Glycine max). A collection of 289 soybean genotypes was grown in three environments, root crowns were excavated, and 12 RSA traits assessed. The first two components of a principal component analysis of these 12 traits were used as additional aggregate traits for a total of 14 traits. Marker–trait association for RSA traits were identified using 31,807 single-nucleotide polymorphisms (SNPs) by a genome-wide association analysis. In total, 283 (non-unique) SNPs were significantly associated with one or more of the 14 root traits. Of these, 246 were unique SNPs and 215 SNPs were associated with a single root trait, while 26, four, and one SNPs were associated with two, three, and four root traits, respectively. The 246 SNPs marked 67 loci associated with at least one of the 14 root traits. Seventeen loci on 13 chromosomes were identified by SNPs associated with more than one root trait. Several genes with annotation related to processes that could affect root architecture were identified near these 67 loci. Additional follow-up studies will be needed to confirm the markers and candidate genes identified for RSA traits and to examine the importance of the different root characteristics for soybean productivity under a range of soil and environmental conditions.Water and nutrient acquisition is a critical function of plant root systems. Root system architecture (RSA) traits are often complex and controlled by many genes. This is the first genome-wide association study reporting genetic loci for RSA traits for field-grown soybean ( Survival and performance of plants depend on efficient exploration of the soil in search for available water and nutrients  Merr.] root system is relatively simple with three distinct morphological components: the primary root, commonly called the taproot, which originates as the radicle from germinating seeds; lateral roots, often referred to as secondary roots that emerge from the taproot; and tertiary and higher-order lateral roots that originate from the secondary and successive order lateral roots  and 2.4 m (2013) in length at Rollins Bottom and 4.6 m in length at Rhodes. Row distance was 0.76 m in all three environments. In 2012 at Rollins Bottom and in 2013 at Rhodes, the experiments were watered with an irrigation gun and a lateral-move irrigation system, respectively. No irrigation was applied at Rollins Bottom in 2013. Pre-plant application of P and K was conducted based on soil test results as recommended by the University of Missouri, Columbia. Weeds were controlled using pre- and post-emergence herbicide applications supplemented by manual weeding as needed. No insecticide applications were conducted. Of the 341 diverse soybean accessions planted, 289 accessions were used for a genome-wide association study (GWAS), while the remaining accessions were excluded because of missing phenotype or marker data. Throughout the remainder of the text, Rollins Bottom 2012 and 2013 experiments and the Rhodes 2013 experiment will be referred to as RB12, RB13, and RH13, respectively.Field experiments were conducted at three locations, namely, Rollins Bottom in Columbia, MO , in 2012 and 2013, and at the Rhodes Farm (Rhodes) near Clarkton, MO , in 2013. At Rollins Bottom, the soil is a Haymond silt loam soil ; and at Rhodes, it is a Malden fine sand . Seeds of 341 maturity group IV soybean accessions, originally obtained from the USDA Soybean Germplasm Collection, were planted in a randomized complete block design (RCBD) with three replications in each environment. Genotypes were planted at ~2.5-cm depth to a density of 25 seeds mRoot crown phenotyping was used to determine the RSA of the top portion of the root system, the root crown York, . When thDescriptive statistics were computed for all measured variables using the PROC MEAN procedures of SAS . Broad-sense heritabilities were determined using a SAS . Principal component analysis (PCA) was performed using original phenotypic values of all genotypes for all root traits in R (version 3.3.2). Data were centered and scaled using the scale function, and then the prcomp function was used to conduct the PCA. The scores, or rotations, from principal components (PCs) 1 and 2 were selected as aggregate traits to be included in the GWAS.To better understand the dependencies among traits, Pearson correlation coefficients were computed in k) ranging from 1 to 10. All soybean accessions were assigned to a subpopulation based on the optimum k (k = 8), and the population structure matrix (Q) was generated for further association analyses. TASSEL 5.2.3 software  markers originated from the application of the Illumina Infinium SoySNP50K iSelect SNP Beadchip  calculation. Clustering of genotypes was conducted with the cladogram function in TASSEL 5.2  among SNPs and identification of haplotype blocks was based upon SNPs within 1-Mb windows as discussed previously    were also taken into consideration based on previous candidate genes reported for root traits in the literature.To identify genes that may be affecting the RSA phenotypes, a region encompassing a ±0.5-Mb window around each of the putative loci was explored for candidate genes in SoyBase (P < 0.001) for all traits. Additionally, strong environment effects (P < 0.001) were observed for all root traits (P < 0.05) only for one half  but not the other half  of the 12 traits  explained 36.05% of the multivariate variation, with the second PC (PC2) explaining another 15.95% of the variation for a total of 52% of the root trait variation being attributed to these first two PCs . The traThe accessions characterized in this study were selected as described in Dhanapal et al.  and origThe SNP markers available for the 289 genotypes were filtered to ultimately include 31,708 polymorphic SNPs with a MAF ≥5% for all analyses. The MAF examined with regard to the genotype frequency showed a peak slightly above 10% and then gradually declined to plateau in the range of 20–50% . Among tk = 8 was determined to be the optimum k, indicating that eight subpopulations could contain all individuals with the greatest probability. Hence, a k value of 8 was selected to describe the genetic structure of the 289 soybean genotypes. The estimated population structure indicated genotypes with partial membership to multiple subpopulations, with few subpopulations exhibiting distinctive identities  against entities . Signifientities .2) between all pairs of markers (only markers with MAF ≥ 5% were included). Pairwise LD estimates were performed on the complete panel using all 31,708 SNP markers. The average r2 of marker pairs suggested that significant LD blocks were observed on several CHRs' regions  followed by LPLN (0.30) and NPL (0.28) . HeritabR2 for the SNPs associated with each trait ranged from 0.04 for LLAA to 0.08 for USLD.Genome-wide association analysis across environments identified both unique SNPs (SNPs that are associated with one trait only) and SNPs common among root traits as well as for PC1 and PC2 . A totalThe genomic locations of significant SNPs marking putative loci for the different root traits are indicated in In some instances, more than one SNP was significantly associated with a particular combination of root traits. Of the 13 different root trait combinations, three root traits, namely, UPLN , USLD , and ALD , were identified by SNPs tagging four other root traits. Additionally, LSLD (ALD and PC1) and NPL (UPLN and PC1) were marked by SNPs that also were associated with two other root traits. Not surprisingly, PC1 co-localized with multiple root characteristics . In contrast, PC2 co-localized with only one trait (LPLN). One of the SNPs (located on CHR 15) associated with PC1 was also associated with three other traits, making it the single SNP associated with the largest number of traits . On CHR 14, one putative locus was marked by eight SNPs associated with both UPLN and NPL, and the other locus was associated with three SNPs that each marked ALD in combination with either LSLD or PC1. The putative locus on CHR 18 was marked by three SNPs that were associated with USLD and ALD. A locus on CHR 16 was marked by two SNPs that were associated with both LSLD and ALD, and a locus on CHR 19 was marked by two SNPs associated with UPLN and PC1. Additionally, the putative locus on CHR 9 was marked by one SNP associated with USLD and ALD and another SNP associated with USLD and LLAA.www.soybase.org; Morales et al., Putative candidate genes were identified within ±0.5 Mb for all significant SNPs showing marker–trait association for all root traits. Across the 14 traits, 70 candidate genes that may underlie associations with root characteristics were identified . Among tThis study employed root crown phenotyping of a soybean diversity panel in three field environments to assess 14 RSA traits including lateral root numbers, secondary lateral root densities, and lateral root angles  and the To our knowledge, this is the first use of PCs in GWAS of root traits. PC1 was heavily influenced by “complexity” traits such as the numbers of primary and secondary laterals, while PC2 was more influenced by all the measures of angle. In fact, this structure agrees with earlier reports in maize where nodal root number and lateral root density influenced PC1, while the angles of various nodes of crown roots influenced PC2 , which has a root system similar to that of soybean  based search using term “root” revealed 19 QTLs that were located within ±0.5 Mb of at least one of two flanking markers close to the putative loci identified in this study. The putative locus identified for LSLD, ALD, LPAR, and PC1 on CHR 2 was located near one QTL previously identified for lateral root number (qLRN1-D1b-1)  for root dry weight of seedlings grown at low-phosphorus availability  at high-phosphorus availability  identified previously  and one QTL for root dry weight (qRDW001), all of which were located near a putative locus identified for LSLD and PC1 on CHR 8. Interestingly, none of the QTLs identified for root weight by different studies   on CHR 3 was co-located with the putative locus identified for PC2, and another one (Q_root_Gm08) was located close to the LPAR locus identified on CHR 8  and root volume 1-3 (qRV1-m-2), were identified to be located near a putative locus for UPAR on CHR 7  for root length on CHR 14 that was identified by Nguyen et al. (qMRL001 and qMRL002) that were located close to a putative locus for PC2 on CHR 6. Additionally, a QTL (Qhti-14-2) for root area and two QTLs (Qrd-14-1 and Qrd-14-2) for root diameter and a QTL (Qcard-14) for change in root diameter identified  were abundant (12 genes) in the vicinity of loci for UPLN, USLD, UPAR, LSLD, LLAA, ALD, PC1, and PC2  family genes, which were found near six putative loci  for UPLN, USLD, ULAA, UPAR, LLAA, ALD, and PC2. Aux/IAAs and ARFs are TFs that regulate the cell-specific transcription of auxin response genes for lateral root development  play diverse roles in plant development and stress responses. Four candidate genes were found in the vicinity of four putative loci on CHRs 4, 5, 9, and 10 identified for UPAR, LPAR, LLAA, and NPL. MYB TFs are reported to mediate ABA–auxin cross-talk in drought stress responses and lateral root growth, providing an adaptive strategy under drought  are a subclass of arabinogalactan proteins (AGPs), play a key role in barley root epidermal cell differentiation and root hair development  proteins often function as molecular “hubs” mediating supramolecular interactions  was found near a previously studied LOX gene  catalyze the conversion of polyunsaturated fatty acids (lipids) into conjugated hydroperoxides. The genome-wide analysis of the lipoxygenase gene family in Other genes found close to putative loci were related to ABC transporter super family, phospholipase D (PLD), BURP domain encoding genes, protein phosphatase 2C (PP2C), ubiquitin C-terminal hydrolases gene (UCHs), glycerophosphodiester phosphodiesterases (GPX-PDE) genes, proline-rich receptor-like protein kinase PER12-related genes, exostosin family protein-related genes, COBRA-like (COB) genes, bHLH protein encoding genes, and TFs, namely, HD-Zip, TPR, AP2/ERF, and WRKY TF family.This study relied on manual measurements and scoring of root traits, an approach that has been used for a variety of crops  of which 31,708 SNPs were used in this study. This information is already available to the public, and the SNP data for our user defined genotypes can be obtained from the following link http://www.soybase.org/snps/download.php. All the data that directly underlie key results and conclusions of this article are attached as The 289 genotypes used in this study are part of 19,652 G. max and G. soja accessions genotyped with 52,041 SNPs (AD, KH, and FF designed the study. AD and KH performed field experiments and collected phenotype data for RSA traits. AD performed SNP–trait association. AD and LY performed other statistical analyses and wrote the manuscript. FF coordinated and supervised the projects. AD, LY, and FF critically revised the manuscript. All authors read and approved the final 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.


