HERC2/OCA2 locus have the largest effect on eye color in these populations, although other genomic regions also influence eye color. We performed genome-wide association studies of eye color in a Canadian cohort of European ancestry  and investigated candidate causal variants. We uncovered several candidate causal signals in the HERC2/OCA2 region, whereas other loci likely harbor a single causal signal. We observed colocalization of eye color signals with the expression or methylation profiles of cultured primary melanocytes. Genetic correlations of eye and hair color suggest high genome-wide pleiotropy, but locus-level differences in the genetic architecture of both traits. Overall, we provide a better picture of the polymorphisms underpinning eye color variation, which may be a consequence of specific molecular processes in the iris melanocytes.Eye color is highly variable in populations with European ancestry, ranging from low to high quantities of melanin in the iris. Polymorphisms in the   •Genome-wide association studies of eye color in 5,641 participants•HERC2/OCA2Multiple independent candidate causal variants were identified across •IRF4, SLC24A4, TYR, and TYRP1Single candidate causal variants observed on or near •Colocalization of eye color signals with expression and methylation profiles Genetics; Genomics; Human Genetics Pigmentation levels in the iris vary among humans, ultimately leading to different eye colors. The melanin pigment in the iris is synthesized in the melanocytes, within organelles named melanosomes . Eye colAt a molecular level, blue irises appear as melanin-free melanocytes, in which molecules in the iris scatter short blue wavelengths to the surface . Green iOCA2, TYR, TYRP1, SLC45A2, SLC24A4, SLC24A5, and IRF4. However, the variant with the largest effect on eye color variation is an intronic SNP (rs12913832) located in an enhancer within the gene HERC2 that regulates the expression of the downstream gene OCA2  and that it does not significantly vary throughout an adult’s lifespan . Furtherene OCA2 . Functio of OCA2 .OCA2. But it has been hypothesized that additional distal elements within the same region may be involved in the regulation of OCA2, a process that often is tissue specific (HERC2/OCA2 region (The SNP rs12913832 is the key regulatory element of specific . In fact2 region .HERC2 and OCA2 that are in low (r2 < 0.2) linkage disequilibrium (LD) with rs12913832 , as well as other distant candidate regulatory variants near or within the APBA2 gene , which is located ∼700kb away from OCA2. However, pinpointing additional causal variants within the HERC2/OCA2 region is challenging due to the complex LD patterns among the genetic variants and the lack of tissue-specific regulatory annotations. For instance, the Gene and Tissue Expression (GTEx) database , along with fine-mapping analyses. We combined these results with gene expression and methylation data of cultured melanocytes by conducting colocalization analyses and transcriptome-wide association studies (TWAS). Our main results indicate that there are several candidate signals in the HERC2 rs12913832 G-allele has a lower frequency than in other provinces , who were genotyped using two genome-wide genotyping arrays See , in whicnces see . In addinces see . Comparences see .Figure 1We performed GWAS of eye color on each genotyping array (genotyped and imputed single-nucleotide polymorphisms (SNPs)) using a linear mixed model and an additive genetic model, using GCTA 1.26.0 , 2014. WTYRP1 , IRF4 , TYR , SLC24A4 , and HERC2 . In addition, we observed a signal on chromosome 6 overlapping the ILRUN gene , a locus that has not been previously associated with pigmentation. We then carried out a meta-analysis using the summary statistics (beta and SE) including the two GWAS on METASOFT v2.0.1 . Q-Q ploWe conducted approximate conditional and joint analyses of association using GCTA-COJO , and usiIRF4, TYRP1, TYR, and SLC24A4 regions, we identified one independent genome-wide significant SNP per locus (see IRF4 and rs1126809 on TYR. The lead SNP on SLC24A4 is in high LD (r2 = 0.98) with rs12896399, a SNP previously associated with pigmentation  is downstream of TYRP1 and has not been previously highlighted in eye color studies. On the HERC2/OCA2 region, we identified six independent SNPs overlapping OCA2 and HERC2 with p values in the conditional analysis exceeding the genome-wide significant threshold  model too, which takes into account heterogeneity among studies   see .IRF4, TYR, SLC24A4, and TYR  was the same SNP highlighted by GCTA-COJO . In contrast, the missense SNP rs1126809 on TYR had a low posterior inclusion probability (PIP = 0.209), due to high LD with other nearby SNPs. Other candidate causal SNPs in the 95% credible set of the TYR locus include intergenic variants and one SNP (rs11018578) on the 3′UTR region of NOX4. On the SLC24A4 locus, the variants with considerable evidence of causality (i.e. log10BF ≥ 2) include intronic SNPs within SLC24A4 and other variants upstream of the gene, including the SNP rs12896399 (log10BF = 2.58)  .TYRP1 locus, all candidate causal SNPs in the credible set had a low (<0.1) PIP, most likely due to high LD among multiple SNPs in the locus  associated with eye color. Within these signals, three SNPs had a PIP >0.98 . Aside from the overlap of rs12913832 (HERC2) with an open chromatin region in foreskin melanocytes, only the SNP rs117007668 is located within an open chromatin region in foreskin melanocytes (By applying a Bayesian fine-mapping approach on the IP >0.98 A. These  of OCA2 . In addianocytes .Table 1S2 ≤ 0.2), with the exception of rs117007668 and rs117744568, which are in high LD (r2 = 0.9)  and suggest that the candidate causal SNPs are not in complete linkage equilibrium  B. We fur2 = 0.9) . The D′ rium See .OCA2/HERC2 locus harbor a putative regulatory function, to consider genetic variants that may have not been present in our dataset after imputation. By using as input the top SNP on each of the five credible sets , although a recent study suggests that this TF does not have a role in normal skin melanocytes (We explored with HaploReg (version 4) , 2016 ifble sets , we idenanocytes .OCA2/HERC2 region (SNPs in SLC24A4 (index SNP: rs4144266) and HERC2/OCA2 (SNPs: rs12913832 and rs4778138)  see .OCA2 locus, OCA2 (rs121918166 and rs74653330) have a measurable effect on eye color variation, in which the alternative alleles decrease melanin levels, even in a heterozygote state. These two SNPs were rare in our sample (MAF <1%); therefore, we did not consider them in our GWAS analyses. By looking at the genotype-level data in these two rare SNPs, we identified 108 individuals heterozygous for either rs121918166 or rs74653330. Among these, 16 individuals were homozygous for the nonblue eye color rs12913832-AA genotype, but none self-reported having blue eye color . In addition, among heterozygous individuals for either one of the rare SNPs (i.e. rs121918166 or rs74653330), 12 individuals were also heterozygous for the nonblue eye color rs12913832-AG genotype and self-reported blue eye color. These results indicate that these two rare variants do not account for the incidences of blue eye color with an rs12913832-AA background in the CanPath, but they may influence the eye color phenotype under an rs12913832-AG background. Furthermore, we identified in our sample a subset of individuals (N = 904) harboring the rs12913832:GG genotype, 21 of whom self-reported brown eye color, 631 green eye color, and 252 hazel eye color, suggesting that the rs12913832:GG genotype does not exclusively yield a blue eye color.By investigating eye color variation in the OCA2/HERC2 haplotypes previously described for eye color in European ancestry populations, with the fine-mapped loci reported in the present study. 