ONAGE birth cohort, telomere length and whole-genome DNA methylation were assessed in cord blood and 26 candidate single nucleotide polymorphism related to ageing or telomere length were genotyped. We identified three genetic variants associated with cord blood telomere length and 57 cis methylation quantitative trait loci (cis-mQTLs) of which 22 mQTLs confirmed previous findings and 35 were newly identified. Five SNPs were found to have significant indirect effects on cord blood telomere length via the mediating CpGs. The association between rs911874 (SOD2) and newborn telomere length was modified by nearby DNA methylation indicated by a significant statistical interaction. Our results suggest that DNA methylation in cis might have a mediation or modification effect on the genetic difference in newborn telomere length. This novel approach warrants future follow-up studies that are needed to further confirm and extend these findings.Telomere length at birth determines later life telomere length and potentially predicts ageing-related diseases. However, the genetic and epigenetic settings of telomere length in newborns have not been analyzed. In addition, no study yet has reported how the interplay between genetic variants and genome-wide cytosine methylation explains the variation in early-life telomere length. In this study based on 281 mother-newborn pairs from the ENVIR Telomeres are ribonucleoprotein complexes that cap the end of eukaryotic chromosomes and function to protect genomic stability. In normal somatic cells, telomere length declines with cell divisions until it reaches a critical length, which triggers cellular senescence and finally leads to subsequent programmed cell death. Therefore, telomere length determines the replicative capacity and the lifespan of a cell . At the The genetic determinants of adult telomere length have been widely investigated. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) related to telomere length  and lifeON early AGEing (ENVIRONAGE), we aimed to investigate the combined genetic-epigenetic regulation of newborn telomere length, assessing biological ageing-related SNPs and DNA methylation. The following hypotheses were tested: 1) genetic variants in adult biological ageing-related genes are associated with newborn telomere length, 2) these genetic variants affect the DNA methylation within the same or nearby genes, which mediate the genetic association with newborn telomere length, and 3) the association between a genetic variant and newborn telomere length is modified by DNA methylation levels in nearby regions.In this study, within a subset of the ongoing prospective birth cohort, ENVIRonmental influence ONAGE birth cohort . Samples were centrifuged at 3,200rpm for 15 min. Plasma was removed and the remainder of the samples with the buffy coats was stored at -80 °C until further analysis.3) were taken at the fetal side directly underneath the chorioamniotic membrane, at approximately 4 cm from the umbilical cord. Contamination by chorioamniotic membrane was avoided by visual examination and dissection. Histological examination confirmed that the fetal placental biopsies mainly contained cytotrophoblasts and syncytiotrophoblast differentiated from trophoblasts.Fetal placental biopsies . DNA purity and concentration were verified using a Nanodrop 1,000 spectrophotometer . DNA was considered pure when the A260/280 was greater than 1.80 and A260/230 was greater than 2.0. DNA integrity was assessed with agarose gel electrophoresis.a priori for genotyping in placental DNA. SNP genotyping was conducted using the Biotrove OpenArray SNP Genotyping Platform at the Dana Farber/Harvard Cancer Center Genotyping and Genetics for Population Sciences Facility. After excluding SNPs with a SNP call rate lower than 95% and samples with a sample call rate lower than 90%, genotype imputation was performed with R package statgenGWAS (version 1.0.7.1) . SNP genotype frequency data can be found in the European Variation Archive with accession numbers PRJEB53351 (project) and ERZ11081188 .Sixteen SNPs reported to be related to telomere length in previous GWAS  and 18 S1.0.7.1)  based on1.0.7.1) . The genONAGE. Array measurements were scanned using an Illumina iScan and the data quality was assessed using the R script MethylAid. DNA methylation data were preprocessed using the minfi package (version 1.38.0) in R , at the service lab GenomeScan , for 377 cord blood samples from ENVIR.0) in R . Brieflyn 10e-16 . One samn 10e-16 . Methylan 10e-16 . For eacn 10e-16 . Preprocn 10e-16 . Subsequn 10e-16 . 787,264+ T cells, and CD8+ T cells) in the cord blood samples were estimated using Bakulski algorithm  protocol . Detailea priori selected covariates: newborn’s sex, gestational age, ethnicity and birth weight, maternal pregnancy complications, pre-pregnancy BMI, parity, education level and smoking status and paternal age, as well as cell type heterogeneity estimated from the DNA methylation data.The statistical analyses workflow is shown in p-value lower than 0.05.In the first step, cord blood telomere length was regressed on each SNP in a multiple regression model. Dominant coding was applied, where the genotype at each SNP was classified into either of the two categories: major homozygote and heterozygote/minor homozygote. To ensure robustness of the results, we additionally applied an additive coding based on the number of minor alleles, which is a commonly used SNP coding method in the literature . Statistwww.mqtldb.org), specifying the timepoint as “newborns” and the distance defining cis-mQTLs as 500 kb Each of the selected 26 SNPs was analyzed against CpG sites available in cis (within ±0.5 Mb from the SNP), by fitting multiple regression models of the M-value of each CpG on the dominant-coded or additive-coded SNPs. The R package limma (version 3.48.1) was used for model fitting and inference. Statistical significance was determined under multiple testing control by Bonferroni correction at a level of 0.05. The identified significant SNP-CpG pairs were compared with a published methylation quantitative trait loci (mQTL) database  genotypes determined at the very beginning of life precede both the change in DNA methylation and the shortening of telomere length, and 2) genotypes are less likely to be changed than DNA methylation and telomere length shortening, which are regulated through more complex