Pto-MIR475b and its four target genes  in 435 unrelated individuals of Populus tomentosa. Expression patterns showed a significant negative correlation  between Pto-MIR475b and its four targets in eight tissues of P. tomentosa, suggesting that Pto-miR475b may negatively regulate the four targets. Single SNP-based association studies identified 93 significant associations  representing associations of 80 unique SNPs in Pto-MIR475b and its four targets with nine traits, revealing their potential roles in tree growth and wood formation. Moreover, one common SNP in the precursor region significantly altered the secondary structure of the pre-Pto-miR475b and changed the expression level of Pto-MIR475b. Analysis of epistatic interactions identified 115 significant SNP–SNP associations (P < 0.01) representing 45 unique SNPs from Pto-MIR475b and its four targets for 10 traits, revealing that genetic interactions between Pto-MIR475b and its targets influence quantitative traits of perennial plants. Our study provided a feasible strategy to study population genetics in forest trees and enhanced our understanding of miRNAs by dissecting the allelic interactions between this miRNA and its targets in P. tomentosa.MicroRNAs (miRNAs) play crucial roles in plant growth and development, but few studies have illuminated the allelic interactions among miRNAs and their targets in perennial plants. Here, we combined analysis of expression patterns and single-nucleotide polymorphism (SNP)-based association studies to explore the interactions between  At-HD-ZIP co-regulate shoot apical meristem and lateral organ formation in Arabidopsis thaliana  are small, non-coding RNAs of 20 to 24 nucleotides and play key roles in post-transcriptional regulation of gene expression in eukaryotes . MiRNAs thaliana . SeveralEucalyptus, Pinus, and Populus  by additive, dominant, and epistatic models, revealing the effect of their interactions on tree growth and wood formation in P. tomentosa.Trees provide essential ecosystem services and form an important part of the bioeconomy, as humans use trees for food, fuel, and biomaterials. However, the use of transgenic and reverse-genetic methods are difficult in forest trees due to their long generation times and high genome heterozygosity . Using a Populus . For insP. trichocarpa . Although miR475b is highly expressed in the developing xylem, the allelic interactions of MIR475b and its targets have not been investigated, and the roles of miR475b in tree growth and wood formation are still unclear.With the development of next-generation sequencing, RNA-seq technology has been widely used to identify miRNAs that are differentially expressed spatiotemporally as well as in response to stress . For exachocarpa . By compP. tomentosa. We identified Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4 as putative targets of Pto-miR475b through bioinformatics prediction and degradome sequencing. We also analyzed their expression patterns to explore the correlation between Pto-MIR475b and its putative targets in eight tissues of P. tomentosa. Association studies in a population of 435 unrelated individuals of P. tomentosa allowed us to dissect the additive, dominant, and epistatic effects of Pto-MIR475b and the four putative targets to identify the complex regulatory network underlying tree growth and wood formation. In addition, we identified a common SNP in the pre-miRNA (precursor region) of Pto-MIR475b that altered the stem-loop structure of Pto-miR475b and changed the expression level of Pto-MIR475b. Our study used a feasible strategy to study population genetics in forest trees that combined expression patterns and association studies to enhance the understanding of the functions of miRNA and to dissect the allelic interactions of an miRNA and its targets in P. tomentosa.Here, we explored the allelic interactions of miR475b and its targets and their potential effects on tree growth and wood formation in P. tomentosa was used for SNP-based association analysis, which were randomly selected in a clonal arboretum , holocellulose content (HC), hemicellulose content (HeC), lignin content (LC), fiber length (FL), fiber width (FW), and microfiber angle (MFA). The growth traits were diameter at breast height (DBH), tree height (H), and stem volume (V). The detailed measurements and phenotypic variation of these 10 traits were described by Pto-MIR475b in P. tomentosa, we used gene-specific primers based on the primary sequence of Ptc-MIR475b from P. trichocarpa, which contained the pre-miRNA region of the Pto-MIR475b sequence and 1,000 bp of flanking region on each side.To clone the full-length sequence of 1 using 3,000 complementary DNA (cDNA) sequences from the mature xylem cDNA library of P. tomentosa. In addition, degradome sequencing was performed to verify the psRNATarget results, which identified Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4 as the putative target genes of Pto-miR475b. Next, we isolated the cDNAs of these candidate target genes from the P. tomentosa mature xylem cDNA library.The putative target genes of Pto-miR475b were predicted by psRNATargetP. tomentosa were pooled together in equal amounts. The pooled RNA samples were used to build degradome libraries for degradome sequencing according to MmeI and ligated to a double-stranded DNA adapter using T4 DNA ligase. The products were amplified  and gel purified. Finally, the purified products were used for sequencing-by-synthesis with Illumina HiSeq2000. The miRNA cleavage sites were identified with the CleaveLand pipeline (P. trichocarpa genome transcripts (v 3.0) (SRX1447192).The total RNA from six tissues  from pipeline  based onP. tomentosa clone, “LM50,” and stored in liquid nitrogen until RNA extraction. The total RNAs were extracted using the Plant Qiagen RNeasy kit  following the manufacturer’s instructions and RNase-free DNase (Qiagen) was used to purify the total RNA. The total RNAs were reverse transcribed into cDNA with the Reverse Transcription System . RT-qPCR was performed on a 7500 Fast Real-Time PCR System using SYBR Premix Ex Taq according to the manufacturer’s protocol. The specific primers for each gene for qPCR (Supplementary Table Actin (Accession number: EF145577). All reactions were performed in three technical and biological repetitions, and differential reactions across classes were tested by ANOVA  as described by Total RNAs were extracted from eight different tissues: root, shoot apex, cambium, developing xylem, mature xylem, phloem, mature leaf, and young leaf. All samples were collected from a 1-year-old Pto-MIR475b and its four target genes, we randomly selected 40 unrelated individuals from the association population. We designed the primers based on the cDNA sequences of these genes and amplified the genomic DNA from these 40 individuals by PCR. The PCR products were purified and sequenced for subsequent analysis. Gene cloning and purification was performed according to Pto-MIR475b, Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4 from the 40 individuals were analyzed and aligned with MEGA 5.0. The 435 individuals, composed the association population, were provided for genotyping all common SNPs [minor allele frequency (5%)] from Pto-MIR475b and four targets according to the methods described by To identify SNPs within Pto-MIR475b, Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4 in the 40 unrelated individuals of P. tomentosa have been deposited in GenBank under the accession numbers KY619249–KY619288, KY619085–KY619124, KY619126–KY619165, KY619167–KY619206, and KY619208–KY619247, respectively.The genomic sequences of 2. Using the methods described above, we selected 10 individuals for each SNP genotype from the association population and conducted RT-qPCR using RNA from mature xylem to test whether the expression levels of Pto-MIR475b and its four target genes varied in the different SNP genotypes. The different expression levels across the different SNP genotypes were tested using ANOVA.Single-nucleotide polymorphisms occurring in the pre-miRNA region of the miRNA gene might affect the secondary structure and expression level of the corresponding miRNA. Here, we predicted the secondary structure of the normal and mutated sequences of Pto-miR475b using RNAfoldw the average number of segregating sites per site  between pairs of SNPs in Pto-MIR475b and its target genes. To assess the pattern of LD in the sequenced region of Pto-MIR475b and its target genes, the decay of LD with physical distance (base pairs) within each common SNP was estimated in 105 permutations of the genotype data by non-linear regression, and singletons were excluded in the LD analyses ; α, SNP effects; v, population effects; u, polygene background effects; e, residual effects; Q, a matrix from STRUCTURE relating y to v; and X, S, and Z are incidence matrices of 1s and 0s linking y to β, α, and u, respectively. The variances of the random effects are Var (u) = 2KVg, and Var (e) = RVR, where K is an n∗n matrix in which the elements are relative kinship coefficients that define the degree of genetic covariance between any two individuals; R is an n∗n matrix where the elements in off-diagonal are 0 and the elements in diagonal are the reciprocal of the number of observations from each phenotypic data point. Vg is genetic variance and VR is the residual variance , which controls for kingship and population structure, in the software package TASSEL Ver. 2.0.1are in R  was usedare in R . The MLMvariance . In our P. tomentosa. We used 1000 permutation tests to calculate the significant levels of the haplotype-based associations and used the positive FDR in Q-VALUE to correct the multiple testing.The haplotype trend regression (HTR) software was used to conduct haplotype association with growth and wood properties of To quantify the modes of gene action, we calculated the ratio of dominance to additive effects (d/a) as described by r2 < 0.1 or different genes), and provide the best five loci for each trait. Finally, an entropy-based measure was used to detect significant interactions between SNP–SNP pairs and calculate