2 < 0.6) with any of our fine-mapped loci. The IrisPlex System is widely used to predict blue/brown eye color based on common genetic polymorphisms (OCA2/HERC2 region (rs12913832 and rs1800407). The rs1800407 SNP is a missense SNP in OCA2, known to be involved in pigmentation variation (SLC24A4 (rs12896399), SLC45A2 (rs16891982), TYR (rs1393350), and IRF4 (rs12203592). Importantly, in our study there is a genome-wide significant association of TYRP1 with eye color, but this locus is not included in the IrisPlex System.We compared the orphisms . Currentariation . This SNHERC2/OCA2 locus . Aside from rs12913832, none of their index SNPs are within the candidate causal SNP sets in our sample, even though rs1800404 was genome-wide significant in our meta-analysis . In addition, they identified three novel loci associated with eye pigmentation: DSTYK (chromosome 1), WFDC5 (chromosome 20), and MPST (chromosome 22). We followed-up each of the three index SNPs in our meta-analyses , but failed to replicate the former two SNPs using a Bonferroni correction , whereas rs5756492 was not present in our meta-analysis. These differences may be driven by population ancestry differences, given that the CANDELA cohort includes recently admixed individuals from Latin America, although the phenotyping approach may also be driving these differences.ILRUN locus identified in the present study was not among their novel signals, nor we were able to replicate it. In addition, they conducted conditional analyses of association to identify secondary signals on the significant loci, in which they identified a total of 115 independent signals, including three signals in chromosome X. Notably, they identified 33 independent signals in the HERC2/OCA2 region and two signals in the nearby gene GABRB3. We followed-up their signals . After considering a Bonferroni correction , 16 SNPs remained significant, all of which overlap known pigmentation genes .Finally, the largest eye color GWAS to date conducted in populations of mainly European ancestry reported several novel loci, which had not been previously associated with eye color, and a subset of them has not been previously associated with any pigmentation trait  . However signals  in our m signals . This seIRF4), rs1126809 (TYR), and rs1129038 (HERC2). Interestingly, even though Simcoe et al. identified several independent loci in the OCA2/HERC2 region, the known rs12913832 SNP is not among them, likely because another SNP in perfect LD has a lower p value, which is similar to what we observe in our GCTA-COJO analysis (i.e. rs1129038). We also compared the same independent SNPs identified by Simcoe et al. with our candidate causal loci as defined by FINEMAP and found three overlapped SNPs: rs12203592 (IRF4), rs1126809 (TYR), and rs13297008 (TYRP1).Lastly, we checked if the independent SNPs associated with eye color identified with GCTA-COJO by cis-QTLs  to explore if there were shared causal signals   using a posterior probability threshold of 0.8. Notably, we did not find colocalized eQTLs on the TYRP1 locus that passed the probability cutoff.We performed colocalization analyses with hyprcoloc  of the eps OCA2) , in whicps OCA2)  overlappOCA2, SLC24A4, and RIN3  of 55%  and 69%  on the UK Biobank and GSA arrays, respectively. Similar to the approach used in a previous study . We did not identify new loci associated with eye color that were successfully replicated, and we focused on performing downstream analysis to pinpoint candidate causal SNPs, specifically on those loci for which a functional variant has not yet been identified or in which there is evidence of more than one independent signal. We found that fine-mapping provides evidence for multiple independent SNPs within the TYRP1, a locus that could potentially improve the eye color prediction system. However, it is important to point out that structural features of the iris  also contribute to color perceptions, but we are not able to distinguish them using the current dataset.One of the caveats of this study is that we utilized eye color categories self-reported by participants of the CanPath cohorts, as categorical classifications do not capture as well iris color variation as quantitative measures . In our SLC24A4, IRF4, TYRP1, TYR, and HERC2/OCA2, similar to what was identified in a recent large GWAS of both categorical and quantitative eye color loci in a Latin American (CANDELA) cohort  are known to alter pigmentation traits (SLC24A5 was rare in our sample (MAF <1%) hence excluded, in line with frequencies observed in the 1000 Genomes Project European populations, in which the alternative allele is nearly fixed. In the case of the SLC45A2 locus, the missense SNP did not reach genome-wide significance .Through our GWAS meta-analysis we identified five known loci associated with eye color, encompassing the genes ) cohort . The onln traits . The misIRF4, rs1126809 on TYR, and rs12913832 on HERC2. Contrary to what has been observed for hair pigmentation  known to alter melanin synthesis within the same gene, in addition to candidate regulatory variants in the upstream GRM5 gene associated with skin pigmentation  the location ∼11kb upstream of the gene and (2) the overlap of a SNP (rs13297008) with open chromatin regions in foreskin melanocytes. In addition, this gene was absent from the TWAS expression weights dataset, suggesting that the gene expression in the current dataset is not sufficiently heritable (i.e. heritability p > 0.01). Therefore, we are not able to provide evidence of the mechanism in which the variants in the locus affect pigmentation variation, nor we are able to nominate a single causal SNP.We did not find colocalization of GWAS SNPs with eQTLs on the The cultured melanocyte expression and methylation QTLs we used for colocalization and TWAS come from newborn foreskin melanocytes , 2021. SHERC2/OCA2 region on chromosome 15 has the strongest effect on eye color variation, such that blue eye color was initially considered a Mendelian trait , but they self-reported non-blue eye color. In addition, there may be rare genetic variants within the OCA2/HERC2 region (i.e. rs121918166 and rs74653330) accounting for the blue eye color individuals under the heterozygous rs12913832 genotype, but larger sample size studies are needed to statistically test this hypothesis. Therefore, the expression of OCA2 might be induced by other regulatory variants in the locus, counteracting the effect of rs12913832, as has been previously proposed , associated with nonblue eye color, along with one or two rs12203592 T-alleles, associated with light eye color (TYR (rs1126809), TYRP1 , and SLC24A4 (rs1289469) may be responsible for the brown eye color in individuals of European ancestry with an rs12913832 homozygous G-allele background (HERC2) and rs12203592 (IRF4).In addition, it is possible that genetic interactions between ye color . Similarye color . Finallyckground . HoweverSLC24A4, IRF4, OCA2). However, we have demonstrated that certain genetic differences come to light when investigating candidate causal variants across the genome. Within the CanPath cohort, we observed that red hair color is driven mainly by multiple candidate causal signals in the MC1R locus and that variants within the same gene also have a significant effect upon blonde hair color. In contrast, variants within MC1R and its antagonist, ASIP, are not associated with eye color, which may be explained by the fact that MC1R is not expressed in iris melanocytes . HERC2/OCA2 is also the most significant locus for eye color. However, the signal from hair color is primarily driven by rs12913832, whereas there are several independent signals in HERC2/OCA2 associated with eye color. Lastly, even though IRF4, a transcription factor that upregulates tyrosinase, has a large effect on both blonde hair and blue eye color, the direction of effect of the causal SNP rs12203592 is opposite for both traits: the derived T-allele is associated with blue eye color, whereas the same allele is associated with the presence of brown hair color .Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact: Frida Lona-Durazo (This study did not generate new unique reagents.This study was approved by the University of Toronto Ethics Committee (Human Research Protocol # 36429) and data access was granted by the Canadian Partnership for Tomorrow’s Health (Application number DAO-034431). The samples in this study correspond to a subset of 5,675 individuals from the Canadian Partnership for Tomorrow’s Health (CanPath), which were sampled in different provinces: Alberta , Atlantic Coast Provinces  , British Columbia , Ontario  and Quebec . We selected the individuals who self-reported having European-related ancestry and for whom self-reported eye colour was available , of which 58.78% were females. The average age across participants was 55 years old (SE ± 0.11).Individuals who self-reported as having European-related ancestry were genotyped between 2012 and 2018 using two different genotyping array chips: Axiom 2.0 UK Biobank (Affymetrix)  and the Global Screening Array (GSA) 24v1+MDP  by the Canadian Partnership for Tomorrow’s Health (CanPath). The number of single nucleotide polymorphisms (SNPs) of these chip arrays ranges between 658,296 and 813,168 SNPs.We performed genotype quality control for each array chip separately by first filtering out variants that deviated in minor allele frequency >0.2 from the 1000 Genomes Project Phase 3 European sample (1KGP-EUR), GC/TA variants with minor allele frequency >0.4 in the 1KGP-EUR and flipping alleles according to the 