Molossus) as our model system. Many species within this genus have low-levels of genetic variation within and between morphologically distinct species, and the relationships among them remain unresolved using traditional Sanger sequencing methods. Given that both de novo and reference genome pipelines can be used to assemble next generation sequences, and that several tree inference methodologies have been proposed for single nucleotide polymorphism (SNP) data, we test whether different alignments and phylogenetic approaches produce similar results. We also examined how the process of SNP identification and mapping can affect the consistency of the analyses. Different alignments and phylogenetic inferences produced consistent results, supporting the GBS approach for answering evolutionary questions on a macroevolutionary scale when the genetic distance among phenotypically identifiable clades is low. We highlight the importance of exploring the relationships among groups using different assembly assumptions and also distinct phylogenetic inference methods, particularly when addressing phylogenetic questions in genetic and morphologically conservative taxa.• Herein we propose a framework for assembling and analyzing Genotype by Sequencing (GBS) data to better understand evolutionary relationships within a group of closely related species using the mastiff bats (• The method uses the comparison of several filter settings, alignments, and tree inference approaches on Genotype by Sequencing data.• Consistent results were found among several approaches.• The methodology successfully recovered well supported species boundaries and phylogenetic relationships among species of mastiff bats not hypothesized by previous methods.  Specification TableGenotyping by Sequencing (GBS), which involves sequencing genomic regions flanking restriction sites. Using GBS, many sequences of short length are obtained, vastly increasing the size of the overall data set in comparison to traditional Sanger methods. This technique provides sequence data for thousands of single nucleotide polymorphisms (SNPs), allowing the detection of small, but consistent genetic variation among genetically similar groups not revealed by standard gene sequencing approaches. GBS has been successfully used in studies of population genetics Advances in genomics technology have allowed the generation of large numbers of molecular markers across the genome, which increases sample sizes and provides additional data to help resolve interpretation of the ecology and evolution of traditionally poorly understood species groups Molossus), a genus with a complex taxonomic history and low levels of genetic variation de novo and reference genome pipelines are often used to assemble next generation sequencing (NGS) data, and that several tree inference methodologies have been proposed for SNP data, we also test if different alignments and phylogenetic approaches produce similar results. These data offer a useful framework for other comparative studies of ecology and evolution using the GBS approach.In this context, we propose a framework for assembling and analyzing GBS data to better understand evolutionary relationships among species of mastiff bats  pipelines, available as part of the TASSEL 3.0 software We assessed the quality of the paired GBS-tags using FastQC. The likelihoods of the possible genotypes were estimated for both pipelines, and the Genotype quality (GQ), which is the difference between the most likely and the second most likely genotype, was calculated also using FastQC. The data were then imported into TASSEL where samples were demultiplexed and filtered. The amount of missing data acceptable for phylogenetic estimates still lacks a consensus. The identification of minimum values for these filters is still debatable and may ultimately depend on the dataset in hand and the question that needs to be answered Similarly, the optimum value for minimum allele frequency (MAF) is not universally agreed upon and there is a trade-off between the use of MAF and the loss of rare alleles. The increase of the MAF value may cause an under-calling of heterozygotes with the loss of biological information, instead of the removal of sequencing errors de novo alignments using VCFtools, and then we simulated how the depth could affect the number of SNPs in both alignments by removing sequences with lower and higher depths than the mean in the TASSEL software. For this simulation we looked at the number of SNPs removed and the agreement between final topologies using the optimum MAF value found for each dataset.Low depth sequences are sequences recovered by relatively few reads and, if not removed, they might lead to serious bias (2014) Molossus and the low percentage of tags that aligned to the Myotis genome (2.5%), precluded use of this method for our dataset. Each tag produced by the GBS method has 64 bp, and therefore we have tried to overcome this problem by removing SNPs that were separated by less than 128 bp in the genome.GBS sequences are short, which decreases the chance of intragenic recombination. However, if the SNPs are closely linked on the same chromosome, they might not be independent. Some studies suggest mapping the SNPs in a reference genome to confront this issue Molossus using the Maximum Likelihood approach (ML) implemented in FastTree To test for divergence in tree inference approaches we removed invariant sites in both alignments and reconstructed the phylogenetic relationships within the genus de novo pipeline identified 418,810 SNPs after error curation in the standard network error remover of TASSEL. The alignment using the reference genome discarded 96.6% of tags that did not align with the Myotis genome and 0.9% of the tags that aligned multiple times, keeping only 2.5% of the short length sequences. After removal of invariant sites, the reference genome alignment produced 55,350 SNPs. Of the 189 specimens included in the Illumina library, 23 were discarded in the de novo pipeline and 16 were discarded in the reference-based pipeline because they had low numbers of raw reads and few loci (>90% of missing data), probably because of low DNA quality. The quality of the isolated DNA depends on the quality of the tissue sample. Fresh tissues produce the highest DNA yield and quality and samples should be stored under conditions that preserve DNA integrity. In addition, repeated freezing and thawing of frozen tissues might reduce the size and quality of DNA. For compromised samples, the initial concentration was lower than required by GBS library preparation (>100 ng/ul), and multiple DNA concentration procedures were required to archive the required DNA quality. After this procedure, thirty microlitres of each sample were sent to sequence, but the remaining DNA aliquot were less than thirty microlitres per individual, too low for repeating the GBS library preparation.After merging the Illumina paired-end sequences, more than nine million tags remained. The Both alignments behaved similarly for all filters. In our dataset, the removal of 10% missing SNPs decreased the total number of markers by less than half. The number of SNPs decreased further when removing 20% to 90% of missing data . Our finde novo and 49% with the reference genome approaches)  , which ide novo alignment in comparison with the reference genome data set. When set to 0.01, this filter discarded approximately two-thirds of the SNPS in the de novo alignment and half of those in the reference genome alignment  and species relationships compared to the linked dataset. However, the relationships within some populations changed slightly, which might indicate a higher effect on population structure analyses.More than half of the SNPS were closely aligned in the genome, which could indicate linked loci. We removed SNPs that were 128 base pairs apart, twice the length of a single tag, to decrease linked SNPs in the dataset . Linkagede novo and reference genome alignments were seven and six respectively, which is consistent with our analysis that shows a plateau in the number of SNPs after setting missing data depth less than six and seven for each respective data set , and this high proportion of low depth tags suggests a high degree of uncertainty about the homology of those sequences The average depth in the data set . The depde novo and reference alignment, respectively. After data filtering, the de novo pipeline yielded 71,801 SNPs and the reference genome pipeline yielded 27,323 SNPs. To remove unlinked and uncertain SNPs, sites with more than 50% of missing data were discarded as were sites less than 128 bp apart. The final data set with unlinked SNPs had 29,448 SNPs for the de novo pipeline and 15,569 SNPs for the reference genome dataset. In addition, we also used more and less conservative filtering values to test for differences in accuracy and consistency in tree topologies.According to the previous results, the sequences were filtered for missing data < 50%, minor allele frequency (MAF) >0.02, heterozygosity >0.01, and depth coverage lower than seven and six for the de novo and the reference genome alignments are congruent when the optimum filtering settings are used, recovering the same clades and the same relationships among species Molossus start to lose support; although, the relationships among them do not change. This outcome is consistent with loss of true rare polymorphic sites, which should not be removed from the dataset. The removal of markers with less than 50% of missing data did not change supports in the phylogenetic trees. However, if markers with more than 50% of missing data are removed and less conservative filtering setting values are used , the topologies between de novo and reference genome alignment lose agreement and relationship among some clades within Molossus are no longer supported by both alignments. These results are consistent with incorporation of sequencing errors. Using the optimum filtering values (The ML and SVDquartets trees with both the g values , the besde novo and reference-based alignments. In our study, relationships within terminal clades with low bootstrap support were affected by the choice of the pipeline, which could have resulted from the difference in number of SNPs retained from both alignments. However, the use of the reference genome in the alignment does not seem to be essential for recovering a robust overall phylogenetic tree, since both phylogenies, de novo and with the reference genome, were similar at the species level when optimal filtering settings values were used.The greater power of next-generation sequencing approaches compared to Sanger methods for answering phylogenetic questions is well established We highlight the importance of exploring the relationships among groups using different assembly assumptions and also distinct phylogenetic inference methods, particularly when addressing phylogenetically conservative groups. All models of molecular evolution are a simplification of the actual evolutionary process, and the inappropriate choice of filters during alignment or tree inference can lead to systematic bias in the phylogenetic reconstruction


