Coxiella burnetii and small ruminant lentivirus in sheep. Genotypes from 513 Rambouillet, Polypay, and Columbia sheep (Ovis aries) were generated using the Ovine SNP50 BeadChip. Of these sheep, 222 animals were subsequently genotyped with the Ovine Infinium® HD SNP BeadChip to increase SNP coverage. Data from the 222 HD genotyped sheep were combined with the data from an additional 258 unique sheep to form a 480-sheep reference panel; this panel was used to impute the low-density genotypes to the HD genotyping density. Then, a genome-wide association analysis was conducted to identify loci associated with absolute monocyte counts from blood. The analysis used a single-locus mixed linear model implementing EMMAX with age and ten principal components as fixed effects. Two genome-wide significant peaks (p < 5x10-7) were identified on chromosomes 9 and 1, and ten genome-wide suggestive peaks (p < 1x10-5) were identified on chromosomes 1, 2, 3, 4, 9, 10, 15, and 16. The identified loci were within or near genes including KCNK9, involved into cytokine production, LY6D, a member of a superfamily of genes, some of which subset monocyte lineages, and HMGN1, which encodes a chromatin regulator associated with myeloid cell differentiation. Further investigation of these loci is being conducted to understand their contributions to monocyte counts. Investigating the genetic basis of monocyte lineages and numbers may in turn provide information about pathogens of veterinary importance and elucidate fundamental immunology.Monocytes are a core component of the immune system that arise from bone marrow and differentiate into cells responsible for phagocytosis and antigen presentation. Their derivatives are often responsible for the initiation of the adaptive immune response. Monocytes and macrophages are central in both controlling and propagating infectious diseases such as infection by  Monocytes are bone marrow-derived immune cells that circulate in the blood with a short lifespan, typically days . They haMonocytes can differentiate along multiple lineages to produce several categories of cells which are still being characterized , 8. In bAfter leaving the marrow, monocytes enter the circulation and can migrate to lymphoid and non-lymphoid tissues. Canonically, they are characterized as classical, nonclassical, and intermediate subsets in humans based on CD14 and CD16 expression profiles , while iThe concentration of monocytes in circulation, under normal conditions, stays within a consistent reference interval that is often measured in complete blood counts in the clinical setting . In mostCoxiella burnetii, an ubiquitous bacterial infection of sheep and goats that also causes the human disease Q fever [C. burnetii actively replicates in monocytes and their derived macrophages [In sheep, monocytes are important in both the propagation and control of several infectious diseases. For example, small ruminant lentivirus, which causes a multisystemic inflammatory disease in sheep and goats , has bee Q fever , subvert Q fever . C. burnrophages , 28, incrophages .ITGA4 and LPAR1 have been found to be important in multiple human populations [Identifying the genetic influences underlying the amount of available circulating monocytes is paramount to characterizing the immune response to diseases influenced by their presence. Genome-wide association studies (GWAS) are a useful methodology employed to identify sites in the genome associated with phenotypes such as monocyte counts. Genotyping a wide array of natural variations across the genome such as single nucleotide polymorphisms (SNPs) or insertion-deletions (indels) followed by a GWAS can identify loci associated with a trait of interest. The genetic influences on monocyte count in humans have been explored extensively using GWAS and related methods. Variations in several leukocyte receptors and other proteins associated with leukocytic differentiation and function have been observed to partially account for circulating monocyte count and occasionally other circulating leukocytes in humans –36. Fromulations –35.The effect of genetic variation on monocyte count in domestic animals has not been studied despite the important influence of the mononuclear phagocyte system in disease. Additionally, the ability of a GWAS to identify loci associated with a trait can be assisted by imputation, a process by which an algorithm estimates genotypes from a lower density genotyping profile to a higher density profile through principles of chromosomal linkage. This is performed when existing high-density (HD) genotypes from a reference population are available to accurately estimate the missing HD genotypes in a new population. This study uses imputed HD genotypes in a GWAS to identify loci and putative candidate genes that are associated with circulating monocyte count in sheep.Ovis aries) at the U.S. Sheep Experiment Station in Dubois, Idaho as part of a previous study [Whole blood was obtained by jugular venipuncture into EDTA-coated vacutainer tubes from ewes  and/or by the U.S. Sheep Experiment Station Animal Care and Use Committee . All efforts were made to minimize any discomfort during blood collection.TM gDNA 3–10 ml Blood Kit using the manufacturers’ instructions . Genotyping services were provided by Geneseek Inc.  using the OvineSNP50 BeadChip  with a set of 54,977 SNPs designed by the International Sheep Genome Consortium [The process of genotyping and imputation is summarized in nsortium .® HD SNP BeadChip, which uses a set of 606,006 SNPs also designed by the International Sheep Genome Consortium [Using the same DNA, a subset of 222 sheep from the lower density dataset  were genotyped again using the Ovine Infiniumnsortium . A separnsortium  with DNAnsortium  but with® HD SNP BeadChip  created by merging the groups of 222 and 258 sheep previously described.Unphased genotype data were converted from PLINK v1.9.ped format , 51 to vEach set was in turn separated into its three component breeds, and imputation of each low-density breed group was performed using its corresponding breed group in the reference panel. Imputation occurred in two major steps. First, the reference panel for each breed was phased with imputation of SNP50-exclusive loci using Beagle v5.1 , 54. Onc−80) were removed. After all quality control measures, 542,255 SNPs remained for absolute monocyte count analysis. Initially chosen fixed effects included age and breed. Thirty principal components were initially generated from the final curated set of genotypes using SVS. The ten principal components with the highest eigenvalues accounted for 84.4% of the variation explained by all thirty, and as such the other principal components were dropped from the analysis . The Quantile-Quantile (Q-Q) plots were constructed using the qqman v0.1.8 package in R [Genome-wide association with the absolute monocyte count was performed using SNP & Variation Suite (SVS) version 8.9.0 . Variants with minor allele frequency below 1% were removed . A Hardy-Weinberg test of equilibrium test was performed, and 768 SNPs at extreme skew  was not within 100 kilobases of any recognized genes in the Oar rambouillet v1.0 assembly [−3. However, rs428401450 is approximately 114 kilobases upstream of the gene GOLIM4, which encodes a type II Golgi-resident protein considered to play a role in cell proliferation and apoptosis in some neoplastic cell lines [GOLIM4, it is not uncommon for GWAS findings to reside within “gene deserts”, with one studying estimating that nearly half of published SNPs associated with disease discovered by GWAS are not within or near genes [Oar rambouillet v1.0 genome [Two genome-wide significant SNPs were identified. One SNP  family of genes [Ly6C defines classical and non-classical monocyte subsets in mice [Regarding the genome-wide suggestive SNPs, one locus  using a BLASTn search [HMGN1 codes for a chromatin accessibility regulator closely linked with myeloid differentiation, being highly expressed in hematopoietic stem cells and progenitor cells but losing expression in differentiated myeloid cells [Two genome-wide suggestive loci are on OAR3 Click here for additional data file.S2 FigThe first and second principal components are plotted against each other for the 513 study sheep, with each point color-coded to show breed.(TIFF)Click here for additional data file.S3 FigThe second and third principal components are plotted against each other for the 513 study sheep, with each point color-coded to show breed.(TIFF)Click here for additional data file.S4 FigThe first and third principal components are plotted against each other for the 513 study sheep, with each point color-coded to show breed.(TIFF)Click here for additional data file.S5 FigThe quantile-quantile (QQ) plot for the mixed model used in the main analysis in the paper.(TIF)Click here for additional data file.S6 FigThe quantile-quantile (QQ) plot for an additional mixed model analysis run after the primary one. The five SNPs with lowest p values were added to the first model as fixed effects.(TIF)Click here for additional data file.S1 TableThe ten principal components used in the analysis matched to sheep study ID.(DOCX)Click here for additional data file.S2 TableThe absolute monocyte count matched to sheep study ID.(DOCX)Click here for additional data file.S3 TableThe distribution characteristics for the absolute monocyte counts are summarized.(DOCX)Click here for additional data file.S4 Table+Allele associated with higher absolute monocyte count.(DOCX)Click here for additional data file.