1KGP-EUR, using a Perl script (version 4.2) . AfterwaEach genotyping array was first phased with EAGLE2 (version 2.0.5)  using thParticipants of the CanPath answered a questionnaire that included self-report on eye colour using the following discrete categories: grey, blue, green, amber, hazel or brown eye colour. These categories were then transformed into a linear scale using R (version 3.5.1)  to buildGenome-wide association studies of eye colour were performed for each genotyping array with a linear mixed model on Genome-Wide Complex Trait Analysis (GCTA- MLMA) 1.26.0 , 2014, u2, as well as a Bayesian posterior probability that an effect exists on each individual study (M)  of each study on the software METASOFT (version 2.0.1) . METASOFtudy (M) .2 statistics.For the meta-analyses results, we generated Manhattan and Q-Q plots using the qqman  and ggplWe used the web-based program SNPNexus  to annotIn order to identify if the genome-wide significant loci of our original logistic meta-analyses were driven by one or more independent variants, we conducted approximate conditional and joint analyses of association (COJO) with GCTA . We perfWe used the program FINEMAP (version 1.4)  to ident10BF < 2 from each of the 95% credible sets, where a log10BF indicates considerable evidence of causality. We annotated the remaining SNPs using SNPnexus (10BF and annotation) as the most likely candidate causal variants. We computed LD correlations between the candidate causal SNP(s) on each locus (i.e. configuration with highest posterior probability and k number of SNPs) and the other variants on each locus using LDStore (version 2.0) and plotted the Posterior Inclusion Probability (PIP) results on R (version 3.5.1)   using ggn 3.5.1) .2 ≥ 0.8 and the core chromatin 15-state model, which is based on several histone marks associated with promoters, enhancers, insulators and heterochromatin.Given that there may be candidate functional SNPs that we did not genotype or did not impute with high accuracy, we explored if markers in the same LD blocks of the credible sets have functional annotations using HaploReg (version 4) , 2016. WIRF4: rs12203592, TYRP1: rs1326779; TYR: rs1126809; SLC24A4: rs4144266), and including as well the five loci fine mapped in the OCA2/HERC2 region  using a linear regression model and the same covariates as in the GWAS. We used a Bonferroni-corrected p-value threshold to account for all the tests performed (0.05/33 tests = 0.0015).We computed statistical interactions using the program CASSI  across tcis-QTL data from primary cultures of foreskin melanocytes, isolated from foreskin of 106 newborn males . Before running the script, we filtered out SNPs with MAF <0.01, SNPs with a genotyping missing rate >0.01 and SNPs that failed Hardy-Weinberg test at significance threshold of 1 × 10−7 using PLINK  (We performed a transcriptome-wide association study (TWAS) by imputing the expression profile of the CanPath cohort using GWAS summary statistics and melanocyte RNA-seq expression data . Using te 10e-7) . We compWe used a bivariate restricted maximum likelihood (REML) approach to test for genome-wide pleiotropy between hair and eye colour using GCTA (--reml-bivar option) , by takiWe first included in the model sex, age and the significant PCs as covariates, and restricted the analysis to SNPs with high INFO score (i.e. INFO >0.8) and MAF >1%. We then explored the correlations between each phenotype (i.e. hair and eye colour) and the eigenvectors of the principal components analysis. If the eigenvectors are correlated with the ancestry (i.e. geography) of the individuals, setting them as covariates may hinder the true genetic correlation between both traits, given that hair and eye colour are themselves correlated with ancestry. Therefore, we ran the genetic correlation a second time using as covariates only the non-significant principal components. In the case of the Axiom UKBB array we used PC3 and PC5, and in the case of GSA 24v1+MDP we used PC4, PC5 and PC8.The quantitative and statistical analyses are described in the relevant sections of the


Pyrus spp.) are divided into russet, smooth, and intermediate. One of the important traits in pear breeding programs is russet on pear fruit skin because it affects the commercial value. In the present study, a high-density genetic linkage map of ‘Whangkeumbae’ (smooth) × ‘Minibae’ (russet) was constructed. In addition, quantitative trait loci (QTL) analysis was performed to identify russet related QTL and develop a cleaved amplified polymorphism sequence (CAPS) marker. Together with SNPs derived from Axiom Pear 70K Genotyping Array and genotyping-by-sequencing derived SNPs and SSRs generated in previous study, an integrated genetic linkage map of ‘Whangkeumbae’ × ‘Minibae’ was constructed. A total of 1263 markers were anchored in 17 linkage groups (LGs) with a total genetic distance of 1894.02 cM and an average marker density of 1.48 cM. The chromosome coverage of ‘Whangkeumbae’ × ‘Minibae’ map was improved because the SNPs derived from Axiom Pear 70K Genotyping Array were anchored. QTL analysis was performed using previous russet phenotype data evaluated with russet coverage and Hunter a. As a result of QTL analysis, russet coverage- and Hunter a-related QTLs were identified in LG8 of the ‘Whangkeumbae’ × ‘Minibae’ map, and SNPs located in the QTL region were heterozygous in the ‘Minibae’. Although the russet coverage- and Hunter a-related QTLs were commonly detected in LG8, the logarithm of odds values of SNPs in the QTL region were higher in QTL related to russet coverage than to Hunter a. The CAPS marker (CBp08ca01) was developed using an array SNP located in the russet coverage related QTL, and the genotype of CBp08ca01 showed a 1:1 ratio in ‘Whangkeumbae’ × ‘Minibae’ . ‘Whangkeumbae’ and ‘Minibae’ were thought to have rr and Rr genotypes, respectively, and the genetic factors controlling the russet formation might be located in chromosome 8. The CBp08ca01 was able to select F1 individuals with less than 30% russet coverage. Thus, it will be a useful tool for marker-assisted selection in pears.The fruit skin types of pear ( Pyrus spp.), which belongs to the Maloideae subfamily in Rosaceae, is one of the most important temperate fruit trees worldwide [Pyrus species have 17 basic chromosomes (x = 17). According to their geographical origin, pears are divided into Asian pears  and European pears (P. communis). The fruit skin types of Asian pear are classified into three types: russet, smooth, and intermediate [Pear (orldwide . Pyrus srmediate .The russet on fruit skin is caused by the accumulation of cork layer and the formation of microcracks . The corP. pyrifolia) × ‘Taihaku’ (P. pyrifolia) and a SSR marker (Mdo.chr8.10) linked with the QTL. Genome-wide associated study (GWAS) was also performed to explore significant single nucleotide polymorphism (SNP) associated with russet using 84 Asian pear accessions and 16 full-sib families. The significant SNPs associated with russet were identified in LG8 [R, a genetic factor of cork layer formation on pear skin, on chromosome 8. In addition, Jiang et al. [P. pyrifolia. A random amplified polymorphic DNA (RAPD) marker associated with russet formation has been developed using two P. pyrifolia populations, ‘Kousui’ × ‘Kinchaku’ and ‘Niitaka’ × ‘Chikusui’ [Previous studies have found quantitative trait loci (QTL) associated with russet on pear fruit skin. Yamamoto et al.  detectedd in LG8  identifihikusui’ . The draSNP array is a high-throughput genotyping method using fixed DNA sequence information . MoleculP. pyrifolia) shows slight russet formation on fruit skin. On the other hand, ‘Minibae’ ((P. pyrifolia × P. ussuriensis) × P. pyrifolia) displays abundant russet formation on fruit skin. Therefore, ‘Whangkeumbae’ × ‘Minibae’ is suitable for the identification of genes or QTL related to russet formation. Previously, a genetic linkage map of ‘Whangkeumbae’ × ‘Minibae’ was constructed using GBS-SNPs and SSRs [1 individuals having russet fruit skin had positive Hunter a. The distributions of Hunter a and russet coverage in ‘Whangkeumbae’ × ‘Minibae’ were diverse, but image analysis evaluated russet more accurately than Hunter a. ‘Whangkuembae’  to improve the genetic linkage map of ‘Whangkeumbae’ × ‘Minibae’ by anchoring array SNPs, (2) to detect russet-related QTL and compare the results of QTL analysis using previous phenotype data evaluated by Hunter a and russet coverage , and (3)The genetic linkage map ‘Whangkeumbae’ × ‘Minibae’ anchored 1263 markers, including 976 array SNPs, 355 GBS-SNPs, and 7 SSR markers . SeventeP. bretschneideri) genome. However, the QTLs for Hunter a ranged from 12,485,588 to 16,154,759 