Adenosine-to-inosine (A-to-I) RNA editing plays important roles in diversifying the transcriptome and preventing MDA5 sensing of endogenous dsRNA as nonself. To date, few studies have investigated the population genomic signatures of A-to-I editing due to the lack of editing sites overlapping with SNPs.In this study, we applied a pipeline to robustly identify SNP editing sites from population transcriptomic data and combined functional genomics, GWAS, and population genomics approaches to study the function and evolution of A-to-I editing. We find that the G allele, which is equivalent to edited I, is overrepresented in editing SNPs. Functionally, A/G editing SNPs are highly enriched in GWAS signals of autoimmune and immune-related diseases. Evolutionarily, derived allele frequency distributions of A/G editing SNPs for both A and G alleles as the ancestral alleles are skewed toward intermediate frequency alleles relative to neutral SNPs, a hallmark of balancing selection, suggesting that both A and G alleles are functionally important. The signal of balancing selection is confirmed by a number of additional population genomic analyses.We uncovered a hidden layer of A-to-I RNA editing SNP loci as a common target of balancing selection, and we propose that the maintenance of such editing SNP variations may be at least partially due to constraints on the resolution of the balance between immune activity and self-tolerance. RNA editing is a process through which the sequence of an RNA is post-transcriptionally altered from that encoded in the DNA , 2. A-toRecent genome-wide searches of editing sites revealed thousands to millions of A-to-I editing events in various species . HoweverHere, we modified our previous approach to achieve robust identification of both non-SNP editing sites and SNP editing sites (editing sites overlapped with SNPs) for samples with matched genomic and transcriptomic data. We applied this approach to samples from Genotype-Tissue Expression (GTEx) and Geuvadis projects and identified SNP editing sites to study the function and evolution of RNA editing in humans.To generate the list of SNP editing sites, we modified our previous pipeline , 22 to rTo validate whether the identified editing sites are bona fide A-to-I editing events, we examined their editing levels in comparison with RNA allelic ratios of known human SNPs. We found that, for both non-SNP and SNP editing sites, the distributions of their editing levels differed from allelic ratios of known heterozygous SNPs, which were centered at 0.5 Fig. b. We alsTo ask whether SNP editing sites are enriched in regions with dsRNA structures required for ADAR activity, we performed two analyses. First, we predicted the editing complementary sequence (ECS) of each editing region (SNP editing site and flanking ± 15 nt) and the shuffled editing region, as previously described . Next, wFinally, to experimentally verify that SNP editing sites are real A-to-I editing events, we examined their editing level changes upon ADAR1 or ADAR2 overexpression in HEK293 cells . We founHaving proved the validity of SNP editing sites, as many SNP editing sites were not overlapped between the two datasets Fig. h, possibA comparison between non-SNP and SNP editing sites revealed the difference in their genic locations and repetitive sequence features. SNP editing sites tended to be in CDS regions and 3′UTR regions, while non-SNP editing sites tended to be in the intergenic regions and intronic regions Fig. a. In addTo ask the possible functional significance of SNP editing sites, we performed Gene Ontology (GO) term analysis. As editing sites in CDS regions and 3′UTR regions may lead to different functional consequences, these two sets of sites were analyzed, separately. We found that genes containing CDS SNP editing sites were highly enriched in muscle-related functions Fig. d. This rTaken together, these results suggest a hidden layer of A-to-I RNA editing events that are likely functionally important.Genome-wide association studies (GWAS) have led to the identification of thousands of SNPs linked to disease susceptibility in complex human diseases . Based oWe next compared the editing levels between non-SNP editing sites, SNP editing sites, and GWAS SNP editing sites, which may be informative for assessing their biological significance. We found that SNP and GWAS SNP editing sites in CDS regions had higher editing levels than non-SNP editing sites, while SNP editing sites in intronic or intergenic regions had similar editing levels as compared with non-SNP editing sites Fig. b. Thus, Balancing selection is the main force shaping the evolution of immunity genes –38. The p values in Additional file p values in Additional file p values in Additional file p values in Additional file p values in Additional file To verify our prediction, we applied population genomic approaches to study the evolution of RNA editing in humans. First, we examined DAF distributions of editing SNPs. A/G and non-A/G editing SNPs were analyzed separately, as they may be subject to different evolutionary constraints. To prevent the ascertainment bias between functional classes, CDS region, UTR of protein-coding genes, and ncRNAs were respectively analyzed. Different DAF distributions were directly compared using the Kolmogorov-Smirnov test, as previously described . IntriguD  of editRecently, DeGiorgio et al. proposed two model-based summary statistics  to detect balancing selection . In thisr2) between pairs of RNA editing SNPs and pairs of control SNPs. Consistent with balancing selection, we found that pairs of RNA editing SNPs had higher LD than pairs of control SNP sites . We compared local LD  [D. melanogaster strains . A total of 743 sites were found to be overlapped with SNPs in DGRP. Similar to human, fly editing SNPs were biased toward A/G or T/C genotypes as compared with the control SNPs  , which ct (DGRP) , which wBalancing selection is a mode of adaptation that leads to the maintenance of variation in a species and potentially an important biological force for maintaining advantageous genetic diversity in populations –47. ImmuSeveral studies have found that editable A’s are more likely to be replaced with G’s during evolution , 48–50. In summary, we uncover a hidden layer of human A-to-I editing SNP loci that are of functional importance, enriched in GWAS signals for autoimmune diseases, and subject to balancing selection. Various types of RNA editing, including A-to-I editing, alter sequence relative to the genome at the RNA level, thus providing a rich resource of RNA variants that potentially produce functionally altered genes. For some of the RNA variants that are beneficial under certain conditions, once the same type of mutation occurs at the DNA level, it may be selectively maintained and become the target of balancing selection. Therefore, we hypothesized that RNA editing, as exemplified in this study with A-to-I editing, may be an unrecognized type of the common target of balancing selection in various species.https://www.ebi.ac.uk/Tools/geuvadis-das/. GTEx project data were downloaded from NCBI, and a list of GTEx data sample IDs is shown in Additional file Geuvadis RNA-seq data were downloaded from We adopted a pipeline that can accurately map RNA-seq reads to the genome . In brieRNA variants were called as we described in Additional file Notably, for SNP editing site call, we considered a site as an authentic SNP editing site only if the corresponding DNA sample had a homozygous genotype. We collected the genotype information from multiple resources and removed a site if its DNA sample had a conflict genotype information between different data resources.https://www.ncbi.nlm.nih.gov/gap/); (2) WES data of Geuvadis project were downloaded from EMBL-EBI database (https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/genotypes/); (3) Genotype Calls (.vcf) from OMNI SNP Arrays, Affy SNP Arrays, and WGS of the 1000 Genomes Project were downloaded from ftp website (http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/). We discarded all insertion and deletion polymorphisms, SNPs with more than two alleles, SNPs monomorphic  in all populations, and SNPs that did not map uniquely to the human genome (hg19).We downloaded all available 1000 Genomes Project and GTEx Project SNP data: (1) Genotype Calls (.vcf) for OMNI SNP Arrays, WES, and WGS of GTEx Project were downloaded from dbGaP database , we considered it as an ECS with statistical significance.Two methods were applied to examine the RNA structure. In the first method , to idenIn the second method , to deteGO term enrichment analysis was performed using R package clusterProfiler .https://www.ebi.ac.uk/gwas/downloads/summary-statistics) were manually checked, and the ones that are not disease-relevant were excluded. Finally, 45 datasets that represent 33 types of diseases were downloaded. For each GWAS dataset, we examined the percentage of editing SNPs that are overlapped with GWAS SNPs with p value < 0.001(%editing_SNP). As a control, we examined the percentage of SNPs with p value < 0.001(%control_SNP). Last, the enrichment score was defined as %editing_SNP/%control_SNP. For a disease with multiple datasets, the dataset with the median enrichment score was shown.A total of 85 GWAS datasets with full GWAS statistics provided in GWAS catalog . Only SNPs with a minor allele frequency (MAF) > 0.001 were used for DAF spectrum analysis.D was calculated by VariScan [D.Tajima’s VariScan . In brieD calculation.We applied two methods to calculate nucleotide diversity (pi). In Fig. In Fig. T1 and T2 statistics were obtained from DeGiorgio et al. .r2) of pairs of A/G editing SNPs and pairs of control SNPs, using VCF data of the 1000 Genomes Project as the input.The software PopLDdecay  was applD. melanogaster inbred lines were downloaded from the Drosophila Genetic Reference Panel Project [http://dgrp2.gnets.ncsu.edu/). The pairwise D. melanogaster/D. simulans alignment files were from UCSC (http://hgdownload.soe.ucsc.edu/goldenPath/dm3/vsDroSim1/). The list of D. melanogaster RNA editing sites was from RADAR database [D. simulans sequence. DAF was calculated based on the genotype information of the 205 inbred lines.The genotype data of 205  Project  (http://database . The ancAdditional file 1: Fig. S1. The pipeline of non-SNP and SNP editing site identification. Fig. S2. The identification of non-SNP and SNP editing sites. Fig. S3. Triplet motif analysis of SNP editing sites. Fig. S4. The verification of SNP editing sites. Fig. S5. Comparison of the ratios of RNA editing SNP and control SNP. Fig. S6. ADAR2 and CASQ2 expression in different types of human tissues. Fig. S7. The cumulative distribution of DAF. Fig. S8. Sliding window analysis of pi for A/G editing SNPs. Fig. S9. non-Alu editing SNPs are subject to a stronger balancing selection compared with Alu editing SNPs. Fig. S10. DAF analysis reveals RNA editing SNPs as the target of balancing selection in flies.Additional file 2: Table S1. The list of GTEx data used for analysis.Additional file 3: Table S2. SNP and non-SNP RNA editing sites.Additional file 4: Table S3.P values of DAF analysis.Additional file 5: Table S4. T1,T2 statistics of RNA editing SNPs.Additional file 6. Review history.