information gains (IGs) to evaluate epistatic effects.Multifactor Dimensionality Reduction 3.0.2 (MDR 3.0.2)  was applPto-MIR475b based on the sequence of Ptc-MIR475b in miRBase and found that miR475b is a poplar-specific miRNA. The sequence contained 21 nt of the mature miRNA region of Pto-MIR475b, 136 nt of the pre-miRNA region, and 1,000 nt of flanking sequence on each side of the pre-miRNA region. The secondary structure showed a typical stem-loop structure, which is the major characteristic of miRNAs . The 21 nt mature sequence of Pto-miR475b and the mature xylem cDNA library of P. tomentosa were analyzed in psRNATarget to predict its target genes, which identified 78 candidate targets. Degradome sequencing, a high-throughput sequencing method used to determine the sequence of RNA ends, by identifying miRNA cleavage sites , Pto-PPR2 (Accession number: KY619166), Pto-PPR3 (Accession number: KY619207), and Pto-PPR4 (Accession number: KY619248) were 1,794, 1,795, 1,915, and 1,900 bp in length, and encoded proteins of 597, 403, 478, and 384 aa, respectively . In addition, the degradome results showed that Pto-miR475b cleaved Pto-PPR1 at 540 nt, Pto-PPR2 at 395 nt, Pto-PPR3 at 1,058 nt, and Pto-PPR4 at 444 nt .We isolated the primary sequence of ge sites . We perfsequence , we founsequence . All fouPto-MIR475b and its four target genes in eight different tissues of P. tomentosa. Pto-MIR475b and its four target genes were expressed in all examined tissues with varied transcript abundance . The expression level of Pto-MIR475b was highest in mature leaves, followed by the root, and lowest in the shoot apex. By contrast, Pto-PPR1, Pto-PPR3, and Pto-PPR4 showed the highest expression in the shoot apex, and Pto-PPR2 had the highest expression in the cambium. Pto-PPR1 and Pto-PPR4 had the lowest expression in mature leaves, whereas Pto-PPR2 and Pto-PPR3 showed the lowest expression in the root and developing xylem. Moreover, the correlation coefficient (r) between Pto-MIR475b and its four target genes ranged from -0.447 to -0.411, which showed a negative correlation and suggested that Pto-miR475b potentially negatively regulates the four target genes.RT-qPCR was used to detect the relative expression levels of Pto-MIR475b and its four target genes, we sequenced these five genes in 40 randomly selected individuals of the association population of P. tomentosa. For Pto-MIR475b, we identified 19 SNPs with a frequency of 1/112 bp . No SNPs were identified in the mature miRNA region, indicating that this region was extremely conserved. In the precursor region, one SNP (Pto-MIR475b-SNP19) was identified with a frequency of 1/135 bp  . For the target genes, we identified 542 SNPs with a frequency range of 1/103 bp (Pto-PPR3) to 1/36 bp (Pto-PPR1). The nucleotide diversity demonstrated that the four target genes were under different selective pressure (Sd) and the non-synonymous diversity (Nd) for the coding region of three of the target genes and the ratio of N/dSd < 1  indicated that the non-synonymous sites experienced purifying selection , 5′-UTR (2.59%), intron region (19.32%), and 3′-UTR (4.38%)  from r2) between each common SNP pair was calculated to evaluate the overall patterns of LD for Pto-MIR475b and its four target genes . The non-linear regression results showed that LD decayed rapidly with r2 values and dropped to 0.1 within approximately 300 bp for Pto-MIR475b, and within approximately 400 to 2,500 bp for each target gene, indicating that the LD of Pto-MIR475b and its four target genes does not extend over the entire genes.The squared allelic correlation coefficient  representing 80 unique SNPs in Pto-MIR475b and the four target genes with nine traits. The phenotypic variance (R2) explained by each SNP ranged from 1.50 to 22.57% (average R2= 12.73%) , which suggested that Pto-MIR475b is potentially involved in tree growth and wood formation. Pto-MIR475b_SNP1 and Pto-MIR475b_SNP2 were located in the flanking regions and associated with FW with different phenotypic contributions . As expected, the SNP in the precursor region, Pto-MIR475b_SNP19, was significantly associated with H with an R2 of 2.62%, indicating that Pto-MIR475b may play important roles in regulating tree height. The other 77 SNPs were distributed amongst the four target genes and were significantly associated with nine traits. Of these SNPs, 10.39% were located in coding regions. For example, Pto-PPR2_SNP64, located in the coding region of Pto-PPR2, caused a non-synonymous mutation of Phe to Ser, which was associated with V with an R2 of 15.14%. Pto-PPR1_SNP109, located in the promoter region of Pto-PPR1, had the largest phenotypic contribution with DBH. Interestingly, we found that 13 of the 77 SNP markers were significantly associated with two traits (DBH and V). For instance, Pto-PPR2_SNP67 caused a missense mutation (His to Tyr), and associated with DBH and V with an R2 of 17.96 and 12.19%, respectively.To test the effects of the SNPs on tree growth and wood properties, we conducted association analysis, which detected 93 significant associations (n = 59) or partially to fully dominant effects . For instance, Pto-PPR1_SNP97 showed additive effects on HC . The different values of the effect on V among the three genotypes of Pto-PPR2_SNP64 were significant, suggesting that the mode of gene action was under-dominance . Pto-PPR3_SNP28 was significantly associated with LC, indicating that the pattern of gene action was a partially to fully dominant effect . Interestingly, one SNP can contribute to different traits with the same mode or different modes of gene action. For example, Pto-PPR2_SNP115 showed both additive effects on DBH  and V . However, Pto-PPR2_SNP48 was significantly associated with DBH and V, but with different modes of gene action, with additive effects on V  and partially to fully dominant effects on DBH  .In addition, we used the ratio of dominance (d) to additive (a) effects to quantify the modes of gene action for SNP–trait associations. Thirty SNP–trait associations showed additive effects (|d/a| ≤ 0.5). The other associations were divided into two modes of gene action, over- or under-dominance  within Pto-MIR475b and its four target genes . Seven haplotypes were associated with multiple traits. For example, the haplotype C-T from block Pto-PPR3_SNP9-11 simultaneously associated with LC, AC, HeC, and HC with different R2 values ranging from 3.19 to 6.80%. The other haplotype (T-A) from this block associated with HeC with an R2 of 4.28%, indicating that the gene has pleiotropic effects. In addition, we found that 20 haplotypes from 18 blocks in Pto-MIR475b and its four targets  associated with FW with an R2 ranging from 0.15 to 23.17% (average R2 of 11.01%). Compared with the single SNP-based association results, we found that six SNPs overlapped in haplotype-based associations. For example, Pto-MIR475b_SNP1 and Pto-MIR475b_SNP2 significantly associated with FW under the single SNP-based association, and the haplotypes (A-T-T and G-C-G) came from the block (Pto-MIR475b_SNP1-3) associated with FW under the haplotype-based association . Pto-PPR4_SNP117 and the haplotype (A-G-A-C) came from the block (Pto-PPR4_SNP117-120) associated with FW under their respective models. These results indicated that the single SNP-based associations support the results of the haplotype-based associations .We used the HTR model to conduct the association studies. We identified 50 common haplotypes (frequency > 0.05) from 28 high LD blocks (Pto-MIR475b and its four target genes with 10 traits (P < 0.01) . In addition, single SNP effects ranged from 0.1 to 8.37%, and the pairwise SNP–SNP effects ranged from 0 to 9.81%. For these SNP–SNP pairs, 11.30% showed an interaction between the Pto-MIR475b and its targets, 63.48% showed an interaction between the target genes, and the remaining 25.22% showed SNP–SNP interactions on the same genes . In addition, we used IG to estimate the epistatic effects of the SNP–SNP pairs. IGs ranged from -0.0808 to 0.0371, and 11.30% of the SNP–SNP pairs were positive IGs. The negative IGs indicated redundancy, which means a partial overlap of function in a number of genes involved in the same processes . All 13 pairs showed negative IGs (-0.0663 to -0.0127) and interaction effects for the associated traits of 0 to 3.6%; and presented 13 unique SNPs, each showing single effects for the associated traits (0.01 to 5.74%).For these 115 significant SNP–SNP pairwise associations, we found that the SNPs in Pto-MIR475b_SNP2 and Pto-PPR4_SNP121 formed an epistatic interaction on MFA, and we calculated the average phenotypic values of each genotype . For Pto-MIR475b_SNP2, the difference between the average phenotypic values of three genotypes  and the total average of phenotypic values (17.557°) ranged from -1.512°  to 7.170° , while for Pto-PPR4_SNP121  the values ranged from -0.177°  to 5.771° (23.327°). The differences between the genotypic combinations and the total average phenotypic values ranged from -1.744° (CC-GG) to 7.776° (TT-GG), which were significantly higher than the phenotypic values of the genotypes of single SNP markers. The significant differences between the average phenotypic values of genotypic combinations and single genotypes of two SNP markers verified the epistatic interactions, which also verified the interactions between Pto-MIR475b and its four target genes.To explore the different effects of single genotypes and genotypic combinations of SNP–SNP pairs, we detected that Pto-MIR475b_SNP19) in the pre-miRNA region on the expression level of Pto-MIR475b, we used RT-qPCR to detect the relative expression in the 20 unrelated individuals . Pto-MIR475b_SNP19 significantly affected the transcript levels of Pto-MIR475b, with the highest level in the AC group , and the lowest level in the AA group (0.216 ± 0.008) . To test whether the altered expression of Pto-MIR475b would affect the expression of the targets, we detected the expression levels of the four target genes in these 20 unrelated individuals. Compared with the average relative expression levels of the different genotype groups, we found that