bp. The 47 significant SNPs (p < 0.0005) in common between russet coverage and Hunter a were anchored in the QTL region. These SNPs are heterozygous paternally (<nn × np>) and both parents (<hk × hk>). Among them, 35 SNPs have the genotype <nn × np>; the locus is heterozygous in ‘Minibae’.QTLs associated with russet coverage and Hunter a were detected in LG8 of the ‘Whangkeumbae’ × ‘Minibae’ map. In the case of QTL associated with russet coverage, it was distributed from 71.2 to 138.0 cM, and logarithm of odds (LOD) values were 4.3 to 11.3 with an LOD threshold of 3.1. QTLs associated with Hunter a in 2019 were distributed 98.8 to 138.0 cM, and LOD values were 3.2 to 7.6. QTLs for Hunter a in 2020 were located from 82.3 to 139.0 cM, and LOD values were 3.2 to 7.5. The LOD thresholds of Hunter a in 2019 and 2020 were 3.0 and 3.2, respectively . The QTL1 DNA samples. After the digestion treatment with RsaI, 398 bp amplicon was generated in ‘Whangkeumbae’ and smooth F1 with the homozygous G allele. On the other hand, two DNA fragments of (134 and 264 bp) were generated in ‘Minibae’ and russet F1 with the R allele (A or G) (2 = 0.65). In addition, the CBp08ca01 could select F1 individuals with less than 30% russet coverage with a selectivity of ~70% selection rate  displayed segregation consistent with phenotype segregation in ‘Whangkeumbae’ × ‘Minibae’, and it was named CBp08ca01 . The pol(A or G) . The genion rate .The development of high-throughput screening technologies for genotyping and phenotyping has accelerated association studies, including QTL and GWAS analyses, in plants. One of the high-throughput genotyping technologies, GBS, was utilized to construct an integrated genetic linkage map of ‘Whangkeumbae’ × ‘Minibae’ . GBS is Although 4.2 times more SNPs were mapped in the ‘Whangkeumbae’ × ‘Minibae’ map than in the previous map , the numKim et al.  suggesteP. bretschneideri genome [P. bretschneideri genome [Previous studies have reported russet-related QTLs located on chromosome 8. Yamamoto et al.  identifii genome . Althougi genome ,8, we coi genome .Thus, wep < 0.0005) included in the QTL region, ~70% SNPs have the paternal heterozygous genotype of <nn × np>  would have rr and that ‘Minibae’ (russet) would have Rr because the significant SNPs in QTL were associated with russet have the genotype <nn × np>. Theoretically, the F1 individuals of ‘Whangkeumbae’ × ‘Minibae’ must show a 1:1 segregation ratio of russet to smooth. However, the F1 individuals displayed various russet coverage [Rr appeared to be affected by the environment, and RR was not [1 individuals of ‘Whangkeumbae’ (rr) × ‘Minibae’ (Rr) showed various russet coverage.Among the significant SNPs (nn × np> . Kikuchicoverage . Heteroz was not . Therefop > 0.05). Because Rr is affected by environmental factors, the CBp08ca01 could distinguish the russet at a 30% level. Therefore, the CAPS marker CBp08ca01 will be used to select individuals that are abundant or scarce in russet in the juvenile phase.In the previous study, RAPD and SSR markers associated with russet were developed in pear ,5,7. HowP. pyrifolia), ‘Minibae’ ((P. pyrifoila × P. ussuriensis) × P. pyrifoila), and 183 F1 individuals grown in the orchard of the Pear Research Station, National Institute of Horticultural and Herbal Science, Naju, Republic of Korea  were used. Fresh young leaves of ‘Whangkeumbae’, ‘Minibae’, and their F1 individuals were collected and preserved at −70 ℃ until DNA extraction. Genomic DNA was extracted with DNeasy Plant Mini Kit  according to the manufacturer’s instructions. The quality and quantity of DNA were measured using Denovix DS-11 spectrophotometry .‘Whangkeumbae’  threshold was as follows: dish quality control (DQC) ≥0.82, QC call rate ≥90%, percentage of passing samples ≥95%, average call rate for passing samples ≥98.5%. The Axiomi et al.  was appl® Pear 70K Genotyping Array into six classes. SNPs were sorted into the six classes of: PolyHighResolution (PHR), NoMinorHomozygote (NMH), MonoHighResolution (MHR), OffTargetVariant (OTV), CallRateBelowThreshold (CRBT), and other. A total of 71,363 sorted SNPs were detected. SNPs with missing values of 10% or less were used.Axiom Analysis Suite software performing Best Practice Workflow was used to classify SNPs of Affymetrix AxiomP. bretschneideri [Linkage analysis was performed using the mapping software JoinMap 5. The genetic linkage map was constructed with regression mapping algorithm and Kosambi’s mapping function. The genotype data consist of filtered 8662 array SNPs, 5965 GBS-SNPs, and 40 SSRs. Among them, the genotype data of 5965 GBS-SNPs and 37 SSRs were obtained from the previous study of Han et al. . Three Shneideri . GeneticQTL analysis was conducted using MapQTL 6.0. Genotype data of SNPs and SSRs mapped in the integrated map of ‘Whangkeumbae’ × ‘Minibae’ and russet phenotype data representing Hunter a and russet coverage  were loaRestriction sites containing SNPs located in the QTL region were identified using NEBcutter . Primer 1 individuals out of 127 F1 individuals for screening candidate CAPS marker. PCR mixture (60 µL) contained HS Prime Taq Premix (2×) , 10 pmole·µL−1 forward and reverse primers, distilled water, and 10 ng·µL−1 DNA. PCR was performed using the T100™ Thermal Cycler , under the following conditions: initial denaturation at 94 ℃ for 5 min, followed by 34 cycles of denaturation at 94 ℃ for 30 s, annealing at 60 ℃ for 30 s, and extension at 72 ℃ for 1 min with final extension at 72 ℃ for 5 min. The 60 µL PCR products were purified with AccuPrep® PCR/Gel Purification Kit  according to the manufacturer’s instructions.Two bulked DNA samples were prepared comprising 10 smooth and 10 russet FThe purified PCR products were digested according to the protocol for each restriction enzyme. The digested PCR amplicon was observed using 2.5% agarose gel electrophoresis with Azure c150 gel documentation system .rr) and ‘Minibae’  by identifying QTL in LG8. The CBp08ca01, which discriminates rr and Rr genotypes at 30% russet coverage, will contribute to increased pear breeding efficiency.The high-throughput genotyping using SNP array and accurate phenotyping using image analysis were able to identify loci associated with russet formation on pear fruit skin. In particular, we could infer the genetic factors related to russet in ‘Whangkeumbae’ (smooth,


Covalent modifications of genomic DNA are crucial for most organisms to survive. Amplicon-based high-throughput sequencing technologies erase all DNA modifications to retain only sequence information for the four canonical nucleobases, necessitating specialized technologies for ascertaining epigenetic information. To also capture base modification information, we developed Methyl-SNP-seq, a technology that takes advantage of the complementarity of the double helix to extract the methylation and original sequence information from a single DNA molecule. More specifically, Methyl-SNP-seq uses bisulfite conversion of one of the strands to identify cytosine methylation while retaining the original four-bases sequence information on the other strand. As both strands are locked together to link the dual readouts on a single paired-end read, Methyl-SNP-seq allows detecting the methylation status of any DNA even without a reference genome. Because one of the strands retains the original four nucleotide composition, Methyl-SNP-seq can also be used in conjunction with standard sequence-specific probes for targeted enrichment and amplification. We show the usefulness of this technology in a broad spectrum of applications ranging from allele-specific methylation analysis in humans to identification of methyltransferase specificity in complex bacterial communities. The covalent modification of cytosine by a methyl group leads to the formation of 5-methylcytosine (5mC), a key epigenetic modification of genomic DNA that occurs in a large number of organisms and represents so far the best-characterized form of DNA modification. In mammals, patterns of methylation are established early during embryogenesis and include X-Chromosome inactivation, imprinting, and the repression of repeats and transposable elements . Global In prokaryotes, the vast majority of genomes contain methylated nucleotides . ContrarTo circumvent these limitations, we developed Methyl-SNP-seq. Similar to the hairpin adapter method previously published , we lockWe applied Methyl-SNP-seq on a variety of biological models: First using human genomic DNA, we assessed whether the joint identification of SNPs and methylation is comparable with commonly used techniques. We also took advantage of the dual readout to question whether allele-specific methylation (ASM) can be identified. We