CD55 and NRIP1 for AGE and ADG, TRIP11 and MIS2 for BF, and VRTN and ZEB2 for LMD, respectively. Generally, in this study, we detected both new and reported variants and potential candidate genes for growth traits of Duroc pigs, which might to be taken into account in future molecular breeding programs to improve the growth performance of pigs.Growth rate plays a critical role in the pig industry and is related to quantitative traits controlled by many genes. Here, we aimed to identify causative mutations and candidate genes responsible for pig growth traits. In this study, 2360 Duroc pigs were used to detect significant additive, dominance, and epistatic effects associated with growth traits. As a result, a total number of 32 significant SNPs for additive or dominance effects were found to be associated with various factors, including adjusted age at a specified weight (AGE), average daily gain (ADG), backfat thickness (BF), and loin muscle depth (LMD). In addition, the detected additive significant SNPs explained 2.49%, 3.02%, 3.18%, and 1.96% of the deregressed estimated breeding value (DEBV) variance for AGE, ADG, BF, and LMD, respectively, while significant dominance SNPs could explain 2.24%, 13.26%, and 4.08% of AGE, BF, and LMD, respectively. Meanwhile, a total of 805 significant epistatic effects SNPs were associated with one of ADG, AGE, and LMD, from which 11 sub-networks were constructed. In total, 46 potential genes involved in muscle development, fat deposition, and regulation of cell growth were considered as candidates for growth traits, including  The huge demand for pork has greatly increased the global production of pork. There is a positive correlation between pig growth rate and meat production, which can be directly used to improve meat production in pig breeding [Pork is a major meat resource for humans, constituting over 31% of all meat consumed worldwide in 2020 , as effective methods for detecting causative mutations associated with economic traits, have been more and more widely used in humans  and domeDuroc boars, due to their maximized production performance, are commonly used as terminal boars in modern three-way crossbreeding production systems , which fully utilize heterosis from both maternal and paternal sides. This is the most efficient way to boost the genetic improvement of growth traits, and this study aimed to detect additive and non-additive effects on growth traits to find causative variants and potential candidate genes for growth-related traits.The data used in this study were all from a nucleus breeding farm in the Jiangxi province of China. After data cleaning, a total number of 2360 Duroc pigs born from 2014 to 2021, including 1559 males and 801 females, were used in our study. Pedigrees can be traced back for three generations. Regarding phenotypes, there were 1854 records for AGE and ADG, 1847 records for BF, and 1651 records for LMD, respectively.ADG and AGE for each individual was calculated with information provided by automated feeding stations. Daily weights were collected from 30 to 100 kg of body weight, and they were used to calculate AGE and ADG. Phenotypes of BF and LMD were measured individually alive at around 100 kg body weight using Aloka 500 V real-time B-mode ultrasound, according to the Chinese national pig performance testing standard. The ultrasound images were taken from the dorsum at a distance of 5 cm from the dorsal midline in the middle of the last third and fourth ribs of each pig. All phenotypes in this study were adjusted to 100 kg using the formulas shown below:(1) AGE:(2) BF:(3) ADG(4) LMDDescriptive information for the phenotypes is presented in p-value < 10−6, and (5) SNPs located in sex chromosomes or unmapped to the reference genome. After the quality control, there were 31,372 high-quality SNPs and 2360 pigs left. Then, missing genotypes were further imputed by Beagle v5.2 software [A total number of 2360 Duroc pigs born from 2016 to 2022 were genotyped using Illumina Porcine SNP50 BeadChip . Data quality control was conducted using the PLINK  software . The gensoftware .The estimated breeding values (EBV) for AGE, ADG, BF, and LMD traits were estimated according to average information restricted maximum likelihood using the pedigree-based best linear unbiased prediction (PBLUP) method and the DMUAI module of DMU software , as descdividual , which wdividual  R packagThen, a linear mixed model, including additive and non-additive SNP effects, was generated by GMAT software , which cp = 0.05/N = 1.59 × 10−6 and p = 1/N = 3.19 × 10−5, respectively, where N is the number of analyzed SNPs. The proportion of DEBV variance explained by significant SNPs was calculated according to [p-values of the epistatic effects were Bonferroni-corrected for multiple testing, which resulted in p < 1.03 × 10−9 as a significance threshold.Then, Bonferroni correction was implemented to define the significant threshold. To avoid missing the true hints of linkage, the genome-wide significant and suggestive levels were set as rding to . The p-vInteraction effects between every two SNPs across the whole genome for growth traits were calculated using GMAT  software . Then, tThe solid spine method in Haploview software   was usedFunctional genes within 1 Mbp centered on each significant SNP were annotated as candidate genes. Identified potential genes were searched using the Ensemble Sus scrofa 11.1 reference genome . Pig QTL10  of SNP additive effects is shown in A total number of 10 SNPs that surpassed the suggestive significance level were detected as being associated with one of the studied traits ; a Manhap-values of the GWAS results were visualized using Manhattan plots and Q-Q plots  was calculated for each individual . DEBV, eMany significant additive and dominance associations were discovered or involved genomic regions previously reported in the literature. Surprisingly, our study identified a number of novel loci associated with dominance effects on growth traits. SNP–SNP networks of epistatic interactions directly revealed the relationships between SNP effects, which could aid in understanding genetic bias with respect to growth traits.In this study, the SNP–SNP interactions which occurred on the same chromosome were discovered as interactions between two SNPs on the same chromosome. However, remarkably, these may have been haplotype effects and not interactions . MeanwhiCD55 molecule (CD55) gene, which plays an important role in adipocyte development and could affect the growth rates of animals by regulating adipocyte production, was identified in nearby candidate SNPs (CNCB10006791 and CNCB10006792) [nuclear receptor interacting protein 1 (NRIP1) gene encodes a nuclear protein also known as receptor-interacting protein 140 (RIP140), which was identified as a candidate gene for AGE with dominant effect. RIP140 is widely expressed and is involved in regulating lipid and glucose metabolism and fat cell regulation [KH RNA binding domain-containing, signal transduction-associated 2 (KHDRBS2) gene encodes an RNA-binding protein, which is involved in mediating uterine endometrial stroma progenitor development [KHDRBS2 is associated with number of teats in Yorkshire pigs [farnesyl-diphosphate farnesyltransferase 1 (FDFT1) gene was reported as being associated with growth rate through the muscle transcriptome in Iberian pigs [Due to the negative genetic correlation between ADG and AGE, the analysis of these two traits used different modeling approaches to detect candidate genes for growth traits. As a result, some commonly detected signals and some differences were also found as well. There were three significant additive SNPs detected for both ADG and AGE, while, partially overlapped with these, there were SNPs identified as being significantly associated with ADG in Yorkshire and Pietrain pigs ,35. The 0006792) ,37. The gulation ,39,40,41elopment . Studiesire pigs  and withire pigs . Meanwhiian pigs .thyroid hormone receptor interactor 11 (TRIP11) gene encodes a protein product that interacts with thyroid hormone receptor β, which functions in regulating lipid metabolism [musashi homolog 2 (MIS2) gene encodes an RNA-binding protein and plays a central role in controlling feed intake and feeding behavior in mammals [For BF, the tabolism  and was tabolism . The mus mammals ,49. A ha mammals . Our stuvertnin (VRTN) gene is known to affect the variation in vertebral number, and, furthermore, an allele of VRTN affects the length of the longissimus muscle, as reported in [VRTN was found in the current study to be associated with LMD. In addition, the Zinc Finger E-Box Binding Homeobox 2 (ZEB2) gene was proved to play a role in skeletal muscle differentiation in pluripotent stem cells [As for LMD, the orted in ,52. Inteem cells . AlthougCD55 and NRIP1 for AGE and ADG, TRIP11 and MIS2 for BF, and VRTN and ZEB2 for LMD, respectively. This study presents novel putative causative variants and genes for future pig breeding programs, which may be used in developing trait-specific marker-assisted selection models.In conclusion, 32 significant SNPs—10 for additive and 22 for dominance effects—and many epistatic interactions were identified for one of four growth traits in a purebred Duroc population through a GWAS fitting additive and non-additive genetic effects. Furthermore, 46 candidate genes with potential functions in muscle development, fat deposition, and regulation of cell growth were considered as candidates for growth traits, including


Some of these genes coded for structural protein and participated in cell wall structure, some were linked to programmed cell death, and others were reported to show abiotic stress response roles in wheat and other plants. In addition, using the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) protocol, the best-performing lines under salt stress were identified. The SNPs identified in this study and the genotypes with favorable alleles provide an excellent source to impart salt tolerance in wheat.High salinity levels affect 20% of the cultivated area and 9%–34% of the irrigated agricultural land worldwide, ultimately leading to yield losses of crops. The current study evaluated seven salt tolerance-related traits at the seedling stage in a set of 138 pre-breeding lines (PBLs) and identified 63 highly significant marker-trait associations (MTAs) linked to salt tolerance. Different candidate genes were identified in  As much as 20% of the cultivated area and 9%–34% of the irrigated agricultural land worldwide is affected by high salinity  from irrigation with brackish water and crop evaporation . A salinsalinity , which usalinity . Salinitsalinity , making salinity . Salinitsalinity .Triticum aestivum L.) has moderate tolerance to salinity  (+ exclusion phenotype is associated with two genes: Nax1 (present at 2A) and Nax2 (present at 5A) in durum wheat, a close relative of bread wheat  . Salt toe xylem) . Previouad wheat . Many QTad wheat . Similarin wheat . GWAS stolerance .Due to the involvement of only two parents in the development of bi-parental populations, the QTL mapping approach fails to disclose the entire genetic architecture for salt tolerance . TherefoThe results of these investigations and identifications may allow the improvement of salt stress tolerance in wheat cultivars. The present study investigated a set of 138 wheat genotypes for salt-stress tolerance at the seedling stage. GWAS was