Pto-MIR475b displayed an inhibited action on the expression levels with its four targets. The expression level of Pto-MIR475b was higher for the AC genotype than the AA genotype, but the corresponding expression levels of the four target genes were lower in the AC genotype than AA genotype .To explore the roles of the SNP  in the precursor region of Pto-MIR475b, which significantly altered the stem-loop structure and changed the minimum free energy of the secondary structure of Pto-miR475b , which affected its transcription and may impair the generation of the mature miRNA , suggesting that Pto-MIR475b_SNP19 can affect the transcription of Pto-MIR475b, which supported the idea that a SNP in the pre-miRNA region can have a regulatory function. We also detected an opposite expression pattern between Pto-MIR475b and its four targets in the different genotypes of Pto-MIR475b_SNP19, suggesting that the transcript abundance of Pto-MIR475b will affect the expression of its target genes. Moreover, the results of the single SNP-based association study showed that Pto-MIR475b_SNP19 significantly associated with H with an R2 of 2.62%, and showed partially to fully dominant effects. These association results also supported the concept that SNPs may play an important regulatory role in miRNA biogenesis . No SNPs were found in the mature miRNA region, indicating that the mature miRNA region is highly conserved (Table 1). Through alignment with other species, we found that miR475b is a Populus-specific miRNA, and is completely conserved compared with the orthologous sequence in P. trichocarpa.In addition, several studies have reported that the flanking sequence may affect the efficiency of cleavage of the primary transcript to form the pre-miRNA. For example,  regions . Here, wPto-MIR475b_SNP1 and Pto-MIR475b_SNP2) were significantly associated with FW, indicating that SNPs in the flanking region may contribute to phenotypic variation. Under the haplotype-based association, we identified eight associations in six haplotypes with three traits. For instance, the haplotypes  in the Pto-MIR475b_SNP1-3 block associated with FW, and the single SNP-based associations showed that Pto-MIR475b_SNP1 and Pto-MIR475b_SNP2 both associated with FW. These results indicated that the haplotype association was supported by the single SNP-based association, suggesting that multiple SNPs composed a haplotype and can lead to phenotypic variation . These haplotype-based association studies illuminated the allelic effects of SNPs in Pto-MIR475b, indicating that Pto-MIR475b may play an important regulatory role in tree growth and wood formation.Next, the results of the single SNP-based associations and haplotype-based associations of 18 common SNPs in the flanking region revealed that two SNPs . For Pto-MIR475b, three common SNPs significantly associated with H and FW. We found that 17 SNPs in Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4 were significantly associated with H and FW (average R2= 8.80%). Additionally, we observed that haplotypes in Pto-MIR475b and its targets associated with the same traits. For instance, three haplotypes from Pto-MIR475b associated with FW, and 17 haplotypes from the four target genes associated with FW. These results suggested that Pto-MIR475b and its four gene targets are involved in the same regulatory pathway for tree growth and wood formation, and the multiple SNPs associated with various traits revealed the pleiotropy of the genes , and demonstrated the allelic interactions between Pto-MIR475b and four putative targets.Through the single SNP-based and haplotype-based associations, we detected 93 significant SNP–trait associations and 50 significant haplotype–trait associations for Pto-PPR2_SNP64, Pto-PPR2_SNP65, Pto-PPR2_SNP67, and Pto-PPR2_SNP77) caused non-synonymous mutations and associated with DBH and V, indicating that Pto-PPR2 may be involved in tree growth  to DBH, indicating that Pto-PPR1 may play a major role in tree growth. We also observed that a single SNP significantly associated with two traits. Thirteen SNPs associated with DBH and V with different R2 values, and the majority of the SNPs occurred in Pto-PPR2 (12 SNPs), with only one in Pto-PPR3. Pto-PPR2_SNP67 caused a missense mutation and associated with DBH and V with an R2 of 17.96 and 12.19%, respectively, with partially to fully dominant effects for both traits, indicating the importance of the SNP marker and the pleiotropy of the gene. Moreover, the target genes were highly expressed in the shoot apex and cambium , and supported the idea that PPRs might play a crucial role in the growth process . For example, Pto-MIR475b_SNP12 interacted with five SNPs from the four putative target genes, and formed five epistatic interactions with H with pairwise effects ranging from 0.01 to 2.42%, and all IGs were negative, indicating that Pto-MIR475b and its four targets interact to co-regulate tree growth and wood formation.Epistasis represents the interactions between genes; thus, we used the epistasis model to verify the interactions between miRNA and its targets. For the 115 SNP–SNP pairs, 11.30% showed an interaction between Pto-MIR475b_SNP2 and Pto-PPR4_SNP121 formed epistatic interactions for MFA with a pairwise effect of 3.6% and an IG of -1.71% . Then we determined the differences between the average phenotypic values of three single genotypes of SNP markers and the total average phenotypic values, and the differences between genotype combinations and total average phenotypic values. The different values of genotype combinations were higher than the single genotypes, indicating the interaction between Pto-MIR475b and its four target genes  and suggesting that epistatic interactions of genotype combinations conferred greater effects on the phenotypic variation. In addition, the genotype of Pto-MIR475b_SNP2 was CC and the genotype of Pto-PPR4_SNP121 was GG, the epistatic effect of the genotype combination (15.21°) for MFA was significantly lower than any single genotype effects , indicating that phenotypic contributions of genotype combinations were less than the single genotype effects. These findings represent examples of epistatic effects and illustrate the allelic interactions of Pto-MIR475b and four putative targets for tree growth and wood formation. In addition, recent studies have discovered that miRNA activity is also impaired by endogenous competing RNAs, a phenomenon termed endogenous target mimicry (eTM)  . For exaPeTMbase , can prePto-MIR475b and its four putative target genes. Analysis of expression patterns revealed negative correlations between Pto-MIR475b and its four target genes in eight tissues. Single SNP-based and haplotype-based association studies showed that significant SNP markers from five genes were involved in the same biological pathways. The epistasis model demonstrated that Pto-MIR475b and its four targets form a complex interactive network affecting phenotypic variation for tree growth and wood formation, and the different genotype combinations of SNP–SNP pairs were further used to dissect the epistatic effects of gene interactions on complex traits.Here, we combined expression profiles and association studies to explore the effects of allelic variation in KY619085–KY619288.Sequence data in this article has been submitted to GenBank: accession numbers DZ conceived and designed the experiment; LX and MQ collected the data and conducted statistical analysis; LX wrote the manuscript; MQ, QD, JC, and JX provided valuable suggestions for the manuscript; MQ, QD, and DZ revised the manuscript; DZ obtained funding and is responsible for this article. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We validated this equation using additional accessions, which exhibited a significant 2R value of 0.430 between the predicted values and actual measurements. The newly detected SNP loci and the model equation could facilitate marker-assisted selection to predict PHS in rice germplasm and breeding lines.Preharvest sprouting (PHS) in rice panicles is an important quantitative trait that causes both yield losses and the deterioration of grain quality under unpredictable moisture conditions at the ripening stage. However, the molecular mechanism underlying PHS has not yet been elucidated. Here, we explored the genetic loci associated with PHS in rice and formulated a model regression equation for rapid screening for use in breeding programs. After re-sequencing 21 representative accessions for PHS and performing enrichment analysis, we found that approximately 20,000 SNPs revealed distinct allelic distributions between PHS resistant and susceptible accessions. Of these, 39 candidate SNP loci were selected, including previously reported QTLs. We analyzed the genotypes of 144 rice accessions to determine the association between PHS and the 39 candidate SNP loci, 10 of which were identified as significantly affecting PHS based on allele type. Based on the allele types of the SNP loci, we constructed a regression equation for evaluating PHS, accounting for an  Rice, possessing a relatively small genome size of approximately 370 Mb, is widely used in genomic studies as a model cereal crop plant. Identifying important genes in rice is an excellent approach for finding functional genes in grass-family crops such as wheat (hexaploid) and barley by comparative genomics . Next gePreharvest sprouting (PHS) in rice panicles is caused by the breakage of adequate seed dormancy, while, under normal seed dormancy, the maturation status of the seed is arrested for various periods of time to allow it to germinate under favorable conditions . PHS is AtABI3 plays a role in the initial ABA-dependent check point for seed dormancy, and its orthologous genes ZmVP-1 in maize, OsVP1 in rice and TaVp-1 in wheat are global regulators of seed maturation in these crops , revealing its central role in controlling seed dormancy. Arabidopsis se crops .Sdr4 is positively regulated by OsVP1, which acts as an intermediate regulator in the genetic regulation of seed dormancy  and bHLH (basic helix-loop-helix) (qSD7-1/qPC7 (Rc locus) is a pleiotropic