LY49B gene that shows strong evidence of being homozygous lethal, despite having high frequencies of heterozygotes in Thoroughbreds and other domestic horse breeds. Variant analysis of whole-genome sequence data identified two SNPs in the 3′UTR of the LY49B gene that may result in loss of function. Analysis of transcriptomic data from equine embryonic tissue revealed that LY49B is expressed in the trophoblast during placentation stage of development. These findings suggest that LY49B may have an essential, but as yet unknown function in the implantation stage of equine development. Further investigation of this region may allow for the development of a genetic test to improve fertility rates in horse populations. Identification of other lethal variants could assist in improving natural levels of fertility in horse populations.Domestic animal populations are often characterised by high rates of inbreeding and low effective population sizes due to selective breeding practices. These practices can result in otherwise rare recessive deleterious alleles drifting to high frequencies, resulting in reduced fertility rates. This study aimed to identify potential recessive lethal haplotypes in the Thoroughbred horse breed, a closed population that has been selectively bred for racing performance. In this study, we identified a haplotype in the  Mutation screens in mice reveal that many genes are essential for development, with knockout of 29% of genes tested resulting in embryonic death by day 143. Although mutations in these genes are expected to be under strong negative selection due to being completely deleterious, many species are estimated to carry between one and two recessive lethal mutations per genome4. However, single mutations are often uncommon in a population, such that unrelated individuals are unlikely to carry the same recessive lethal mutations7. The likelihood of an individual inheriting two copies of the same lethal mutation is dramatically increased by inbreeding events, whereby alleles that are identical by descent are inherited from a common ancestor10.There is estimated to be a high rate of natural embryonic mortality in mammals. A large proportion of these embryonic losses occur soon after fertilisation, such that pregnancies often go undetected, with the only sign being reduced fertility18. This is often due to a limited number of sires with desirable characteristics making large genetic contributions to the population19. Moreover, population bottlenecks due to domestication and breed formation have also resulted in increased deleterious mutation loads and diminished gene pools in many domestic breeds23. These processes lead to a reduction in genetic diversity indices including effective population size, which is defined as the theoretical population size that shows the same rate of loss in genetic diversity as the study population (Ne)25. Reduced genetic diversity can increase the risk of drift and inbreeding events in future generations of a population. Lethal mutations that have reached high frequencies are often detected by deviations from the Hardy–Weinberg equilibrium with a lack of homozygotes for one allele12. Characterisation of such mutations can assist in improving breeding decisions to increase fertility rates in these populations and prevent these mutations from drifting to higher frequencies27.In recent years, a number of studies in livestock have identified embryonic lethal mutations at high frequencies due to intensive selective breeding practices28, to date there has been no published comprehensive characterisation of common embryonic lethal alleles in horse populations. Despite the large variety of domestic horse breeds found throughout the world, many breeds suffer from low within-breed diversity and small Ne31. Some horse breeds with large census population sizes also experience low genetic diversity due to intense artificial selective breeding practices and closed population structures31. Maintaining good fertility rates is particularly important for horse populations due to the seasonal nature of breeding and the low individual fertility output, as mares produce only one foal from an eleven month gestation period32. Despite the extensive use of hormonal therapies to increase covering success in many domestic horse populations, per cycle pregnancy rates in some breeds only average around 65%, suggesting the presence of unknown variables that may reduce fertility33.The identification of high frequency lethal variants is of particular interest in domestic horse populations. Although a recent study has identified some candidate mutations34. The population has since been intensely selected for the improvement of athletic abilities36, resulting in contemporary Thoroughbred horses being characterised by high levels of inbreeding and a small Ne39. Due to selective breeding practices, all Thoroughbred horses can trace their ancestry back to a small number of individuals from the foundation of the breed38. Genetic diversity in the Thoroughbred breed has been reduced in recent decades due to the increased commercialisation of popular stallions providing large genetic contributions to the population40. Although such practices are in line with selective breeding principles41, they could also inadvertently increase the frequency of embryonic lethal variants in the population. Reproductive technologies such as artificial insemination are banned in the Thoroughbred population, making the maintenance of high levels of natural fertility imperative. Additionally, Thoroughbred horses have been used as foundation stock for other popular horse breeds including The Quarter Horse, Standardbred, and many Warmblood breeds30. Therefore, identification of lethal variants in Thoroughbreds is also likely to assist in the breeding management of these populations. We also aimed to determine the frequency of any potentially lethal variants identified in the Thoroughbred population in other horse breeds and examine their transcriptomic profile in embryonic tissue.In this study, we aimed to characterise variants at high frequencies that may cause lethality in the Thoroughbred horse population. The Thoroughbred breed is of particular interest due to the closed population structure since the foundation of the studbook in the eighteenth centuryn = 156) identified only two adjacent, linked SNPs that significantly deviated from the Hardy–Weinberg equilibrium with an absence of homozygotes (Table n = 370) also showed an absence of homozygotes for these SNPs  and 6:38278874 (rs68663106), which are found in an intronic region of the 30, and their associated SNP data, the SNPs of interest were present in heterozygous state across most phylogenetic branches of domestic horse breeds. Of the 32 breeds in this dataset, 23 had at least one heterozygote for both SNPs of interest. Notably, this two-SNP haplotype was not found in genotype data from one branch of the tree which contains the North Swedish Horse (n = 19), Norwegian Fjord Horse (n = 21) and Exmoor Pony (n = 24)  Table . A largeorse n = 9, Norwegn = 137) (Table n = 380) and Norwegian-Swedish Coldblooded Trotters (n = 641), being 20% and 40% respectively (Table n = 19), French Trotter (n = 17), Paint (n = 15), Morgan (n = 19), Mongolian Paulista (n = 19) and Tuva (n = 15) breeds may also have a high proportion of heterozygotes for this haplotype in their populations  Table . The pro%, 7 Tablns Table .42 identified three variants that may result in changes to protein structure or expression, so these represent the most likely candidates to cause lethality in homozygous state , was located in an exonic region of the LY49B gene. Alignment of the 3′UTR of the five functional LY49 genes in Equus caballus revealed that the region containing the SNP 6:38276955G > A (rs1139567427) is highly conserved in all members of the LY49 gene family  were found within the 3′UTR (3′untranslated region) of the LY49B mRNA were not detected in equine trophectoderm tissue collected on day 16 of development. However, LY49B mRNA was observed in trophectoderm tissue collected on days 23 and 24 of development (Table LY49B mRNA transcripts were detected in microarray data from equine chorion and chorionic girdle tissue between days 27 and 34 of development (Table LY49B (Table Measurable levels of nt Table . Inner cLY49B gene on ECA6 (Table LY49B gene belongs to the LY49 (Killer cell lectin-like receptor subfamily A) family of receptors, which consists of five functional members in Equus caballus43. Other species (including humans) have a functionally similar, but structurally different gene family called KIR (Killer cell immunoglobin line receptors)44. The LY49/KIR gene family are expressed across various types of immune cells, and mediate their function through bindings to MHC-145. The LY49B gene is expressed in myeloid cells where it regulates their activity through an inhibitory effect, possibly to prevent their spontaneous activation46. Despite the important role that they play in immunity, the function of LY49 genes in development is currently unknown. In humans, incompatibilities between foetal KIR and maternal MHC (HLA) genotypes are associated with an increased risk of miscarriage and preeclampsia49. Additionally, knockdown of LY49 in mice showed a high rate of implantation failure51. These findings indicate that LY49B may play an important role in maternal/foetal compatibility and implantation success in horses.Analysis of genotype data identified a two-SNP haplotype as a strong candidate for harbouring a variant that causes lethality in homozygous state. The SNPs identified in this preliminary analysis mapped to an intronic region in the A6 Table . The LY4LY49B was first expressed in equine trophoblast tissue during the placental development stage. The first evidence of LY49B expression was found on day 23–24 of development . DNA was extracted from the hair samples of Norwegian-Swedish Coldblooded Trotters and Swedish Warmbloods by incubating the samples for 2 h at 56 °C with Chelex 100 Resin  and Proteinase K . The Proteinase K was then inactivated by incubating for 10 min at 95 °C and DNA resuspended in low TE . DNA was extracted from blood samples using the Qiasymphony DSP DNA mini kit .n = 102) or the Affymetrix 670 K Chip  (n = 54). Common genotyped SNPs between the two arrays were scanned for deviations from the Hardy–Weinberg equilibrium with an absence of homozygotes for one allele using PLINK (version 1.9)67. The p values were adjusted using a false discovery rate correction with the R package “qvalue”68. Since SNPs with an absence of homozygotes could indicate a calling error, the search was narrowed to only include adjacent SNPs that fit such criteria.Genotype data from a representative sample of Thoroughbreds were used to identify SNPs with a high proportion of heterozygotes, but an absence of homozygotes for one allele. Genome-wide SNP data were generated for 156 Australian Thoroughbred horses by genotyping samples on either the Illumina 70 K Chip  (n = 370) typed on the Affymetrix 670 K Chip69 and these were added the Thoroughbred sample. The SNP frequencies were then characterised from genotype data from Swedish Warmbloods (n = 380)70 and Norwegian-Swedish Coldblooded Trotters (n = 646)71 typed on the Affymetrix 670 K Chip. Publicly available data from Exmoor Ponies 71, Quarter Horses 72 and horses of 32 different domestic breeds 30 were also included in this preliminary scan for SNP frequencies. In these data, raw intensities were plotted to check for calling errors. If potential calling errors were detected, SNPs were recalled using a mixture model fitted with an expectation–maximization algorithm in R.The frequencies of the candidate SNPs were then examined in publicly available genotype data from Japanese Thoroughbreds (https://www.ebi.ac.uk/ena) which included horses of different domestic breeds  and additional Thoroughbred samples   Table .73. Duplicate