applied to determine the extent of variation in response to salt stress, to identify molecular markers linked to salt tolerance, to search for candidate genes favorable for salt tolerance, and to identify salt-tolerant genotypes.The study was conducted on a set of 138 diverse wheat pre-breeding lines (PBLs) developed at CIMMYT . These 1This investigation followed a completely randomized design (CRD). Initially, seeds from each line were treated with 10% NaOCl for 5 minutes followed by three washes with distilled water. In a growth chamber  with controlled temperature of 28 ± 2°C and a 10-h photoperiod, 25 seeds of each genotype were grown on Whatman no. 1 filter paper moistened with a salt solution . The expOn the eighth day of germination tests, the performance of the seedlings was assessed by recording the following morphological characteristics . The mea5 plants. DNA was extracted using the cetrylmethylammonium bromide (CTAB) method and quantified on a Nano-Drop instrument (http://www.diversityarrays.com/dart-application-dartseq), as described by http://www.diversityarrays.com/sequence-maps).The genotyping used the flag leaves at the booting stage of TC1FSPSS 16.0 was used to generate the descriptive statistics. All other analyses, including three way ANOVA, phenotypic histograms (using the “ggplot2” package) , circulaWe used STRUCTURE version 2.3.4 to analyze population structure  where thp-values <10−3 were defined as significant, whereas markers p-values less than the reciprocal of the number of markers (<1.45 × 10−4) were defined as highly significant associations (after Bonferroni correction) (TASSEL V5.2.43 software was used to perform marker-trait associations using the mean data for each treatment . The currection) .e-values < 10−4 were selected.Sixty-nine base-pair-length sequences for each highly significant marker including 48 bp flanking regions of SNP marker were subjected to BLAST  using the NCBI  database. The BLAST search was conducted using the genome assembly IWGSC RefSeq v2.1 . Hits wirXij) was standardized initially. This was followed by factor analysis to characterize the ideotype matrices. In the final step, an MGIDI index was computed by measuring the Euclidean distance between genotypes and ideotype scores using the following equation:γij represents the score represents of ith genotype  in the jth factor  and t and f are the number of genotypes and factors, respectively. The score of the ideal genotype was represented by γj. The lower the MGIDI value of a genotype, the closer it is to the ideal genotype . The CL in S1 was higher (2.94) than those in S0 and S2 . The RCL in S1 was also higher (106.78) than that in S2 (85.62). Among all traits, the highest decrease was observed in SL, which decreased from 9.38 (S0) to 6.08 (S1) and 3.25 (S2). The values for RSL_S1 and RSL_S2 were 67.30 and 33.02, respectively. A decreasing trend was also observed in RL, from 9.35 (S0) to 6.08 (S1) and 3.25 (S2). In contrast, the RRL in S1 was 65.67 and 35.36 in S2. The mean R/S in the control group was higher (1.14) than those for the S1 (0.98) and S2 (1.11) treatments. The relative R/S was higher in S2 (112.64) compared to that in S1 (99.50). The highest SVI was observed in the control group, with a mean value of 1708, followed by S1  and S2 . The mean RSVI in S1 (50.77) decreased to 20.38 in S2 .Genotypes (G), treatments (T), and GxT showed significant differences in TG, RN, CL, SL, RL, R/S, SVI, RCL, RSL, RRL, RR/S, and RSVI. Among G and T, RTG showed highly significant differences, whereas no significant differences were observed for G×T. Only genotypes differed significantly in RRN, as compared to treatment plus G×T .Most traits were positively correlated in the untreated control, although SL_S0 was negatively correlated with TG_S0 and R/S_S0 . Likewisx-axis  for the different traits observed in this study  number, whereas chromosomes 3A (three including one highly significant), 6A (three including two highly significant) and 7B (three significant) exhibited the lowest numbers of MTAs . SixteenRegarding traits, RN_S2 showed the highest number of MTAs (35 including thirteen that were highly significant), whereas RL_S0, RN_S0, RTG_S2, SL_S1, SVI_S0, TG_S2, and TG_S0 showed the lowest numbers of MTAs (one) for each trait. Twenty-eight MTAs (including eighteen that were highly significant) and 22 MTAs (including six that were highly significant) were detected for RR/S_S1 and RN_S1 respectively. We also detected 20 MTAs associated with SL_S0, thirteen with RRN_S2, and seven with RSL_S1 including eleven, eight, and two that were highly significantly MTAs respectively. Nine MTAs associated with SL_S0 (with no highly significant association), while six MTAs were detected for R/S_S2, RCL_S1 (including one significant MTA), and RRN_S1. Five MTAs were linked with RL_S2 (one significantly MTA) and SL_S0 (with no highly significant association). RR/S_S2 and CL_S2 were linked to four markers. RR/S_S2 was associated with one highly significant MTA. We also detected three MTAs for CL_S1, RL_S1, RRL_S2, and RSL_S2 and two MTAs for RCL_S2, RRL_S1, and RSVI_S2, with no highly significant association .e-values < 10−4 were selected. Twelve candidate genes were identified through this analysis. These included putative disease resistance RPP13-like protein 1, disease resistance protein RGA2-like (involved in conferring disease resistance), glycine-rich cell wall structural protein 1-like , two metacaspase-1-like proteins (play roles in programmed cell death), sphinganine C4 monooxygenase 1-like (plays a role in sphingolipid biosynthesis), two 60S ribosomal protein L22-like proteins , glyceraldehyde-3-phosphate dehydrogenase GAPA1, subtilisin-like protease SBT1.7, mRNA-decapping enzyme-like protein, and calmodulin-binding protein 60 D-like . These candidate genes along with their physical locations and functions are shown in BLAST was performed for highly significant SNP markers. Hits with 100% identity and The first two principal components explained significant variation , with eigenvalues ≥1 , where PWheat seedling development has three parts: germination, emergence, and early growth. All three stages are especially sensitive to salinity stress . The mosOwing to the significance of the germination stage in plant tolerance against salinity stress , germinaThe root is the first plant organ that experiences salt stress; thus, it plays a significant role in sensing the salinity level in the nearby environment  by signaThe coleoptile protects the first leaf of the future wheat plant, which also functions as the driver to propel the leaf outside the soil crust. Coleoptile strength and success is tantamount to the successful establishment and early plant vigor. Stress, may enhance the CL and shorten the SL . In the Rht-B1 on chromosome 4BS  and one QTL on chromosome 6DL (QRSRp.ipk-6D) that were associated with the R/S ratio under osmotic stress. We detected two highly significant MTAs (M1987 at 107.75 cM and M1019 at 116.66 cM) in S1 and S2 on chromosome 3D. Two MTAs, M11763 at 90.8 cM and M1188 at 121.22 in S1, were also detected on chromosome 6D, suggesting that both these loci were associated with R/S control under both salt and osmotic stresses.The R/S ratio is also disturbed under salt stress; however, this response is more tied to water stress than to salt stress . IncreasBrassica napus (Hibiscus species (QSVI.2A.SG), 4A (QSVI.4A.SG), 6D (QSVI.6D.SG) and 7B (QSVI.7B.SG) have been reported. We detected one MTA (M38 at 214.7 cM) on chromosome 7A that was associated with SVI_S0 on chromosome 7A. M38 was also associated with RL. Likewise, two MTAs (M7489 at 258.98 cM on chromosome 1B and M3286 at 113.89 on chromosome 7A) were also detected with RSVI_S2 on chromosome 7A, indicating that a wide variety of loci determine the SVI in wheat and are strongly dependent on RL and SL.Damaged plants show decreased viability, as represented by SVI . This isca napus  and Hibi species . In cont species , variousGAPA1 has an abiotic stress response role in wheat and other plants  was found on chromosome 5D. Glycine-rich proteins (GRP) are reportedly involved in stress responses including salinity, drought, etc., in many plants . Moreover plants  and was esponses . Furtheresponses . Calmoduesponses . In addiesponses . ABA medesponses . The anaPCA analysis disentangles a large data set into a small number of unrelated groups that can easily be plotted along independent linear axes. Closely linked variables in the same group may hint at latent relationships among them. Multiple traits often make it difficult to choose the best-performing genotypes. Therefore, several counter multivariate approaches such as cluster analysis, factor analysis, and PCA including other indices  have beeAccording to the IMGIDI-based selection of genotypes, we identified the 10 best accessions  for all treatments . We obseTriticum aestivum L. could be a valuable reference for future studies. The best-performing lines with desirable allele combinations can be incorporated into wheat breeding programs.This study comprehensively dissected the performance of diverse bread wheat germplasm against different levels of salt stress. A total of 138 lines were screened at the seedling stage for seven traits at 0, 150, and 250 mM NaCl. We identified 195 significant SNPs/loci and 63 highly significant loci related to different traits. Most of the associations were present on the A genome, especially on chromosome 2A, and strengthened our findings regarding salinity tolerance. A total of 12 candidate genes were associated with highly significant SNP markers. The chromosomal localization of many of the important candidate genes such as Plastidial GAPA1, Metacaspase-1, etc., and their role in salt stress were also reported previously. These results of the extensive study of salt stress tolerance in