gene that promotes the expression of key genes for ABA biosynthesis and seed pigmentation  . qSD7-1/entation . Althougentation  and bothentation , the molWe previously analyzed the variation in PHS and seed germination among various rice genetic resources to increase the number of available alleles for PHS, as most studies of PHS and seed dormancy have been performed using limited resources and alleles from some well-known accessions ; In thisThe seed germination test after cultivation in Korean middle and southern region for 2 years revealed similar variation of seed germinability (data not shown), and these reflected that some genetic factors artificially selected during rice domestication might affect the diversity of seed germination besides environmental effects. In the current study, we searched for SNPs showing distinct allelic distribution between PHS resistant and susceptible accessions based on genome re-sequencing of representative accessions. We then genotyped the putative PHS-associated SNPs in rice germplasm and constructed a regression equation for the quantitative trait PHS, which could facilitate breeding for PHS resistance in rice.Table 1 and Supplementary Tables The flowering date were tagged by panicles of each accession, and seeds and panicles having same flowering date , revealing similar flowering times (early August), were selected as representatives by PHS resistance  at 42 DAF among the diverse rice genetic resources (Twenty-one samples (esources  in that Oryza sativa L. (IRGSP-1.0.27) using Bowtie2 with default parameters  version 3.4.46 (∗DP) higher than 0.1, where DP is the unfiltered read depth, (2) FS higher than 200 to reduce false positives, and (3) Phred-scaled quality score lower than 30.Genomic DNA was extracted from the 21 rice samples using a Gentra Puregene Cell Kit for Plants . Library construction and sequencing data collection were conducted using Illumina’s official protocol with a 101 bp paired-end read length. Trimmomatic-0.33  was usedrameters . The mapn 3.4.46  was usedp-value of the total number of tests was below 0.05. Significantly enriched SNPs were annotated with SNPeff.To identify specific SNPs in the PHS groups (PHS resistant and susceptible group), Fisher’s exact test implemented in SNPSift was conducted to analyze the resulting genotype count data  contraryjaponica accessions and 64 indica accessions) showing PHS variations (Supplementary Table japonica accessions (mainly Korean landraces) were used to validate the regression model.Thirty-nine candidate SNP loci (for genotyping diverse rice genetic resources) were selected, including SNPs in germination-related genes with hormonal action, such as ABA perception and signal transduction, as well as SNPs in genes expressed specifically in florets and grains . The IFC Controller utilizes pressure to control the valves in the chips and to the load samples and genotyping assay reagents into the reaction chambers. The EP-1 system was used as an endpoint image reader. Specific target amplification (STA) reactions were performed in a GeneAmp PCR System 9700 from Applied Biosystems.For SNP genotyping, 5 μL of STA mix was prepared for each sample, containing 1 × Qiagen Multiplex PCR Master Mix  and 50–60 ng of genomic DNA. After diluting the STA mix, an allele-specific SNP genotyping assay was carried out with the SNPtype assays protocol in the Fluidigm 192.24 Dynamic Array IFC User’s Guide (PN 68000098 N1). After PCR amplification, the endpoint fluorescent image data were acquired on the EP-1 System. Data were analyzed using Fluidigm SNP Genotyping Analysis software to obtain genotype calls.japonica and indica ecotypes have different genetic constitutions, the accessions were separately analyzed based on ecotype 3.One-way analysis of variance (ANOVA), Duncan’s multiple range test (DMRT), correlation analysis, and regression analysis were conducted using R software (ver. 3.2.3)Table 1). The genomes of these selected accessions were sequenced at an average of ∼25.7 × coverage, with an average of 9.59 Gb total bases (8.01–10.82) and 94.9 million reads (Supplementary Table Oryza sativa L. (IRGSP-1.0.27) using Bowtie2 with default parameters  based on dominant and recessive models , respectively, and the common SNPs detected in both model were 16,753 loci  and the 14 japonica accessions, respectively , followed by intron variants (31.1 and 28.9%), 3′ UTR variants (16.5 and 16.9%), and 5′ UTR variants (9.1 and 9.5%).Among the SNPs showing allelic differentiation between PHS resistant and susceptible accessions, the number of SNPs in genic regions was approximately 4,100 and 1,000 among the 21 total accessions . Among the SNPs detected genome-wide, an average of 55 SNPs per bin revealed allelic differentiation between PHS resistant and susceptible accessions. Chromosome-1 has the highest number of SNPs, with an average of 142 per bin, followed by chromosome-11 (78 per bin) and chromosome-12 (69 per bin). Chromosome-8 has the fewest SNPs (12 per bin). Among the japonica accessions, only an average of 13 SNPs per bin revealed allelic differentiation between the PHS resistant and susceptible groups. Chromosome-11 has the highest number of SNPs, averaging 36 per bin, followed by chromosome-12 (34 per bin) and chromosome-4 (16 per bin). The fewest SNPs were found on chromosome-10 (three per bin). We compared the locations of the SNPs with publicly reported QTLs related to PHS and dormancy along the rice chromosome, finding that some chromosomal regions contain high numbers of these SNP loci. Region-1  has a high density of SNPs overlapping with QTL Sdr6 , respectively (Table 3). Furthermore, the existence of an awn was also significantly associated with PHS and GI values. Three significant SNP loci were associated with PHS and GI values in 64 indica accessions. Specifically, among significant SNPs associated with PHS in japonica and indica rice, V2 is a significant variant in an upstream region of the rice homolog ortholog of AtBG1, which functions in ABA conjugation in Arabidopsis. V5 is a variant in the intron region of a ROS homeostasis gene, and the S4 locus is located in the upstream region of OsVP1. S13 and S21 are missense variations in the BRCT domain-containing protein gene and the Guanine nucleotide-binding protein gene, respectively. S3 and S9 are located in a Dynamin family protein gene, representing a missense variation and an extra stop codon, respectively. S16 and S19 are missense variations in CINNAMYL ALCOHOL DEHYDROGENASE 4. Finally, S1, which is significant only in the indica group, is a missense variation in a hypothetical protein gene.Among the approximately 20,000 SNP loci revealing distinct allelic distribution between PHS resistant and susceptible accessions, we genotyped 144 rice germplasm , containing the S3 and S9 variations associated with GI, overlaps with reported QTLs including Sdr1 , BRCT domain containing protein (S13 variation), and Hypothetical protein (S1 variation) are highly expressed in embryos and endosperm compared with other tissues, whereas G-protein (S21 variation) is expressed at low levels in embryos and endosperm during seed maturation (data not shown). Dynamin family protein (S3 and S9 variation), THIOREDOXIN H-TYPE (V5 variation), and OsCAD4 (S16 and S19 variation) are more highly expressed in endosperm than in embryo tissue, while BETA-GLUCOSIDASE 36 (V2 variation) is expressed at lower levels in endosperm than in embryo tissue.Seven new PHS- and GI-associated loci  in the ing Sdr1 , qDT-SGCT-SGC3.1  and qSD-1  - 14.479(V2) - 11.629(V5) - 20.62(S4) + 22.544(S21) + 12.209(S3) + 10.864(S13) + 11.767(Awn)] (Table 4). The regression model is composed of eight factors, including six SNP loci , one InDel locus of qLTG3-1, and the awn character.We conducted regression analysis based on the genotypes of 11 significant loci including japonica accessions and measured the PHS value at 42 DAF . The 2R (coefficient of determination) value between the predicted PHS estimates and the actual measurements was 0.430 .To validate the estimation model for PHS, we genotyped 56 additional Korean The regulation of PHS has become a crucial issue due to global climate change, as unpredictable moisture conditions can lead to PHS, which reduces both the quality and quantity of usable grain. Several studies investigated the molecular mechanism underlying PHS caused by the breakage of adequate dormancy, including the identification and fine-mapping of QTLs related to seed dormancy and PHS . While Gp-value < 0.05), respectively, including approximately 4,100 and 3,700 variations in genic regions based on dominant and recessive models, respectively. We constructed a density plot of SNPs showing allelic differentiation between PHS resistant and susceptible accessions, finding different densities in different chromosomal regions. Some regions containing high numbers of these SNPs overlap with previously reported QTLs associated with PHS and seed dormancy; Region-1 and Region-2 of Chromosome-1, Region-3 of Chromosome-3, Region-6 of Chromosome-7, and Region-7 of Chromosome-12 overlap with previously reported QTLs and fine-mapped genes  are included in the chromosomal region harboring germination-related QTLs such as Sdr1 (Hd8 (QTL for heading date), qDT-SGC3.1 . This locus is included in the chromosomal region of germination-related QTLs Sdr6  included seven genetic factors and one phenotypic factor (absence or presence of an awn). We validated the regression equation model, finding that the 2R value between the predicted and actual values was 0.430 . As PHS is a quantitative trait that is regulated by complex factors, it is reasonable to expect that the adjusted 2R value and the correlation between actual and predicted values would be low.Using the genotypes of significant SNP loci, we constructed a regression equation for PHS in Several QTLs and significant SNPs have been reported for seed dormancy and PHS, and finding additional factors associated with PHS might increase the resolving power of our equation. Based on this study, we will try to elaborate the regression equation for PHS including previously reported high quality SNPs, and this might be valuable for the molecular breeding for PHS resistance.G-AL: performed research, analyzed data, wrote the paper. Y-AJ: phenotyped samples, analyzed data. H-SL: performed research, wrote the paper. DH: performed research, analyzed data. J-RL: performed research, analyzed data. M-CL: performed research, analyzed data. S-YL: designed research, performed research. K-HM: designed research, performed research. H-JK: designed research, performed research, wrote the paper.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.


