difficult to address. Due to failed genotyping, the genotypes of the P0 grandparents of the existing cross, or the F1 parents used to make F2s, are not known. Thus the direction of the allelic effects in the cross is actually not known, although the paper is generally written as if they went in the expected direction . This is not a trivial point, and it affects the conclusions of the paper. For example, the difficulty contrasting alleles would have invading different genetic backgrounds is less of an issue if existing planktotrophic and lecithotropic populations already harbor lots of alleles with non-concordant phenotypic effects.This issue could be addressed by carrying out additional genotyping to try to confirm whether the planktotrophic and lecithotropic alleles segregating in the cross do in fact come from either the planktotropic and lecithotropic grand parental populations. Note that the additional genotype calls would only be in the regions harboring key QTLs, rather than on a whole genome basis. With a focused genotyping effort, it may be possible to determine these key genotypes on limited or poor quality material still available from the original grandparents. Alternatively, it may be possible to infer the likely grandparental genotypes in the key QTL regions, based on the differential frequency of linked markers found in additional individuals sampled from the starting populations.If you cannot produce these data, a thorough rewrite will be required to make sure all claims are supported, and the manuscript will be reviewed again.The reviewers make an important point about the direction of the QTL effects, and we have generated new data to address it. Unfortunately, we were not able to add genotypes from the original parents due to the limited, degraded DNA. We instead used a population genetic approach to infer the source population for the alternative haplotypes in our genetic map. We collected 25 individuals from each population and used Pool-seq methods to estimate allele frequencies in each. We then calculated the likelihood of alternative population assignments for each founder chromosome. We find strong support from the allele frequencies for a specific population assignment for each founder haplotype, and the inferred assignments are consistent with the directional effects described in our manuscript. This has important implications for interpretation because it demonstrated that the QTL effects are acting in the direction we expected; they are complementary not contrasting effects. We have added the details of our analysis to the mapping part of the Materials and methods section and we provide the underlying data and analysis scripts as part of Supplementary file 3.Less substantially, please clarify in what sense Figure 4 shows maternal "masking", and in general improve description, i.e. clearly label what exactly is the offspring of one cross, how many individuals were scored for each chaetae number.1 mothers), but we see the same pattern where the number of chaetae depends on the zygotic genotype at the two QTL. At the same time, we would predict that because of the maternal planktotrophic contribution that all of these offspring would develop some chaetae, but some that have lecithotrophic paternal alleles do not, demonstrating the incomplete zygotic penetrance of this trait.We changed the figure legend to clarify the cross distributions. The offspring in these 30 crosses have a different maternal background from the rest of our dataset (which have F


Here, we discuss approaches to avoid this curse of dimensionality (n ≪ p), which will involve analyzing intermediate phenotypes such as molecular traits and component traits related to plant morphology or physiology. Because these approaches will rely on novel data types such as DNA sequences and high-throughput phenotyping images, Breeding 4 will call for analyses that are complementary to traditional quantitative genetic studies, being based on machine learning techniques which make efficient use of sequence and image data. In this article, we will present some of these techniques and their application for prioritizing causal loci and developing improved varieties in Breeding 4.In the past, plant breeding has undergone three major transformations and is currently transitioning to a new technological phase, Breeding 4. This phase is characterized by the development of methods for biological design of plant varieties, including transformation and gene editing techniques directed toward causal loci. The application of such technologies will require to reliably estimate the effect of loci in plant genomes by avoiding the situation where the number of loci assayed ( This perspective article considers technologies that have contributed to the evolution of plant breeding and focuses on innovations that will shape its future, amid a growing world population and changing climate. Since the Neolithic revolution, plant breeding has gone through distinct phases, separated by radical transformations. Within each of these major technological phases, we see a pattern that we would now call a shift from monogenic to polygenic focus. While it is tempting to believe this pattern is the product of greater understanding, it is most likely the product of how adaptation proceeds, as suggested by the Fisher–Orr geometric model Fisher . A populThe first phase, which we refer to here as Breeding 1, accounts for the first 10,000 years of crop improvement. Early plant breeders domesticated useful species and unintentionally promoted domestication traits, such as reduced shattering in maize  in experimental populations, using genetic markers such as restriction fragment length polymorphisms  than there are observations at the genotype level : n ≪ p. As a result, model parameters cannot be solved without simplifying assumptions. So, even though Breeding 3 models may accurately predict genetic merit, they should not be used to infer effects of many QTL. This curse of dimensionality is a critical issue in Breeding 3, and it severely hampers applications based on estimated QTL effects, especially for complex traits. In models based on few markers, omitted marker information results in models that fail to account for the confounding effects of loci. For example, apparent QTL effects may be caused by unobserved causal loci that are physically linked to genetic markers. In marker screens involving single-marker analyses or stepwise model selections , estimated effects may fluctuate randomly around their true values. These random fluctuations can reach such extremes that the most significant estimates might be highly overestimated in magnitude: this is the winner’s curse, also known as the Beavis effect in plant breeding  by means of regularization, such that their estimates are distorted  allowed breeders to exploit family relationships and more accurately estimate breeding values Fisher . Neverthg Beavis ; Xu 2003 Gianola . All thrn ≪ p) in order to make reliable inferences about numerous effects of loci. More exhaustive assays of genetic variability, using whole-genome or haplotype sequence data, will allow scientists to directly query the effects of observed loci, instead of estimating them indirectly through linked genetic markers. Furthermore, analyses involving higher n and/or lower p will permit more reliable inference of causal loci. This new framework will make use of sequence or image data and will therefore benefit from innovative machine learning approaches, different from the linear regression models typically used in Breeding 3  can be used to produce data on more genotypes, or endophenotypes (intermediate molecular traits such as gene expression levels or metabolic activity) can be used to provide many phenotypes per genotype . Differently, neural networks may capture biologically relevant effects of polymorphisms beyond additive effects and should be able to model effects of interactions between polymorphisms in local nonlinear functions  has consisted of two steps: (1) segmentation, in which regions of interest for feature extraction are isolated ; and (2) analysis of structure, in which component traits are predicted from regions of interest  , with high enough accuracy for applications in Breeding 4.For each genotype, estimation of component traits would provide useful replication over time, as opposed to replication across plants. This type of replication should be useful to increase the prediction accuracy for component traits on each plant, making use of correlation of measurements over time to fit time-series functions to the data  for estimating component traits as parameters of such functions   and to eliminate confounding by physical linkage of loci. Then, with reasonable scale in field trials, effects of loci can be measured to define reliable plant ideotypes for developing improved varieties by genomic selection or genome editing.Ultimately, Breeding 4 will aim to detect causal variants as precisely as possible for subsequent breeding or editing, using approaches complementary to traditional QTL mapping techniques. This effort will rely on various types of data: DNA information (either in the form of genetic markers or DNA sequences), genomic annotation (on epigenetic status or evolutionary constraints) and phenotypic data, including both agronomic traits of interest and intermediate traits such as endophenotypes or component traits. Breeding 4 has become possible because of rapid progress in genotyping technologies  as well as phenotyping technologies (image acquisition and robotics). This phase will be characterized by a shift in focus from genetic marker data to well-annotated haplotype data. The shift from genetic markers to haplotypes will further reduce the cost of genomic prediction and help expand Breeding 3 to new crops and programs. In Breeding 4, it will allow contextualized sequence data to be used as inputs to models predicting endophenotypes. Fortuitously, Breeding 4 is concurrent with the development of machine learning methodologies, in particular neural networks such as CNNs or RNNs, which are appropriate for estimating the effects of polymorphisms on endophenotypes (from DNA sequences) or analyzing high-throughput phenotyping data for predicting component traits (from images). Realistic depiction of the genotype-phenotype map for these simpler traits will allow to estimate effects for prioritizing a handful of putative causal variants that are worth assessing in subsequent analyses on agronomic traits Fig. . Two plaGPR, SEJ and ESB all contributed to developing the article's outline, reviewing the literature and writing the manuscript.