The reduction of VLC-PUFA levels in lipid fractions of the organs from the herein sampled farmed fish suggesting a yet undetected effect on these compounds of high vegetable oil aquafeed formulations, that currently dominate the market.Very long-chain (> C24) polyunsaturated fatty acids (VLC-PUFA) play an important role in the development of nervous system, retinal function and reproductive processes in vertebrates. Their presence in very small amounts in specific lipid classes, the lack of reference standards and their late elution in chromatographic analyses render their identification and, most important, their quantification, still a challenge. Consequently, a sensitive and feasible analytical methodology is needed. In this work, we have studied the effect of chain length, as well as the number and position of unsaturations (or double bonds) on the response of GC-APCI-(Q)TOF MS, to establish an analytical method for VLC-PUFA quantification. The developed methodology allows the quantification of these compounds down to 2.5 × 10 Among them, the n-3 (or “omega-3”) eicosapentaenoic acid  and docosahexaenoic acid , and the n-6 arachidonic acid , are important for normal growth and development, and, in mammals, they play pivotal roles in the inflammatory response. Importantly, dietary consumption of EPA and DHA has been found beneficial in cardiovascular and cerebrovascular conditions, and some types of cancer3. Marine fish, particularly oily species, represent a major source of n-3 LC-PUFA in the humans diet, and as a consequence, these compounds have been of special concern for researchers4. Farmed fish represent an ever-increasing portion of fish consumed by humans5. The rapid expansion of aquaculture in the last decades has urged the need to use non-fish derived raw materials in the feed. Consequently, aquafeed formulations have remarkably decreased the levels of LC-PUFA, hence impacting the nutritional value of the final product because of the reduced levels of the health-promoting n-3 LC-PUFA such as EPA and DHA, and interestingly, compromising the health of the farmed fish itself whereby LC-PUFA have critically important functions as described above for mammals.Long-chain (C24) PUFA (VLC-PUFA) and, therefore, the abovementioned reduction of LC-PUFA in fish diets has been hypothesized to compromise the physiological demands of VLC-PUFA in farmed fish. It is now well established that the elongation of very long-chain fatty acid protein 4 (Elovl4) is a pivotal enzyme determining the bioconversion of LC-PUFA into VLC-PUFA7. VLC-PUFA have been found in low concentrations in retina, sperm and brain of mammals, as a part of phosphatidylcholine (PC), sphingomyelin (SM) and cerebrosides (CE)7, and have been therefore considered essential for development of nervous system, retinal function and reproductive processes10. Since their detection in bovine retinas by Aveldaño et al. in 198711, the study of VLC-PUFA has been mainly focused on mammals14. Surprisingly, the detection and characterization of VLC-PUFA has been barely studied in fish despite the potential detrimental effects noted above associated with a reduction of metabolic precursors (i.e. LC-PUFA) in current aquafeed formulations15.LC-PUFA are biosynthetic precursors of the so-called very long-chain (> C6. Liquid chromatography (LC) coupled to different mass spectrometers (MS) (with electrospray ionization (ESI) or atmospheric pressure chemical ionization (APCI)) in normal and reverse phase has been applied to the analysis of lipids containing VLC-PUFA18. However, although a derivatization step to fatty acid methyl esters (FAME) is required for fatty acid determination, gas chromatography (GC) coupled to MS with electron ionization (EI) is the most frequently used technique for the analysis of VLC-PUFA20. Pioneer studies from the 80’s reported VLC-PUFA of up to 40 carbons21. Nevertheless, resolution of quadrupole analyzers (over 1 Da), and deficient chromatographic separation, made very challenging to obtain accurate and reliable identification of VLC-PUFA. Moreover, the use of EI source provokes an extensive fragmentation of FAMEs and involves the partial or total loss of the highly diagnostic molecular ion (M+·) generating the same EI spectra for unsaturated FAMEs (both n-6 and n-3) with different chain length, which hinders the identification process.VLC-PUFA are present in very small amounts in specific lipid classes of particular tissues including brain, retina and gonads. Moreover, the absence of commercially available standards and their late elution in standard chromatographic analyses has hindered the identification of VLC-PUFA in animals22. This soft ionization technique has allowed to obtain the parent molecule, enabling the accurate identification of VLC-PUFA by means of measuring protonated molecule at accurate mass. Moreover, the study of the mass spectrometric behavior enables the discrimination between n-3 and n-6 compounds22. The application of this technique has also allowed, for the first time, the identification of VLC-PUFA with chains up to 44 carbons in the SM and PC fractions in the total lipids from fish based on their accurate mass and fragmentation pattern23.Recent studies have applied APCI source with quadrupole time-of-flight mass analyzers (QTOF) coupled to GC to the qualitative analysis of VLC-PUFA in fish.24 expressed semi-quantitative results of VLC-PUFA content in rat testis as percentages of the total fatty acids in each lipid class. Méndez et al.25 and Liu et al.13 based the quantitative analysis of VLC-PUFA on the internal standard (IS) method by the determination of mass response factor, extrapolating the data from existing standards C30:0-C36:0. In the above studies, the extrapolation of the response factor did not take into account the dependence of the instrument response on chain length, nor the degree and the position of unsaturations. Liu et al.14 developed a method to quantify VLC-PUFA expressed as fatty acids molar percentage, but the response of a single quadrupole mass analyzer shows trends caused by chain length, number of unsaturations and their position, and the need of performing extrapolations.While the above-mentioned absence of reference standards makes difficult the identification of VLC-PUFA, their quantitation, aggravated by their extremely low abundance pointed out above, is almost an unexplored field. Furland et alSparus aurata)23, providing valuable information, for future studies in the fields of lipid biochemistry and aquaculture, about the presence of these novel molecules in wild and farmed fish tissues.The methodology established in this work is able to identify, and also quantify, VLC-PUFAS with chains up to 44 carbons in fish organs, improving previous methodologies taking advantage of the GC-APCI-QTOF, not used before to the best of our knowledge. The effect of chain length, number of unsaturations and their position to the response of GC-APCI-QTOF was studied in order to establish a quantification method of VLC-PUFA, taking into account the possible variations of the VLC-PUFA response respect PUFA standards available. This methodology was applied in the identification and quantification of VLC-PUFA known to be present in the PC, SM and CE lipid fractions of eye, brain and gonads of wild and culture gilthead sea bream . Mass spectra of the different VLC-PUFA peaks were studied for further identification of the compounds. The mass accuracy of [M+H]+ was evaluated in the low-energy (LE) function, and mass errors were below 3.2 ppm in all cases. Table .23).Like in a previous work26 revealed no statistical differences and no trends regarding length of chains and position and number of unsaturations  from equimolar mixtures  of each saturated FAME  and PUFA methyl esters  were studied to elucidate the effect of chain length, as well as position and number of unsaturations, on the instrumental response. Comparison of slope values from saturated FAMEs and PUFA calibration curves, respectively, using Student’s t-test based in both the standard error of the regressions and the standard error of the slopesSlopes from FAMEs presented a variation coefficient of 5.6% with a confidence interval at 95% of ± 4.9% (n = 7). Regarding PUFA methyl esters, statistical analysis showed also that slopes were statistically equal and position and number of unsaturations did not show relation with instrumental response. Values of coefficient of variation and confidence interval at 95% (n = 8) were 4.6% and ± 4.2%, respectively (n = 8) Figs. , S2. In –4 pmol/mg Lipid (limits of quantification down to 2.5 × 10–3 pmol/mg Lipid).Table Table elovl4 genes is circumscribed to brain, eye and gonads of fish27. It has also been described that the expression patterns are linked to phenotypic influences, essentially the diet29. Table 44 VLC-PUFA were only present in PC from brain of wild S. aurata individuals. Figure It is a well-known fact that the highest expression of t-test, 99% confidence level) than wild specimens. Considering that VLC-PUFA are biosynthesized in fish through Elovl enzyme-mediated reactions from LC-PUFA precursors30, these results suggest that diet can impact VLC-PUFA homeostasis and, potentially, the biological functions that these compounds exert in vision and reproduction. On one hand, natural diets of a carnivorous fish like the gilthead sea bream are rich in LC-PUFA and therefore, biosynthesis of VLC-PUFA guarantees the physiological demands in key tissues. On the other, compared to wild fish, dietary supply of LC-PUFA in farmed fish, albeit likely covering their minimum nutritional requirements, is largely lower due to the high inclusion levels of non-marine ingredients in current aquafeed formulations31. Thus provision of adequate levels of LC-PUFA in the diet affects more strongly eyes and gonads, being these organs more dependent on an adequate dietary provision of VLC-PUFA precursors (i.e. LC-PUFA)36 generally found in lower amounts in substituted diets37.As can be observed in Fig. 24–C28 (short-chain VLC-PUFA), C30–C38 (medium-chain VLC-PUFA) and C40–C44 (long-chain VLC-PUFA). Short VLC-PUFA are more abundant in eyes, whereas the long-chain VLC-PUFA are mainly located in brain. Moreover, eyes preferentially concentrate medium-chain VLC-PUFA. To note, the amounts of VLC-PUFA with chains of less than 40 carbons detected in the different tissues are one order of magnitude lower than the rest and, interestingly, the SM of wild specimens seems particularly rich in these compounds, especially in brain, as compared to the cultured ones. Distribution by chain length may be relevant since it has been suggested that the properties of VLC-PUFA are directly linked to their structure presenting simultaneously properties of both saturated and polyunsaturated fatty acids in each of the extremes38.Figure 40.In conclusion, the method developed in the present study allows the quantitation of VLC-PUFA in the lipids of target tissues of fish and reveals their distribution in different lipid classes. Furthermore, the application of the method to samples collected from wild and farmed individuals of the gilthead sea bream provides evidence of the impact of currently used feed formulations on VLC-PUFA biosynthesis. Although it can be argued that the present findings should be limited to the particular fish stocks/individuals sampled, the extensively documented phenotypic fingerprint on the fatty acid composition of cultured fish makes generalization tempting. Such generalization was beyond the objectives of the present work, but quantitation of VLC-PUFA will lay the groundwork for future studies linking their presence to gene expression and phenotypic performance to ascertain the effects of these compounds in visual acuity, fertility, breeding and growth2 atmosphere. Working solutions were prepared by diluting stock solutions in hexane.In this research, commercially available fatty acids and PUFA analytical standards were selected. Free form and methylated (ME) saturated fatty acids from 18:0 to 24:0 were purchased from Fluka , and from 26:0 to 30:0 from Dr. Ehrenstorfer . PUFA including 20:4n-3, 20:4n-6, 20:5n-3, 22:4n-3, 22:4n-6, 22:5n-3, 22:6n-3 and 24:5n-3 standards were purchased from Supelco . Stock solutions  were prepared by dissolving solid reference standards in hexane or by diluting reference standard solutions in hexane and subsequently stored in a freezer at – 20 °C under a N3), diethyl ether and toluene were purchased from Merck Millipore . Methanol (MeOH) was purchased from VWR . Sulfuric acid, glacial acetic acid and potassium chloride (KCl) were purchased from Panreac . Iodine and butylated hydroxytoluene , used as antioxidant, were purchased from Sigma Aldrich . All reagents were analytical grade. HPLC-grade water was obtained by purifying double distilled water in a Milli-Q Gradient A10 system  .Hexane  was purchased from Scharlab . Chloroform  equipped with an Agilent 7683 autosampler was coupled to a quadrupole orthogonal acceleration time-of-flight