pathways. Therefore, it was assumed that the genetic variants at a certain SNP altered the methylation level at certain CpGs, which subsequently led to a change in newborn telomere length. Mediation analyses were performed with the R package mediation (version 4.5.0). cis-interaction between CpG and SNP was assessed by including a statistical interaction term (SNP*CpG) in the model of cord blood telomere length. The model is stated as:The Characteristics of the study population, comprising 281 mother-newborn pairs, are summarized in OBFC1), rs9420907 (OBFC1) and rs17653722 (KRT80), had estimates significantly different from zero in the dominant coding model. Rs9419958 (OBFC1) was confirmed in the additive coding model, while the other two SNPs, rs9420907 (OBFC1) and rs17653722 (KRT80), were only borderline-significant under additive coding.The model estimates under dominant coding and additive coding, respectively, are shown in r2 > 0.5), such as rs1475398 (LEPR), rs10493379 (LEPR) and rs1343981 (LEPR), rs9419958 (OBFC1) and rs9420907 (OBFC1), behaved consistently in their association with cord blood telomere length in terms of the effect size, direction and effect significance.The 26 SNPs were not completely independent, as suggested by RTEL1) had no CpGs within the 0.5 Mb neighborhood. In total, the association was tested on 12,194 SNP-CpG pairs. There were 57 cis-mQTL identified from both the dominant models and the additive models under Bonferroni correction (http://www.mqtldb.org/), (GAPDH) and cg07142400 , which confirmed the findings in mQTLdb, where one more copy of the minor allele G at rs4764600 was associated with more than 0.50 increase in the M-value at cg07142400 . Most of the SNPs were associated with multiple CpGs that were not located in the same gene. SNP rs3027234 (CTC1) had the largest number of associated CpGs and the involved twelve CpGs mapped to seven genes. The SNP with the second largest number of associated CpGs was rs4764600 (GAPDH), with the nine CpGs distributed over five genes. On the other hand, five CpGs were associated with more than one SNP in other genes and all the other CpGs were only associated with one SNP.The number of CpGs tested on each SNP is summarized in rrection . Matchinb.org/),  22 out oDCAF4), the direct and indirect effects were in the same direction and the proportion mediated was 68%  and cg24223887 (gene unknown) under Bonferroni correction , indicating that the higher the methylation level at cg24223887 (gene unknown), the weaker the association between the number of A allele at rs911847 (SOD2) and cord blood telomere length. Within the same model, the SNP rs911874 (SOD2) main effect was significant under the Bonferroni correction for SNP main effects, which has not been identified in the candidate SNP association analysis without SNP-CpG interactions  increase in cord blood telomere length. The CpG main effect of cg24223887 was nominally significant .A SNP-CpG interaction effect was identified between rs911874 , rs10936599 (TERC) and rs755017 (RTEL1)) (OBFC1 (rs9419958 and rs9420907) and in KRT80 (rs17653722) were found nominally significantly associated with cord blood telomere length, where the associations for the two SNPs in OBFC1 gene were robust across coding methods and were of the same effect direction as the published GWAS’s  was found strongly associated with cord blood telomere length, while in the discovery study it was associated with age at death and a skeletal marker of biological age ) . This mid GWAS’s . The OBFongation . KRT80-epithelia . Conditiical age . SOD2 enn beings .cis-mQTL for the candidate SNPs and all available nearby CpGs. Only cis-acting mQTL and interactions were investigated since the trans-interplay explains much less variation and tends to be polygenic (CXCR4), rs11125529 (ACYP2), rs511744 (SIRT3) and rs17653722 (KRT80), were significantly associated with one CpG each, and all the other SNPs were associated with multiple CpGs. While rs3027234 (CTC1) was the mQTL with the largest number of CpGs, the SNP with the largest number of CpGs in the 500 kb neighborhood was rs40184 (DAT1). This might indicate that the presence of mQTL is not randomly distributed across the genome, but is enriched within a certain set of hub SNPs.We assessed olygenic . Part ofolygenic  where thSIRT5)-cg01064902 (SIRT5) and rs412658 (ZNF676)-cg22620746 (ZNF257) confirmed the findings reported in mQTLdb. However, these SNP-CpG pairs have not been reported yet as being involved in a causal relationship with any diseases or phenotypes.We found that DNA methylation at specific CpGs mediated the genetic association with newborn telomere length. All indirect effects were nominally significant, which is logical because the CpGs used to construct the mediation model were involved in mQTLs and associated with cord blood telomere length at the same time . None of the SNPs in the mediation model was associated with cord blood telomere length in the candidate gene association analysis, and neither were they when conditioned on the mediation by a CpG. Therefore, no SNP had a significant direct effect. Among the five SNP-CpG pairs used in the mediation analysis, rs2841505 (SOD2) and cg24223887 (gene unknown) (results not shown). Published epigenome-wide studies have reported DNA methylation modulating the genetic impact on cardiovascular disease-related traits . Thirdly, the SNP selection was only based on genome-wide association studies, while it could be more precise and sensible to select targets based on genes that are well studied and known to be relevant for telomere length, such as telomerase or shelterin genes as suggested in the Telomerase Database . We partly confirmed telomere length- or ageing-related genetic variants and mQTLs in newborns identified in previous studies. Our results suggest that DNA methylation might alter the effect of genetic variants on newborn telomere length, and identified DNA methylation loci that might be crucial targets in future investigations of telomere biology. Potential mediating effects by DNA methylation might be present, but knowledge of biological pathways is required for a sound causal inference. Through the current study, we have shown the potential importance of investigating the interplay between different molecular levels in regulating telomere length. This novel approach should be evaluated in future large-scale studies.