Maize ear inflorescences at the floral meristem stage were collected from two inbred lines, one chromosome segment substitution line that carried hlEW2b (sub-CSSL16), the receptor parent lx9801, and the Zheng58 × sub-CSSL16 and Zheng58 × lx9801 hybrid lines. A total of 256 metabolites were identified, including 31 and 24 metabolites that were differentially accumulated between the two hybrid lines and between the two inbred lines, respectively. Most of these metabolites are involved in complex regulatory mechanisms important for maize ear development. For example, nucleotides are basic metabolites affecting cell composition and carbohydrate synthesis. Additionally, nicotinate and nicotinamide metabolism is important for photosynthesis, plant stress responses, and cell expansion. Moreover, flavonoid and phenolic metabolites regulate auxin transport and cell apoptosis. Meanwhile, phytohormone biosynthesis and distribution influence the cell cycle and cell proliferation. Our results revealed that changes in metabolite contents may affect the heterotic performance related to ear width and yield in maize hybrid lines. This study provides new clues in heterosis at the metabolomics level and implies that differentially accumulated metabolites made distinct contributions to the heterosis at an early stage of ear inflorescences development.Heterosis can increase the yield of many crops and has been extensively applied in agriculture. In maize, female inflorescence architecture directly determines grain yield. Thus, exploring the relationship between early maize ear inflorescence development and heterosis regarding yield-related traits may be helpful for characterizing the molecular mechanisms underlying heterotic performance. In this study, we fine mapped the overdominant heterotic locus ( Heterosis can increase grain yield and has been applied to improve agricultural production for a few hundred years. Three classical genetic hypotheses  were proposed early last century to explain the genetic basis of heterosis. The overdominance hypothesis has been validated using the SINGLE FLOWER TRUSS (SFT) locus of tomato3. However, these hypotheses did not end the century-long debate regarding whether they fully explain the genetic basis of heterosis because they represent largely conceptual models and do not describe the underlying molecular mechanism. Advances in high-throughput technologies have enabled researchers to develop new strategies for dissecting the genetic or molecular mechanism regulating heterosis. However, multiple omics-based researches  in plants for hybrids and their parents, have been conducted to explore the cause of heterosis, the genetic mechanism mediating heterosis remains unclear up to now6.Heterosis refers to the phenomenon that hybrids perform better than their parents regarding phenotypic traits, including biomass, development rate, fertility, nutrient quality, disease resistance, and crop yield8. Moreover, based on transcriptomics and proteomics, additive and non-additive effects have been estimated between parents and hybrids10. Due to a large amount of redundant information was generated along with the high-throughput data obtained by genomics, transcriptomics and proteomics technologies, making it even more difficult to elucidate the genetic mechanism of heterosis at one or multiple loci. As so far, at the single-locus level, SFT is the first identified overdominance gene associated with heterosis, has been cloned in tomato3. Although considerable efforts have been made to dissect the genetic basis of heterosis in plants, there is no appropriate method for analyzing heterotic performance because of the complexity of genetic backgrounds and controversies regarding the underlying mechanism.Genomics-based studies revealed that the complementation of two parental genomes makes an important contribution to heterosis12. Compared with other populations, such as recombinant inbred lines (RIL) or doubled haploid populations, CSSLs enable the identification of the chromosomal segment responsible for heterotic effects under the same genetic background in multiple environments. Testcross populations have been widely applied to detect quantitative trait loci associated with heterosis14. For example, Wang et al.12 used 66 testcross F1 lines developed from the corresponding CSSLs to demonstrate that partial dominance and overdominance represent the genetic basis of the heterosis of yield and yield-related traits in rice hybrids. Using a set of 202 CSSLs and the corresponding testcross population, 15 heterotic loci (HLs) for plant height were detected in rice13. Integrating the backcross and testcross populations of CSSLs may clarify the genetic and biological basis of heterosis.To eliminate the influence of genetic backgrounds, backcross populations of chromosome segment substitution lines (CSSLs) were developed and used for characterizing the relationship between heterosis and grain yield as well as the associated components in tomato and rice16. However, relatively few studies comparing the concentrations of secondary metabolites in hybrids and their parents have been conducted in maize, especially involving two inbred lines (one origin inbred line and its progeny with a single chromosome segment from a backcross), with the corresponding hybrids crossed with a common test parent.Recent metabolomics-based investigations have more thoroughly clarified the metabolic activities of many crops, with potential implications for breeding new high-yielding varieties with improved nutritional composition. Metabolites are useful for efficiently predicting the hybrid performance of several crops, including maize and rice17. Ear traits including ear length, ear width, kernel row number and kernel number per row, are determined in developing immature maize ears19. During ear inflorescence development, the floret meristem (FM) stage is critical for ear development and heterosis20. Consequently, this stage is ideal for studying heterosis. Furthermore, metabolites represent the basic compounds accumulating in plant organs that influence the phenotype. However, there is very little or no available information regarding the relationship between secondary metabolites and heterosis. Thus, analyzing the secondary metabolites in hybrids and their parents may provide new insights and clarify the genetic mechanism underlying heterosis. In this study, we collected immature maize ears during the FM stage from two inbred lines  and their corresponding hybrids crossed with a common tester, Zheng58. We integrated the results of our targeted metabolomics approach with relevant gene expression data to (1) explore the metabolic processes involved in early maize ear development; (2) identify metabolites associated with heterosis; (3) construct a metabolites interaction network explaining heterotic performance. These results may help to elucidate the mechanism underlying heterotic performance at the metabolomics level.Heterosis of ear traits has significant positive correlations with maize yieldhlEW2b on chromosome 2 between markers umc1555 and umc1465, and determined that it accounts for 8.5% of the ear width heterosis over the standard control (Zheng58 × lx9801)21. To more precisely locate hlEW2b, 152 homozygous sub-CSSLs were identified among 1,652 F2 individuals derived from the CSSL125 × lx9801 cross by genotyping with 12 new polymorphism markers within the hlEW2b region. Meanwhile, a test population was generated by crossing 152 homozygous sub-CSSLs with the test parent Zheng58 . Screening with molecular markers resulted in the mapping of hlEW2b to a 1.98-Mb region between 7HY-304 and 1HY-100 ] was a homolog of Zm00001d002893 [encoding Walls-Are-Thin 1 (WAT1) related protein], which could disrupt auxin transport to regulating cell elongation23. Moreover, several candidate genes encode some important enzymes, for example, Zm00001d002880 encodes an isopropylmalate dehydrogenase, Zm00001d002889 encodes a soluble inorganic pyrophosphatase, Zm00001d002922 encode an inositol polyphosphate multi kinase beta, Zm00001d002912, Zm00001d002916 encode the glyoxal oxidase-related protein and five candidate genes  