Arctocephalus gazella). We then mapped the raw Illumina reads to the new hybrid transcriptome using BWA and BOWTIE2 before calling SNPs with GATK. The resulting markers were pooled with two existing sets of SNPs called from the original 454 assembly using NEWBLER and SWAP454. Finally, we explored the extent to which SNPs discovered using these four methods overlapped and predicted the corresponding validation outcomes for both Illumina Infinium iSelect HD and Affymetrix Axiom arrays.Single nucleotide polymorphism (SNP) discovery is an important goal of many studies. However, the number of ‘putative’ SNPs discovered from a sequence resource may not provide a reliable indication of the number that will successfully validate with a given genotyping technology. For this it may be necessary to account for factors such as the method used for SNP discovery and the type of sequence data from which it originates, suitability of the SNP flanking sequences for probe design, and genomic context. To explore the relative importance of these and other factors, we used Illumina sequencing to augment an existing Roche 454 transcriptome assembly for the Antarctic fur seal  contains supplementary material, which is available to authorized users. High throughput sequencing and cost efficient genotyping technologies are revolutionising the study of wild organisms . For exaIn non-model species, SNPs are often mined from transcriptome assemblies, as these are smaller and simpler to generate than genomes. Nevertheless, there are a variety of alternative methods available for read mapping and variant discovery and it is not always straightforward to know which of these to use. Relatively few systematic comparisons of the available programs have been carried out and most have mainly been based on genomic data from humans , 10. TheSNP discovery can facilitate multiple genotyping approaches. Genotyping by sequencing approaches such as RAD, ddRAD and 2bRAD –19 allowArctocephalus gazella). A transcriptome assembly based on Roche 454 sequencing is already available for this species, from which two SNP datasets were generated using NEWBLER and SWAP454 respectively [An opportunity to quantify the extent of overlap between different SNP discovery methods and to explore the consequences for validation success is provided by a study of Antarctic fur seals , which were assembled de novo to generate 26,266 contigs of mean length 904 bp. Blasting these contigs to the 454 backbone, we found that 15,520 (59.0 %) successfully mapped at an e-value threshold of 1e−10. After annotating the unmapped contigs, around 50 % were removed, either due to a lack of homology to known sequences or because the top BLAST hit revealed similarity to known bacterial or viral sequences. Most of the remaining 5452 annotated contigs showed sequence similarity to the Weddell seal (Leptonychotes weddellii) and the walrus (Odobenus rosmarus) and were thus concatenated to the original 454 transcriptome. This yielded a ‘hybrid transcriptome’ comprising a total of 28,548 contigs . To investigate homology to the dog (Canis familiaris), we mapped these contigs to the most recent and complete build of the dog transcriptome. 23,587 (82.6 %) of the seal contigs mapped to 35,724 (64.7 %) of the dog transcripts, suggesting that a reasonably large proportion of the fur seal transcriptome has been captured.To improve upon the existing 454 transcriptome, which comprises 23,096 contigs of mean length 971 bp, we conducted an additional round of Illumina sequencing  to be estimated more accurately than for the 454 data. We therefore selected the subset of 4679 SNPs that were called from both the 454 and Illumina datasets and compared their respective parameter spaces. Two obvious differences emerge between the two datasets Fig. . First, We also used the same approach to compare all of the SNPs called from the 454 data with all of the SNPs called from the Illumina data. Again we found marked differences between the two datasets Fig. . For then = 32,727, 94.3 %, Step 1 in Fig. Although most studies present the total number of putative SNPs identified from transcriptome assemblies, when developing a custom SNP array it is important to consider the likelihood of each SNP successfully validating with a given genotyping technology. We therefore tested the total set of 34,718 SNPs for compatibility with both Illumina Infinium iSelect HD and Affymetrix Axiom high density SNP arrays . Direct comparison of SNPs called from the 454 and Illumina data also revealed marked differences in their MAF distributions, the former being dominated by SNPs with a MAF of around 0.4 while the latter show a more even MAF distribution. While we cannot yet say which of these is the most accurate portrayal of the true underlying distribution, we suspect that the Illumina data are closer to the mark because, at least in theory, greater depth of coverage should allow in silico allele frequency distributions to be estimated more accurately. This finding could thus explain why studies often find no association between in silico and realised allele frequencies –38.SNP discovery is an important goal of many studies and features prominently in many publications describing transcriptomes –41. HoweWe also tested whether the probability of successful validation varied according to the method by which a given SNP was called. Above and beyond the pattern described above, we found that SNPs called only from the 454 data (using either NEWBLER or SWAP454) were less likely on average to successfully validate than SNPs called only from the Illumina data (using BOWTIE2 or BWA in combination with GATK). This suggests that a larger proportion of SNPs called from the 454 data may be spurious, in line with the lower depth of coverage of the 454 data, the fact that only around a quarter of these SNPs were called by both NEWBLER and SWAP454, and the limited overlap between SNPs called from the 454 and Illumina data. This finding would also be consistent with our previous work on fur seals in which we experimentally validated a panel of putative SNPs derived from the 454 transcriptome using Illumina’s GoldenGate assay . This stFinally, we found that the probability of successful validation was greater for SNPs detected using more than one method than for SNPs flagged by a single method. The highest overall validation success rate was obtained for SNPs called by two methods while a marginal reduction was found for SNPs called by three or four methods. To explore this further, we calculated the proportion of the total number of SNPs called by each of the four methods separately for SNPs called by one, two, three or four methods respectively. We found that the peak in validation success corresponding to SNPs called by two methods can be explained by a greater proportion of those SNPs having been called by GATK after mapping with either BOWTIE2 or BWA . Each set of SNP calls was then hard-filtered using GATK’s VariantFiltration tool based on the following criteria: fisher strand bias <30, quality by depth >2, unfiltered read depth ≥10, read mapping quality ≥40. SNPs consequently flagged with anything other than ‘PASS’ were removed from the datasets. We also removed SNPs if read support for the minor allele was less than three.To mine SNPs from the hybrid transcriptome, we generated two In order to determine the extent of overlap between SNPs called by different methods, we revisited two sets of SNPs called from the 454 transcriptome using NEWBLER and SWAP454 respectively , 34. A sgetfasta to extract the 121 bp SNP flanking sequences required for Illumina assays and the 71 bp flanking sequences required for Affymetrix assays. Loci with insufficient flanking sequence were discarded, as were a small number of SNPs that did not match the corresponding base in the genome sequence. The suitability of the resulting flanking sequences for each assay’s hybridization technology was then determined by generating Illumina Assay Design Tool (ADT) scores for the 121 bp SNP flanking sequences and Affymetrix p-convert scores for the 71 bp SNP flanking sequences. These were obtained from both Illumina and Affymetrix directly. SNPs assigned an ADT score of <0.8 were discarded from the Infinium dataset. For the Affymetrix dataset, SNPs with forward and/or reverse sequences designated ‘not recommended’ or ‘not possible’ were discarded.We generated a global list of SNPs representing all of those called from (i) the 454 transcriptome using NEWBLER and SWAP454 and (ii) the hybrid transcriptome using BWA and BOWTIE2 in combination with GATK. We then implemented the steps outlined below to obtain subsets of SNPs suitable for designing Illumina Infinium iSelect HD and Affymetrix Axiom SNP assays respectively. Firstly, we used the BEDTOOLS command blastn in BLAST with an e-value threshold of 1e−12. From this, we determined the alignment length of the top blast hit (a full and continuous mapping indicates that a SNP and its flanking sequences lie fully within an exon) and the total number of mappings (a proxy for sequence uniqueness).For each SNP, we recorded the depth of coverage, minor allele frequency (MAF), ADT score (for Illumina assays) or p-convert score (for Affymetrix assays). We then mapped the corresponding Illumina and Affymetrix flanking sequences to the Antarctic fur seal reference genome  using blk-fold cross validation. This approach splits the 144 observations into k = 5 non-overlapping subsets of approximately equal size, uses one subset as a validation sample and the remaining four subsets as training data in order to generate the best predictive model. This best model was then used to output the probability of each SNP successfully validating given values of the predictor variables using the predict function in the bestglm package in R [Given the above information, we used two approaches to identify SNPs with high likelihoods of validation success for each SNP. First we simply filtered for SNPs whose flanking sequences match completely and uniquely to the genome, as these two characteristics have been shown to have a major affect on validation success . Second,age in R . A given


Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements. Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability. Plants have evolved unique mechanisms to respond to variable environments because they are fixed in a specific location and cannot seek shelter or alter their environment. These plastic responses include changes in physiology, metabolism, growth, and development in response to biotic and abiotic stresses3. Natural populations with a greater capacity for phenotypic plasticity have been shown to have greater fitness than populations that are less able to respond to their environment4, supporting an evolutionary advantage conferred by plastic responses. Conversely, plasticity can also confer an evolutionary disadvantage in novel environments when there has not yet been selection on genetic variation for plasticity5. Plasticity is heritable, and therefore can be intentionally selected for or against in manmade populations6. Artificial selection in the form of modern crop breeding has yielded remarkably productive cultivars that are stable across diverse conditions, but it is not clear whether phenotypic plasticity is among the traits that have been selected for or against.The expression of an individual’s phenotype is a function of its genotype (G), the environment experienced during its lifetime (E), and the complex relationship established by the differential sensitivity of certain genotypes to specific environmental influences throughout their lifetime. This variable plastic response7. Heritable plasticity may contribute to a population’s success in novel habitats, but may also contribute to divergence of populations in different environments8. If a population is introduced to and becomes successful in a novel habitat, but is subsequently restricted to that habitat by other forces, alleles that contributed to plastic adaptation in the new environment could trend towards fixation in the absence of gene flow from other populations12. We hypothesize that as these loci under selection for fitness in the new environment trend toward fixation, their ability to confer plasticity is also subsequently reduced.Variable plasticity of genotypes across differing environments can be quantified as G × E. Proposed mechanisms of the genetic basis for G × E include overdominance, pleiotropy, epistasis, linkage, and epigenetic causes15. Genes involved in stress response can be classified as either coding for proteins that directly protect against stress or as coding for products that regulate downstream gene expression16. DNA polymorphisms that lie within gene promoter regions have been associated with G × E variation for flowering time in Arabidopsis17. The complex nature of G × E, along with results from attempts to map genes responsible for G × E variation19 suggest that genetic control of G × E is highly polygenic. One of the hypothesized reasons that some mapping studies only explain small amounts of variation is because highly complex traits are controlled by numerous polymorphisms with small effects20. The evidence for regulatory regions controlling stress responses, combined with mapping evidence that points to G × E variation being controlled by many small-effect loci, lead us to hypothesize that G × E variation is disproportionately controlled by numerous regulatory mechanisms.Expression of abiotic stress responses by plants is a complicated process, thought to involve (among other mechanisms) differential response of gene regulatory networks21. There is a deficit of large-scale field experiments that study species in the context of defined, variable growing conditions. Crop species grown in typical field production environments can provide such an experimental structure.Studies that discuss the genetic control and modulation of plasticity and G × E variation have been conducted either in the context of natural populations or model species evaluated in contrasting conditions within controlled environments. Surveys of existing natural populations, long used by ecologists, do not have the structure of replicated, designed experiments that cultivated species can provide. On the other hand, evaluations in controlled environments can impose extreme conditions leading to overestimation of the variation found in natural conditionsZea mays L.) G × E Project, launched in 2014, is a part of the Genomes to Fields (G2F) initiative (www.genomes2fields.org). This project has measured phenotypes of maize hybrids across a geographically and climatically diverse transect of the North American maize growing landscape.The Maize  as well as phenological traits , which display great variability.Maize, as both a model species and a crop grown worldwide, is an ideal candidate for replicated, field-based studies of G × E variation across a wide range of environments. Maize was domesticated in southwestern MexicoThis study leverages data generated by the Maize G × E project to study the relationships between allelic variation and G × E. Specifically, we investigate how loci showing evidence of differential selection affect G × E and where single nucleotide polymorphisms (SNPs) associated with G × E tend to be located in the genome. We test the following hypotheses: (1) genomic regions that have experienced changes in allele frequency due to selection for productivity in temperate conditions explain less G × E variation than regions in which allele frequency was unaffected by selection; and (2) G × E variation is disproportionately controlled by regulatory mechanisms.To test the first hypothesis, we use high quality resequencing data in groups of temperate and tropical inbred maize lines to identify regions that show high divergence in allele frequency between the two groups. We then use SNP data from hybrids grown as part of the Maize G × E project to estimate G × E variance explained by those divergent regions relative to a set of SNPs that show little to no divergence Fig. , and pro25 on the hybrids grown for the Maize G × E project. We use the slope and mean squared error (MSE) parameters from those regressions as the response variables in genome-wide association studies (GWAS). We then examine the genomic location of polymorphisms associated with G × E in relation to nearby genes, providing evidence for enrichment of associations in regulatory regions of the genome  they were selected in both temperate and tropical material; (2) they were selected in temperate material and experienced little or no selection in tropical material; (3) they experienced little or no selection in temperate material and selection in tropical material; or (4) they are the result of random genetic drift. Our conclusions are contingent on the assumption that the majority of high FST regions were selected on in the temperate material, i.e., that the third and fourth categories are not disproportionately large. Analysis of nucleotide diversity in the high and low FST regions revealed most of the high FST regions had low nucleotide diversity in both temperate and tropical materials  is similar to median nucleotide diversity previously reported for known selection candidates in maize (0.0021)27. We used a variance components approach  to calculate the phenotypic variance of 552 hybrids attributable to SNP-by-environment interaction of SNPs with differential FST. Due to concern that different allele frequencies between high and low FST SNPs within the set of hybrids could affect the variance estimates, we compared the minor allele frequency (MAF) distributions for the high FST and low FST SNPs but observed no major differences  and 20.8% (plant height) of the non-environmental variance  and low (putatively unselected) FST SNPs in our random effects model, allowing us to partition the phenotypic variance that was attributable to environment, genetic effects, and G × E of presumably selected and unselected genomic regions  and environment than by the interaction between high FST SNPs (n = 1248) and environment  derived from the experimental design random effects model.To determine whether significant SNPs controlling G × E are primarily genic or non-genic, we first quantified plasticity of each hybrid using a method similar to that originally described by Finlay and WilkinsonTo categorize the variants identified from GWAS, we assigned variants into classes based on proximity to the closest annotated gene model. We pooled the top 50 most significant SNPs from GWAS of each trait’s slope, resulting in 250 slope-associated SNPs for the five traits considered. MSE-associated and trait-per-se-associated SNPs were pooled in the same manner. There was no systematic co-localization of slope-, MSE- or trait-per-se-associated SNPs  for slope-associated SNPs in the upstream gene-proximal region relative to what would be expected from the null distribution. A Bonferroni (5 tests per parameter) corrected type I error rate of 0.05 is 0.01 (0.05/5 = 0.01). The upstream gene-proximal region corresponds with the typical location of promoters and short-range regulatory elements. The rest of the distance distribution for non-genic slope-associated SNPs, and the entire distribution of non-genic MSE-associated SNPs, are similar to the distribution formed from all SNPs. Both slope- and MSE-associated genic SNPs were reduced  relative to the all-SNP distribution. The decrease in genic SNPs contrasts with the results of Wallace et al.30, who found a strong enrichment of genic SNPs in GWAS hits of phenotypic traits per se. GWAS hits for the traits per se in this study