This paper characterizes laser-generated guided waves in a metal-lined composite-overwrapped pressure vessel (COPV) to assess typical damage, including interfacial debonding and low-velocity impact damage. First, an eigenfrequency approach that avoids additional coding is utilized to theoretically analyze the dispersion characteristics of a COPV. The theoretical results show that interfacial debonding significantly alters dispersion curves, and the wavenumber of the L mode is sensitive to impact damage. Experimental verifications were conducted based on the full wavefield acquired using a scanning laser-ultrasonic system with a repetition rate of 1 kHz. By comparing the experimental dispersion curves with the theoretical ones, it was found that the metal-composite interface was not bonded. In addition, a local wavenumber estimation method was established to detect the impact damage by obtaining the spatial distribution of the wavenumber of the L mode. Composite-overwrapped pressure vessels, which reduce weight while improving structural performance, have been increasingly used in aerospace, automotive and other industrial fields . Three tType III COPVs are usually used for long-term hydrogen storage . AccordiUltrasonic-guided waves (UGWs) are promising for damage detection in COPVs due to their long propagation distances in hollow-cylindrical waveguides and high sensitivities to various damage . n), axisymmetric torsional mode T, and non-axisymmetric flexural mode F , and the thickness of the single layer was 0.2 mm. The elastic constants and densities of the aluminum- and carbon-fiber-reinforced polymers are given in The inner and outer diameters of the aluminum liner were 176 mm and 180 mm, respectively. The filament winding angles were of the order [90The eigenfrequency approach was implemented in COMSOL Multiphysics , a generIn the eigenfrequency approach, uin Zone  accordinThe dispersion curves are drawn in In the extreme case where the metal and the composite are not bonded at all, the guided wave propagating in the outer layer cannot enter the metal liner, and vice versa. As shown in The results show that when the COPV is debonded, the dispersion curves vary significantly, indicating that the dispersion curve can be a good choice for detecting interfacial debonding damage. Furthermore, compared to the L mode, the dispersion curves of the L mode were more sensitive when interface debonding was present. More quantitative studies on the dispersion characteristics at various degrees of interfacial debonding states are required in the future.The damage produced by low-velocity impact testing and quasi-static indentation loading can be considered identical ,31. TherAccording to the theoretical investigation, the wavenumber of the L mode is suitable for impact damage detection. This section describes the local wavenumber estimation method for impact damage imaging by evaluating the wavenumber of the L mode at each spatial point of interest. Because the laser-ultrasonic wavefield contained multiple UGW modes, the mode isolation should have been performed before estimating the local wavenumber. Three-dimensional Fourier transformation is defined as follows:x and y axes, respectively. Equation (18) converts the ultrasonic signals from the time–space region to the frequency–wavenumber domain. Here, Different filter windows regarding the wavenumber or frequency can be applied independently to the frequency–wavenumber domain signals, thus achieving mode isolation. In this paper, a two-dimensional Tukey window was used to retain the wavenumber region of interest and eliminate the rest, while a one-dimensional Gaussian window was used to filter the frequencies.The Tukey window curve is shown in The Tukey window specifies an all-pass region Here, A Gaussian-shaped frequency domain window is defined asBF is the 3 dB wavenumber bandwidth. By multiplying the Tukey and Gaussian windows, the original frequency–wavenumber domain data In The estimation of the local wavenumber for each spatial point, The generation of the space-wavenumber data kc, asThe frequency–wavenumber domain signal of one isolated mode, The inverse Fourier transform method was applied to the narrowband signal e-summed  across tFinally, the local wavenumbers were estimated by determining the wavenumber bin with the highest magnitude for each spatial point according to Equation (24).i.ii.f0, the central frequency of the mode to be isolated;iii.CL and CH, the lower and upper cutoffs in the Tukey window;iv.BM, the half-power taper bandwidth in the Tukey window;v.BF, the 3 dB frequency bandwidth in the Gaussian window;vi.BK, the 3 dB wavenumber bandwidth in the Gaussian window;vii.kc, a bank of possible wavenumber values.In summary, the key parameters in the local wavenumber method include:In this section, a metal-lined COPV cylinder is manufactured with interfacial debonding and impact damage. A laser-generated ultrasonic imaging system is used to scan the CPOV structure. The laser-ultrasonic wavefield is visualized. Dispersion curves are analyzed experimentally and compared with those calculated theoretically to evaluate the interface debonding. In addition, the impact damage is visualized based on the local wavenumber estimation method.A laser-generated imaging system was introduced ,27 for dThe scanning laser-ultrasonic system has numerous advantages, such as its non-contact, remote excitation, high repetition rate and high resolution. The first two advantages are more pronounced in curved structures, such as COPVs. The laser pulse was manipulated to scan the COPV specimen with an area of 100 mm × 200 mm at a spatial interval of 0.5 mm along both scanning axes with real-time mirror control. The scanning repetition rate was 1 kHz, allowing for a rapid scanning process of only 80 s. The laser pulse energy was carefully selected to prevent ablation on the surfaces of the COPVs. The time domain signals received by the sensor at each scanning grid were amplified with the AE9922 tool (NF Corporation) using 30 dB at a sampling rate of 5 MS/s. After a full scan, a three-dimensional wavefield data matrix The wavefield snapshots of the laser-generated guided waves in the COPV are shown in As previously stated, interfacial debonding can be assessed by studying the dispersion characteristics of the guided waves . x = 0 mm were converted to the frequency–wavenumber domain using the two-dimensional Fourier transform method [As shown in m method . Figure The theoretical dispersion curves were also plotted. It was found that the theoretical dispersion curves of the COPV with the bonded metal-composite interface were very different from the experimental spectrum. In addition, as expected, the theoretical dispersion curves of the COPV without the aluminum liner matched well with the experimental ones, demonstrating that the structure was debonded at the metal-composite interface. The slight misalignment of the dispersion curves may be attributed to the unavoidable differences between the actual materials and those listed in Based on the theoretical analysis in In the local wavenumber estimation method, the main parameters were set as follows. i.For the Tukey window to filter the wavenumber domain, ii.For the Gaussian window to filter the frequency domain, iii.For the two-dimensional Gaussian window to filter the possible local wavenumber, −1 and 187 m−1, respectively.In addition to the Tukey window, an additional treatment was applied to the wavenumber domain signal to eliminate the reflected L modes by setting the intensity value of the negative wavenumber to zero. To reduce the effect of the wave propagating beyond the axial direction, another Gaussian window was applied to the wavenumber domain with a central wavenumber of The local wavenumber estimation method carries more information because it is closely related to structural or material properties ,26. The To verify the detected depth of the impact damage, a depth gauge with an accuracy of 0.01 mm  was used to measure the depth of the crater. The average value of the multiple measurements was approximately 0.38 mm, which was very close to the result estimated with the local wavenumber method.The conventional C-scan method was utilized to verify the in-plane size of the impact damage. An ultrasonic probe with a central frequency of 5 MHz was used to generate and receive ultrasonic pulses with a sampling frequency of 100 MHz. A two-dimensional spatial drive motor system was established to accomplish the in-plane motion of the transducer. The amplitude of the first ultrasonic peak reflected from the COPV surface was collected to form the C-scan image. The boundary of the impact damage in the C-scan result was provided as black curves in This paper investigated the ultrasonic-guided wave method for the non-destructive evaluation of a filament-wound COPV with a metal liner. An eigenfrequency method was utilized for the analysis of the dispersion characteristics of the COPV without the need for additional coding. A laser-ultrasonic system was used to rapidly scan the COPV to detect the interfacial debonding and impact damage. The conclusions drawn are as follows.i. In the eigenfrequency approach, a specific unit cell of the COPV with a Floquet BC was demonstrated as feasible for analyzing the dispersion characteristics. The accuracy of the method was validated with the SAFE method. In fact, the eigenfrequency method is applicable to any type of waveguide, including pipes, plates, etc., and to both isotropic and anisotropic materials.ii. Based on the eigenfrequency method, the effects of the interfacial debonding and impact damage on the wave characteristics were investigated. The dispersion curves were suitable for the assessment of metal-composite interface debonding, and the wavenumber of the L mode was selected for impact damage detection.iii. The laser-ultrasonic wavefield contained both circumferentially and longitudinally propagating waves and exhibited multi-mode characteristics. By comparing the experimental dispersion curves with the theoretical ones, it was demonstrated that the metal and composite materials were not bonded to the metal–CFRP interface. Quantitative studies regarding the interfacial debonding state need to be carried out in the future.