mass spectrometer, Xevo G2 QTOF , equipped with APGC v2.0 as ionization source, working in positive APCI mode. A fused silica VF-5HT capillary column with a length of 15 m × 0.32 mm i.d. and a film thickness of 0.10 μm  was used for GC separation. The oven temperature was programmed as follows: 130 °C (1 min); 20 °C/min to 340 °C; (2.5 min) with a total runtime of 14 min. Pulsed splitless (25 psi) injections of 1 μL were carried out at 280 °C with a splitless time of 1 min. Helium 99.999%  was used as carrier gas at a flow of 4 mL/min.2 was used as auxiliary gas at 200 L/h, as cone gas at 5 L/h and as make-up gas at 300 mL/min. The APCI corona discharge pin was operated at 1.7 μA and the cone voltage was set to 20 V.The interface and ionization source temperatures were set to 340 °C and 150 °C, respectively. NThe QTOF was operated at 2.5 spectra/s acquiring the mass range m/z 50–1000. The TOF MS resolution was approximately 15,000 (FWHM) at m/z 264. Acquisition was done in MSE mode in which two alternating acquisition functions with different collision energies were generated: the low-energy (LE) function, selecting a collision energy of 4 eV to avoid or minimize fragmentation, and the high-energy (HE) function, with a collision energy ramp ranging from 25 to 40 eV to obtain a greater range of fragment ions.Heptacose  was used for the daily mass calibration. Internal calibration was performed using octafluoronaphthalene  as lock mass .In order to work under proton transfer conditions, an uncapped vial containing water was placed in a designed holder into the APCI source door to enhance protonation. MS data were acquired in centroid mode and processed by the ChromaLynx XS application manager . Mass-Fragment software (Waters) was used for mass spectra interpretation.Eyes, brains and gonads (n > 5) were collected from wild (western Mediterranean) and farmed specimens of gilthead sea bream from local  markets.The study was reviewed and approved by the ethics committees of the Spanish Research Council (CSIC), Local Government  and Institute of Aquaculture “Torre de la Sal” under the framework of project AGL 2013-40986-R . All methods were performed in accordance with the relevant guidelines and regulations, including ARRIVE guidelines.23. Crystalline lenses were removed from eyes. Total lipids were extracted using the method of Folch et al.41 Subsequently, an aliquot of total lipids (~ 200 mg) was further developed by thin layer chromatography  using a polar solvent system (methyl acetate:propan-2-ol:chloroform:methanol:0.25% (w/v) aqueous KCI (25:25:25:10:9 by vol). The three fractions where VLC-PUFA were detected in previous studies23, namely SM, PC and CE, were scrapped off the plate42, eluted in chloroform:methanol  containing BHT , and used to prepare FAMEs43. FAME samples were stored in hexane containing BHT (0.01%) under nitrogen at -20ºC  was estimated from the chromatograms of sample extracts fortified at the lowest level tested  for a signal-to-noise ratio of 3. LOQ was calculated as the concentration for a signal-to-noise ratio of 10.A direct calibration with internal standard (IS 27:0) calculated as the arithmetic mean of the responses of each of the eight available PUFA standards was used to quantify VLC-PUFA. Calibration curves were made by triplicate with five concentrations . Internal standard was added to standards and samples before injection. Goodness of the regressions were checked by means of residuals study. Trends were not observed and residuals values were below 5% in all cases. Values of rIn order to verify quantitatively the whole procedure, samples of eyes were fortified with the saturated FA standards with greater number of carbons (28:0 and 30:0), and with the IS used (C27), with the aim of simulating the VLC-PUFA molecules as best as possible. Recovery experiments (n = 5) at two different levels for 28:0 and 30:0 (0.02 and 2 pmol/µL), and at 0.25 pmol/µL for 27:0 (concentration added of IS), were performed. The fortified samples were left to stand for 1 h prior to extraction. Precision, expressed as relative standard deviation (%) was calculated from five replicates processed in the same conditions.26.Calculations and statistical tests were carried out using MS EXCEL 2013 . Slopes were statistically compared among them using Student’s t-test based in both the standard error of the regressions and the standard error of the slopesSupplementary Information.


Oryza sativa) and two root hair traits, root hair length (RHL) and root hair density (RHD). Root hairs are outgrowths of single cells on the root epidermis that aid in nutrient and water acquisition and have also served as a model system to study cell differentiation and tip growth. Using lines from the Rice Diversity Panel-1, we explored the diversity of root hair length and density across four subpopulations of rice . GWA analysis was completed using the high-density rice array (HDRA) and the rice reference panel (RICE-RP) SNP sets.Genome wide association (GWA) studies demonstrate linkages between genetic variants and traits of interest. Here, we tested associations between single nucleotide polymorphisms (SNPs) in rice  associated with root hair density in rice. We identified candidate genes in these regions and present those with previously published expression data relevant to root hair development. We re-phenotyped a subset of lines with extreme RHD phenotypes and found that the variation in RHD was due to differences in cell differentiation, not cell size, indicating genes in an associated genomic region may influence root hair cell fate. The candidate genes that we identified showed little overlap with previously characterized genes in rice and Root hair length and density are quantitative traits with complex and independent genetic control in rice. The genomic regions described here could be used as the basis for QTL development and further analysis of the genetic control of root hair length and density. We present a list of candidate genes involved in root hair formation and growth in rice, many of which have not been previously identified as having a relation to root hair growth. Since little is known about root hair growth in grasses, these provide a guide for further research and crop improvement.The online version contains supplementary material available at 10.1186/s12870-022-04026-5. We present genomic regions associated with root hair length and density in rice. These quantitative traits are potential breeding targets for improved nutrient acquisition.Secale cereale L.) could have up to 14 billion root hairs, providing an additional 400 m2 of surface area for soil exploration [Root hairs are small tubular outgrowths of single epidermal cells that contain a number of transporters for nutrient and water acquisition . Early rloration –7.Arabidopsis and other species including rice [Arabidopsis [32P and a root surrounded by mesh that could only be penetrated by root hairs indicated that root hairs contributed 63% of total plant P uptake in barley [Two separate root hair traits, length and density, increase under low phosphorus (P) conditions in ing rice –11. Thering rice –19. Undebidopsis . Studiesn barley . Both ann barley , 22. Sinn barley , root han barley , 25. Roon barley , 26 and n barley , 28. Roon barley . Recent n barley . FurtherArabidopsis [Arabidopsis and other Brassicaceae compared to other plant families, including members of the Poaceae [Arabidopsis forms longitudinal files of hair and non-hair cells, whereas other plants form hairs over the entire root surface [Brachypodium, the initial division of cells at the root tip is uneven, resulting in a pattern of alternating hair and non-hair cells along the length of the root [Arabidopsis as players in epidermal cell differentiation [In addition to their agronomic importance, root hairs have been used as a model system to study differentiation and growth of single cells, since they develop from specific cells of the epidermis  and elonbidopsis , 31–45.  Poaceae , 47. Ara surface , 48–51.  surface , 52. In the root . Though ntiation , 41, 53,ntiation , 47.OsEXPB5, OsEXPA8, and OsEXPA17) [OsCSLD1) [OsSNDP1) [OsFH1) [RBOH genes [RHC) [WUSCHEL-related homeobox gene [OsNOX3) [ROOT HAIR DEFECTIVE-SIX LIKE (RSL) class I genes partially control root hair elongation in Arabidopsis, Brachypodium, and rice, indicating conservation of some basic mechanisms [Arabidopsis [In rice, several genes have been identified that play a role in root hair elongation, including root hair-specific expansins (sEXPA17) , 55, a cOsCSLD1) , a Sec14OsSNDP1) , a formi [OsFH1) , three ROH genes , a putates [RHC) , a WUSCHbox gene , a NADPH[OsNOX3) , and a b[OsNOX3) . The ROOchanisms –68. RSL chanisms . These sbidopsis .Oryza sativa, we undertook a genome wide association (GWA) study to identify genomic regions that control root hair length (RHL) and density (RHD). Leveraging natural variation provides unique insights into the genetics underlying quantitative traits, including RHL and RHD, and can potentially identify genes that were missed or overlooked in mutant screens. We phenotyped a subset (314 lines) of the Rice Diversity Panel-1 (RDP1), a collection of Oryza sativa varieties that represent the global diversity of cultivated rice [indica, aus, temperate japonica, tropical japonica, and aromatic), averaging one SNP every 540 bases. The SNPs are well-distributed across the genome, and the mix of private (subpopulation-specific) and universal SNPs allows for GWA analysis to be performed across and within subpopulations. The imputed SNP set in RICE-RP comprises 4.8 million SNPs, or a SNP every 89 bases, vastly increasing SNP density across the genome for Rice Diversity Panel-1 (RDP1) accessions. We used subsets of the RICE-RP for whole-genome association analysis and the complete set of high-density SNPs for single chromosome analysis. This allows for more detailed examination and interrogation of genomic regions found to be associated with root hair traits in rice while reducing the false-discovery rate.To take advantage of the natural diversity in ted rice , 71. We ted rice . The HDRUsing the HDRA in combination with the imputed SNP set, we identified four genomic regions associated with RHL and 14 genomic regions for RHD. The significant SNPs were not linked to genes previously identified to play a role in root hair formation. We re-phenotyped a subset of lines from the initial phenotyping panel based on differing marker profiles within a significant genomic region and discovered that these lines differed in their root epidermal patterning, with more cells elongating into root hairs. This finding indicates that genes within the target genomic region may play a seminal role in rice root epidermal patterning.JAPONICA lines had denser root hairs than INDICA lines, with tropical japonica (trj) lines having the greatest root hair density  associated with RHD (n = 514) or RHL (n = 277) or both traits (n = 14)  resulted in 805 genes being significantly -SNP located at 18,292,657 bp ) allele, and the majority (n = 55) of lines carrying the alternate allele (n = 59) were from the trj subpopulation , and trj carrying the alternate allele as part of H2, H3 or H4  include all ind (n = 72) and aus (n = 47) lines and a minority of tej (n = 4) and trj (n = 5) lines. Lines carrying the alternate allele at this SNP have longer root hairs than those carrying the reference allele , two were