encode the peroxidase. These candidate genes maybe contribute to heterosis, more work needed to narrow down the mapping interval and confirm the functional gene. Furthermore, according to the heterosis for ear width and the genotype of the homozygous recombinant lines, one sub-CSSL16 line with the shortest donor chromosomal segment (1.98 Mb) harboring hlEW2b was selected for a metabolomics analysis.A total of 42 candidate genes, including 30 functional genes coding proteins, 3 transcription factors and 8 unknown functional genes, were found in this mapping region  (Table 16 in two environments (Supplementary Table hlEW2b), we calculated the additive and dominant value of HL (hlEW2b) based on the following equations: a = (P1–P2)/2 and d = F1−(P1 + P2)/2, here, a is the additive effect, d is the dominant effect, P1 refers to the trait value of Zheng58, P2 stands for the trait value of sub-CSSL16, and F1 refers to the value of the trait for the hybrid Zheng58 × sub-CSSL16). Theoretically, a ratio of estimated dominance to the absolute value of additive effect (d/a) >1, =1, <1 were regarded as overdominance, dominance and partial-dominance, respectively24. Our results revealed that the HL (hlEW2b) of ear width exhibits high overdominance   between the hybrids of Zheng58 × sub-CSSL between 16 and lx9801, were distributed together along the Y-axis. The PCA model also revealed a generally good biological reproducibility within groups because the three biological replicates of each sample exhibited satisfactory clustering. The general trends for Zheng58 × sub-CSSL16 and Zheng58 × lx9801 indicated their metabolite profiles were similar. This observation was supported by the fact Zheng58 × sub-CSSL16 and Zheng58 × lx9801 were clustered in one subclass    model is a statistical method for supervised classification that can be exploited to predict the group among new and unlabeled samples49) Fig. . This ob74) Fig. . A total74) Fig. .Figure 316 vs. Zheng58 × lx9801 and sub-CSSL16 vs. lx9801). Furthermore, 25 metabolites were differentially accumulated between Zheng58 × sub-CSSL16 and Zheng58 × lx9801, while 18 metabolites were only differentially accumulated between sub-CSSL16 and lx9801  in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801  belongs to purine nucleotide was 1.99-fold higher in Zheng58 × sub-CSSL801 Fig. . Additio801 Fig. . In term801 Fig. .Figure 5surE , IMPDH  and GMPS  that involved in the purine metabolic pathway were expressed at lower levels in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801 , ADA , and ADK  , which is involved in zeatin biosynthetic pathways, was significantly upregulated in Zheng58 × sub-CSSL16  analyses. A total of 15 candidate genes associated with major secondary metabolic pathways were selected for an analysis of the secondary metabolism related to heterosis during the early stage of maize ear development. During the nucleotide metabolism, the genes of NMNAT , which is an important structural gene related to nicotinamide mononucleotide (NaMN) synthesis, was significantly upregulated in Zheng58 × sub-CSSL16  was one of the most significantly upregulated DEGs (P = 5.25E−03) in Zheng58 × sub-CSSL16 vs. Zheng58 × lx9801 group. Meanwhile, URH1 catalyzing nicotinamide riboside (NR) to nicotinamide was upregulated in Zheng58 × sub-CSSL16  populations, have been developed for identifying heterotic loci26. Hua et al.27 identified 44 HLs for grain yield and yield-related components in rice using IF2 population that were developed from paired crosses of recombinant inbred lines, while Tang et al.28 detected 13 HLs for grain yield and its components in maize using the same genetic population. Compared with the primary segregated population and other permanently segregated population, the CSSLs and receptor parent test hybrids used in this study may be more appropriate for dissecting the genetic basis of heterosis. First, the influence of the genetic background can be eliminated during the identification of HLs in comparisons between the test population and the control hybrid Zheng58 × lx9801. Second, accurate phenotypic values may be obtained for the test hybrids under different environmental conditions during the identification of HLs.Heterosis is considerably influenced by the genetic background and environmental conditions. In previous studies, several primary segregated populations, such as BC15. High-throughput technologies were recently applied to accurately measure the accumulation of metabolites, resulting in large amounts of metabolomics data for analyzing rice hybrid performance16. In another study, Lima et al.29 detected more robust metabolite changes  in hybrid lines than in inbred lines. They also observed that a few secondary metabolites could be used as biological markers to predict heterosis. In this study, we conducted a metabolomics analysis with one CSSL and its receptor parent as well as test hybrids to identify nucleotides and their derivatives, alkaloids, flavonoids, phenolics, and phytohormones mediating heterosis for maize ear development. Our data suggested that several important metabolic pathways affect the heterosis for maize ear development.Predicting the generation of high-performance hybrids is an integral part of maize breeding. However, only small subsets of potential crosses have been evaluated under field conditions for selecting elite hybrids. Thus, predicting hybrid performance is important for breeding programs. Advanced molecular technologies enabled the acquisition of system-wide genome data for predicting hybrid performance30. In this study, the enzyme of surE participated in the substrate cycles and influence the balance of nucleotides and nucleosides pools at the cellular levels31, which have the lower expressed levels in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801. Additionally, the enzymes of IMPDH and GMPS exist in one branch of the guanine nucleotide synthesis and IMPDH controlling the gateway to guanine nucleotides for most organism32. The conversion of inosine 5′-monophosphate (IMP) to xanthosine 5′-phosphate (XMP) is the first committed and rate-limiting step in guanine nucleotide biosynthesis, which was catalyzed by IMPDH. Subsequently, XMP is converted to GMP through the action of GMPS32. In this study, some genes in the same branch of guanine nucleotide biosynthetic pathway were expressed at lower levels in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801, including surE, IMPDH and GMPS. These observations suggested that the conversion of inosine to GMP was less activity in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801. Another branch of guanine nucleotide regeneration is the recycling of guanine. The APRT belongs to the phosphoribosyl transferases family. Each APRTs can reutilize purine bases (such as guanine), being substrates for the synthesis of high-energy nucleotides, and convert them into mononucleotides (such as GMP), instead of nucleotides de novo synthesis consuming high-energy33. In this branch of guanine nucleotide regeneration, two genes encoding APRT were significantly upregulated in Zheng58 × sub-CSSL16 comparing with in Zheng58 × lx9801. This could largely explain the high accumulation of GMP in the Zheng58 × sub-CSSL16. Moreover, there are two enzymes potentially contribute to the adenosine metabolism in plant, ADA and ADK. One major route for adenosine salvage may be catalyzed by ADA in specific subcellular compartments34, and ADK as the housekeep enzymes phosphorylate adenosine to adenosine monophosphate (AMP) in Arabidopsis thaliana35. Moreover, in many organisms, AMP not only as an essential component in purine nucleotide pools, but also as the substrate be catalyzed to produce isopentenyladenine37. Furthermore, hydroxylation of isopentenyladenine was a key step, which was catalyzed by CYP735A in tZR biosynthetic pathway37. In this study, some upregulated expression genes involved in the adenosine metabolism including ADA, ADK and in the trans-zeatin riboside (tZR) biosynthetic pathway including CYP735A were recruited in Zheng58 × sub-CSSL16, implied that expression pattern of these genes might be responsible for the increased biosynthesis