−1 and 187 m−1, respectively. Based on the local wavenumber map, the size and depth of the impact damage were roughly estimated. The C-scan method was used to verify that the in-plane size and depth were close to that measured using the depth gauge.iv. The impact damage was visualized with the local wavenumber estimation method, and the wavenumber of the L mode in the healthy and damaged regions were approximately 174 m


This study identified several variants and candidates with strong associations to coat color in Angus cattle by using genome sequencing data. In particular, the MC1R variants, which are a truncated MC1R protein obtained by altering the MCIR coding sequence region, result in lighter coat color and further enrich the genetic basis between DNA damage and melanin production caused. This study not only helped understand the hereditary basis of different coat colors, but also provided new ideas for future research on meat phenotypes. However, the putative candidate genes or markers identified in this study require further investigation to confirm their phenotypic causality and potential effective genetic relationships.ZCCHC14, ANKRD11, FANCA, MC1R, and LOC532875 [AFG3-like protein 1]), considerably diverged between black and red Angus cattle. The strongest associated loci, namely, missense mutation CHIR18_14705671 (c.296T > C) and frameshift mutation CHIR18_12999497 (c.310G>-), were located in MC1R. Three consecutive strongly associated SNPs were also identified and located in FANCA, which is widely involved in the Fanconi anemia pathway. Several SNPs of highly associated SNPs was notably enriched in ZCCHC14 and ANKRD11, which are related to myofiber growth and muscle development. This study provides a basis for the use of potential genetic markers to be used in future breeding programs to improve cattle selection in terms of coat color and meat phenotype. This study is also helpful to understand the hereditary basis of different coat colors and meat phenotypes. However, the putative candidate genes or markers identified in this study require further investigation to confirm their phenotypic causality and potential effective genetic relationships.Angus cattle have made remarkable contributions to the livestock industry worldwide as a commercial meat-type breed. Some evidence supported that Angus cattle with different coat colors have different feed-to-meat ratios, and the genetic basis of their coat color is inconclusive. Here, genome-wide association study was performed to investigate the genetic divergence of black and red Angus cattle with 63 public genome sequencing data. General linear model analysis was used to identify genomic regions with potential candidate variant/genes that contribute to coat color and feed conversion rate. Results showed that six single nucleotide polymorphisms (SNPs) and two insertion–deletions, which were annotated in five genes ( Angus cattle were certified in 1892 as one of the ancient British meat breeds native to Aberdeen in Northern Scotland. This breed has many excellent production performances. Its beef nutrients and meat quality occupy an irreplaceable position in the high-end beef market , fecundiThe advent of next-generation sequencing (NGS) technology has led to the confirmation of the molecular basis of a large number of phenotypes in various domestic animals . AlthougIn this study, the wide genomic divergence of Angus cattle with different coat colors was investigated by using a NGS dataset to explain the hereditary basis of coat color and help understand the genetic determinants that may cause differences in production performance.https://github.com/OpenGene/fastp (accessed on 1 June 2021)) [https://github.com/OpenGene/fastp, custom filter parameters . The HQRs were mapped to the cattle (Bos taurus) genome (ARS-UCD1.2) by using Burrows-Wheeler Aligner (version v 0.7.15) (http://bio-bwa.sourceforge.net/ (accessed on 1 June 2021)) [http://broadinstitute.github.io/picard/ (accessed on 1 June 2021)). Single nucleotide polymorphisms (SNPs) and insertion deletions (InDels) were identified and annotated by GATK Haplotype Caller [www.cog-genomics.org/plink/1.9/ (accessed on 1 June 2021)) and filtered by individuals with call rate >90% and minor allele frequency >0.99.The public sequencing data of 63 Angus cattle, including 21 red Angus and 42 black Angus, obtained by a previous study  were dowe 2021))  on the be 2021)) , and pote Caller . The fine Caller  with cushttps://cytoscape.org/ (accessed on 1 June 2021)).Coat color was divided into two categories. Red and black coats were as 1 and 2, respectively. For the genome-wide association study (GWAS), the SNP and InDel datasets were filtered by PLINK  (versionp < 4.3464 × 10−9). Among them, six SNPs were located in the exon or intron of four functional genes ), and two SNPs were distributed in the intergenic sequence (p < 0.05). Seven of these GO terms were related to pigment synthesis (FANCA and MC1R) were remarkably annotated (Corrected p < 0.05) in three known single pathways (Fanconi anemia (FA) pathway, melanogenesis, and neuroactive ligand–receptor interaction). The strongest association (SAM) with Corrected p = 5.461 × 10−15 was identified in CHIR18_14,705,671 (c.296T>C) in MC1R. In addition, two InDels (CHIR18_12999497 and CHIR18_14705684) located in MC1R and ZCCHC14, respectively, exceeded the Bonferroni significant threshold (p < 3.40316 × 10−8). These couple genes were remarkably enriched (Corrected p < 0.05) in 13 GO terms and two single pathways  to be replaced by proline (E+-type) at the 99th amino acid residue. Moreover, the ED-type MC1R individual remains sensitive and produces eumelanin [+/E+ homozygous, and the ED gene frequency of black Angus was 0.9125 at the c.296T > C locus  . In the n humans ,29.FANCA, which is enriched in the FA pathway. FANCA mutations are most commonly observed in patients with FA [Notably, three consecutive strongly associated SNPs  were annotated to  with FA ,31, and  with FA . Its cli with FA ,35. A pr with FA . Further with FA .LOC532875 could be associated with the excellent production performance of red Angus. AFG3, as a homolog of LOC532875 and a member of the subfamily of the AAA protein family, plays an important role in mitochondrial metabolism (MM) and oxidative phosphorylation (MOP) [Subsequently, a previous study stated that red Angus has a better gain-to-feed conversion rate than black Angus and that red cows are assigned to Canadian production category 1 (≥59% lean meat), whereas black cows have higher back fat thickness . In the on (MOP) ,40. Richon (MOP) ,42. In pon (MOP) .ZCCHC14 and ANKRD11, respectively. Some studies determined that one cause of myotonic dystrophy is CCHC type zinc finger nucleic acid binding protein [FHL3, MYOG, BAG3, SMAD3, and HIF1AN) [MYOG, which acts as an essential regulator of adult myofiber growth and muscle stem-cell homeostasis, interacted with ZCCHC14 [ANKRD11 plays an important role in KBG syndrome [HDAC3, HDAC4, SRC, ARRB2, and APEX1) [ZCCHC14/ANKRD11 is related to MOP, a large number of genes  that have been proven to interact with these genes display a positive regulation in oxidative phosphorylation [Moreover, two significant SNPs (CHIR18_12999497 and CHIR18_14389309) that exceeded the threshold were annotated in  protein ,45,46,47 HIF1AN) , such assyndrome . It inted APEX1) ,52. Whicd APEX1) , the negd APEX1) , and thed APEX1) . Coincidrylation ,57. TherLRRC30 and LOC781276 should not be ignored. LRRC26, as a family member of LRRC, is associated with the big potassium (BK)α subunit. LRRC26 could increase the channel voltage and apparent Ca2+ sensitivity in arterial muscle cells to induce vasodilation and maintain the expression of functional BK channel γ subunits in muscle cells [Finally, another remarkably divergent SNP (CHIR24_40137470) located in the intergenic region between le cells ,59. Furtle cells ,61. Manyle cells  and muscle cells ,64.The coat color and feed conversion rate/lean meat ratio of domestic animals are phenotype with complex molecular basis. In this study, a series of SNPs and InDels strongly diverged between red and black Angus, as identified by GWAS method. This study could help further understand the hereditary basis of these phenotypes and support the use of available molecular markers to improve cattle breeding.