identified in ALL and JAPONICA (chromosomes 7 and 12), one was uniquely identified in aus (chr 1), one was uniquely identified in trj (chr 8) and regions on six chromosomes were uniquely identified in ind  is 158 kb in size and centers around the MS-SNP located at 2,035,050 bp  found only in the aus subpopulation that results in denser root hairs. A list of the genes in this region can be found in Supplemental Table ALL is 219 kb in size and centers around the MS-SNP located at 18,637,547 bp , trj (17), and ind (3) subpopulations  all belong to the ind1 subgroup. We identified six major haplotypes across the D2 region . H4 is found in only three varieties, Ming Hui, Djimoron, and SL 22–613. Of the 93 genes in this region, 44 have previously been found to be expressed in root hairs or root hair forming cells. The MS-SNP is nearest to LOC_Os01g72740, a cytochrome p450 expressed in root hairs. All genes in this region can be found in Supplemental Table The D3 ALL lines. It lies on chromosome 6 and lines carrying the alternate allele at the MS-SNP located at 1,510,956 bp have less dense root hairs , with the region on chromosome 12 also appearing in the JAPONICA-specific analysis. Region D12 is 1.1 Mb in size and is centered around the MS-SNP at 10,332,255 bp. The alternate allele is a rare, JAPONICA specific variant that results in less dense root hairs  and one from trj (KU 115). The MS-SNP is upstream of LOC_Os10g20510, a gene expressed in both root hairs and root-hair forming cells. Other genes in this region can be found in Supplemental Table ALL and JAPONICA with the MS-SNP located at 27,218,429 bp  in  H2 Fig. . Five li H2 Fig. . Haplotyind1 subgroup lines carry the alternate allele at the MS-SNP. The MS-SNP is located within LOC_Os09g37240, a glutathione S-transferase expressed in root-hair forming cells. The change in the MS-SNP from its reference identity, C, to its alternate identity, G, results in a predicted nonsynonymous mutation within exon 15 of 19, resulting in a shift from arginine to serine, and the gene contains a root-hair element cis-factor in the promoter. In the analysis using genic SNPs, this gene was not significantly associated with RHD. Other genes in this region include LOC_Os09g37230, a kinase expressed in hairs and cells, LOC_Os09g37200, a transferase expressed in hairs and cells, LOC_Os09g37100, a phospholipase expressed in hairs and cells, and LOC_Os09g37080, also expressed in hairs and cells. Lines carrying the alternate haplotype were Ming Hui, SL 22–613, Djimoron, Tadukan, Seratoes Hari, Co 18, Sigadis, Chiem Chanh, and Slo 17.Region D11 is centered around the MS-SNP located at 21,513,108 bp on chromosome 9   in Arabidopsis, which has a different cell differentiation pattern than the grasses, making the conservation of mechanisms unlikely. To investigate the relationship between genes identified in this study and those identified previously, we first compared our list of candidate genes within significant GWA regions with a list of genes previously identified as having a role in root hair formation in rice  in their putative promoter regions , than the sparse lines  . Using the SNP identities in Fig. To further investigate the mechanism by which genomic regions identified in this study modulate RHD, we re-phenotyped a subset of  12 Fig. . Three o 12 Fig.  to threean) Fig. . We alsoan) Fig.  and obseArabidopsis, a Brassicaceae species, which has a root hair patterning mechanism that differs greatly from other plant families, especially grasses. Here, we used natural variation and linkage analysis to identify genomic regions and candidate genes potentially related to root hair length and density in rice. Many of the candidate genes that we discovered have not been previously identified as playing a role in root hair formation or growth. We present a condensed list of 43 candidate genes in Supplemental Table cis-acting RHE in their putative promoter sequence. We include LOC_Os09g37240 in this list due to the SNP-level evidence described above.Root hairs have been studied as both a target trait for crop improvement and as a system to understand cellular patterning, differentiation, and growth. Our understanding of root hair development is largely limited to Arabidopsis is largely controlled through a series of transcriptional feedback loops, resulting in the phenotype of hair and non-hair cells [Arabidopsis. It is unknown whether these same negative feedback loops control epidermal cell patterning in rice, and whether genes expressed in root-hair forming cells result in root hair formation or if genes expressed in non-hair cells repress the formation of root hairs. Recent work has shown that less severe mutations of a key player in cell differentiation, TTG1, results in significantly altered root hair patterning of the Arabidopsis epidermis [We integrated previous studies , 74 profir cells , 77–81. pidermis , suggestpidermis , 84; howpidermis  and our Arabidopsis ortholog of this gene, AT2G32730, RPN2a, is differentially phosphorylated under auxin application, indicating a possible role in root hair growth [Previous research based on biparental mapping (Aus276 x IR64) identified a region on chromosome 8 that was significantly associated with root hair length, measured via a rank-scoring method in field-grown plants . Plants r growth . This suindica subpopulation and six from the diversity panel as a whole (ALL). Two of the regions identified in ALL were also significant in the clade-level JAPONICA analysis. The ind lines carrying the largest number of favorable alleles were from diverse geographic regions. Ming Hui (China) and Sigadis (Indonesia) are classified as belonging to the ind2 subgroup and Co18 (India), RTS4 (Vietnam), and SL 22–613 (Sierra Leone) are ind3 lines [Of the 14 genomic regions associated with RHD in this study, six were identified based on variation in the d3 lines .Arabidopsis glutathione S-transferases have been shown to be up-regulated by ethylene and play a role in early epidermal patterning [cis element in their promoter region. LOC_Os12g18880 is OsMAGO2, which is involved in multiple developmental pathways [Arabidopsis plants with a mutation in a Mago ortholog have altered root hair differentiation patterns [OsUPL2, encodes a HECT-domain E3 ubiquitin ligase and has been shown to be related to panicle size and grain number [We identified LOC_Os09g37240, a glutathione S-transferase expressed in root-hair forming cells in region D11 with a non-synonymous mutation that may alter protein function that is expressed in root-hair forming cells and contains a RHE in its putative promoter region. This gene, also known as Os09g0544400, has been previously shown to be associated with rice grain weight , though tterning , lendingpatterns . LOC_Os1n number . Though n number .In this study, we present a list of genomic regions and candidate genes for two quantitative traits, root hair length and root hair density. These traits have complex and independent genetic control in rice. Many of the genes we present as candidates have not been previously identified as having a relation to root hair growth. Exploiting natural variation, complemented by other studies using induced mutations, provides us with new insight into mechanisms that may control root hair formation. Root hairs are both critical to plant performance and serve as a basic model for understanding cellular patterning and tip growth. The genes and genomic regions presented here can be productively investigated as targets for selection, to further our knowledge about root hair growth and grasses, and to deepen our understanding of basic plant biology.A selection of 314 lines of the Rice Diversity Panel-1 , plants were harvested. Three nodal roots from each plant were stored in 70% ethanol after cleaning. For observation, three root segments 5–10 cm from the root tip were stained with 0.5% toluidine blue in water and imaged at 40 × magnification . Root hair length and density were measured in separate images in ImageJ . Length Broad sense heritability, or repeatability, was calculated as previously described  using thwhere σ2G represents genetic variance, σ2e represents residual variance, and r is the number of replicates. Genetic coefficients of variation (GCV) for each root trait were calculated as:where μ is the population trait mean.ALL)  > 0.05 in each subpopulation, as well as a count of at least three minor alleles and 30% maximum missing data, were included. When the analysis was run across all 314 lines in the panel, SNPs present at MAF > 0.05 in individual subpopulations were combined and three additional PC covariates were added to the model. When analyses were run on individual subpopulations , no covariates were used in the model. When analysis was run on the JAPONCIA clade, one covariate was used. Analysis was completed using the “RGWAS.normal” function of the RAINBOWR package with a genomic relationship matrix created using the A.mat method and mixed models residuals implemented through obtaining the zeta values through the “modify.data.res” function [First, we used the HDRA SNP set obtained from function .For our second analysis, we conducted LD-block genome-wide level analysis with the HDRA data. We created linkage disequilibrium (LD) blocks in PLINK (v 1.9) using the “—blocks” command and default values. These blocks were then used with the “RGWAS.multisnp” command from RAINBOWR with a Gaussian kernel, an additive test-effect, and a score-based test method.https://snp-seek.irri.org/download.zul . For the third analysis, we created six, non-orthogonal smaller genome-wide datasets that were less computationally taxing and less likely to result in false-positive discoveries. SNPs were pruned in PLINK1.9 using both the indep and indep-pairwise functions using window sizes, step sizes, and variance inflation factor (VIF) thresholds or r2 thresholds (used by indep-pairwise) of 50, 5, 2 (VIF), 0.5 (r2); 50, 5, 1.5 (VIF), 0.2 (r2); 25, 5, 2 (VIF), 0.5 (r2). This resulted in six SNP sets, ranging in size from 705,712 to 1,189,943 SNPs. We then completed genome-wide association analysis using the “RGWAS.normal” function with the same MAF and missing parameters as the HDRA SNPs.The third, fourth, and fifth analyses used the RICE-RP SNP data, obtained from The fourth analysis used all RICE-RP SNPs from a single chromosome. Analysis was run using the same MAF and missingness parameters as whole-genome analysis.Finally, for the fifth analysis we analyzed association on a gene-block level . SNPs wip < 0.0001 threshold [th percentile cutoff implemented as the critical r2 value. Haplotypes were constructed by using the identity of each SNP in the Nipponbare reference genome. Blocks were examined visually and representative SNPs from each region were used to create the representative haplotypes presented here. Only SNPs with less than 10% of lines carrying a heterozygous or missed read were used, and lines with heterozygosity at the selected SNPs were excluded from further analysis.Genomic regions associated with root hair phenotypes were identified as regions with three or more SNPs within 200 kb of each other and significant below the hreshold . Regionshreshold , and addhreshold ). Local hreshold  with a 9Arabidopsis homology was determined using the orthologous groups generated by the Rice Genome Annotation Project (http://rice.uga.edu/annotation_pseudo_apk.shtml).Lists of known root hair genes