did not differ from the all-SNP distribution in any category.When we compared the distributions of distance to the nearest gene for non-genic slope- and MSE-associated SNPs with the distance distribution of all SNPs Fig. , we obseThese results provide evidence that plastic response in maize is disproportionately associated with non-genic regions of the genome and that in the case of type II stability, variation is attributable to the upstream gene-proximal region where short-range regulatory elements are frequently located. We do not see a similar pattern for type III stability, indicating that different types of plastic response might be under different types of regulation.ST regions; and (2) that G × E variation is disproportionately controlled by regulatory mechanisms, supported by enrichment of upstream gene-proximal variants associated with stability.The results presented here provide evidence to support the hypotheses that (1) genomic regions selected for high productivity in temperate environments show reduced contribution to G × E variation, supported by the difference in grain yield G × E variance explained by high and low FST statistic can reliably identify genomic regions that have been differentially selected; high FST values can also occur due to random genetic drift, which we assume here to have had less of an effect at the high FST regions than selective forces. Analysis of nucleotide diversity among genomic regions with high and low FST revealed that the majority of high FST regions had low nucleotide diversity in both temperate and tropical materials, supporting the idea that genetic differentiation in high FST regions is due to divergent selection. Additionally, while FST between temperate and tropical materials has been calculated with as few as 16 and 11 individuals in each group27, even the sample size of 30 and 30 used in this study may result in considerable noise surrounding estimates of FST. Finally, different sets of lines used for calculating FST may also result in the identification of differing selection candidate loci, but the approach of defining groups based on genetic differences across the axis separating temperate and tropical lines is the most compelling given that maize originated in tropical regions and adapted to temperate locations.Our results provide evidence that regions of the maize genome which were presumably selected during modern temperate maize breeding contribute less to grain yield G × E variance than genomic regions that do not appear to have been selected. These results rely heavily on the assumption that the F31. To reduce G × E, breeders attempt to select lines that perform consistently across a target population of environments . For example, breeders try to produce cultivars that perform reliably despite year-to-year fluctuations in weather patterns. In the case where limited plastic response confers stability, the low G × E contribution of selected regions may have a desirable effect by enabling germplasm to perform predictably across environments. Modern temperate maize as a whole was heavily selected for stable grain yield but not explicitly for stable plant height. The large difference in G × E explained by high and low FST SNPs for grain yield, compared to the small difference for plant height, reflects this systematic difference in how the two traits were selected. Under the framework of G × E being the result of susceptibilities exposed in specific environments, the low G × E and higher stability conferred by high FST regions may be a sign of effective selection by maize breeders against alleles that are deleterious in modern temperate breeding programs. When exploiting G × E, breeders attempt to select lines that perform particularly well in specific environments. For example, most modern maize is bred to mature more or less quickly depending on where it will be grown. If genetic potential for plastic response is limited, it decreases breeders’ ability to identify cultivars that truly excel in specific environments. That is, reduced G × E contribution of selected regions may have the undesirable effect of constraining performance potential when cultivars are bred for specific locales. Based on these findings, we are unable to know whether this pattern of decreased G × E variation attributable to selected regions is unique to the adaptation of North American maize germplasm, or whether similar trends would be seen in other highly selected populations of maize  or in different species.We interpret these results as evidence that genomic regions that originally contributed to the adaptation of maize to temperate North American growing conditions may now limit the ability of modern North American temperate germplasm to adapt to different environments. Through continued breeding, the modern germplasm pool will produce future hybrids that are adapted to new environmental conditions, yet may no longer contain alleles that once conferred plasticity. Limited plastic response can be either beneficial or antagonistic in cultivar development. Frequently, G × E is the result of biotic or abiotic stress susceptibilities that are exposed in particular environments. Breeders’ approach to G × E has traditionally been to either reduce or exploit the phenomenonOur second hypothesis, that phenotypic plasticity is disproportionately controlled by regulatory regions, is also supported by the results of this study. Because the experimental design was unbalanced and hybrids were assigned to locations based on expected maturity, there is likely to be some degree of genotype-environment correlation. This is an inherent limitation of experiments that measure crop productivity in relevant field conditions. Results for this experiment rely on the assumption that parameters of the Finlay–Wilkinson regressions are good estimators of their true values despite measuring hybrids in a non-random subset of environments.30 found enrichment for associations between phenotypes and variants in both gene-proximal and genic regions. The associations were mapped using phenotypes per se—that is, measurements of physical traits. Our results from mapping the stability of cultivars for various traits across a range of environments revealed a similar pattern of gene-proximal associations with type II stability, but differ in that we observed a depletion of genic associations with both type II and type III stability. Our mapping of phenotypes per se did not reveal any deviations from the null distribution. This contrasts with the aforementioned study, which observed enrichment for genic and gene-proximal variants associated with traits per se using inbred maize lines. Our study was conducted with hybrids, in which major allele effects may be more likely to be complemented or otherwise buffered, complicating additive mapping efforts. The deviation of stability-associated SNPs from the expected distribution established by the full set of SNPs provides evidence that we are seeing some true mapping associations rather than purely false positives or noise. We hypothesized that phenotypic plasticity is largely controlled by regulatory elements rather than genes per se, and the enrichment of gene-proximal variants associated with type II stability provides support for this hypothesis. The enrichment of upstream associations in gene-proximal regions precludes the possibility that we had an enrichment of gene-proximal variants only because they were in linkage disequilibrium with genic causal mutations. If that were the case, we would have expected to see enrichment for both upstream and downstream gene-proximal variants. Observing enrichment of upstream gene-proximal variants for type II stability but not type III stability means we are seeing gene-proximal association with differences in the linear response of cultivars across environments, but not with variability around that linear response. Due to the way type II and type III stability were calculated (slope and MSE), estimates of type II stability provide some degree of smoothing across environments while estimates of type III stability are more sensitive to stochastic differences between environments. As a result, it is unclear whether the lack of upstream gene-proximal enrichment for type III associated variants is attributable to differential control of type II and type III stability or just experimental noise. The observed decrease in genic associations with both type II and type III associated variants was not an effect that we predicted, but may be cautiously interpreted as further evidence that stability is modulated even less by structural genes per se than anticipated. These findings could be further validated with the addition of denser SNP data, more phenotype data for an array of traits, and a larger number of phenotyped and genotyped individuals.A previous studyThe G2F Maize G × E project is an ongoing experiment that is accumulating phenotypic data across years and locations. The analyses presented here are an example of the project’s utility, which will only increase as data continues to grow. This experiment fits a yet unfilled niche in the study of phenotypic plasticity and adaptation; specifically, that of a large, multi-environmental replicated experiment that has some of the attractive features of both natural population and controlled-environment studies.https://doi.org/10.7946/P2V888. Female parents of hybrids were classified into eight pools based on genetic background. Briefly, those pools were: (1) Recombinant inbred lines (RILs) from the Intermated B73/Mo17 population (IBM)32; (2) RILs from the nested association mapping (NAM)33 population involving B73/Oh43; (3) RILs from the NAM population involving B73/Ki3; (4) Public and expired plant variety protection (PVP) lines belonging to the Iodent group; (5) Public and expired PVP lines belonging to the Stiff Stalk group; (6) Public and expired PVP lines belonging to the Lancaster group; (7) Public lines originating from the Texas A&M AgriLife corn breeding programs; (8) RILs developed by the University of Wisconsin’s biomass breeding program. The first maize inbred