next applied Methyl-SNP-seq in conjunction with exome capture and compared the resulting data to standard DNA-seq based exome sequencing. Finally, we applied Methyl-SNP-seq on a bacterium and a synthetic microbial community to identify methylation sites and evaluate whether a reference genome can be de novo–derived from the Methyl-SNP-seq reads.Supplemental Fig. 1A).To capture the original four nucleotide sequence information as well as cytosine modification, Methyl-SNP-seq is taking advantage of the double-stranded nature of DNA to duplicate the sequence information into a linked copy of the original strand. Importantly, this copy is amended to be resistant to bisulfite conversion by replacing all the cytosines with deamination-resistant modified nucleotides. Thus, the copied strand conserves its original four nucleotide content while the original strand undergoes deamination at unmethylated cytosines. Both strands are connected as one molecule and are sequenced using Illumina paired-end sequencing, resulting in one read containing the sequence information while the other paired read contains the methylation information  that compares Read1 with Read2 considering the conversion and complementary nature of the paired-end reads. This step, called the read deconvolution step, accurately identifies each cytosine and its methylation status. More specifically, a T in Read1 pairing with a C in Read2 corresponds to an unmethylated C, whereas a C in Read1 pairing with a C in Read2 corresponds to a methylated C , base calibration is performed using Bayesian statistics, which considers the corresponding nucleotide on the reference genome, the substitution type, and the sequencing cycle. Thus, the adjusted Phred quality score reflects the Bayesian probability that the base recorded on Read1 is true. If a reference genome is unavailable (reference-free read deconvolution), the Phred quality score, in case of pair disagreement, is assigned to 0.Supplemental Fig. 1B). The pipeline for processing and deconvoluting the linked paired-end reads is freely available in GitHub (Methods) and Supplemental Code. The outputs of the deconvolution pipeline are in a standard format compatible with existing algorithms designed for genome assembly, genetic variant calling , and meg., GATK ). The abSupplemental Figure 1B shows the data analysis workflow used for this experiment. Both replicates show similar QC and alignment metrics  indicating that the Methyl-SNP-seq protocol is reproducible.As proof of concept, we tested Methyl-SNP-seq using gDNA from the widely studied human cell line GM12878 (lymphoblastoid cell line) for which a large number of sequencing and methylation data sets are publicly available. Unmethylated lambda DNA was spiked into the human gDNA to monitor the bisulfite conversion efficiency. Methyl-SNP-seq libraries were performed in duplicate using the same source of starting material to monitor the reproducibility of the method (Methods). Whole-genome sequencing was performed using Illumina NovaSeq resulting in an average of 1.5 billion 100 bp paired-end reads per replicate. During the deconvolution step of Methyl-SNP-seq, an average of 84% of reads were successfully deconvoluted and more than 95% of the deconvoluted reads were mapped to the reference human genome using Bowtie 2 . To obtaSupplemental Fig. 2A).We first assessed the ability of Methyl-SNP-seq to detect genetic variations in the human GM12878 cell line. To increase coverage, filtered reads from the two replicates were combined for variant calling. Genetic variants were identified using the GATK pipeline  followinSupplemental Fig. 2B). Consequently, by using the common set of variants defined by both deconvoluted read and Read2, we could correct the variant calling error and improve the accuracy . Therefore, we chose the common variants between deconvoluted read and Read2 as the Methyl-SNP-seq defined genetic variants.Because both deconvoluted read and Read2 represent the original genome sequence, we can call genetic variants using either one. Overall, variants found using either the deconvoluted read or Read2 show a high level of agreement: 95% of SNPs found with the deconvoluted read being identical to those found with Read2 A. AccordUsing this set of common variants, we found 1,296,699 and 1,903,083 homozygous and heterozygous SNPs, respectively (see Data access). A total of 98% of the SNPs identified using Methyl-SNP-seq were confirmed by JIMB WGS with a better agreement for homozygous SNPs  compared with heterozygous SNPs  A,B. Our Supplemental Table 2). More specifically, the profiles of true-positive SNPs substitution observed in Methyl-SNP-seq are similar to the ones obtained by WGS .We performed standard quality controls for variant calling  and found that both the Methyl-SNP-seq and the WGS data sets displayed comparable metrics for both SNPs and indels .We next evaluated the performance of Methyl-SNP-seq in identifying and quantifying cytosine methylation. The methylation status of individual cytosine was determined in the read deconvolution step and was added to the mapped BAM file so that the base-resolution methylation information can be calculated using conventional methylation calling tools such as Bismark ; MethodsWith 1.6 billion reads from the two replicates combined, we acquired 54 million CpG sites, for which 45 million had at least 5× coverage (see Data access). These numbers are comparable with that of the WGBS method downsampled to a similar number of reads (53 million sites with ≥5× coverage) A,B. The Methylation patterns of CpG islands have been shown to affect gene expression and are linked to disease phenotypes . TherefoAttempts to infer SNPs from WGBS have been previously published  but requUsing the whole-genome Methyl-SNP-seq experiment performed on human NA12878 described above, we identified a total of 34,909 ASDMRs genome-wide. Supplemental Table 4). For example, we detected 2 ASDMRs overlapping with the known imprinted cluster of the GNAS-AS1 gene .Allele-specific methylation is also often associated with gene imprinting. Using a set of ASDMRs that are reported to be associated with known imprinted gene clusters in the human genome as a reference , we wereAS1 gene B. These XIST) gene  (PDK3 and MBTPS2 (Allele-specific methylation (ASM) is also known to be associated with X-Chromosome inactivation in female cells via regulating the X-inactive specific transcript (ST) gene . Accordi C. In add  such as d MBTPS2 D,E.2 = 98.3, df = 1, P-value < 1 × 10−9, fold change = 1.5) . However, ASDMR CpGs are not enriched in active enhancers identified by H3K27ac modification (fold change = 0.9). In addition, ASDMR CpGs are significantly depleted in the promoter regions marked with histone H3K4me3 modification . ASDMR CpGs are also enriched in the genomic regions with repressive histone mark H3K9me3 . This histone mark is associated with heterochromatin and frequently coexists with DNA methylation. H3K9me3 is also reported to play a role in establishing imprinted X-Chromosome inactivation in mice  compared with the standard exome targeted capture with the exception that it had lower coverage of AT-rich regions (AT_DROPOUT = 9). We also applied stringent filters to remove PCR duplicates, etc., consequently having about 11 million deconvoluted reads from two replicates. Although the Twist exome panel is designed to capture the gene body rather than the promoter regions, we still found 11,783 CpG islands captured with coverage above 50. Like the whole-genome sequencing, the methylation quantification of these CpG islands was consistent with the WGBS and Nanopore result  cannot be used because these methods rely on differentiating between a genuine T and a C-to-T conversion using a reference genome. The Methyl-SNP-seq method, on the other hand, identifies cytosine methylation directly on the paired-end reads in a reference-independent manner. Additionally, it reports the methylation status of individual cytosine sites with sequence context information at single-base resolution and at single-molecule level, which is most suitable for methylation motif studies. Furthermore, our Methyl-SNP-seq method also reports the original genomic sequences that can be used for genome assemblies of a single organism or a mixed population.Escherichia coli K12. We first investigated whether we could assemble the deconvoluted reads into a reliable reference genome. Using the Velvet assembler (E. coli data (16 million deconvoluted reads) with high genome coverage (94% of the genome covered) and high sequence identity (2.21 mismatches per 100 kbp)  A, which k-mers (8-mers) having methylated or unmethylated cytosine from these reads. We applied a binomial