reads were flagged using Samblaster (version 0.1.22)74, and base recalibration was performed using Genome Analysis Toolkit (GATK) (version 4.0.8.1)75. Variants (SNPs and INDELs (insertions and deletions)) were called using Haplotype Caller and then filtered using the standard hard filtering recommendations in GATK76. The individual SNPs were then filtered to only include high quality allele calls with an average filtered depth over 10 and a Phred score over 20.The SNP array used in the initial genotyping analysis was developed based on coordinates of the EquCab2.0 reference genome. For consistency, we used the EquCab2.0 assembly as a reference for the whole-genome sequence analysis. The EquCab2.0 assembly was also used because of an issue with resolution in the area of interest on the newer EquCab3.0 assembly. The raw reads were mapped to the EquCab2.0 reference genome using BWA-MEM algorithm from Burrows-Wheeler Alignment Tool (version 0.7.17)67. Only SNPs with an r2 value of over 0.8 and a D′ value > 0.9 were shortlisted. The effects of each SNP on gene structure and function was characterised using SIFT (version 4G)42. The conservation of variants across taxa was analysed using the NCBI Conserved Domain Database Search77.Variants that were linked to the SNPs identified from the genotype data were produced using the LD function in PLINK (version 1.9) with a window size of 5 Mbn = 3) and trophectoderm tissue  from the Functional Annotation of ANimal Genomes (FAANG) equine biobank (available from ENA under the project name PRJNA223157)78. Adaptors were trimmed using bbduk from BBtools (version 37.98)79. Reads were aligned to the EquCab 2.0 genome using STAR (version 2.7.2b)80. Counts were generated using featurecounts from Subread package(version 1.5.1)81, then quantified in fragments per kilobase/million (FPKM) using the R package “edgeR”82 with the Equus_caballus_Ensembl_94 file used for annotation. Microarray data for chorion (n = 19) and chorionic girdle (n = 19) tissue collected from horse embryos between days 27–34 of development83 were also examined for gene expression levels.Publicly available RNA sequence data were used to examine expression levels of the genes of interest in embryonic tissue. The data included equine inner cell mass tissue  Written informed consent to use the animals in this study was obtained from the owners of the animals. The hair samples from Swedish Warmblood horses were originally collected for parentage testing and stored in the biobank at the Animal Genetics Laboratory, SLU so ethics approval was not applicable. Hair and blood samples of Norwegian-Swedish Coldblooded Trotters were collected under approval from the Ethics Committee for Animal Experiments in Uppsala, Sweden (Number: C 121/14). All the methods were performed in accordance with the guidelines set out by the respective Animal Ethics Committees and the guidelines contained in the Guide for the Care and Use of Laboratory Animals. No experimental procedure was performed on live animals. All other data was downloaded from publicly available repositories.https://doi.org/10.1371/journal.pone.0218407, 10.1371/journal.pone.0152966, 10.1093/jhered/est079 and 10.1371/journal.pone.0054997). The exceptions are genotype data from Australian Thoroughbred, Swedish Warmbloods and North Swedish Coldblooded Trotters, which are available on request but restrictions apply to the availability of these data which were used under license for the current study and so are not publicly available.The whole-genome sequence data used in this study is publicly available for The European Nucleotide Archive. Genotype data for Japanese Thoroughbreds, Exmoor Ponies, Quarter Horses and other domestic horse breeds can be found in the supplementary information of their respective papers (Supplementary information


GWAS has been proven to be a powerful tool for detecting genetic variants associated with economically important traits such as production,The experimental population consisted of 445 Chinese Holstein cows, with parity no. 2, raised at four farms in Shandong province of China. Hair follicle samples were collected individually within four days in the same season. In total, 29 body conformation traits were analyzed by GWAS. There were 23 phenotypes measured individually on 1–9 scores, which were body height, height at front end, body depth, chest width, stature, angularity, rump angle (RA), pin width, loin strength, foot angle, heel depth, bone quality, rear legs – side view, rear legs – rear view, udder depth, udder texture, median suspensory, fore udder attachment, front teat placement, attachment height, attachment width (AW), rear teat placement and teat length. The remaining six functional body conformation traits, measured on 1–100 scores, were stature score, rump system score, feet and legs score, udder system score, mammary system score and conformation final score (FS). The definitions of these conformation traits for cattle are available on the website of Canadian Dairy Network (https://www.cdn.ca/articles.php). The summary statistics of these phenotypes are listed in Table P‐value of the Hardy–Weinberg Equilibrium test less than 1.0E − 6. Finally, 421 animals with 20 632 SNP genotypes were retained for the subsequent GWAS analysis. Physical map length, the number of SNPs and SNP density on each chromosome based on reference genome UMD3.1, before and after the data cleaning procedure, are shown in Table Each experimental animal was genotyped by the GGP BovineLD V3 SNP chip, containing 26 151 public SNPs. Because all of the animals in these study populations were female, the SNPs from the X chromosome were also counted. The data quality control procedure excluded individuals with more than 10% missing genotypes and SNPs with a call rate of less than 90%, MAF less than 3% or A fixed‐effects linear regression model was used to carry out the GWAS according to a previous method.P < 0.05) association effects on 14 body conformation traits (Table MON1B (MON1 secretory trafficking family member B) gene, were also found to be associated with length of productive life in Holstein cows.LIG1 (Ligase I) gene region, was reported to be associated with body weight at birth.SLC24A4  receptor‐associated protein like 1] gene, were found to be significantly associated with median suspensory in this study. It was reported previously to affect udder attachment and height.CACNA1D  gene region. Coincidentally, this SNP was also reported to be associated with the length of productive life and udder cleft.The GWAS based on the mixed‐effect linear regression model identified 79 SNPs with genome‐wide significant  association effects on 14 body conformation traits. Among these significant SNPs, 74 of them are newly detected in this study, five have been reported in previous literature and 26 are located in genes and are worth further investigation to potentially identify the causative mutations underlying the QTL.In summary, a GWAS using linear statistical model was conducted on 29 body conformation traits in a Chinese Holstein cattle population, and 79 SNPs were found to have genome‐wide‐significant  SNP association effects on body conformation traits of rump angle  and final score  obtained by mixed‐effect linear regression model.Click here for additional data file.Table S1 Descriptive statistics of the 29 conformation traits used in the GWAS.Table S2 Distribution of SNP markers by chromosomes before and after quality control.Table S3 The chromosomal distribution of significant SNPs associated with body conformation traits.Table S4 The SNPs identified associated with multiple body conformation traits of dairy cows.Table S5 Multiple SNPs located in the SLC24A4 gene region were significantly associated with body conformation traits of dairy cows.Click here for additional data file.


However, knowledge of the genetic variability of masson pine germplasm is still limited. In this study, the genetic diversity and population structure of masson pine germplasm were assessed using 204 wild accessions from 10 main distribution regions using 94,194 core single-nucleotide polymorphisms (SNPs) obtained from transcriptome sequencing data.Masson pine The average expected heterozygosity was 0.2724, implying abundant genetic diversity within masson pine germplasm. Analysis of molecular variance (AMOVA) revealed that 3.29% of the variation was sourced from genetic differentiation. Structure analysis identified two geographically distinct groups. Discriminant analysis of principal components (DAPC) showed that one of those groups was further divided into two clusters. Sichuan and Chongqing provenance is the geographical origin, which diffused outward along two different lines. Oleoresin yield is reflected in the evolution of the two groups, and exhibits two different trends along the two lines of diffusion. The oleoresin yield may be associated with the genes of chitinase, CYP720B, cytochrome P450, ABC transporter, and AP2/ethylene-responsive transcription factor (ERF) based on SNPs and expression.SNP markers from transcriptome sequencing are highly capable of evaluating genetic diversity within different species, as well as the genetic control of objective traits. The functions of these genes will be verified in future studies, and those genes strongly associated with oleoresin yield will be used to improve yields by means of early genotype selection and genetic engineering. Pinus massoniana Lamb.) is a commercially important conifer for supplying timber and oleoresinin China. The natural distribution of this species extends from 21°41′N to 33°56′N and from 102°10′E to 123°14′E, with a planting area of 2 million hectares [As a dominant native tree species, masson pine (hectares .P. taeda) and could be increased 1.5- to 2.4-fold in one generation through selection.As a secondary substance obtained from masson pine, oleoresin is an important natural product that is used as the source for many different compounds in the chemical industry , 9, for Molecular markers are very useful in identifying germplasm, assessing biodiversity, and describing the geographic patterns of genetic variation. Single nucleotide polymorphisms (SNPs) are commonly used in genetic studies. Taking advantage of next generation sequencing (NGS) technologies, millions of SNPs for crops can be rapidly developed at low cost . These hAssociative transcriptomics has contributed significantly to identifying sequence polymorphisms and transcript abundances linked to phenotypic variation, especially in non-model species , 23. In P. taeda [Research into and the breeding program of masson pine have been hampered by a lack of high-quality genome sequences because of its extremely large and complex genome, as presumed by that of the closely related species P. taeda . To overA total of 1,326,230 SNPs and 153,459 insertions/deletions (InDels) were detected from the transcriptomes of the 204 genotypes using GATK packages [P. massoniana germplasms from the main regions where the species is distributed was investigated based on 94,194 SNPs. The observed heterozygosity (H0) value was lower than the expected heterozygosity (He) value for each population, ranging from 0.2211 (Guangxi) to 0.2358 (Sichuan and Chongqing). The He values were similar among the different populations, and ranged from 0.3011 (Jiangxi) to 0.3124 (Sichuan and Chongqing) (Table F index) ranged from 0.2242 (Sichuan and Chongqing) to 0.2714 (Guangxi), with an average value for the overall population of 0.2731, indicating that the SNPs in the Sichuan and Chongqing population had the highest polymorphism. Putative differences among the nine populations were tested by AMOVA based on 94,194 SNPs  revealed three genetic clusters driving the partitioning of diversity within our panel Fig. c. Cluste− 2) was higher than those of Cluster II (2.77 × 10− 2) and Cluster III (2.83 × 10− 2) and exhibited the highest level of diversity. This result is also supported