of tZR in Zheng58 × sub-CSSL16.Biosynthetic and catabolic enzymes could regulate the pool size of nucleotides and nucleosidesNMNAT, as the central enzyme to control this conversion of nicotinamide adenine dinucleotide (NAD) to NaMN38, was significantly upregulated in Zheng58 × sub-CSSL16. In addition, SDT1 are responsible for production of NR in cells and its activity is correlates with reduced cellular NAD39, which was one of the most significantly DEGs between Zheng58 × sub-CSSL16 and Zheng58 × lx9801. These two enzymes have significant impact on the cellular NAD homeostasis, which are the regular steps in NAD metabolism39. Moreover, URH1, as an apparent hydrolytic enzyme, makes a greater contribution to the conversion of NR to nicotinamide, and maintains the higher expression levels than other enzymes in this pathway of NR to nicotinamide salvage40. The expression of URH1 was upregulated in Zheng58 × sub-CSSL16, which may result in the higher accumulation of nicotinamide in Zheng58 × sub-CSSL16 than Zheng58 × lx9801, thus, more trigonelline was synthesized from the nicotinamide.In the nicotinate and nicotinamide metabolic pathway, the expression of de novo guanine nucleotide biosynthesis35. Salvage pathways are essential for the biosynthesis of nucleic acids in all organisms. In plants, they also help regenerate nucleotide pools by converting nucleosides to nucleotides, and they contribute to plant cell metabolism and catabolism41. To preserve energy, the purine salvage pathway is activated to regenerate guanine from GMP, while consuming only one adenosine triphosphate (ATP)42. Guanine nucleotides are important components of nucleotide pools. A small increase in nucleotide pools is accompanied by an increase in carbohydrate content43. In the present study, the purine salvage pathway affected the accumulation of GMP and guanine, with higher levels in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801. These results may be explained by the fact that carbohydrates, such as sucrose (2.16-fold change) and D-(−)-threose (1.56-fold change), accumulated more in Zheng58 × sub-CSSL16 than in Zheng58 × lx9801  provided evidence that NAD is an important coenzyme for photosynthesis and plant stress responses53. Additionally, trigonelline synthesis during seed germination may contribute to the detoxification of surplus nicotinamide54. Moreover, trigonelline induces cell expansion, which is an important determinant of grain width55. In this study, the abundance of trigonelline (1.69-fold change) and nicotinamide (1.71-fold change) increased in Zheng58 × sub-CSSL16  and influencing the early stage of maize ear inflorescence development. Moreover, we also detected other flavonoids. For example, tricetin is a flavonoid exhibiting cytostatic properties by inducing the production of endogenous reactive oxygen species involved in apoptosis60. Tricetin concentrations were higher in Zheng58 × lx9801 than in Zheng58 × sub-CSSL16 Arabidopsis thaliana64. Regarding auxin distribution, PIN is the crucial factor facilitating polar auxin transport, while SLR/IAA14 is a key mediator of auxin redistribution that is regulated by 4-methylumbelliferone66. In this study, 4-methylumbelliferone was detected during the early development of maize ear inflorescences. Additionally, 4-methylumbelliferone accumulated more in Zheng58 × lx9801 than in Zheng58 × sub-CSSL16  with an ear width significantly different from that of Ludan900221. In this study, various mapping populations, including backcross and inbred populations, were developed in the winter of 2013 (for backcrossing) in Sanya, Hainan province, China  and in the summer of 2014 (for selfing) in Xinxiang, Henan province, China . The sub-CSSL test population was constructed by crossing sub-CSSLs with Zheng58 in the winter of 2014 in Sanya. Additionally, the phenotypes of the sub-CSSL test population and the control hybrid, Ludan9002, were evaluated at two locations, Xinxiang and Hebi , in 2014 and 2015. Plants were grown with two replicates and a randomized complete block design. The control hybrid was also planted after every five testcross plants. Plots consisted of two rows (4 m long and 0.6 m wide) that were separated by 0.22 m. The final plant density was 67,500 plants per hectare. The plants were grown using standard agricultural practices.Previously, a maize CSSL panel of 184 lines was developed used two maize inbred lines (Chang7-2 as donor parent and lx9801 as the receptor parent), and a corresponding testcross population was constructed by crossing the CSSL panel with a test parent Zheng58. Of the 169 heterotic loci (HL) associated with grain yield were detected in the testcross population of Zheng58 × CSSL after comparing with the control hybrid Ludan9002 (Zheng58 × lx9801)16, which contained the shortest donor chromosomal segment harboring hlEW2b, was compared with other homozygous lines in the sub-CSSL population. Two inbred lines, sub-CSSL16 and lx9801, along with their corresponding testcrosses, Zheng58 × sub-CSSL16 and Zheng58 × lx9801, were planted in two-row plots in four environments (Xinxiang and Hebi in 2015 and 2016). In each plot, 10 ears were harvested at physiological maturity for phenotypic measurements. Ear length (cm), ear width (mm), ear weight (g), kernel row number, and kernel number per row were measured using standard procedures to confirm the presence of hlEW2b. Raw data underwent a one-way analysis of variance (ANOVA) using the SPSS 17.0 program . The HL associated with ear width were considered to exist in sub-CSSL16 when the ear width in Zheng58 × sub-CSSL16 differed significantly from that of Zheng58 × lx9801 (P < 0.05).A maize inbred line, sub-CSSLhttp://www.maizegdb.org/data_center/map). Additionally, SSR sites were identified with the SSR Hunter program67. Primers were designed using the Primer 5.0 program . Details regarding the primers used for mapping are listed in Supplementary Table hlEW2b within 1.98-Mb region based on the filtered working gene list of the maize genome downloaded from MaizeGDB . Candidate genes were annotated according to InterProScan (http://www.ebi.ac.uk/interpro/scan.html).In this study, we developed molecular markers for fine mapping. The simple sequence repeat (SSR) markers in the region flanked by umc1555 and umc1465 on chromosome 2 were obtained from the IBM 2008 Neighbors maize linkage map  on June 20, 2016. The ear development stage was confirmed based on the leaf age index and microscopic observation. Immature ears at the FM development stage were collected. Three biological replicates were collected for each sample, which comprised five randomly collected fresh immature ears. All samples were immediately frozen in liquid nitrogen and stored at −80 °C prior to metabolite analyses.Two hybrids, Zheng58 × sub-CSSLSamples were freeze-dried in a vacuum and then homogenized using an Retsch MM400 Mixer Mill  with zirconia beads (90 s at 30 Hz). Metabolites were extracted from 100 mg powder with 70% aqueous methanol containing 0.1 mg/L lidocaine. Samples were incubated overnight at 4 °C and then centrifuged at 10,000 × g for 10 min. A 400-µL aliquot of the extract was filtered through an SCAA-104 membrane , and the filtrate was stored in sample bottles for a subsequent analysis by liquid chromatography (LC) and tandem mass spectrometry (MS/MS). The LC-MS/MS analysis was completed with a Shim-pack UFLC Shimadzu CBM30A HPLC system and a 4500 QTRAP ESI–triple quadrupole–linear ion trap mass spectrometer (Applied Biosystems) equipped with an ESI Turbo Ion-Spray interface, which was operated in a positive ion mode and controlled with the Analyst 1.6 program .68. Briefly, the multiple reaction monitoring model was applied to identify the precursor ion of the target material. Many ion fragments were formed after a collision-induced dissociation, and characteristic ions were filtered from the fragment ions. The retention time and mass-to-charge ratio were measured for all detectable ions. Metabolites were quantified according to integral correction and normalization methods for analyzing mass spectra for metabolites in different samples . Metabolites were analyzed qualitatively and