model with Bonferroni adjustments to identify the 8-bp sequences having significantly higher methylation levels. These sequences were further grouped using hierarchical clustering to uncover the consensus methylated motif indicative of methyltransferase specificity(ies). Two context clusters were found from 128 significantly enriched 8-mers. CCAGG and CCTGG can be further combined into CCWGG (with W = A or T), revealing the correct specificity for the E. coli Dcm methyltransferase  to mimic a simple mixed bacteria population. Using 0.6 million deconvoluted reads, we found three motifs instead of the two motifs expected for these strains  for library preparation. For human whole-genome Methyl-SNP-seq sequencing, we used 4 µg of NA12878 gDNA and the unmethylated lambda DNA as spiked in to monitor the bisulfite conversion efficiency. The genomic DNA was fragmented using 250 bp sonication protocol using a Covaris S2 sonicator. We set up two technical replicates. For human exome targeted Methyl-SNP-seq sequencing, 4 µg of NA12878 gDNA was fragmented using 400 bp or 500 bp sonication protocol.E. coli genomic DNA (MG1655 strain) or 2 µg of mixed bacterial DNA (containing 1 µg of E. coli MG1655 genomic DNA and 1 µg of C. acetobutylicum genomic DNA). The genomic DNA was fragmented using 250 bp sonication protocol.For bacteria Methyl-SNP-seq sequencing, we used 2 µg of Supplemental Figure 1A, the fragmented gDNA was end-repaired and dA-tailed (NEB Ultra II E7546 module), then ligated to a custom hairpin adapter using NEB ligase master mix . The incomplete ligation product (fragment having only one or no adapter ligated) was removed using exonuclease (NEB ExoIII M0206 and NEB ExoVII M0379). Two nick sites were created at the uracil positions in the hairpin adapters at both ends after being treated with UDG  and EndoVI . The nick sites were translated toward 3′ terminus by DNA polymerase I  in the presence of dATP, dGTP, dTGP, and 5-methyl-dCTP. The nick translation causes double-stranded DNA break when DNA polymerase I encounters the other nick on the opposite strand. The resulting fragments have one end ligated to a hairpin adapter and a blunt end on the other side. The blunt end was dA-tailed and ligated with Y-shape methylated Illumina adapter . The ligated product was bisulfite converted using Abcam Fast Bisulfite conversion kit . The bisulfite-converted product was amplified using NEBNext Q5U Master Mix  . The resulting indexed library was used for Illumina sequencing or target enrichment.As shown in To perform targeted sequencing, about 200–300 ng Methyl-SNP-seq indexed library was used in a pool for target enrichment. The whole-human exome regions were enriched from the pooled libraries using the Twist Human Core Exome panel  following the manufacturer's instructions. The enriched DNA fragments were further amplified using NEBNext Q5 Master Mix  and NEBNext Library Quant Primer Mix  for sequencing following the manufacturer's instructions.E. coli or mixed sample) were sequenced using an Illumina NextSeq 550 sequencer for 150 bp paired-end reads.The human Methyl-SNP-seq libraries (WGS sequencing and targeted sequencing) were sequenced using an Illumina NovaSeq 6000 sequencer for 100 bp paired-end reads. The bacteria Methyl-SNP-seq libraries (The sequence of the custom hairpin adapter (46 bp) is: 5′-(p)CCACGACGACGACGACGAGCGTTAGGCTCGTCGTCGTCGTCGUGGT-3′Supplemental Protocol in Supplemental Material.A detailed protocol is provided as C. acetobutylicum genomic DNA to prepare an EM-seq library (NEB E7120) as directed by the manufacturer. The library was sequenced using an Illumina NextSeq 550 sequencer for 75 bp paired-end reads.We used 100 ng of Reference genome and other annotation files:GRCh38 human reference genome:ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gzLambda reference genome:https://www.ncbi.nlm.nih.gov/nuccore/215104Known human SNP files used for GATK Base Quality Recalibration:ftp://ftp.broadinstitute.org/bundle/hg38/dbsnp_138.hg38.vcf.gzftp://ftp.broadinstitute.org/bundle/hg38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gzftp://ftp.broadinstitute.org/bundle/hg38/1000G_phase1.snps.high_confidence.hg38.vcf.gzJIMB NA12878 SNP VCF file used for ASM identification:ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/NA12878_HG001/NISTv3.3.2/GRCh38/HG001_GRCh38_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-X_v.3.3.2_highconf_PGandRTGphasetransfer.vcf.gzHuman CpG island annotation was generated using the “CpG island track” from UCSC browser.Data processing for Methyl-SNP-seq:Supplemental Material.The commands used for data processing are provided in https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/TrimRead.py) using Trim Galore! version 0.6.4 . For human NA12878 Methyl-SNP-seq sequencing, the bases of the last cycle (cycle 100) for both Read1 and Read2 were further trimmed because of poor quality.The sequencing reads were trimmed for both the Illumina adapter and the hairpin adapter , including the following steps:(1)Base determination and methylation extraction. For the same Illumina cycle, if Read1 base is a C and Read2 base is a C, it results in a C in the deconvoluted read and a 5mC in the methylation report; whereas if Read1 base is a T and Read2 base is a C, it results in a C in the deconvoluted read and an unmethylated C in the methylation report.(2)Base quality score adjustment. For the mismatching positions that Read1 bases are different from Read2 bases except for the Read1-T Read2-C case, Read1 bases are used but the sequencing quality scores are adjusted to 0 in the deconvoluted reads.If the reference genome is not available, for example, Methyl-SNP-seq for https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/Deconvolution WithCalibration). The step (1) base determination and methylation extraction is the same as the reference-free read deconvolution. But reference-dependent read deconvolution uses a statistical model for the base quality score adjustment in step (2) as shown below.If the reference genome is available, for example, Methyl-SNP-seq for human NA12878, a reference-dependent deconvolution was performed to increase the performance ((2) Base quality score adjustment. For the mismatching positions, by comparing to the reference genome, a Bayesian probability is calculated, which reflects the likelihood of being able to trust the Read1 base. Therefore, Read1 bases are used but the sequencing quality scores are adjusted based on the Bayesian probability in the deconvoluted reads.In a typical Methyl-SNP-seq experiment, about 10%–15% of reads are undeconvolutable. Data suggest that the partial exonuclease activity to eliminate unligated/partially ligated fragments is the main reason for undeconvoluted reads in our sequencing result. Incomplete digestion by the exonuclease results in regions of single-stranded DNA that are subsequently filled in during the end-repair step resulting in double-stranded DNA molecules that can be ligated and amplified. After sequencing, these fragments represent conventional paired-end bisulfite reads mapping to flanking regions of the genome with the exception that the fill-in regions are resistant to bisulfite conversion. This scenario is supported by the fact that (1) a notable fraction of the undeconvoluted reads map to the genome close to their pair and (2) an optimized exonuclease treatment decreased the percentage of undeconvoluted reads.For human NA12878 Methyl-SNP-seq, the deconvoluted reads were aligned to the GRCh38 human reference genome using Bowtie 2 (version 2.3.0) default parameter for single-end mapping with the addition of read group identifier defined by ‐‐ rg-id and ‐‐rg. These identifiers  were necessary for Base Quality Score Recalibration by GATK for variant calling.