Glycine max (L.) Merr.], an important oilseed crop, is a low-cost source of protein and oil. In Southeast Asia and Africa, soybeans are widely cultivated for use as traditional food and feed and industrial purposes. Given the ongoing changes in global climate, developing crops that are resistant to climatic extremes and produce viable yields under predicted climatic conditions will be essential in the coming decades. To develop such crops, it will be necessary to gain a thorough understanding of the genetic basis of agronomic and plant root traits. As plant roots generally lie beneath the soil surface, detailed observations and phenotyping throughout plant development present several challenges, and thus the associated traits have tended to be ignored in genomics studies. In this study, we phenotyped 357 soybean landraces at the early vegetative (V2) growth stages and used a 180 K single-nucleotide polymorphism (SNP) soybean array in a genome-wide association study (GWAS) conducted to determine the phenotypic relationships among root traits, elucidate the genetic bases, and identify significant SNPs associated with root trait-controlling genomic regions/loci. A total of 112 significant SNP loci/regions were detected for seven root traits, and we identified 55 putative candidate genes considered to be the most promising. Our findings in this study indicate that a combined approach based on SNP array and GWAS analyses can be applied to unravel the genetic basis of complex root traits in soybean, and may provide an alternative high-resolution marker strategy to traditional bi-parental mapping. In addition, the identified SNPs, candidate genes, and diverse variations in the root traits of soybean landraces will serve as a valuable basis for further application in genetic studies and the breeding of climate-resilient soybeans characterized by improved root traits.Soybean [ Glycine max (L.) Merr.] is an important oilseed crop and an inexpensive source of protein and oil. In regions of Southeast Asia and Africa, soybeans are commonly used as a source of traditional food and animal feed and for diverse industrial purposes. In addition to these uses, given its symbiotic associations with nitrogen-fixing rhizobacteria, cultivating soybean crops can make a valuable contribution to fixing atmospheric nitrogen, thereby reducing reliance on the excessive application of chemical fertilizers and thus facilitating sustainable cultivation [Soybean [tivation ,2.To meet the growing demand for food that will be necessary to sustain human populations in the coming years, it will be necessary to boost productivity. However, given the current yields of major crops, it will be difficult to meet the projected future demand. In addition to population growth, the predicted changes in global climate will potentially have wide-ranging ramifications with respect to human health, world trade flow, food prices, and the ecosystem, and could also imperil global food security ,4. ConseTypically, it is considered that the landraces of cultivated crop plant species, including soybean, are characterized by wide genetic diversity and carry multiple favorable alleles and genes that confer greater adaptability to adverse conditions than that shown by modern cultivars ,13,14. CqWT_Gm03) linked to waterlogging tolerance and the regulation of RSA and root plasticity in soybean. Furthermore, based on a combined approach using genotyping-by-sequencing in conjunction with whole-genome sequencing, Seck et al. (2020) recently identified 10 QTL regions associated with total root length and primary root diameter in soybean [The increasingly widely adopted genome-wide association studies (GWASs) have facilitated the discovery of the immense allelic diversity harbored within natural populations with both high resolution and accuracy, and have thereby enabled the identification of genomic regions/QTLs linked to desirable traits. Using high-density SNP markers, researchers have succeeded in discovering genomics regions, loci, or alleles for root traits in numerous plant species, including soybean ,16,17,18 soybean . All theApart from the markers identified in these studies, however, there has generally been a lack of relevant markers for root traits in molecular breeding, and few underlying genes have been identified for RSA ,20,22,23In this study, we aimed to examine the phenotypic association among root traits, elucidate their genetic basis, and identify significant SNPs associated with regions/loci controlling root traits. To this end, we phenotyped 357 soybean landraces at the early vegetative (V2) stage of growth and conducted GWAS using a soybean 180K SNP array. This study will help us comprehend the genetic basis of soybean root development.2, with a mean value of 294.382 cm2 . For phylogenetic tree construction, we used all 357 landraces, which were compared based on genetic similarity matrix information. The results of both PCA and phylogenetic analysis were consistent with the aforementioned population structure . Phyloger2 = 0.1, the average LD decay distance of the 357 soybean landraces was approximately 200 kb, 2r = 0.2  (10log (p) value (5.78) was the LAL-associated AX-90477018 located at 7419551 bp on Chr. 7, followed by SNP AX-90402282 located at 58817483 bp on Chr. 18 with a −log10 (p) value of 5.28.To identify SNPs that are significantly associated with the seven assessed root traits , we conducted GWAS using a mixed linear model (MLM) and accordingly identified 112 SNPs with significant associations, among which 28, 23, 21, 16, 16, 4, and 4 SNPs were associated with LAD, NT, DIAM, LAL, NF, LENGTH, and SA, respectively, with a threshold of ≥4 − log10(p) . The SNPWe also established that 24 of the identified SNPs were co-associated with two of the seven traits. Among the 28 significant SNPs associated with the LAD trait, 17 SNPs present on Chr. 3 (6 SNPs), Chr. 6 (7), Chr. 14 (2), and Chr. 18 (2) were also commonly associated with DIAM. Likewise, three SNPs were identified as commonly associated with LENGTH and NT on Chr. 11 and a further four SNPs distributed on Chr. 8 were found to be commonly associated with NF and SA. Manhattan and quantile-quantile plots of all the significant SNPs associated with individual traits are shown in https://bar.utoronto.ca/eplant_soybean/, accessed on 16 April 2022) database to analyze the expression patterns of 55 candidate genes in different tissues. The result showed that all candidate genes were expressed in soybean root tissues  in the root, and data analysis was completed for these six genes using RNA-Seq soybean libraries (4085) and compared with other tissues . The result showed differential expression levels of six selected candidate genes in the othertissues , adzuki bean (Vigna augularis), and common bean (Phaseolus vulgaris). A total of 13,191 core gene clusters were found in the four species, and 960 gene clusters were unique to soybeans  level  at (1) level . Genetic1) level A and a