by the He value, which revealed the sequence diversity based on heterozygous sites  to 0.303 (Cluster I vs. Cluster II), while the pairwise fixation index Fst ranged from 0.024 (Cluster II vs. Cluster III) to 0.110 (Cluster I vs. Cluster II), and the Nei’s and Fst genetic distances of Cluster I vs. Cluster II were higher than those of Cluster I vs. Cluster III  of Cluster I  than between those of other populations, implying a strong variation in the genome for the new natural adaptation when first transferring from the basin to the plateau habitat. The nucleotide diversity is slightly higher for the progenitors of the Sichuang/Chongqing population  was localized in the transcript annotated as chitinase class I . The results of a quantitative real time polymerase chain reaction (qRT-PCR) using 10 high- and low-yielding oleoresin accessions showed higher expression levels for these two transcripts in high-yielding oleoresin masson pines , are an important class of enzymes involved in the biosynthesis of diterpene resin acids as the main content of oleoresin . One CYPP. taeda [The sequences of AP2 domain transcription factor and ABC transporter were associated with oleoresin yield in P. taeda . In thisP = 8.73E− 16) and expression level (P = 4.83E− 08) simultaneously. The SNP resulted in non-synonymous mutation, with the transition of codon CTC to TTC. However, the function of the tubulin alpha chain during the biosynthesis of oleoresin is unclear.In addition, one SNP from the transcript of the tubulin alpha chain (c20772_f1p4_1467) was significantly associated with oleoresin yield in the sequence  were slightly lower than the He values (0.30 across the populations), suggesting that frequent inbreeding events have occurred within the populations  [Masson pine has continuous native distributions in various regions of China. Hamrick et al.  found th be 0.22 . In thisvestris) , 38. In vestris) .Fst and Nei’s genetic distance . However, the elevation of the floor of the Sichuan Basin only ranges from 250 to 750 m. Although the glaciations also occurred in the Sichuan Basin during the Quaternary, the cooler climate did not lead to the extinction of plant and animal species, such as the surviving giant–panda (Ailuropoda melanoleuca). Therefore, the results suggest that the Sichuan Basin is one main refugium for many species [Climate is one of the main driving factors for adaptive evolution of forest trees , 41. In  species . These s species , 47, 48.P. taeda) as a reference. Our study showed that the Sichuan Basin was the geographic origin of masson pine. This accords with previous results reported by Qin [To explore the evolutionary history of masson pine, we used loblolly pine  would lead to lignin being ectopically deposited in the stems of Arabidopsis spp., and the walls of the lignified cells were not thickened. The function of chitinase might affect the transportation rate of oleoresin from living cells to resin ducts.Chitinase plays a key role in modifying the structure of cell walls. Zhong et al.  found thMost of the cytochrome P450s have been reported to be involved in the progress of secondary metabolism . The CYPOsEREBP1 belonging to the ERF family causes increased expression of genes related to lipid metabolism in rice [P. taeda, one SNP in the AP2 domain transcription factor was also associated with oleoresin yield [Ethylene can induce the biosynthesis and formation of traumatic resin ducts in many conifers . AP2/ERF in rice . In P. tin yield , which wIt is important to understand the genetic architecture of masson pine in order to improve the oleoresin yield in the genetic breeding process. Although the genome of masson pine has not been sequenced, we obtained satisfactory results for genetic diversity, population structure, and trait–gene association based on 94,194 SNPs using the full-length transcriptome as a reference. Masson pine is clearly differentiated into two groups, and Sichuan and Chongqing provenance have been shown to be its geographical origin, from which masson pine diffuses outward along two distinct lines. Oleoresin yield exhibits two different trends along the two lines of diffusion and is associated with the genes of chitinase, CYP720B, cytochrome P450, ABC transporter, and AP2/ERF, some of which were also confirmed as being present in other conifers. The functions of these genes will be verified in future studies.A clonal test of masson pine, located at Laoshan Forest Farm in the western part of Zhejiang Province, China , was used for this study. This trial included 400 clones (genotypes) obtained from 10 provinces and municipalities. In the 1980s, a national technical cooperation group for masson pine was established in China, and the scions for these clones were identified and provided by each provincial technical cooperation group authorized by the local government. Robust shoots as scions from wild trees were collected from the upper crowns of masson pine in April 1985. Subsequently, the scions were grafted onto two-year-old local seedlings of masson pine using the pith-cambium pairing grafting method and carried out by the Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou. In the following year, the clonal trail was established using these grafted seedlings to give a completely randomized design with 10 repetitions and 2.0 m × 3.0 m spacing between individual trees. Experimental research on these plants, including the collection of plant material, complied fully with institutional, national, and international guidelines. Field studies were conducted in accordance with local legislation. The authors complied with the Convention on International Trade in Endangered Species of Wild Fauna and Flora .In this study, 204 healthy clones of masson pine from 10 provinces and municipalities of China were selected randomly , and the quality of the three libraries was assessed using the Agilent 2100 Bioanalyzer. Subsequently, SMRT sequencing was carried out using a Pacific Biosciences RS II  platform at Biomarker Technologies Corporation, Beijing, China.Total RNA from each sample was separately extracted and evaluated according to Liu’s method . BrieflyThe mRNA was obtained from high-quality total RNA for each sample using the magnetic beads enrichment procedure. Fragmentation buffer was used to fragment mRNA randomly. The first- and second-strand cDNA were synthesized. All of the cDNAs were purified using AMPure XP beads. After end repairing, adding A, and adaptor ligation, the fragment size of the purified cDNA was selected using the AMPure XP beads. The cDNA fragments were then enriched by PCR amplification, and the quality of the cDNA library for each sample was assessed using the Qubitfluorometer and the Agilent 2100 Bioanalyzer. Finally, the qualified cDNA library of each sample was paired-end sequenced on the Illumina HiSeq™ 2000 sequencing platform.Low-quality reads were filtered out based on the following four rules: (1) If one end of a pair-end read had > 5% “N” bases, then the pair-end read was removed; (2) For each pair-end read, if one of them had an average base quality less than 20, then they were both removed; (3) For each read, we trimmed its 3′ bases if their quality scores were less than 13. The trimming was stopped at the base with a quality score ≥ 13. Following trimming, if the number of remaining bases was less than 40, then the pair-end reads were removed; (4) Duplicates of pair-end reads were removed. Clean data were then used to call both SNPs and InDels.Filtered reads were then mapped to the reference sequences (full-length transcriptome) using the BWA-MEM algorithm of the Burrows Wheeler Aligner. SNPs were called using the Haplotype Caller in GATK across the 204 samples of masson pine. Finally, low-quality SNPs  were removed. InDels were called using the same pipeline as SNP calling. Raw InDels were filtered to reduce the false positives using GTAK variant filtration with the parameters: FS > 200, QD < 2, Read Pos Rank Sum < − 20.0.H0), expected heterozygosity (He), minor allele frequency (MAF), and inbreeding coefficient (F) were estimated using PLINK software . Variation among populations, among clones within populations, and within clones was calculated via analysis of molecular variance (AMOVA) using Arlequin software .The genetic parameters of observed heterozygosity (P. taeda) was first downloaded from the NCBI database (SRX4454630) and then aligned with the full-length transcriptome sequences. Subsequently, SNPs were called from the genome of the loblolly pine. Phylogenetic tree visualization and editing assignment were then performed using ITOL (http://itol.embl.de). The divergence time between masson pine and loblolly pine was obtained using the online Time Tree software (http://timetree.org). Finally, the divergence time for each germplasm was calculate and visualized using the MCMCtree program in the Phylogenetic Analysis by Maximum Likelihood (PML) package (http://abacus.gene.ucl.ac.uk/software/paml.html) [For the phylogenetic tree, the genome of the loblolly pine (ml.html) .https://speciationgenomics.github.io/ADMIXTURE) [K = 1 to K = 10. The most likely number of ancestral genetic groups was determined by the minimum K value on the cross-validation curve.ADMIXTURE software (MIXTURE)  was usedK that maximizes the variation between groups. The Bayesian information criterion (BIC) was calculated for K = 1–40, and the K value with the lowest BIC was selected as the optimal number of clusters. A discriminant analysis was then performed on the first 120 principal components using the function DAPC to efficiently describe the genetic clusters.For DAPC, genetic data were first transformed into uncorrelated components using principal component analysis (PCA). The number of genetic clusters was then defined using k-means clustering, an algorithm that looks for the value of https://www.maizegenetics.net/tassel) [P-value corresponding to each association was calculated, and the association was significant when the P-value ≤1.06E− 5, which was estimated using 1/n named Bonferroni correction (n is the number of SNP markers).The association between SNPs and oleoresin yield was carried out using the mixed linear model (MLM) using Trait Analysis by aSSociation, Evolution and Linkage (TASSEL) (/tassel) . The P-v−ΔΔCt method [Ten high- and ten low-oleoresin-yielding RNA samples were used in qRT-PCR. The primer pairs (Additional file t method . ElongatAdditional file 1 Table S1. Statistical results following combination of full-length transcripts and unigenes of 204 accessions of masson pineAdditional file 2 Table S2. Numbers of reads mapped to reference sequences for 204 accessions of masson pineAdditional file 3 Table S3. SNPs and InDels obtained using transcriptomes of 204 accessionsAdditional file 4 Table S4. Effect of SNP mutation for each unigeneAdditional file 5 Table S5. Core accessions with 90.7 and 95% allele proportionsAdditional file 6 Figure S1. Cross-validation error rate for each K valueAdditional file 7 Figure S2. Population structure and the corresponding groups of 204 masson pines when K = 2 and K = 3.Additional file 8 Table S6. Three genetic clusters identified by DAPC based on SNP markersAdditional file 9 Table S7.Nei’s and Fst genetic distance calculated for three clusters inferred by DAPCAdditional file 10 Figure S3. Phylogenetic relationships among a masson pine population and P. taeda as the outgroupAdditional file 11 Table S8. Oleoresin yield in the xylem of 204 clones of masson pineAdditional file 12 Figure S4. Quantile-quantile plots resulting from the transcriptome-based association study data for oleoresin yieldAdditional file 13 Table S9. SNP markers significantly associated with variation in oleoresin yield in masson pineAdditional file 14 Table S10. Origin of the 204 clones of masson pineAdditional file 15 Table S11. Primers designed from the sequences of the transcriptome library in masson pine using Primer Premier 3.0