quantitatively.Metabolites were identified and quantified according to a published protocol2-transformed before undergoing a multivariate statistical analysis with the SIMCA-P 13.0 program . The data then underwent an unsupervised principal component analysis (PCA), which is a widely used unsupervised dimensionality reduction method, to visualize general clustering and trends among the samples. To identify differentially accumulated metabolites for each group, the partial least squares discriminant analysis (PLS-DA) was applied to remove irrelevant variables and maximize the variance among samples25. The efficiency and reliability of the PLS-DA model was assessed by a cross-validation with R2. The predictive performance was described by the Q2 model. The variable importance in the projection (VIP) reflected the contribution of each variable to the model. Variables with a VIP value > 1.0 were considered to influence the separation of samples in the PLS-DA score plots69. In addition to the multivariate approaches, fold-change ratios were used to assess the significance of each metabolite. A VIP value > 1.0 and a fold-change ratio ≥1.5 or ≤0.67 were set as the thresholds for screening potential metabolites with significant differences in abundance between samples70.Metabolite data were log® Reagent  and then treated with DNase I. The quality of the purified RNA  was characterized by 1% agarose gel electrophoresis and with the Nano LabChip kit . Purified RNA was quantified at OD260nm by using a NanoDrop™ One . The RNA was then used as the template for a reverse transcription with the PrimeScript™ RT reagent kit with gDNA Eraser . A quantitative real-time polymerase chain reaction (qRT-PCR) assay was conducted with the SYBR Premix Ex Taq Kit (Takara) and the BioRad iQ Cycler  according to the manufacturer′s instructions. The Primer 5.0 program was used to design the qRT-PCR primers, which are listed in Supplementary Table −ΔΔCt method was applied to estimate relative gene expression levels. Additionally, gene expression data underwent a one-way ANOVA with the SPSS 17.0 program. The significance threshold was set as P < 0.05.Total RNA was extracted from each sample using the TrizolSupplementary information


This approach identifies two known and one novel loci. In the second multivariate method, we applied principal component analysis (PCA) to the same 15 correlated FBC traits. We then tested for marker associations with each PC in univariate linear mixed models implemented in GEMMA. We show that the FBC composite phenotype as assessed by each PC expresses information that is not completely encapsulated by the individual FBC traits, as this approach identifies three known and five novel loci that were not identified using both the standard univariate and multivariate GWAS methods. Across both multivariate methods, we identified six novel loci. As a proof of concept, both multivariate methods also identified known loci, HBB and ITFG3. The two multivariate methods show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with the standard univariate GWAS in the same dataset.Genome-wide association studies (GWAS) have found hundreds of novel loci associated with full blood count (FBC) phenotypes. However, most of these studies were performed in a single phenotype framework without putting into consideration the clinical relatedness among traits. In this work, in addition to the standard univariate GWAS, we also use two different multivariate methods to perform the first multiple traits GWAS of FBC traits in ∼7000 individuals from the Ugandan General Population Cohort (GPC). We started by performing the standard univariate GWAS approach. We then performed our first multivariate method, in this approach, we tested for marker associations with 15 FBC traits simultaneously in a multivariate mixed model implemented in GEMMA while accounting for the relatedness of individuals and pedigree structures, as well as population substructure. In this analysis, we provide a framework for the combination of multiple phenotypes in multivariate GWAS analysis and show evidence of multi-collinearity whenever the correlation between traits exceeds the correlation coefficient threshold of  Genome-wide association studies (GWAS) have discovered loci associated with a extensive range of human traits and diseases. Mostly, the standard univariate GWAS approach has been performed in a single trait framework without putting into consideration clinical relatedness and correlations among phenotypes. However, as many human traits are highly correlated, given the usual stringent statistical genome-wide significance threshold, such analyses may have a number of limitations including difficulties in identifying genetic risk factors implicating pleiotropic effects . CurrentMultivariate linear mixed models have been extensively used in a range of genetics studies . RecentlGeneral Population Cohort is a population-based open cohort of roughly 22,000 inhabitants around 25 neighboring villages of Kyamulibwa, which is a subcounty of Kalungu district in countryside south-west of Uganda . The cohThe data collection of GPC Round 22 study contained five main stages which took place in 2011 over the course of the year: mobilization (recruitment and consenting), mapping, census, survey, and feedback of results and clinical follow-up. The census consisted of a family questionnaire and questionnaire for the individual recruited from within the family. The family questionnaire was completed by the head of family or another responsible adult or emancipated minor member of the household. The household census questionnaire focused on sociodemographic information about the household, such as the quality of the house, property ownership, and employment of workers. The individual survey questionnaire captured information on members of a household including position within household, marital status, resident status, childbirth, and fertility, tribe, and religion. Information on lifestyle and health was obtained using a standard questionnaire. This included biophysical measurements and blood samples . To asseBriefly, we applied stringent quality control filtering to carry out a succession of sequential quality control steps on ∼5000 Uganda samples genotyped on an Illumina array. Specifically, a total of 2,314,174 autosomal variants were genotyped on the illumine HumanOmni2.5-8 array. We excluded 39,368 autosomal variants who did not pass the stringent quality control cutoff for the variants . We alsoImputation was carried out on pre-phased data with IMPUTE2  using a Fifteen FBC traits were measured using the Beckman Coulter ACT5 Diff CP hematology analyzer . We carrFollowing merger of imputed genotype and sequence data, we first examined if systematic differences existed between imputed genotype data and sequence data . We carrn = 20,594,556) that met an MAF threshold of at least 0.5%.Following exclusion of 904,283 variants  that showed <90% concordance in genotypes between the sequence and imputed genotype data (for 343 samples that had been genotyped and sequence), PCAs did not show any systematic differences between imputed genotype and sequence data. We inspected the first ten PCs to ensure that systematic differences did not represent an important axis of variation in the genetic data. Following filtering, a total of 39,312,112 autosomal markers in the joint set of 6,407 samples were taken forward for analyses. For GWAS association analyses, we only included a subset of variants  approach for analysis . In thisFor computational efficiency, and to avoid correlation effects due to LD, we LD pruned the data prior to calculation of the GRM matrix for each LOCO analysis. We carried out sensitivity analyses using different r2 thresholds for pruning, to examine whether type I error was appropriately controlled on examining genomic inflation factors from QQ plots. We finally used all markers with an MAF >1%, pruned to an r2 threshold of 0.5, using PLINK  with theP-values were calculated using the likelihood ratio test.We also included a covariate to indicate whether data originated from imputed genotyped individuals or sequenced individuals to allow