l1) level . In our 2r = 0.2 within approximately 200 kb, which is slightly lower than the previously reported LD decay in soybean (235–375 kb) [In self-pollinated crops, the rate of LD decay is typically higher than that detected in cross-pollinated plants . Based o–375 kb) ,30,31, a–375 kb)  and (150–375 kb) , respectn = 357) to identify genetic loci associated with root traits. The primary goal of this study was to determine the genetic basis of phenotypic variation in the soybean root system. To this end, we used a 180KSoyaSNP array for our analyses of root traits. In GWAS studies, marker density is of particular importance with respect to establishing entire genome coverage and capturing all the available haplotypic variation, with a larger number of markers being conducive to obtaining sufficient LD to identify significant marker–trait associations. To the best of our knowledge, the marker dataset used in the present study is the second-largest examined to date for root trait GWAS, after that recently reported by Seck et al. [Although numerous studies have examined the agronomic traits of soybean, very few have adopted a GWAS approach to assessing the genetic basis of root traits in soybean. To facilitate such studies, a necessary prerequisite is genetic diversity among the assessed germplasm resources. In terms of allelic diversity for various agronomic traits, the most extensive differences can be found among wild soybeans, followed by landraces, with cultivated soybeans being characterized by being the least diverse. Accordingly, for GWAS in the present study, we used a diverse collection of landraces −log value >4.0 for the all-measured traits using an MLM model. The phenotypes showed high stability across replicate analyses, and we observed significant genetic diversity among the 357 landraces with respect to the seven assessed root traits. Moreover, we also succeeded in detecting a large number of SNPs associated with the traits of interest. Based on comparisons of the significant SNPs detected in the present study and those previously reported for root and shoot traits, we identified significant SNPs on chromosome 13 that differ from the SNPs previously reported for root traits, although interestingly these SNPs map to locations within the vicinity of those detected for NT  [In this study, we identified a total of 112 significant SNPs associated with one or more of the seven assessed root traits, which enabled us to identify genomic regions associated with desired traits. Similarly, recent GWAS studies conducted for root traits in soybean detected 10 genomic regions for the length and diameter of roots and four QTL regions for the number of lateral roots ,23. Give0433497) ,37,38. S0433497) . To the Glyma.03g065700 and Glyma.07g096000) associated with soybean root development and root tips. Furthermore, six genes that showed higher expression in the root , North Korea (58), China (90), and Japan (94). The seeds of these germplasms were sown in polyvinyl chloride tubes (8 cm in diameter and 40 cm in height) containing horticultural soil  in a randomized block design with three replicates. All landraces were grown for 40 days and were harvested at the V2 stage of growth.For greenhouse experiments, individual plant samples were removed and measured at the V2 growth stage, and having been rinsed with clean fresh water, their root samples were measured. Seven root traits, namely, average root diameter (DIAM), link average diameter (LAD), link average length (LAL), total root length (LENGTH), number of root forks (NF), number of root tips (NT), and surface area (SA), were scanned and analyzed using the Win-RHIZO system . We had performed this root phenotyping previously  and used® 180k SoyaSNP array following a previously described method [https://faculty.washington.edu/browning/beagle/beagle.html, accessed on 14 April 2022) based on a hidden Markov process algorithm. The number of subpopulations (k) was determined based on cluster analysis, and further ancestry estimation was performed based on ADMIXTURE analysis [k = value of 3. Linkage disequilibrium (LD) analysis was performed using genome-wide SNP markers, and correlation (r2) values among the 84,210 SNP markers were calculated using QTLmaxV2 software based on PLINK, with a window size of 400 SNP markers, and estimated as the squared values of the correlation coefficient between SNP marker pairs [For DNA extraction, we collected samples of young trifoliate leaves from individual plants of each of the 357 soybean landraces, from which the genomic DNAs were isolated using the magnetic bead method  following the manufacturer’s instructions. Genotyping of the 357 individuals was performed using an Axiomd method . The genanalysis . A bar panalysis ,45 for aer pairs .www.cog-genomics.org/plink/2.0/, accessed on 14 April 2022) [Phylogenetic analysis was performed using the similarity identity-by-state coefficient matrix calculated using Plink v2.0 (il 2022) , and a p−log10(p) values ≥ 4 at alpha = 5 to search for significant SNPs.All GWAS analyses were performed for seven root traits of the 357 landraces using GWASpro (QTLmaxV2)  with a lhttps://soybase.org/SequenceIntro.php, accessed on15 April 2022) according to the Wm82. a2. v1 soybean reference genome. Candidate genes were searched within a sequence range of 100 kb up and downstream each significant SNP using the genome browser, and functional annotation of the genes was performed using the Phytozome database [We used the most significant SNPs detected in GWAS to identify potential candidate genes, based on annotation using the SoyBase database  database and a heatmap was constructed for shortlisted genes using TBtools software . Furthermore, selected genes were used for differential expression analysis at different tissues using web-based publicly available RNA-Seq soybean libraries’ (4085) data with default setting  [To determine the candidate genes expression level, we first performed an analysis using the ePlant (er 2022) . Furtherer 2022) .We used phenotypic data for the 357 landraces obtained in our recently published study . To deteIn this study, using GWAS, we determined the genomic regions associated with seven selected root traits in soybean landraces and identified the 112 most significant SNPs associated with the measured traits. Furthermore, based on annotation information and publicly available expression data, putative candidate genes were identified in the vicinities of the SNPs. These promising candidate genes could potentially serve as molecular targets for further studies conducted to evaluate their role in the regulation of root development. Moreover, the numerous SNPs identified in this study can be used as a potential resource in soybean breeding programs, with a view toward marker development for root traits. Collectively, our findings will provide a basis for identifying the genetic components underlying phenotypic variation in the root traits of soybean landraces.