in rice were constructed from the literature , 99–108 To determine if cell size was influencing root hair density, a selection of lines  were grown in conical containers  until the third leaf stage (approximately three weeks of growth). The medium consisted of 40% sand, 40% vermiculite, 15% soil ), and 5% perlite. Plants were fertigated with Yoshida nutrient solution and grown in the same greenhouses as previously described.At harvest, three nodal roots were collected from each plant and stored in 70% ethanol. A segment 5–10 cm from the root tip was isolated from each root and stained with 0.05% toluidine blue. A collection of z-stack images (minimum eight images per sample) was collected on a Nikon Diaphot at 200 × equipped with a digital camera . Three roots per plant were imaged from four plants per genotype. As many cells as could be detected from the center of the root  were measured and designated as hair and non-hair cells. Image stacks were reconstructed in ImageJ  and haircis-acting elements in the putative promoters (2 kb upstream from the transcription start site) known as root hair elements (RHE) by downloading FASTA promoter sequences from the Rice Genome Hub (https://shinyapps.southgreen.fr/app/downloadgenesequences). We queried the 16 and 17 bp long sequences previously identified as RHEs using the Find Individual Motif Occurrences (FIMO) tool of the MEME suite (http://meme-suite.org/tools/fimo) [We queried our generated list of candidate genes for ls/fimo) . The seqls/fimo) , which ils/fimo) .Additional file 1: Supplemental Tables 1-7. Supplemental Table 1. Genotypes and mean phenotypes used in this study. Supplemental Table 2: broad-sense heritability (h2) and genotypic coefficient of variance by subpopulation for RHL and RHD. Supplemental Table 3: Genes significantly associated with RHD and RHL using gene-block chromosome-wide analysis. Supplemental Table 4: All genes within significant genomic regions as identified by GWAS. Supplemental Table 5: Genes previously identified to play a role in rice RH formation or growth. Supplemental Table 6: Genes with RHE (cis-acting root hair elements) in the putative promoter region. Supplemental Table 7: Condensed list of candidate genes.Additional file 2: Supplemental Figures 1-32. Supplemental Figure 1. Genome- wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for ALL RHD. Supplemental Figure 2.ALL RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 3. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for ALL RHL. Supplemental Figure 4.ALL RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 5. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for aus RHD. Supplemental Figure 6.aus RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 7. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for aus RHL. Supplemental Figure 8.aus RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 9. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for ind RHD. Supplemental Figure 10.ind RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gen blocks. Supplemental Figure 11. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for ind RHL. Supplemental Figure 12.ind RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 13. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for JAPONICA RHD. Supplemental Figure 14.JAPONICA RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 15. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for JAPONICA RHL. Supplemental Figure 16.JAPONICA RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 17. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for tej RHD. Supplemental Figure 18.tej RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 19. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for tej RHL. Supplemental Figure 20.tej RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 21. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for trj RHD. Supplemental Figure 22.trj RHD chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 23. Genome-wide association analysis using the HDRA SNPs, HDRA LD blocks, or pruned sets of the RICE-RP SNPs (p1-p6) for trj RHD. Supplemental Figure 24.trj RHL chromosome-wide association analysis using the RICE-RP SNPs or the SNPs collapsed into gene blocks. Supplemental Figure 25. Haplotype exploration of the region on chromosome 5 associated with RHL in ALL. Supplemental Figure 26. Region D4 from ind for RHD on chromosome 5 lacks support across multiple association runs. Supplemental Figure 27. Region D7 associated with RHD from ALL and JAPONICA lines. Supplemental Figure 28. Region D9 associated with RHD in trj on chromosome 8. Supplemental Figure 29. Region D10 associated with RHD in ALL on chromosome 9. Supplemental Figure 30. Haplotype exploration of the region on chromosome 1 associated with RHD in aus. Supplemental Figure 31. Haplotype exploration of the region on chromosome 10 associated with RHD in ALL and JAPONICA. Supplemental Figure 32. Haplotype exploration of region D14 on chromosome 12 associated with RHD in ALL and JAPONICA.


Mouse substrains are an invaluable model for understanding disease. We compared C57BL/6J, which is the most commonly used inbred mouse strain, with eight C57BL/6 and five C57BL/10 closely related inbred substrains. Whole-genome sequencing and RNA-sequencing analysis yielded 352,631 SNPs, 109,096 indels, 150,344 short tandem repeats (STRs), 3,425 structural variants (SVs), and 2,826 differentially expressed genes (DE genes) among these 14 strains; 312,981 SNPs (89%) distinguished the B6 and B10 lineages. These SNPs were clustered into 28 short segments that are likely due to introgressed haplotypes rather than new mutations. Outside of these introgressed regions, we identified 53 SVs, protein-truncating SNPs, and frameshifting indels that were associated with DE genes. Our results can be used for both forward and reverse genetic approaches and illustrate how introgression and mutational processes give rise to differences among these widely used inbred substrains.  •Detection of numerous polymorphisms among 14 inbred C57BL substrains•A subset of regions with dense polymorphisms is not due to new mutations•2,826 genes showed significant differential expression among the C57BL substrains•Association of differentially expressed genes with likely causal variants Mortazavi et al. identify polymorphisms  and gene expression differences among 14 inbred C57BL substrains. Genomic regions with dense polymorphisms between substrains are consistent with differential fixation or introgression rather than new mutations. They highlight significant associations between genomic variants and gene expression. Little generated the C57BL/6 inbred strain a century ago, the C57BL/6J has become the most commonly used inbred mouse strain. Closely related C57BL/10 substrains,Genetic differences between closely related laboratory strains have been assumed to be the result of accumulated spontaneous mutations.,Numerous studies have reported phenotypic differences among various C57BL/6- and C57BL/10-derived substrains, which are likely attributable to genetic variation. For C57BL/6 substrains, these differences include learning behavior,,−5 mutations per locus per generation,−8),,Whole-genome sequencing (WGS) technology provides a deep characterization of single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), short tandem repeats (STRs), and structural variations (SVs). SNPs that differentiate a few of the C57BL/6 substrains have been previously reported.cis-regulatory variants, making it possible to narrow the number of potentially causal mutations without requiring the creation of intercrosses.In an effort to create such a resource, we performed WGS in a single male individual from nine C57BL/6 and five C57BL/10 substrains (∼30× per substrain) and called SNPs, indels, STRs, and SVs. In addition, to identify functional consequences of these polymorphisms, we performed RNA sequencing (RNA-seq) of the hippocampal transcriptome in 6–11 male mice from each substrain, which allowed us to identify genes that are differentially expressed A. This aProcessing of WGS data from the 14 C57BL substrains  allowed RNA-seq analysis on 106 hippocampal samples identified 16,400 expressed genes and 2,826 differentially expressed genes  in C57BL/6 and C57BL/10 substrains .RNA-seq data were also used to validate WGS SNPs and indels in protein-coding regions of the mouse genome see . We obseDespite the lower sequencing depth on the X chromosome that was expected for male mice, we did not observe a difference in the density of private SNPs discovered on chromosome X compared with the autosomes (7.2 SNPs/Mb on X versus average 7.5 SNPs/Mb on the autosomes). However, the validation rate of coding SNPs on chromosome X was lower than on the autosomes (89% for chromosome X versus 97% on the autosomes).We also detected 582,795 heterozygous SNP calls. For 92% of these heterozygous calls, all samples showed heterozygous genotypes, which suggests that most reflect segmental duplications (SegDups) rather than true heterozygous SNPs. Consistent with this idea, many apparently heterozygous SNPs were in known segmental duplication or tandem repeat regions (45%). For the remaining apparently heterozygous SNPs, we obtained a very low validation rate when using our RNA-seq data .39http://www.informatics.jax.org/snp), which further suggests that they are not due to new mutations in the C57BL/10 lineage. We used the MGI database to identify strains that were similar to these 28 segments. No single strain matched all 28 segments. However, domesticus origin; however, two large segments on chromosomes 4 and 11 showed apparent musculus origin. We checked the subspecific origin of these 24 regions in C57BL/10J, C57BL/10ScNJ, and C57BL/10ScSnJ reported in Yang et al.musculus origins in the MGI database) showed musculus origins as well. Among the remaining 22 segments, all but three showed domesticus origins, and three segments (two on chromosome 4 and one on chromosome 11) showed some evidence of musculus origin.The distribution of variants across the genome is shown in Additionally, we identified 9,218 SNPs  in which none of the C57BL/6 or C57BL/10 substrains matched the reference genome (mm10). One explanation is that some of these SNPs represent errors in mm10, perhaps related to the use of bacterial artificial chromosomes or other technical issues.cis) polymorphisms.We found that 2,826 of 16,400 expressed genes (17.2%) were differentially expressed among the 14 substrains (FDR 0.05). We refer to these as DE genes. We assumed that many of the DE genes were due to local (cis-window (1 Mb upstream of gene start and 1 Mb downstream of gene end) for association with the corresponding DE gene. Specifically, we tested the association between the cis-variants and the median of DE gene expression by a linear regression test, using Limix.In order to identify genomic variants that might be causally related differential gene expression, we tested all identified variants  in the Kcnc2, which had significantly reduced expression in C57BL/6JEiJ  using Limix.For each category, the p values obtained by the LMM are corrected by the Benjamini-Hochberg procedure to obtain a FDR. We identified 53 significant (FDR < 0.05) associations between DE genes and features, which are reported in cis-regulatory elements of genes. For example, there are three copies of the gene Srp54 in the mouse reference genome, but