for any systematic differences between data . A MAF threshold of 0.5% was applied in GEMMA analysis. The 20674434 variants that passed all quality control (QC) criteria were tested for associations using the standard univariate (UV-GWAS), multivariate approach (MV-GWAS) and principal component approach (PC-GWAS). These methods were described in the Sub-sections “Univariate GWAS Method (UV-GWAS), Multivariate GWAS Method (MV-GWAS), and Principal Component GWAS Method (PC-GWAS).” For each analysis, the P-values for the 15 traits in QQ plots  and MCH highly correlated with MCV (r = 0.92)  would be 5 × 10−8/15 (3.33 × 10−9). In order to address the potential introduction of type II errors via the application of this rigorous correction, we present all our results using the standard genome-wide significant threshold of P-value ≤5 × 10−8 was met, but we highlight result with Bonferroni corrected significant threshold.There are many methods such as Bonferroni or Sodak for multiple comparisons tests. These methods exploit the correlation structure between genetic variants to estimate the effective number of independent tests, and then use standard techniques for independent tests to calculate an appropriate significance threshold. In standard univariate GWAS (such as our UV-GWAS), the standard significance threshold of 5 × 10p < 5.0E-08 to define association. We defined a locus to be novel if it had not been associated with any FBC trait in any previous GWAS and its P-value is less than or equal to 5 × 10−8. In order to define whether a locus was known or novel, we searched the NHGRI database for loci reaching statistical significance at a level 5e-08 associated with FBC trait. This was supplemented by a literature review.For each strategy , we applied the typically significance threshold of With UV-GWAS method, we analyzed each 15 trait individually and identified 4 novel association signals. This method also confirmed 4 known loci associated with blood traits .HBB gene associated with RDW. The HBB locus is found to be associated with RBC distribution width in our main standard univariate analysis. As previously observed in regions affected with malaria, this variant has reached high frequencies as a result of balancing selection because it can provide resistance against the parasite, and reduces the severity of malaria among carriers. This signal was also identified by MV-GWAS and PC-GWAS.We identified important functional variants such as the sickle cell variant (rs334) in the ITFG3 is not known, it is known to be expressed in an erythroleukemia cell line, and other common SNPs of this gene have been implicated with red blood cell indices in European and Asian GWASs , and monomorphic in Europeans. This signal is reported in our study.UV-GWAS identified 277 genome-wide statistical significant variants in association signal UV-GWAS found 4 genome-wide statistical significant variants in the known locus TPM4. TPM4 plays a crucial role, in association with the troponin complex, in the calcium reliant on regulation of vertebrate striated muscle tightening .Both CTB-30L5.1 and AC008834.1 are Uncharacterized, and do not code for protein. CTB-30L5.1 is an RNA Gene which is affiliated with the ncRNA class while AC008834.1 is a processed pseudogene.HBB and ITFG3) had been previously reported to be associated with at least one of the FBC traits   . The var.81e-08) .rs4837892 in TTLL11 (tubulin tyrosine ligase-like family) is associated with FBC . TTLL11 We identified four novel genome-wide statistical variants at chromosome 20 in the gene OTOR. This gene is known to be associated with posttraumatic stress disorder in GWAS catalog .One variant was identified in the gene COL1A1 to be associated with blood cell. This gene encodes the pro-alpha1 chains of type I collagen whose triple helix comprises two alpha1 chains and one alpha2 chain. The COL1A1 gene provides instructions for making part of a large molecule called type I collagen.Collectively, the three methods identified fifteen loci including ten novel loci associated with FBC traits. Two of the novel loci are intergenic variants and not shown in ATF3 (rs3123543) using MV-GWAS strategy, PDZRN4 (rs7296503), ANKRD26 (rs112505971) and TTLL11 (rs4837892), OTOR (rs9917425), COL1A1 (rs3840870) using PC-GWAS strategy and AC008834.1 (rs7725036), CTB-30L5.1 (rs12534473), two intergenic variants  using UV-GWAS. As a proof of concept, both methods also identified known associated loci HBB and ITFG3. Additionally, UV-GWAS solely identified an additional variant at known loci TPM4 and R3HDM1 while PC-GWAS exclusively identified known association locus HLA-C. The MV-GWAS has been reported to be especially powerful when the genetic correlation between traits differs from the environmental. I think this effect is not present in the PC-GWAS, because it makes PCs based on phenotypic correlations. Therefore the two methods can be sensitive for different correlation patterns between the traits. The methods complement one another and show also show that multivariate genotype-phenotype methods increase power and identify novel genotype-phenotype associations not found with univariate GWAS in the same dataset.To assess the spectrum of genetic variants associated with FBC traits in Uganda, we performed standard univariate and two multivariate GWAS methods to examine association of 15 FBC traits in 6407 individuals in a pooled data from UGWAS with UG2G sequence data. Across the three methods, we identified eight novel loci. They include P-values compare to the standard univariate and PC-GWAS methods.One limitation of the MV-GWAS approach is its sensitivity to highly correlated traits. Sensitivity analyses showed that issues with multicollinearity may occur, that manifest as inflated QQ plots, and unconventional Manhattan plots particularly due to rare variation using the MV-GWAS strategy. The inflation is mostly for variants with <1% maf, but not all variants causing the inflation are in this category. It seems that rare variants are much more susceptible to unstable estimates with multicollinearity. Evidence of multi-collinearity was seen whenever the correlation between traits exceeded the ±0.75 threshold in MV-GWAS strategy. However, this approach exclusively identified a novel locus ATF3 with generally lower R3HDM1, TPM4).Though the PC-GWAS approach captured well the variation across FBC traits simultaneously in this study and identified more novel loci compare with the other two methods, it cannot be a replacement for both the standard univariate GWAS and MV-GWAS because there are still a number of known loci that were not identified by PC-GWAS in our study but were identified in the standard univariate GWAS. The multivariate methods evidence that multivariate genotype-phenotype method increase power and thus identify novel genotype-phenotype associations not found with univariate GWAS in the same dataset. Though the MV-GWAS improves P-value much better, the PC-GWAS strategy found more novel loci.To demonstrate the strength of these multivariate GWAS methods when used to complement each other, we collectively identified six novel loci  subject to replication and both methods also identified three known association loci (These multivariate methods could maximize novel loci discovery for other correlated phenotypes, such as lipid traits, liver function, cancers, anthropometry, immune disease, and others and might help to speed up drug discovery across a range of Cardiometabolic traits as previous studies have shown that FBC may serve as markers of proinflammatory state of metabolic syndrome and promoter of atherosclerotic risk .This study was approved by the Science and Ethics Committee of the UVRI, the Ugandan National Council for Science and Technology, and the East of England-Cambridge South NHS Research Ethics Committee United Kingdom.SF, DG, MS, and PK designed the study. SF performed the analyses. TC carried out the quality control and imputation. DG, MS, and PK directed the project. SF and ON wrote the manuscript. All authors contributed to the interpretation of the results and writing the article.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.