A key genomic region was identified for resistance to FSR at 168 Mb on chromosome 6 in GWAS and haplotype regression analysis, which was validated by QTL mapping in two populations.Fusarium verticillioides. The pathogen invades the plant individually, or in combination with other stalk rot pathogens or secondary colonizers, thereby making it difficult to make accurate selection for resistance. For identification and validation of genomic regions associated with FSR resistance, a genome-wide association study (GWAS) was conducted with 342 maize lines. The panel was screened for FSR in three environments using standard artificial inoculation methodology. GWAS using the mixed linear model corrected for population structure and kinship was done, in which 290,626 SNPs from genotyping-by-sequencing were used. A total of 7 SNPs, five on chromosome 6 showing strong LD at 168 Mb, were identified to be associated with FSR. Haplotype regression analysis identified 32 haplotypes with a significant effect on the trait. In a QTL mapping experiment in two populations for validating the identified variants, QTLs were identified with confidence intervals having overlapped physical coordinates in both the populations on chromosome 6, which was closely located to the GWAS-identified variants on chromosome 6. It makes this genomic region a crucial one to further investigate the possibility of developing trait markers for deployment in breeding pipelines. It was noted that previously reported QTLs for other stalk rots in maize mapped within the same physical intervals of several haplotypes identified for FSR resistance in this study. The possibility of QTLs controlling broad-spectrum resistance for PFSR in general requires further investigation.Fusarium stalk rot (FSR) of maize is an economically important post-flowering stalk rot (PFSR) disease caused by  Fusarium verticillioides , Gibberella stalk rot (GSR) caused by Fusarium graminearum, anthracnose stalk rot (ASR) caused by Colletotrichum graminicola, Diplodia stalk rot caused by Diplodia maydis, Late wilt caused by Harpophora maydis  and Charcoal rot caused by Macrophomina phaseolina. These pathogens could occur in isolation or in combination, along with secondary colonizers and other abiotic stress-related factors to cause severe incidences of late-stage post-flowering stalk rots. As these diseases affect the crop at a much later stage in the crop life cycle, the economic impact of the disease is too high and hence is one of the biggest challenges faced by small and marginal farmers.Due to the population and socioeconomic growth, the global food requirement is expected to be double the current demand by 2050  the lines are grouped into early, medium and late maturity groups. A subset of 342 inbred lines from this panel were included in GWAS.2:3 populations were developed for validation of genomic regions identified in GWAS and HTR analysis. The first population FSR-1 developed from a cross between the resistant parent CML578, is an orange flint line derived from waterlogging stress-tolerant population in Asia. This line was also used as a resistant parent for QTL mapping for charcoal rot resistance  campus at Hyderabad  for two years and CIMMYT managed farm at Daulatabad for one year . For linkage mapping analysis, two FFusarium verticillioides samples were isolated from the previously infected maize stalks. Infected stalks were cut into 5–10 mm small pieces, washed with 0.6% sodium hypochlorite for 1 minute and rinsed with sterile distilled water for 3–4 times under aseptic conditions. Excess water was blot dried on sterile tissue paper and infected leaf pieces were placed on Petri plates carrying pure culture Potato Dextrose Agar (PDA). The plates were incubated at 28 °C for 3–5 days, the growing hyphal tips were transferred to PDA allowed to grow for 8-10 days at 28 °C, spores were isolated using single spore isolation method.F. verticillioides for artificial inoculation was done on wooden toothpicks by the method proposed by Jardine and Leslie  were generated for each genotype using the META-R software.All phenotypic data analyses were carried out in META-R software  platform. The GBS libraries were constructed as reported by Elshire et al. Genomic DNA of the association mapping panel and two Fhttps://www.biosearchtech.com/services/genotyping-services, Herts, UK). F2:3 mapping populations FSR-1 and FSR-2 were genotyped with a set of 272 and 268 markers, respectively, based on parental line polymorphism.For two mapping populations for the QTL mapping study, markers were selected across the 10 chromosomes from the Illumina Goldengate assay. The lines were genotyped with Kompetitive Allele Specific PCR (KASP) assays developed from sequences of random and GWAS-identified SNP markers at LGC Genomics, London. (GWAS was carried out based on three models: (i) G-test in which only genotypic data (G) was used, (ii) General linear model (GLM) where genotypic data was corrected for structure (Q) using 10 principal components (PCs) and (iii) single locus mixed linear model (MLM)  where genotypic data was corrected for Q using 10 PCs and kinship matrix (K) to avoid false associations. In earlier GWAS studies in the CAAM panel, principal component analysis did not reveal a clear population structure with the first three principal components. The Scree plot plotted with all the eigenvalues suggested 10 principal components will be able to adjust the existing population structure  inferred the severity of inflation test statistics by plotting the observed versus expected negative log10 P value that were generated for all the models studied according to established procedures. Q–Q plots from the three models were represented in one plot using R-studio software , SNP = SNP marker (fixed effects), PC = principal component coordinate from the PCA (fixed effects), K = kinship matrix (random effects), α is the vector of PC, β and μ are the vectors of SNP and K, respectively, and ε is the error. Manhattan plots were used for visualization of GWAS results by plotting 2 values between adjacent SNPs and physical distances between those SNPs as described in Rashid et al. 2 threshold of 0.1  and estimated as 126,120. The suggestive P value threshold to control the genome-wide type 1 error rate was estimated as 7.92 ×10-6 which was considered as the significance cut-off for the association  algorithm (Excoffier and Slatkin Linkage disequilibrium (LD) was estimated based on pairwise r3 families and 114 from FSR-2 with 166 F3 families were polymorphic between their respective parental lines and were used for constructing the linkage map. BLUP values of disease severity scores of FSR on 1-9 scale were used in QTL analysis. The walking steps in QTL scanning was 1cM and LOD threshold of 3.383 and 3.951 were estimated, respectively, in FSR-1and FSR-2 populations based on 1,000 times permutations analysis . Broad-sense heritability (h2) estimates were moderate to high, ranging from 0.43 to 0.61, with across trial heritability of 0.51  and the heritability estimate was 0.40. The second F2:3 mapping population FSR-2 showed a trial mean of 5.20 with disease score ranging from 3.89 to 7.57. Highly significant  genotypic variance was observed, and the heritability