Shorter telomere length (TL) has been associated with poor health behaviors, increased risks of chronic diseases and early mortality. Excessive shortening of telomere is a marker of accelerated aging and can be influenced by oxidative stress and nutritional deficiency. Plasma n6:n3 polyunsaturated fatty acid (PUFA) ratio may impact cell aging. Increased dietary intake of marine n-3 PUFA is associated with reduced telomere attrition. However, the effect of plasma PUFA on leukocyte telomere length (LTL) and its interaction with genetic variants are not well established.A nested coronary artery disease (CAD) case-control study comprising 711 cases and 638 controls was conducted within the Singapore Chinese Health Study (SCHS). Samples genotyped with the Illumina ZhongHua-8 array. Plasma n-3 and n-6 PUFA were quantified using mass spectrometry (MS). LTL was measured with quantitative PCR method. Linear regression was used to test the association between PUFA and LTL. The interaction between plasma PUFAs and genetic variants was assessed by introducing an additional term (PUFA×genetic variant) in the regression model. Analysis was carried out in cases and controls separately and subsequently meta-analyzed using the inverse-variance weighted method. We further assessed the association of PUFA and LTL with CAD risk by Cox Proportional-Hazards model and whether the effect of PUFA on CAD was mediated through LTL by using structural equation modeling.p = 0.018) and increased CAD risk (p = 0.005). These associations were mainly driven by elevated plasma total n-3 PUFAs, especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) (p < 0.05). There was a statistically significant interaction for an intergenic single nucleotide polymorphism (SNP) rs529143 with plasma total n-3 PUFA and DHA on LTL beyond the genome-wide threshold (p < 5 ×  10− 8). Mediation analysis showed that PUFA and LTL affected CAD risk independently.Higher n6:n3 ratio was significantly associated with shorter LTL (Higher plasma n6:n3 PUFA ratio, and lower EPA and DHA n-3 PUFAs were associated with shorter LTL and increased CAD risk in this Chinese population. Furthermore, genetic variants may modify the effect of PUFAs on LTL. PUFA and LTL had independent effect on CAD risk in our study population. After meta-analysis, the interaction reached genome-wide level of significance  but no significant interaction was detected  in transformed fibroblasts [Our interaction analysis indicated an intergenic SNP, rs529143, could modify the association between n-3 PUFA/DHA and LTL. Carriers of the minor C allele with low n-3 PUFA/DHA (lower tertile) had shorter LTL while those with high n-3 PUFA/DHA (higher tertile) had longer LTL. Regional genes (100 kb) around rs529143 include multiple phospholipase genes such as tabolism . Functioroblasts , 41, whiroblasts , 43. Morroblasts . The intP > 0.05).Our study has several potential limitations. First, measurements of LTL in our study were mean TL in leukocytes and therefore may not reflect TL dynamics in other tissues . The meaWe report in this study an inverse association of plasma n6:n3 ratio with LTL and CAD risk and that this association was mainly driven by total plasma n-3 but not n-6 PUFA. Higher plasma levels of both EPA and DHA were associated with longer LTL and decreased CAD risk. We additionally identified an intergenic genetic variant, rs529143 that was observed to modify the association between plasma n-3 PUFA/DHA level and LTL.Additional file 1: Table S1. Interaction between genetic variants and plasma PUFA on telomeres in SCHS_CAD cases and controls. Table S2. Association between genetic variant and telomeres. Table S3. The mediation effect of telomeres on the association between plasma PUFA and coronary artery disease. Table S4. Interaction between genetic variants and PUFA intake on telomeres in SCHS


Bos Taurus, and the cattle in southern China are primarily Bos indicus. Cattle from southern China are characterized by a specific phenotype and adapted to the local environment. This study explored the genetic diversity, degree of admixture, and selection signature in eight local cattle breeds in southern China. The lowest level of heterozygosity was found in Hainan and Nandan cattle from Hainan and Guangxi province, respectively, whereas the highest level of heterozygosity was detected in Zhaotong cattle from Yunnan province. A neighbor-joining phylogenetic tree analysis clearly separated Lufeng cattle from other breeds, whereas Leiqiong and Hainan cattle have some crossover. Based on linkage disequilibrium-filtered single nucleotide polymorphisms (SNPs), the admixture analysis revealed two clusters corresponding to the taurine and indicine cattle lineages, and the local cattle breeds from southern China showed a certain degree of admixture. When K = 4 and 9, we found a slight separation among Leiqiong, Lufeng, and Hainan cattle. Meanwhile, we performed a selection signature analysis in Hainan, Leiqiong, and Lufeng cattle distributed in the extreme south of China, using the integrated haplotype score (iHS), Rsb statistic, and BayeScan software. Using the iHS approach, we identified 251, 270, and 256 candidate regions in Lufeng, Leiqiong, and Hainan cattle, respectively. Moreover, we identified 184, 174, and 146 candidate regions in pairwise comparisons of Leiqiong vs. Lufeng, Leiqiong vs. Hainan, and Hainan vs. Lufeng cattle using the Rsb approach. In addition, we identified 76 loci with a total of 48 genes under selection, based on the FST approach. Several candidate genes under selection were found to be related to meat quality, immunity, and adaptation to the local environment in southern China. Our results provide significant information about the genetic differences among the cattle breeds from southern China and the possible cause of difference in breed-specific characteristics. Selection signature analysis identified a few candidate SNPs and genes related to certain important traits of these cattle. In general, our results provide valuable insights into the genetic basis of specific traits under selection in certain local cattle breeds.Chinese local cattle with a high level of genetic diversity mainly originate from two subspecies; the cattle in northern China are primarily  Bos taurus cattle (humpless) and B. indicus cattle (humped)  investigate the population structure of the cattle breeds in southern China, (2) identify the selection signature associated with important traits of the cattle, and (3) pinpoint the potential candidate genes associated with adaptive traits in southern Chinese local cattle.The southern Chinese local cattle  used in this study were owned by farmers. Before sampling, the objectives of the study and procedures involving animal sample collection were explained to the farmers, and they provided consent to collect samples from their animals. Animal welfare and health conditions were observed during the sampling process. All animal research protocols were approved by the Institutional Animal Care and Use Committees (IACUCs) of South China Agricultural University . Other animal genetic data used in this study were derived from previous analyses that have been specifically licensed , Nandan cattle , Pingwu cattle , Wenshan cattle , and Zhaotong cattle  , Leiqiong cattle , and Lufeng cattle . The breed name, abbreviation, and other information are presented in A total of 201 individuals from eight cattle breeds were included in the study. The data on five of these breeds were obtained from a previous study, including Liangshan cattle  and expected heterozygosity (He) were estimated using PLINK v1.9 with the option “hardy.” We estimated the molecular inbreeding coefficient based on runs of homozygosity, as described previously  , we extracted 11,455 SNPs to assess population admixture using STRUCTURE 2.3.4 , a total of 62,486 SNPs were obtained for the multidimensional scaling analysis. Pairwise genome-wide identity-by-state (IBS) distances were estimated for sample clusters using PLINK v1.9 (-mds-plot 4). After strict LD-based filtering  was estimated using selscan with default settings   and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were estimated using Metascape with default parameters  from HNC, NDC, LFC, and LQC . The breed WSC was located in the middle of the two clusters, suggesting admixture from other breeds. In the second dimension, there was no obvious clustering, and LFC and LQC were scattered clustering well .K = 2, we found that HNC, LFC, and LQC displayed a high degree of indicine ancestry. In contrast LSC, ZTC, and PWC exhibited large proportions of taurine content. Additionally, when K = 4 and 7, HNC, LFC, and LQC were admixed with several assumed lineages in a complicated fashion, while LSC, ZTC, and PWC show fewer lineages. At K = 9, we found that LFC contained a unique lineage  varying from 2 to 10. At  lineage , which mNext, a neighbor-joining tree was constructed  to WSC . The secWe explored the genomic regions under recent selection in southern Chinese local cattle breeds by estimating an iHS analysis of three representative cattle breeds . The |iHS| values were estimated to visualize the distribution of selection signatures across autosomes . In totaST values using BayeScan v2.1 , we identified 48 candidate genes located in the 50 kb regions upstream and downstream of the selected SNP sites . Detailed results of KEGG pathway and GO analysis were presented in We investigated the potential genetic differences among the above three cattle breeds by estimating the Fcan v2.1 . After sNP sites . A totalectively . Then, wST) to identify candidate signatures of positive selection in HNC, LQC and LFC. A large number (815) of candidate genes were found; however, only 18 candidate genes were found by two of the methods, and only 1 candidate gene was found by three methods. The lower overlap between iHS and Rsb analyses may be explained by the reduced power of iHS to detect regions where alleles have almost reached fixation. Moreover, candidate genome regions identified by iHS might not be detected by Rsb if the favorable alleles/haplotypes were subjected to selection in the reference populations , while the FST method is suitable for the ancient selection signal   are found to be related to meat quality and fatty acids in LFC cattle. CLSTN2  plays an important role in promoting adipocyte proliferation in visceral adipose tissue and subcutaneous fat and is associated with mammalian obesity  could increase the marbling fat  linked to immunity and homeostasis in LFC cattle. BCAR3, PRNP, and TRAPPC9 have been previously reported to potentially regulate specific diseases. BCAR3 is related to the entry of Mycobacterium avium ssp. paratuberculosis into the host cell or the immune response initiated in response to a Mycobacterium avium ssp. paratuberculosis infection  with similar functions are also found in LQC cattle. TMSB4 plays an important role in the development of the immune system; it encodes thymosin b4, which is a hormone mainly involved in functions such as endothelial cell differentiation, angiogenesis, inflammation, wound healing, apoptosis, and tumorigenesis  were found to be enriched in the cell adhesion molecular pathway, which is related to immunity and disease. CDH5 promotes the growth of endothelial cells and maintains the normal function of endothelial cells. CDH5 is one of the main components of endothelial adhesion and connection, which maintains the integrity of the endothelial barrier and controls the migration of leukocytes during injury and infection  are not available.YLiu and LX drafted the manuscript and performed the experiments. YLiu and DL collected the blood samples. YLiu, GZ, and LY performed data analysis. LX, DL, and YLi conceived the study and participated in its design and coordination and helped to draft the manuscript. All authors have read and approved the final 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.