Relatedness between training and test populations and heritability were among the principal factors affecting accuracy. Our results demonstrate the potential of using spectral indices as proxy measurements for selecting lines with increased yield potential and for improving prediction accuracy to increase genetic gains for complex traits in US Pacific Northwest winter wheat.Secondary traits from high-throughput phenotyping could be used to select for complex target traits to accelerate plant breeding and increase genetic gains. This study aimed to evaluate the potential of using spectral reflectance indices (SRI) for indirect selection of winter-wheat lines with high yield potential and to assess the effects of including secondary traits on the prediction accuracy for yield. A total of five SRIs were measured in a diversity panel, and F5 and doubled haploid wheat breeding populations planted between 2015 and 2018 in Lind and Pullman, WA. The winter-wheat panels were genotyped with 11,089 genotyping-by-sequencing derived markers. Spectral traits showed moderate to high phenotypic and genetic correlations, indicating their potential for indirect selection of lines with high yield potential. Inclusion of correlated spectral traits in genomic prediction models resulted in significant ( Trait phenotyping is a major constraint in plant breeding due to the number of lines that need to be evaluated in multiple environments and trials. Traditional phenotyping strategies are usually based on visual scores, which are costly, labor- and time-consuming, subjective, and sometimes destructive . The timImprovement of grain yield is a priority for many wheat breeding programs ; howeverIn comparison to phenotyping, genotyping has gained significant advances in previous years, mainly due to the expanding use of DNA-based marker platforms. For example, single nucleotide polymorphisms (SNPs) have allowed for inexpensive and efficient genomics-based breeding, quantitative trait loci (QTL) analyses, genome-wide association mapping, and marker-assisted selection of different crops ,16,17,18Genetic improvement for traits via indirect selection, using secondary traits such as SRI, should be measured in genetic populations closely representing the area where the improvement is being targeted . The Pacp < 0.001) phenotypic correlations of SRI with grain yield were observed across different developmental stages  phenotypic and genetic correlations with yield were observed for all the measured SRI. Genetic correlations ranged between −0.58 (NWI-1 (Gf2)) and 0.67 (SR (heading)). Using NDVI, NDRE-1, and NDRE-2 resulted in a relative selection efficiency of greater than 1  for the first grain fill. Negative selection efficiency was observed for the water-based index NWI-1, ranging between −0.67 (Gf2) and −0.38 (heading).Broad-sense heritability for the spectral traits were low to moderate, ranging between 0.16 and 0.63 for NWI-1 (heading) and NDRE-1 (Gf1), respectively . Heritabn = 115) were selected based on yield per se and SRI and the number of lines selected using both methods were compared. Selection based on SRI across environments resulted in correctly selecting 14% to 66% of the top 25% highest yielding lines across all environments  and 0.65 (LND17) . Root mep < 0.001) were observed for mean genomic prediction accuracy with and without the spectral measurements as fixed effects in the model for both CV and independent predictions , 0.27 (NDVI + NWI-1), and 0.33 (NDVI + SR). Predicting yield for the DH populations resulted in higher prediction accuracy compared with using the F5 lines, with or without the fixed effects in the model.Using one, and two and three SRI led to a 75% and 42% increase in prediction accuracy, respectively, for the DH lines relative to using the F5 as test populations. Roger’s distances between the diversity training panel and the DH lines was 0.29, whereas genetic distance between the DP and the F5 populations was 0.32. Using the DH populations as validation sets resulted in a higher mean prediction accuracy (0.23) compared with using the F5 panels (0.15) with and without the presence of spectral traits as fixed effects in the prediction model.p < 0.05) compared with the accuracy for different locations (0.22 vs. 0.15). Prediction accuracies within LND environments were significantly higher (p < 0.0001) compared with the predictive accuracy within the PUL environments. Using LND to predict PUL and PUL to predict LND resulted in mean accuracy of 0.11 and 0.18, respectively.Using the LND18 dataset as training population showed the highest average prediction accuracy when fixed effects, either as a single trait or in combination with other indices, were included in the model (0.34), followed by using LND_Com (0.30) for the independent predictions. Overall, no significant difference was observed when using the combined datasets (LND_Com and PUL_Com) and using single environments as training sets for predictions  in the presence of fixed effects. Accuracy within similar environments was significantly higher  through fitting significant markers with >5% genetic variance as fixed effect in a weighted ridge regression model. Maximum gains in accuracy were also observed when combinations of multiple height and phenology genes were included as fixed effects in a prediction model for yield in a historical US winter-wheat panel [Previous studies have shown the potential of increasing prediction accuracy for yield through the inclusion of fixed effects in the model, either in the form of genomic information for major genes, QTL associated with the trait of interest, or phenotypic data for correlated secondary traits. In soft winter wheat, Mason et al.  recentlyin wheat . Throughin wheat  showed tin wheat . Galianoin wheat  previousat panel .In the current study, the inclusion of spectral traits in the model either alone or in combination resulted in a 13% gain in prediction accuracy under the CV. Overall, CV within a single winter-wheat panel resulted in a significantly higher mean prediction accuracy for grain yield, with and without the presence of spectral traits in the model compared with the independent predictions. This result was consistent with previous studies where CV showed higher predictive power than across-population independent validations ,41,42,43H2 = 0.15) and F5_PUL17 (H2 = 0.13) as test panels resulted in 35% lower prediction accuracy compared with using the DH datasets  for predictions with and without the presence of spectral traits in the model under independent validations. Altogether, the low prediction accuracy for yield observed in this study, particularly under the independent validations, could be a consequence of the lack of apparent genetic relatedness among the populations used, low to moderate heritability of the target trait, as well as the differences in the environments used for predictions. Using the water-based index NWI-1, which is negatively correlated with grain yield, resulted in similar prediction accuracy compared with using a positively correlated trait such as SR. In some datasets, using this index resulted in higher accuracies than using NDVI and SR, either alone or in combination. This indicates that a negatively correlated trait could still be used for indirect selection given that it has a significant and strong correlation with the target trait. Other prediction models for yield could also be developed to further assess how the inclusion of correlated secondary traits could affect prediction accuracy across multiple environments, years, and populations evaluated in the US PNW growing conditions.Using the more related DH panels as test sets resulted in higher prediction accuracy compared with using the F5 in the presence of SRI as fixed effects (mean prediction accuracy of 0.23 vs. 0.15), indicating the relevance of relatedness between the training and validation panels. This is consistent with previous studies that show the importance of using genetically related populations for predictions ,47,48. OIt should be noted that adding secondary traits as fixed effects in the model is not always advantageous for increasing prediction accuracy. In the case of Moore et al. , for exaWe were also interested in looking at the effects of the number of spectral indices modeled on the ability to predict grain yield. Altogether, we observed an increased prediction accuracy when all three spectral traits were included as fixed effects in the model compared with using only one or two traits in the CV and independent prediction scenarios. In the current study, the use of multiple genetically intercorrelated traits has been shown to increase model prediction accuracy. Incorporating NDVI, NWI-1, and SR resulted in an 81% and 26% increase in the prediction accuracy compared with using only one or two traits as fixed effects, respectively; whereas