we found significant variability in the number of copies across the substrains; the number of copies was strongly associated with expression of Srp54 (Lpp gene. The Lpp gene has a tandem duplication of the first two exons in two substrains (C57BL/10ScCr and C57BL/10ScNHsd) that creates two copies of the TSS, which probably accounts for its ∼2-fold increased expression (SVs can affect gene function by (1) varying the dosage of a full-length gene, (2) deleting or inserting exons producing alternative isoforms of a gene, or (3) rearranging the of Srp54 A. Thus, pression B.Figure Wdfy1 gene. This gene has a tandem duplication of exons 4–6 in the C57BL/6J substrain, which is also present in the mm10 reference genome. We found that this duplication is associated with a paradoxical decrease in Wdfy1 gene expression  C57BL/6J embryonic stem (ES) cells showed that Wdfy1 expression was increased ∼2-fold compared with control (sibling) C57BL/6J ES cells . Unexpectedly, many of the polymorphisms that differentiated the C57BL/6 and C57BL/10 substrains were concentrated in a few haplotypes, comprising just 5% of the genome. These polymorphisms appear to be due to either introgression of an unrelated individual or incomplete inbreeding at the time that the C57BL/6 and C57BL/10 lineages diverged. Setting these introgressed regions aside, we tried to identify variants that were causally related to differential gene expression by focusing on the dels see . Inflatidomesticus origin and were similar to haplotypes found in multiple non-C57BL inbred strains. The two largest haplotypes appeared to be of musculus origin and were also similar to multiple non-C57BL inbred strains. The exact sequence of events that led to this situation is impossible to deduce, but these patterns are clearly due to breeding events  rather than spontaneous mutations; this conclusion is based on several observations: (1) the density of the polymorphisms, (2) the abrupt boundaries of the regions/haplotypes, and (3) the fact that the SNPs in these introgressed regions are found in other inbred strains, which would not be the case if they were due to spontaneous mutations. A previous microarray study performed on 198 inbred mouse strains also identified SNP differences between C57BL/6J and three C57BL/10 substrains  for all the 28 introgressed segments that we identified;,An unexpectedly large subset of variants  was concentrated in 28 highly diverged haplotypes that were present in all C57BL/10 strains and represented just 5% of the genome. These dense clusters of genetic variation (1 SNP/425 bp) perfectly differentiated C57BL/10 from C57BL/6, and likely reflect introgression from another strain. Intriguingly, the smaller haplotypes appeared to be of cis) variants and expression of DE genes. Variable copy number segmental duplicated regions were shown to be highly enriched for significant associations, as were genic SVs and loss-of-function SNP/indels . These SNPs are apparently due to the accumulation of new mutations, and their identification was the original goal of our study. Dendrograms based on these SNPs recapitulated the historically recorded relationships among the substrains B. For thP/indels C.Srp54 ; mice that are homozygous for C57BL/6J allele are more active in the open field test. Whether Wdfy1 is actually the cause of either association cannot be resolved by our study.We presented several examples to highlight how different classes of variants underlie DE genes. For example, variable copy number segmental duplications led to both increased and decreased expression of Srp54 A. In anop54 , the large fraction of differentially expressed genes  could indicate that many DE genes are due to trans-eQTLs. Producing crosses between pairs of strains will be necessary to address the relative importance of cis- versus trans-eQTLs in the observed DE genes; it is possible that such crosses could identify one or more major trans-regulatory hot spots.Despite the numerous examples in which likely causal variants were identified, a majority of the causal variants underlying DE genes remain unknown. Many are likely to be due to variants in regulatory regions that have not been distinguished from other nearby variants with the same strain distribution pattern . Although we focused on the possibility that DE genes were due to nearby variants , another novel application of our dataset would be to select two strains that are divergent for a coding or expression difference and to use that cross to study gene function. This reverse genetic approach  has not been attempted using closely related substrains but is conceptually similar to characterization of a knockout mouse. This approach is limited by the available polymorphisms. Although it would be necessary to account for the impact of linked polymorphisms, most of the polymorphisms would be unlinked and would not confound the interpretation of results.In summary, we have created a dataset that elucidates the differences among C57BL strains and can be used for both forward genetic (RCC) and reverse genetic approaches. We identify previously unknown introgressed segments that differentiate the C57BL/6 and C57BL/10 lineages. Our results can also be used to explore mutational processes and highlight the tendency of inbred strains to change over time due to the accumulation of new mutations and genetic drift.cis-eQTLs, which limited out search space to nearby variants; however, it is possible that some of the DE genes were caused by distant polymorphisms (trans-eQTLs). Even after assuming that the causal variant was due to a cis-eQTL, there were typically multiple candidates, meaning that only variants with strong priors  could be confidently associated with a given DE gene. As a result, we could not confidently determine which variant was causal for the majority of DE genes. Addressing this problem would have required crosses, and even then, linkage disequilibrium would have made it very difficult to distinguish between putative cis-eQTL variants.This study has several limitations. First, only a single mouse per substrain was used for the WGS, meaning that some of the reported variants may not be fixed within a given substrain, and other unfixed mutations may not have been detected because they were heterozygous or not present in the single individual used for WGS. Because the RNA-seq used multiple individuals, some of the DE genes might have been due to these unfixed mutations, but the causal alleles for such DE genes would not have been detected by our analyses. Second, we performed RNA-seq for only a single tissue (hippocampus). This means that eQTLs that are specific to certain tissues, specific to less common cell types, or specific to certain developmental time points or environmental conditions or eQTLs with modest effect sizes were not detected. Third, we did not use long-read sequencing, which would have improved our ability to detect SVs. Another limitation of this study is that we used only male mice; therefore, sex-specific DE genes may have been missed. The female littermates were used for behavioral studies that we hope to publish in the future. Finally, because we did not perform any crosses, our association analyses implicated all of the variants that had the same strain distribution pattern as the DE gene in question. We assumed that most DE genes were due to aap@ucsd.edu).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Abraham A. Palmer  was extracted from spleens using a standard “salting-out” protocol. Sequencing libraries were prepared using a TruSeq DNA LT kit, as per the manufacturer's instructions. Subsequently, sequencing data was generated by Novogene at an average depth of ∼30x coverage on an Illumina HiSeq X Ten (paired-end 150bp) .Total RNA was extracted from 110 hippocampal samples using Trizol reagent . RNA was treated with Dnase (Invitrogen) and purified using Rneasy columns . RNA-sequencing library prep and sequencing was performed by the University of California San Diego Sequencing Core using Illumina TruSeq prep and Illumina HiSeq 4,000 machine  pathway, we performed RNA-Seq on mouse embryonic stem cells (mESCs) from a Upf2−/− strain of C57BL/6J that has impaired NMD and control mouse mESCs from C57BL/6J. Samples with an RNA integrity index of >8  were used for RNA-Seq analysis. The University of California San Diego Sequencing Core performed library preparation using ribosomal RNA depletion protocol followed by paired-end sequencing (100 cycles) using a HiSeq4000.To determine whether SVs of the Reads were mapped to mouse reference transcriptome (mm10) using the splice-aware alignment software HiSat2,anova function in R, and adjusted the p values by computing the false-discovery rate (FDR) using the p.adjust function in R, with the Benjamini-Hochberg procedure. We obtained 2,826 DE genes among C57BL/6 and C57BL/10 substrains combined, 1,210 DE genes within C57BL/6, and 104 DE genes within C57BL/10 substrains with FDR<0.05.To identify Differentially Expressed Genes (DE genes) we performed analysis of variance using the Upf2−/− samples and three controls from the NMD assay were mapped to the mouse reference genome (mm10) by HiSat2,edgeRR, however, we only analyzed Wdfy1 expression in an effort to detect differences in NMD between Upf2−/− and control samples.Reads from three replicates of We used SpeedSeqIn each substrain, we validated a subset of WGS variants which were in the protein coding regions of the mouse genome (exons and UTRs obtained from Ensembl annotations), and had at least 3× coverage in our RNA-Seq data .Genotypes from the WGS data in the protein coding regions were compared with variants detected in RNA-Seq data, and validation rates for SNPs and indels as well as different categories of variants including C57BL/10-specific, singleton and monomorphic variants were shown in In order to validate the 183 (58 SNPs and 125 indels) predicted loss-of-function variants by VEP,dist and plotting the dendrograms with hclust in R v3.6.1.When computing the identity-by-state (IBS) matrix for dendrograms, we LD-pruned the SNP panel with Plinkhttps://github.com/HipSTR-Tool/HipSTR-references). The reference STR set was generated using Tandem Repeats FinderWe used HipSTR v0.6 with default parametersSVs were detected using a combination of approaches. First, we called SVs with LUMPYWe filtered out SV calls that overlapped 50% or more with the gap regions of the mouse reference genome, as well as the calls with length smaller than 50 bp and larger than 1 Mbp. A more stringent >1,000 bp length filter was applied to CNVnator calls. We then filtered out non-homozygous calls and calls that were homozygous for the alternative allele in all substrains.Concordant calls from LUMPY and CNVnator with 50% or greater reciprocal overlap and the same genotypes were merged and the breakpoints reported by LUMPY were used. Consensus calls that overlapped with annotated segmental duplications (SegDup) in the reference genome were excluded, and instead SegDup copy number was assessed directly from read depth signal using mosdepth v0.2.6The details regarding association analyses are presented in the results sections as well as in the figure legends, and the software information is presented in the key resources table. p-value of 0.05 after correction for multiple testing (Benjamini-Hochberg procedure) was used to identify significant associations. The t-test and Chi-squared tests in