Triticum aestivum L.) grains with a diversity panel of 207 bread wheat varieties. To uncover authentic quantitative trait loci (QTL) controlling Zn accumulation, the varieties were planted in three locations. In total, 29 unique loci associated with Zn grain accumulation were identified. Notably, seven non-redundant loci located on chromosomes 1B, 3B, 3D, 4A, 5A, 5B, and 7A, were detected at least in two environments. Of these quantitative trait loci (QTL), six coincided with known QTL or genes, whereas the highest effect QTL on chromosome 3D identified in this study was not reported previously. Searches of public databases revealed that the seven identified QTL coincided with seven putative candidate genes linked to Zn accumulation. Among these seven genes, NAC domain-containing protein gene (TraesCS3D02G078500) linked with the most significant single nucleotide polymorphism (SNP) AX-94729264 on chromosome 3D was relevant to metal accumulation in wheat grains. Results of this study provide new insights into the genetic architecture of Zn accumulation in wheat grains.Micronutrient deficiencies, and especially zinc (Zn) deficiency, pose serious health problems to people who mainly depend on cereal-based diets. Here, we performed a genome-wide association study (GWAS) to detect the genetic basis of the Zn accumulation in wheat ( Over two billion of the world’s population suffer from diseases associated with mineral and essential vitamin deficiencies ,2,3. ZinTriticum aestivum L.) is one of the most important cereal crops worldwide and its products provide ~20% of the energy and proteins in human diet [Hexaploid wheat  could facilitate the understanding of the genetic basis of a complex quantitative trait such as zinc accumulation in wheat grains, thus providing a theoretical basis to guide crop mineral nutrients improvement ,19. Usin® array. The main purposes of this study were to: (1) investigate novel significant molecular markers or loci associated with potential candidate genes for Zn grain accumulation; (2) provide useful information to better understand the molecular and genetic mechanisms of Zn accumulation in common wheat, and (3) identify germplasms with higher than average Zn grain content that could be used as donors for biofortification breeding. Results of this study could contribute to the development of strategies to improve the nutritional quality of wheat.Nevertheless, despite the number of genetic studies performed on wheat to identify the genetic regions associated with Zn grain content, the molecular mechanisms behind Zn accumulation in wheat grain remain unclear and no molecular markers which could facilitate the breeding for Zn content have been identified. The use of a different set of germplasm and of higher resolution genotyping techniques could help to shed light on the mechanisms behind the accumulation of this mineral in wheat grain. In the present study we conducted a GWAS on a diverse panel of 207 common wheat cultivars grown in three different environments and genotyped using the high-resolution Wheat Breeders 660 K AxiomAcross the three environments, a continuous and wide variation of Zn grain concentrations was observed. As reported in All the 207 lines included in the association panel were genotyped using the wheat 660K SNP array. After filtering the markers for minor allele frequency >0.05 and missing data of <10%, were obtained a total of 244,508 SNPs which were used for the following analysis . The GWAp ≤ 1.0 × 10−4) with variation in Zn grain content  contained 4 SNPs with 2 haplotypes (Hap1A and Hap1B) and block2 (Hap2) contained 14 SNPs with also 2 possible haplotypes (Hap2A and Hap2B)  compared to the haplotype Hap1B+Hap2B (23.08%), with this last combination being associated with higher Zn grain content in both the SQ, KF, and BLUP environment  A. Intereironment B. In thiironment . When anironment C.http://www.wheat-expression.com) and then, based on both the gene annotation information and the gene expression profiles, seven promising genes were selected as candidate genes relevant to Zn accumulation in wheat grains -like superfamily protein. Differently, the SNP AX-110922471 located at chromosome 3B, was associated with the gene encoding a CTP synthase (TraesCS3B02G252000), involved in metal transport. The gene TraesCS5A02G475200 that encodes for a heavy metal transport/detoxification superfamily protein associated with the homeostasis of Zn in plants, was located around 30kb away from the significant SNP marker on chromosome 5A (AX-110931014). Finally, the SNP AX-111012263 on chromosome 7A was linked with the predicted gene TraesCS7A02G263700 encoding for a transcription factor. Notably, when analyzing the gene expression database, all the above-mentioned genes were expressed in wheat grains (The recently annotated wheat genome reference sequence (IWGSC RefSeq v2.0) was employed to identify the candidate genes possibly associated with the seven major QTLs for Zn grain accumulation. The expression profiles of the candidate genes were obtained from the public database of Wheat Expression Browser . It is important to consider this factor because different traits have different sensitivities to the model used for the GWAS analysis and have specific trait dependence. For example, Liu et al.  tested bu et al. , FarmCPUu et al. ,30, wherSlight differences were observed among the Zn concentrations in the grains of the association population planted in different locations, which may be attributed to varying environmental conditions of different locations A. The indurum wheat × wild emmer wheat RIL population were identified stable QTL on chromosomes 5A, 6B and 7A, explaining 1.3% to 23.5% of the grain zinc variation [QGZn.cimmyt-3B_1P2 and QGZn.cimmyt-5B_P2 identified in the RIL population developed from the cross SeriM82 × CWI76364 [QGZn.sar_1BTSK and gwm397-gwm269 loci, respectively, the first identified in a RIL hexaploid wheat mapping population [Many QTLs for Zn accumulation in grains have been detected by bi-parental linkage mapping ,31,32,37ariation . These lariation . TherefoCWI76364 . The twopulation  and the pulation . InteresNAC transcription factor. This gene family contain a highly conserved DNA binding NAC domain in the N-terminal, which accelerates the senescence and also affects nutrient remobilization from leaves to developing wheat grains during grain filling period [V-type proton ATPase. This protein has a typical ATP synthesis activity, with an important function for membrane trafficking, and serves as generator of an electrochemical gradient driving the accumulation of nutrients within plant cells vacuole, which may be related to cells ion homeostasis [tetratricopeptide (TPR) repeats, which may mediate protein-protein interactions and chaperone activity thus affecting the activity of osmotic stress responses and metal ion binding [serine/threonine-protein kinase, which serves as the mediator for the regulation of voltage-dependent ion channels according to protein phosphatases [TraesCS3B02G252000 encoding a CTP synthase, which using L-glutamine as the nitrogen source catalyzes CTP from UTP and is involved in the conversion between glutamine and CTP. However, the glutamine is an important N-source in organisms, and previous research have shown that sufficiently high nitrogen utilization could facilitate Zn from vegetative tissues to grain, while affecting the Zn concentration in grains [TraesCS7A02G263700 and TraesCS5A02G475200, respectively, the first encoding for a basic helix-loop-helix transcription factor (bHLH) and the second for a heavy metal transport/detoxification superfamily protein. Overexpressing bHLH transcription factor will increased tolerance for Zn toxicity [heavy metal transport/detoxification superfamily protein might be involved in altered zinc homeostasis in wheat. Interestingly, all the above-mentioned candidate genes exhibited high expression levels in wheat grains  with great positive effect for Zn accumulation in wheat grain. The above results imply that grain Zn accumulation is largely controlled by multiple genetic loci with mainly additive effects.Grain yield is the major target of any breeding program. However, higher yield (and greater grain size) is often associated with considerable decreases in the concentrations of Zn in wheat grains, because of the so-called “dilution effect” ,48,49. FBased on the pedigree, physiological characteristics, and adaptive fields, a representative sample of 207 wheat cultivars was used in the association population. The selected cultivars originated from the Yellow and Huai Valley and Southwestern Wheat Region of China, including Henan, Hebei, Shaanxi, Shanxi, Shandong, Jiangsu, Sichuan, and Yunnan provinces. The association population was planted at Yuanyang , Kaifeng  and Shangqiu  of the Huang-Huai Plain, China, during October 2016 to June 2017. The three sites are located in northern (YY) and east (KF and SQ) Henan Province, with an average annual temperature of 14.3 °C, 15.0 °C and 14.2 °C, respectively. The average annual rainfalls of YY, KF and SQ are 556 mm, 650 mm and 623 mm, separately. The major soil type of the three locations are fluvo-aquic soil with different sand content , developed from alluvial sediments of the Yellow River . In each3/HClO4, 80/20, v/v, analytical reagent grade, Merck, Darmstadt, Germany) through a heating block . The Zn concentration was determined using atomic fluorescence spectrometry . Each sample from the three locations was analyzed three times to generate technical replicates and the average values among three technical replicates were used for further analysis  to determine the accumulation and distribution of Zn in wheat grains. Approximately 20g grain samples of each entry were threshed by hand and carefully removed the broken grains and foreign materials. The samples were stored in glassine bags to ensure that no contact with any metal containers. The Zn grain content was estimated following the protocol of Zhang et al. . Brieflyanalysis .A frequency map was made using R software version 3.5.3 . Descriptive statistics  for all of the phenotypic data from each location, was conducted in R software with the “psych” package. Best linear unbiased predictors (BLUPs) values for each cultivar across the three locations were obtained by fitting the mixed linear model in R package “lem4” and were computed according to the formula: Y = (1|Line) + (1|Loc) + (1|Rep%in%Line: Loc) + (1|Line: Loc). In the above formula, Y represents the trait value, the values in the parenthesis indicate the random effects, “:” indicates interactions, and “1|” means groups. “Line” and “Loc” indicate all the testcrosses and environments, respectively. Additionally, “Rep” refers to the replications in one location. Then, the BLUP values were combined to reduce the prediction bias caused by the unbalanced data. Finally, the BLUP values for the Zn contents of each accession across three environments were used as the input data for the further association mapping analysis.® array according to the Axiom 2.0 Assay Manual Workflow protocol [Total DNA was extracted from young leaves tissue of the accessions using a modified CTAB procedure previously reported . The DNAprotocol . After gprotocol . In totaK = 1–10). A burn-in of 1000 iterations followed by 1000 MCMC (Markov Chain Monte Carlo) replicates was used to estimate the number of subpopulations. The optimal K value was determined using the method of ∆K [p-values was then adjusted accordingly to 1.0 × 10−4 in order to control the genetic type I error rate for this population.The population structure was calculated using the Structure software version2.3.4  with admixture model. An admixture model with ten replicates for each genetic group . After GWAS, the DNA sequence flanking the SNP markers significantly associated with Zn accumulation, were analyzed against the “IWGSC RefSeq v1.0” using Basic Local Alignment Search Tool (BLAST) in NCBI databases with default parameters (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The physical positions of SNP markers were then obtained from the public database of JBrowse (http://202.194.139.32/jbrowse/?data=Chinese_Spring). Candidate genes transcripts and their corresponding annotation information were obtained from the website of IWGSC (https://urgi.versailles.inra.fr/jbrowseiwgsc/gmod_jbrowse/IWGSC_RefSeq_v1.0). For the loci without appropriate candidates, the gene nearest to the peak SNP was assigned. All of the potential candidate genes and their corresponding annotations within all detected loci were listed in Based on the reported common wheat reference genome sequence, the high confidence wheat gene list was downloaded from the International Wheat Genome Sequence Consortium (IWGSC) website (The results obtained from the GWAS analysis conducted in the present study provide an overview on the genomic regions involved in Zn grain accumulation in a wheat diversity panel. A total of 29 loci were significantly associated with Zn accumulation in wheat, and seven loci were associated with Zn grain accumulation in multiple environments. The associated PVE values ranged from 11.97% to 24.77% in Yellow and Huai wheat region. Putative candidate genes associated with the most significant QTL were identified and could be possible targets to elucidate mechanisms of wheat grains Zn accumulation in the future. The SNPs most highly associated with Zn grain accumulation could be converted into molecular markers to improve the efficiency of breeding zinc-rich wheat varieties. Overall, this study improves the understanding of the genetic architecture behind Zn accumulation in wheat grains, thus facilitating the future improvement of Zn grain concentrations in wheat breeding programs.