(1)https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/MarkUniread.py). Here, for Bowtie 2 single-end mapping, the unique mapping is defined as the read having only AS tag or AS score != XS score ;removal of multiple mapping using a custom script ((2)https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/MarkDup.py). Here, for Bowtie 2 single-end mapping, the PCR duplicates are identified as reads aligned to the same position as well as having the same sequence;removal of PCR duplicates using a custom script ((3)https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/AddXMtag.py). The XM tag is defined by Bismark to mark methylation call string and used to extract methylation status;addition of an XM tag reflecting the methylation status. Based on the methylation report generated in the read deconvolution step, an XM tag is added to each mapped read in the sam file using a custom script ((4)removal of reads having incomplete bisulfite conversion using Bismark (version 0.22.3) filter_non_conversion.To achieve high accuracy, the following steps were taken to filter the aligned data before variant calling and methylation status determination:The resulting filtered deconvoluted reads from two replicates were combined to be used for variant calling and methylation determination. There were 1.6 billion and 11 million filtered deconvoluted reads for human WGS and exome-targeted Methyl-SNP-seq, respectively.https://github.com/elitaone/Methyl-SNP-seq/tree/main/Read_Processing/MarkUniread.py). To achieve a similar coverage, we downsampled to use 1.6 billion filtered JIMB WGS reads (Read1 and Read2) for variant calling.Whole-genome sequencing of human NA12878 generated by JIMB NIST Genome in a Bottle  JIMB WG, we procWhole-genome bisulfite sequencing (WGBS) of human NA12878 generated by ENCODE (ENCSR890UQO) was compared with Methyl-SNP-seq for methylation quantification. For a fair comparison, we reduced the paired-end WGBS data to 99-bp long and trimmed the Illumina adapters. Next, the read pairs were aligned to the human GRCh38 genome using Bismark (version 0.22.3). The properly paired reads were further filtered before methylation determination by: (1) removing PCR duplicates using SAMtools markdup; (2) filtering out alignments having incomplete bisulfite conversion using Bismark filter_non_conversion. The two ENCODE replicates were combined to about 1.6 billion filtered reads (Read1 and Read2) for methylation quantification.We performed variant calling on the filtered data set as mentioned above using GATK (version 4.1.8.1), following GATK best practice recommendations for germline short variant discovery. First, BaseCalibration  was applied to the filtered data set to calibrate the systematic errors made by sequencing. Next, the calibrated reads were used for variant calling using HaplotypeCaller. Finally, FilterVariantTranches was applied to filter raw SNVs using ‐‐info-key CNN_1D and ‐‐snp-tranche 99 ‐‐indel-tranche 99. For human targeted Methyl-SNP-seq sequencing, an additional filter “DP < 6” was applied to remove SNPs with low coverage. In this study, only SNVs on the somatic chromosomes, Chr X and Chr M, were reported and used for analysis.Methyl-SNP-seq genetic variants were defined as the common SNVs identified by both deconvoluted read and Read2. We used vcfeval from RTG Tools (version 3.11)  to compaFor both Methyl-SNP-seq and WGBS, the methylation information was extracted on the filtered reads and read pairs, respectively, using bismark_methylation_extractor (version 0.22.3) with the following parameters: ‐‐merge_non_CpG ‐‐bedGraph.https://github.com/nanopore-wgs-consortium/NA12878/blob/master/Genome.md)  . The Nannome.md) . The metnome.md)  call-metThe methylation level of UCSC annotated CpG islands (CGI) was defined as:CGI methylation = number of methylated CpG Cs in the region/number of CpG Cs in the region.Only the CGIs having coverage (number of CpG Cs in the region) above 50 were used for comparison between different methods.To discover the allele-specific methylation loci in the NA12878 genome, we used the heterozygous SNPs detected by Methyl-SNP-seq and confirmed in the JIMB NA12878 SNP VCF file . We spliE. coli and mixed samples, respectively. The following parameters were used for Velvet assembly to obtain the best result: for E. coli, k = 81 -fastq -short -exp_cov 13 -cov_cutoff 9 -min_ contig_lgth 500; for mixed sample, k = 75 -fastq -short -exp_cov 15 -cov_cutoff 8 -min_contig_lgth 500. The assembly quality was estimated using the QUAST web interface  based on 16.4 million and 36.7 million deconvoluted reads for nterface .E. coli or mixed samples . The 8-mers including 3 bp upstream of and 4 bp downstream from either a 5mC or unmethylated C were extracted for each read. The reads including more than one methylated C were excluded from this analysis . The numbers of 8-mers containing either 5mC or 5mC and unmethylated cytosine were counted. We used Binomial statistics with Bonferroni Correction to determine the 8-mer sequences that have significantly higher methylation levels compared with the background. The P-value is calculated using the following formula:We randomly chose 2% deconvoluted reads to identify the methyltransferase recognition site in k is the number of 8-mers having 5mC; n is the number of 8-mers having 5mC and unmethylated cytosine; P0 is the average methylation level. We used a custom script to perform this statistical analysis to extract the significantly enriched/methylated 8-mers .For each 8-mer sequence, https://github.com/elitaone/Methyl-SNP-seq/tree/main/Methylation_Motif_Calling/clusterMotif.py). The number of clusters (‐‐number) can be decided based on the cluster heatmap. Specifically, in this study, we assigned the significantly enriched sequences into two clusters for E. coli and three clusters for the mixed sample  under accession number GSE206253.All raw and processed sequencing data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


Our work may facilitate fine-map regulatory mechanisms underlying risk loci of cancer and discover the biomarkers or therapeutic targets for cancer prognosis. The BCaQTL database will be an important resource for genetic and epigenetic studies.Genome-wide association study (GWAS) has identified thousands of single nucleotide polymorphisms (SNPs) associated with complex diseases and traits. However, deciphering the functions of these SNPs still faces challenges. Recent studies have shown that SNPs could alter chromatin accessibility and result in differences in tumor susceptibility between individuals. Therefore, systematically analyzing the effects of SNPs on chromatin accessibility could help decipher the functions of SNPs, especially those in non-coding regions. Using data from The Cancer Genome Atlas (TCGA), chromatin accessibility quantitative trait locus (caQTL) analysis was conducted to estimate the associations between genetic variants and chromatin accessibility. We analyzed caQTLs in 23 human cancer types and identified 9,478 caQTLs in breast carcinoma (BRCA). In BRCA, these caQTLs tend to alter the binding affinity of transcription factors, and open chromatin regions regulated by these caQTLs are enriched in regulatory elements. By integrating with eQTL data, we identified 141 caQTLs showing a strong signal for colocalization with eQTLs. We also identified 173 caQTLs in genome-wide association studies (GWAS) loci and inferred several possible target genes of these caQTLs. By performing survival analysis, we found that ~10% caQTLs potentially influence the prognosis of patients. To facilitate access to relevant data, we developed a user-friendly data portal, BCaQTL ( As the most common type of genetic variation, single nucleotide polymorphism (SNP) plays a vital role in complex human diseases. Genome-wide association studies (GWAS) have identified thousands of SNPs associated with human traits or diseases . HoweverAs one of the high-throughput sequencing, ATAC-seq was used to identify open chromatin regions which usually represent cis-regulatory elements , 10. IncThe Cancer Genome Atlas (TCGA)  generatehttps://tcga-data.nci.nih.gov/tcga/). For better performance in QTL analysis, we imputed autosomal variants for all samples in 23 types of cancer using IMPUTE2 (p > 1e-6 (estimated by Hardy-Weinberg R package). To eliminate the impact of population structure on chromatin accessibility, we used smartpca in the EIGENSOFT program to generate the principal component factors. We included the first five principal components in the covariates.We obtained the genotype matrix of 898,620 SNPs detected using the Affymetrix SNP 6.0 array from the TCGA data portal ( IMPUTE2  (1000 Ge IMPUTE2  as the rhttps://tcga-data.nci.nih.gov/tcga/), containing ATAC-seq bam files of 410 tumor samples from 404 TCGA donors across 23 cancer types. The processing strategy described in Corces et al. . Then, to obtain independent peak sets in each cancer type, we combined peak sets from all the samples in that cancer type. Next, we used the same iterative removal procedure to obtain independent peaks. We only kept peaks that: (i) were observed in over two samples, (ii) have an SPM ≥ 5, (iii) did not span the gap region, and (iv) were on the autosomes. These SPM matrices were used in caQTL mapping and downstream analysis.Finally, to remove the hidden batch effects or other confounders in the chromatin accessibility data, the PEER R package was used to generate the PEER factors from chromatin accessibility data to minimize the batch effect. Reported that PEER factors equivalent to 25% of the sample size were selected to avoid the batch effect of the chromatin