estimate was 0.48  in Year-3 and the highest in Year-1 (0.41) with an across genotypic variance of 0.14, that was found to be highly significant  at 168Mb  followed by the SNP S6_168690080 . The phenotypic variance explained by the identified significant SNPs in GWAS ranged from 8.38 to 6.16%. In all seven associations identified, the major allele was found accountable for conferring resistance to FSR and the narrow sense heritability with the associated SNPs was found to be 0.61. Significant SNPs detected in GWAS were located within six gene models based on the physical positions of the SNPs with respect to B73 version 2 of reference genome (http://ensembl.gramene.org/Zea_mays). Most of the gene models identified were found to have functional domains involved in tolerance to biotic and abiotic stresses, plant growth and development, resistance to viral and fungal pathogens. LD (r2) was estimated in the interval of ~360 Kb that covered the peak on chromosome 6 between 168.43 and 168.79 Mb. Average pairwise r2 observed for 52 SNPs in this interval was low at r2 =0.23. The five most significant SNPs on chromosome 6 were in strong LD with an average pairwise r2 of 0.77 , followed by GLM (G+Q model), which was corrected using 10 principal components. The least genomic inflation was observed in Single locus MLM (G+Q+K) model where the genomic data was corrected for both population structure (Q) and Kinship (K) as detected in QQ plot  identified in GWAS, and were used in haplotype regression analysis on the estimated BLUP values of disease scores. HTR analysis detected 32 haplotypes across 9 chromosomes  with Bonferroni P value ≤ 0.05, that explained phenotypic variance ranging from 3.75 to 10.91% for FSR resistance. Haplotype Hap_6.3 on chromosome 6 formed by four SNPs  at 168 Mb had the least Bonferroni P–value (3.63×10−8) followed by Hap_8.3 on chromosome 8  in this population. In the FSR-2 population, two QTLs were detected on chromosome 6 and on chromosome 1 at the threshold of LOD 2.5, but no QTL could be identified based on the LOD threshold obtained with 1000 permutations  in this population. QTLs qFSR6 and qFSR6-1 identified on chromosome 6 in FSR-1 and FSR-2, respectively, were found to have one common marker flanking the identified QTLs.One QTL was identified in FSR-1 on chromosome 7 between PZA00616_13 and PZA02643_1 at 122 Mb and 128 Mb, respectively, based on the physical coordinates of the flanking markers. At a lower LOD threshold of 2.5, two more QTLs were identified in FSR-1 on chromosome 5 and 6. QTL qFusarium verticillioides is one of the important fungal pathogens causing PFSR of maize. It is a hemibiotrophic pathogen which initiates the disease through roots from soil-borne inoculum and spreads through the nodes and other aerial parts of the plants. FSR is a part of the PFSR complex, where stalks are infected with one or many stalk rot pathogens, which gets exacerbated under abiotic stress conditions like less soil moisture and high usage of fertilizers. PFSR diseases are predicted to either increase or change in pathogen spectrum under the climate change scenario , suggesting a polygenic nature of the trait, and the results of this study could be used to optimize the most efficient breeding strategy for trait improvement. Genotypic variation was significant in the CAAM panel and the mapping populations, suggesting the superior quality of the phenotypic data that can be used for identification of genomic regions.viz, F2, double haploid (DH), recombinant inbred lines (RILs), etc. and has an enormous advantage for QTL detection power. Nevertheless, it has limitations like low mapping resolution, limited allele sampling and longer research time. Contrary to linkage mapping, GWAS uses historical recombination from an assembly of lines to analyse marker-trait relationship  released from different CIMMYT regional centres across geographies were also the part of panel. In Asian environments, CAAM panel was earlier used in mapping for resistance to diseases like Sorghum downy mildew, Northern corn leaf blight & Charcoal rot , qFSR6 in FSR-1 and qFSR6-1 in FSR-2, albeit with minor effects in these populations. One of the flanking markers, PHM3466_69 at 167 Mb on chromosome 6 was found to be common between these QTLs identified in the two different mapping populations. This region could be of major significance for this trait as it is infrequent to detect stable QTL for complex traits in genetically unrelated populations   and fungal blast caused by M. oryzae  associated with FSR resistance on chromosome 6 at 168Mb. Further, haplotype regression test also detected three haplotypes (Hap_6.2 Hap_6.3 and Hap_6.4) having a significant effect on the trait at this genomic region. A clear peak was observed in the Manhattan plot covering the region that spanned 168.43 Mb to 168.79 Mb in the GWAS analysis of which five SNPs which showed the strongest association with the trait were with moderately strong LD with an average pairwise rqRfg2 for resistance to Gibberella stalk rot (GSR) caused by Fusarium graminearum between 257.3 Mb and 277.9 Mb on chromosomal bin 1.10. In this study, we identified a haplotype, Hap_1.4 at 272 Mb, within the physical interval where qRfg2 was fine-mapped. Similarly, Hap_8.2 and Hap_8.3 identified at 159 Mb in this study were detected within the physical coordinates of the confidence interval of a QTL (146.4-158.9 Mb) for GSR resistance reported by Ma et al. Macrophomina phaseolina, causing Charcoal rot in maize (Rashid et al. qRfg1 reported for GSR resistance by Yang et al. In studies where genetic variants associated with phenotypic traits are analysed, phenotypic variance explained for any trait increases by using haplotypes for trait regression than single markers, and therefore haplotypes allow the identification of genomic regions responsible for controlling a larger part of variation for the trait of interest (Maldonado et al. 2:3 mapping populations FSR-1 and FSR-2 were employed for QTL analysis of FSR resistance. QTLs on chromosome bin 6.07-08 were detected in both the mapping populations with a common flanking marker at 167 Mb. Considering the significance of this region for resistance to FSR, it has to be further studied for fine-mapping and validating in breeding populations for development of deployable trait markers. Interestingly, three genomic regions were identified with haplotypes co-localized with reported QTLs for other stalk rots like GSR and charcoal rot resistance. This raises an interesting proposition of common genomic regions contributing to broad-spectrum stalk rot resistance mechanisms.Genome-wide association studies and linkage mapping were used to investigate the genetic architecture of Fusarium stalk rot resistance in maize. GWAS in a panel of 374 maize lines evaluated for FSR in three environments detected seven highly significant SNPs associated with the trait, of which, five were on chromosome 6 at 168 Mb. Haplotype regression analysis revealed 32 haplotype blocks on chromosome 1, 2, 3, 4, 5, 6, 7, 8 and 10 with significant effect on the trait. Four haplotypes were detected on chromosome 6, of which three haplotypes were located at 168 Mb. For validation of the SNPs identified through GWAS, two F