sequenced34, B73, is a parent of pools 1–3 and a founding member of the Stiff Stalk group represented by pool 5. Pools 4 and 6 represent the Iodent and Lancaster groups which are commonly crossed to Stiff Stalk materials in public and private breeding programs. Pool 7 represents temperate and exotic germplasm selected for adaptation to Texas35, while pool 8 contains RILS derived from diverse parents showing segregation for various biomass related traits. Pools 1 through 8 were crossed with up to five male testers , and each pool-by-tester family was designated as a “set”  was nested within the whole-plot factor (set). This design is also referred to as a sets-in-replicates design. Two complete replicates of each hybrid were planted at each location; within each replicate, each set was grown in a block, and block order was randomized within replicate. Hybrid order was randomized within each whole-plot block. The locally adapted hybrid check selected by each investigator at each location was incorporated into each block within each replicate. Weather data were collected at each location  was recorded as pounds per bushel. Root lodging and stalk lodging were recorded respectively as the number of plants leaning more than 15 degrees from vertical and as the number of plants with broken stalks between the ground and the primary ear node. Stand count was recorded as the number of plants per plot at harvest. Grain moisture was measured as the percent water content in the grain at the time of grain harvest. Grain yield in bushels per acre assumed a 56 pounds per bushel conversion, 15.5% grain moisture, and used plot area measured without the alley. The calculation for grain yield was grain yield = (plot weight)×(1–0.01×moisture) × (area−1) ×920.5401. Ear height and plant height were measured on one to five representative plants per plot depending on the location while all other measurements were representative of the entire plot. Full phenotypic data can be found at https://doi.org/10.7946/P2V888.Eleven morphological and agronomic traits were measured for all hybrids and across all locations. Methods for their measurement were standardized project-wide. A detailed description of phenotyping guidelines is available at E represents the environmental effect; S is the set effect, e is the error term. Models were fit in R36 using the lmer function in the lme4 package37. Variance component estimates were expressed as a percentage of the total variance. Predictions of hybrid effects were recorded as the best linear unbiased predictions (BLUPs) for hybrid line nested within set.As detailed in the field experimental design section, hybrids were classified into sets based on the female pool and the male tester. Hybrid genotypes were grown in a modified split-plot design. To calculate the variance attributable to each element of the field experimental design, we modeled each phenotype as http://www.panzea.org/#!genotypes/cctl). DNA for the remaining inbreds was extracted and genotyped using genotype-by-sequencing (GBS) following the protocol described by Elshire et al.38 at 96 plex. Genotypes were called using the Tassel5-GBS Production Pipeline with the ZeaGBSv2.7 Production TagsOnPhysicalMap(TOPM) file that was built using information about ~32,000 additional Zea samples39 . Imputation was performed with FILLIN40 using the available set of maize donor haplotypes with 8k windows . FILLIN has been shown to have an imputation accuracy of 0.996 on temperate inbred materials representative of the germplasm used in this study. All GBS samples used are listed in Supplementary Data https://doi.org/10.7946/P2V888.A set of 336 inbred lines were used to generate the hybrid sets tested in the 2014 experiment. Sequencing data for 232 of the inbred lines used in this evaluation were downloaded from the ZeaGBSv2.7 Panzea release  and the hybrid genotype for each hybrid at each locus was computed as the mean of its two parents at that same locus.39 and Hapmap 3.126  were used to perform a Multidimensional Scaling (MDS) analysis on the 916 Zea accessions described in Hapmap 3.1, following the procedure described by Romay et al.41 Based on the location of the inbreds that were part of Hapmap 242, inbreds with coordinates <−0.5 on the first coordinate and above 0 on the second coordinate were classified as temperate selected while inbreds with coordinates > 0.5 on the first coordinate and greater than 0 on the second coordinate were classified as tropical selected , and missing SNP data. Thirty individuals from each group was the sample size that best balanced the amount of missing data between temperate and tropical lines. Previous publications have computed FST between tropical and temperate materials with as few as 16 and 11 individuals per group (respectively)27. FST43 between the two groups was calculated for each SNP in Hapmap 3.1 using VCFtools44. The unweighted average was calculated to determine an FST value for every 20-SNP interval. From the FST results, 1248 SNPs in the hybrid lines were identified as present in regions that are more probable to have been subject to selection , while 263,243 SNPs in the hybrid lines were chosen as present in regions that are unlikely to have been selected . Per-site pairwise nucleotide diversity was assessed in the high and low FST regions within the temperate and tropical inbreds to assess evidence of divergent selection vs. directional selection within individual subpopulations. Nucleotide diversity calculations were performed with the Hapmap 3.1 sequencing data using SAMtools45 and ANGSD46. Because the high and low FST SNP groups were chosen based on mean values of 20-SNP windows, some of the SNPs that were included in the high FST group do not have an individual FST greater than 0.5. The 736 high FST SNPs that overlap between Hapmap 3.1 and the hybrid line genotypes were used to evaluate allele frequencies between the temperate and tropical groups, as well as FST values at individual SNPs  denotes the random effect of the jth environment such that p marker covariates m = 1,..,p) and their correspondent marker effects ST and each environment such that ST and the environments with ST values were above 0.5 and ST set of 263,243 SNPs. This model was fit 1000 times with random subsets of the low FST SNPs, and the calculated variance components were recorded. Models were fit using the BGLR48 package in R36. Residuals across all model fittings followed a distribution approaching normality, and heuristic assessment of equal variance between environments49 was satisfied. Therefore, common transformations of phenotypic data were not explored.We calculated G × E variance explained by SNPs with high and low FST SNPs using the same model as described above, but with the ST SNPs, and ST SNPs. The model was fit 1000 times for both grain yield and plant height and the calculated variance components were recorded.Because presence of G × E variance is dependent on the presence of both genetic and environmental variances, we tested for the presence of genetic variance attributable to high and low FThe hypothesis being tested assumes that hybrids used in this field evaluation are representative of only temperate selected germplasm. A small number of hybrids had inbred line Ki3 as a parent, which is of tropical origin and as such could contain alleles that are not representative of germplasm selected for growth in temperate conditions. The variance decomposition analysis described above was run with hybrid lines containing Ki3 parentage both included and excluded, with no differences observed. With the hybrid lines containing Ki3 parentage removed from the data set, 552 unique hybrids were included in each model fitting.25. Environmental means were calculated using 21 check hybrids that were grown in at least 20 of 21 environments. Ear height, plant height, number of days to silk and anthesis, and yield values for each hybrid were regressed on the respective environmental means by simple linear regression: i in environment j, j, and Hybrid stability was calculated using a method similar to the Finlay–Wilkinson regression50  with the synthetic hybrid genotypic data. Synthetic hybrid SNPs were filtered to 413,796 SNPs with <80% missing data, a mean of 20% missing, and 95% of SNPs having less than 61% missing. GWAS was performed using the software GAPITp-values from each GWAS for slope were pooled. If any of the top 50 SNPs were within 5 kilobases of each other and in LD (52. Results from each GWAS for MSE and the traits per se were pooled in the same manner. Base pair (bp) distances from the pooled SNPs to the closest gene were calculated in a manner similar to that described by Wallace et al.30, but rather than calculate the absolute distance to the nearest gene we also calculated whether each SNP was upstream or downstream (5ʹ or 3ʹ) based on annotated gene orientation in the B73 AGPv2 reference genome (ftp.gramene.org/pub/gramene/maizesequence.org/release-5b/filtered-set/ZmB73_5b_FGS.gff.gz). A null distribution of distance to the closest gene was calculated using all 421,142 SNPs in the hybrid genotype data set. We chose 50 SNPs per trait/parameter combination because it closely represented the proportion of total SNPs used in the Wallace et al.30 study. For the null, slope, MSE, and trait per se distances, SNPs were categorized as either upstream or downstream and as genic (within a gene), gene-proximal (1–5000 bp to closest gene), or intergenic (>5000 bp to closest gene). Tests for enrichment or reduction of slope- or MSE-associated SNPs in each position category were performed against the null distribution using a two-sided exact binomial test.The 50 SNPs with the lowest ST regions can be found in Supplementary Software https://github.com/joegage/GxE_scripts.Scripts for modeling variance attributable to high and low Fhttps://doi.org/10.7946/P2V888. All relevant data are available from the authors upon request.Hybrid phenotypic data, inbred genotypic data, weather data, metadata, and readme files are publicly available at Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary SoftwareSupplementary Data 1