using two traits showed a 44% gain over using only a single index for predictions when performing independent validations. In contrast, the number of traits included in the model did not affect prediction accuracy for yield under CV. Crain et al. . In the n = 456 lines) consisting of cultivars and breeding lines from regional breeding programs. The other four panels consisted of winter-wheat breeding lines from Washington State University. These panels consisted of two F4:5 (F5) and two double haploid (DH) trials, each consisting of multiple crosses and multiple lines within each cross, as is common in many breeding programs. The DP was grown in Lind (LND) and Pullman (PUL), WA in 2015–2018, whereas the F5 and DH panels were planted in 2017 and 2018 in LND and PUL, WA .Five different panels of soft winter-wheat lines adapted to PNW growing conditions of the US were used in this study, which included a diversity panel /(R800 + R700) [R800 − R750)/(R800 + R750); NDVI = (R800 − R680)/(R800 + R680) [R970 − R900)/(R970 + R900) [R900/R680 [Rn indicates reflectance at wavelength n of light (measured in nm). Grain yield (t·ha−1) was assessed by harvesting whole plots, using a Wintersteiger® NurseryMaster combine.Spectral reflectance data was collected using a CROPSCAN handheld multiple spectral radiometer . The radiometer was attached to a pole and placed approximately 1 m above the canopy for taking measurements at the middle of each plot. Filters that reflect radiation at 16 different wavelengths between 430 and 970 nm were installed on the radiometer. Reflectance data were taken at three developmental stages: heading (Feekes stage 10.2), early grain-fill , and late grain-fill , with each measurement taken ~10–15 days apart. Spectral data collection was within a four-hour solar window (between 10:00 a.m. and 2:00 p.m.) on windless days with little or no cloud cover. Normalized Difference Red Edge-1 (NDRE-1), Normalized Difference Red Edge-2 (NDRE-2), Normalized Difference Vegetative Index (NDVI), Normalized Water Index (NWI-1), and Simple Ratio (SR) were calculated as follows: NDRE-1 = ( + R700) ; NDRE-2  + R680) ; NWI-1 = + R900) ,13; SR =900/R680 , where RY is the trait of interest; µ is the mean effect; Bi is the effect of the ith block; G corresponds to the un-replicated genotypes; C is the effect of the replicated checks on each block; I is the effect of check identifier; Ei is the effect of the ith environment; Ei × C, Ei × G, and Ei × I are the effects of environment by check, environment by genotype, and environment by check identifier interactions, respectively; Bk(Ei) is the effect of block k nested within environment i; and ε is a normally distributed residual effect with a mean of zero [Adjusted values were calculated for an augmented design, using the augmented complete block design with R  in the  of zero . All effH2) for SRI across all locations for each developmental stage and for grain yield across environments was calculated by considering genotype, replications, environment, and genotype by environment interactions as random effects, using the following formula: n and r are the number of environments and replications per environment, respectively.Broad-sense heritability (Gr), response to selection (R), correlated response (CR), and relative efficiency of indirect selection (RE) were calculated according to Falconer [G =rxy is the covariance between traits x (SRI) and y (grain yield); R = Hxσx, where Hx is the square root of heritability for trait x (SRI), and σx is the genotypic standard deviation for trait x (SRI); CR = HxrGσy; where Hx is the square root of heritability for trait x (SRI); rG is the genetic correlation between SRI and yield, and σy is the genotypic standard deviation for yield; and RE = CRx/Ry; where CRx is the correlated response of the trait x (SRI) with yield; and Ry is the response to selection for yield. These parameters were calculated to estimate the efficiency of selection for grain yield using the spectral indices.Phenotypic correlations between SRI and grain yield were calculated by using JMP Pro v.11.0 . GeneticFalconer , using tn = 115) were selected based on yield per se and the SRI measurements in LND and PUL for 2017 and 2018 for the DP. The number of lines selected based on actual yield and SRI-based selection across environments were compared. PCA for SRI and yield across growth stages was conducted in JMP Pro v.11.0.To gain a better understanding of the relationships between secondary reflectance indices and grain yield in different developmental phases, predictions were conducted by fitting the spectral indices measured across growth stages as independent variables in least squares (LS) regression models for yield in JMP Pro v.11.0. Root mean square error (RMSE) was calculated for the yield models in four site-years . The predictive LS model with the best fit was chosen by using the Bayesian Information Criterion. Relationships between adjusted and predicted yield based on the spectral measurements were visualized in biplots, using JMP Pro v.11.0. The top 25% of lines  RefSeq v1.0 [k-nearest neighbor joining imputation) function [The training and the test panels were genotyped, using genotyping by sequencing  throughSeq v1.0 , using tSeq v1.0 , followeSeq v1.0 ,76. Aftefunction  implemenGenomic prediction accuracy for yield was assessed, using cross-validations (CV) using the DP and independent predictions. In the single population five-fold CV, 80% of the DP lines were used to predict the remaining 20%, with and without the presence of SRI in the prediction model. CV was conducted to assess the level of prediction accuracy which could be achieved prior to performing across-population predictions. Independent validations were then implemented, using the winter-wheat DP evaluated in LND and PUL, between 2015 and 2018, as training population and Washington State University F5 and DH winter-wheat breeding lines as test panels. The F5 panels evaluated in 2017 consisted of 61  and 501  individuals, whereas the DH panels planted in 2018 comprised 449 (DH_LND18), and 761 lines (DH_PUL18). BLUE and BLUP estimates for yield and spectral traits  were used for genomic predictions. Prediction accuracy was calculated as the Pearson correlation coefficient between the BLUE and BLUP estimates and the genomic estimated breeding values for grain yield. A total of nine datasets for the DP were used to train models to predict yield with or without the SRI as fixed effects in the model. Relatedness among the winter-wheat panels used for predictions was evaluated by calculating Roger’s genetic distance . These gWGu + ε, where u represents the vector of marker effects, G is a genotype matrix under an additive model, and W is design matrix relating lines to phenotypes or observations; ε is the vector of errors with variance σ2ε [β + Zu + ε, where X is a design matrix for fixed effects β, and Z is the design matrix for random effects u [A ridge regression best linear unbiased prediction (RRBLUP) model implemented in the ‘rrBLUP’ package  in R 6969 througance σ2ε . Measureffects u . The RRBffects u ,81.The present study showed the potential of indirect selection of high-yielding winter-wheat lines, using SRI collected from HTP. Moderate to high genetic and phenotypic correlation with yield, as well as generally higher heritability, were observed for the SRI, making them suitable for selection of lines with high yield potential. Altogether, relative selection efficiency at the first grain fill stages was higher, indicating that this is the optimal stage where indirect selection through SRI could be implemented. The red-edge indices, NDRE-1 and NDRE-2, could serve as alternatives to the more traditional vegetation indices such as NDVI, based on the relative efficiency of selection observed in the present study. Improved prediction accuracy under CV and independent validations were observed when SRIs were included as fixed effects to predict yield. Using indices in combination further provided better accuracies than using a single spectral trait for predicting yield. Integrating HTP and genomic prediction approaches could therefore facilitate increased genetic gains in winter-wheat breeding programs. Genomic predictions could help increase selection intensity and decrease the time to complete a breeding cycle, as selections would be based on the predicted genomic breeding values, consequently increasing gains achieved through selection. An increased selection accuracy resulting from incorporating HTP traits in the prediction model would also result in selecting lines with improved genetic performance. Overall, the relevance of using SRI as an indirect selection tool and their effects on increasing prediction accuracy was demonstrated, indicating that genetic gains for grain yield could be achieved for PNW winter wheat.