accessibility data (We obtained the data on chromatin states from the Roadmap Epigenomics Consortium. We combined related chromatin states and redefined the states as follow: (i) 1_TssA, 2_TssFlnk, 3_TssFlnkU, 4_TssFlnkD, and 14_TssBiv to promoter, (ii) 5_Tx and 6_TxWk to transcribed, (iii) 7_EnhG1, 8_EnhG2, 9_EnhA1, 10_EnhA2, 11_EnhWk, and 15_EnhBiv to enhancer, (iv) 16_ReprPC and 17_ReprPCWk to polycomb, (v) 12_ZNF/Rpts to ZNF repeats, (vi) 13_Het to heterochromatin and 18_Quies to quiescent. We calculated the coverage of each ATAC-peak for each chromatin state using the BEDTools “coverage” command. The peak was assigned to the chromatin state with the highest coverage, except for quiescent. Only when the peak has a coverage of 100% with quiescent will it be assigned to quiescent. Otherwise, that peak will be assigned to the chromatin state with the second-highest coverage. Background peaks were selected randomly by the bedtools shuffle command.https://jaspar.genereg.net/downloads/) and restricted to TFs expressed in cancer. To do this, we obtained the expression data of all cancer types from TCGA and TFs with a median TPM > 1 marked as expressed in that cancer type. Only the expressed TFs were included in TF enrichment and motif disruption analyses.We downloaded all of the 838 non-redundant transcription factors (TF) binding motifs for vertebrates from JASPAR CORE 2022   software , we testcaQTL was mapped using the MatrixEQTL  R packagColocalization analysis could find the causal variation that caQTLs and eQTLs may share. To do this, we used the Bayesian test for colocalization implemented in the coloc R package to assess the probability that cis-caQTLs and cis-eQTLs shared the same causal variant. The cis-eQTL data used in this analysis came from our previous research . We adopTo identify the QTL pairs for the colocalization test, we extracted the lead caQTL of the caQTL peak for each caQTL peak. The eQTL gene correlated with this lead caQTL was tested for colocalization. If multiple genes are linked with the same lead caQTL, we first obtained the lead eQTL SNP of each eQTL gene, and then we calculated the LD between the lead eQTL SNP and the lead caQTL SNP. Only the eQTL genes whose lead eQTL SNP has the highest LD with the lead caQTL SNP were retained. For each caQTL peak, this workflow helped us obtain a peak-gene pair that showed an association with the same SNP. Then, we applied the Bayesian model implemented in the coloc R package to all peak-gene pairs. Colocalization results with a PP4 > 0.8 were regarded as solid colocalization evidence.Using the colocalized peak-gene pairs, we applied model-based causal meditation analysis implemented in the mediation R package to investigate whether the effect of a cis-eQTL SNP is mediated by influencing chromatin accessibility. To do this, we extract the chromatin accessibility and gene expression data of all matched samples for each colocalized pair. We fitted two statistical models to estimate the effect direction of the SNP. In the mediator model, we hypothesize chromatin accessibility as the mediator and gene expression as the outcome. Moreover, we reverse the mediator and the outcome in the outcome model. The average causal mediation effects (ACME) calculated by coloc were used to characterize the mediation effects in a specific model. In addition, we used the nonparametric bootstrap  instead of the quasi-Bayesian Monte Carlo simulation for variance estimation.2 > 0.5 calculated by LDlink (https://ldlink.nci.nih.gov/). The caQTL SNPs falling into the LD regions were identified as GWAS-related caQTLs. LocusZoom  - lg(p_strong) was used for quantifying the difference in motif match between alleles  software  and the homozygous carriers of the minor allele (aa) were merged into one group and tested against the other. In the recessive model, the heterozygous donors (Aa) and the homozygous carriers of the major allele (AA) were merged into one group and tested against the other group. The Kaplan–Meier plot was used for visualization.https://github.com/macs3-project/MACS) to get open chromatin regions. By adopting the strategy described by Corces et al. (We downloaded the ATAC-seq data of 410 samples derived from 404 tumor donors across 23 cancer types from The Cancer Genome Atlas (TCGA). We then performed peak calling using MACS2  binding sites in these ATAC-peaks. We found 374 TFs significantly enriched  in all of the 23 cancer types, including 125 oncogenes on the list of oncogenes provided by the Network of Cancer Genes  , and four of them were in the oncogenes list mentioned before. It is worth noting that the rs4283211-BRCA_162467-IDH2 pair has a significant mediating effect , which means that the effect of SNP on gene expression is entirely through chromatin accessibility. Another example was the pair rs2071487-BRCA_9060-GSTM1 (p = 0.004) and significant direct effect (p < 0.001), indicating that the impact of this SNP on the expression of GSTM1 may be exerted through the accessibility changing of BRCA_9060 (chr1:110230129-110230630).To assess the evidence that chromatin accessibility mediates the effect of a SNP on local gene expression, we performed mediation analysis on our 141 colocalized SNP-peak-gene pairs. Under a threshold of adjusted p < 4.6e-4, CTCF, RREB1, TFAP2A, BNC2, and BACH2. According to previous reports, CTCF induced the expression of Nm23-H1 and was related to cell migrations in MDA-MB-231 cell lines  is an important regulatory pathway of genetic variants , 40, 41 ll lines . In our 2 > 0.5, we identified 173 GWAS-related caQTLs mapped to three GWAS loci. Previous studies have not reported clear causal SNPs and genes for these three GWAS loci. Among these caQTLs, only two caQTL SNPs, rs9289981 -based-colocalization analysis on our caQTLs and GWAS SNPs downloaded from the GWAS Catalog , 43. UsiLMTK2 after rs6465658 (BHLHA15 (p = 4.02e-4) but showed no significant effect on gene expression in breast cancer. The three SNPs showed high LD with each other  associated with survival time. For example, the G-to-C mutation of rs2223850 was significantly correlated with a worse prognosis . An example was the caQTL rs9289981, a GWAS-related caQTL significantly related to the overall survival time of BRCA patients. Individuals with T/T genotype exhibited worse prognosis than those with A/T and A/A  and identified 730 caQTL SNPs significantly .Publicly available datasets were analyzed in this study. This data can be found here: TCGA data portal  and Huazhong Agricultural University Scientific & Technological Self-innovation Foundation (11041810351 to JG), and the Fundamental Research Funds for the Central University HZAU (Grant No. 2662017JC048 to XN).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


ST. These analyses identified 33 SNPs as discriminating between high and low WSC populations and putatively under selection. One SNP was located in the intron of ERD6-like 4, a gene coding for a sugar transporter located on the vacuole membrane. A genome-wide association study using a subset of 605 white clover individuals and 5,757 SNPs, identified a further 12 SNPs, one of which was associated with a starch biosynthesis gene, glucose-1-phosphate adenylyltransferase, glgC. Our results provide insight into genomic regions underlying WSC accumulation in white clover, identify candidate genomic regions for further functional validation studies, and reveal valuable information for marker-assisted or genomic selection in white clover.Increasing water-soluble carbohydrate (WSC) content in white clover is important for improving nutritional quality and reducing environmental impacts from pastoral agriculture. Elucidation of genes responsible for foliar WSC variation would enhance genetic improvement by enabling molecular breeding approaches. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) associated with variation in foliar WSC in white clover. A set of 935 white clover individuals, randomly sampled from five breeding pools selectively bred for divergent (low or high) WSC content, were assessed with 14,743 genotyping-by-sequencing SNPs, using three outlier detection methods: PCAdapt, BayeScan and KGD-F Trifolium repens L.) is sown in temperate pastures globally, as it provides high quality forage for ruminants and is a source of bioavailable nitrogen, fixed through symbiosis with soil Rhizobium bacteria  that differentiate populations with divergent phenotypes. This approach most commonly involves Fed tests  or princed tests  to idented tests . Both oued tests .Selective breeding to create experimental white clover populations with divergent levels of foliar WSC was previously undertaken in two New Zealand breeding programmes, with five discrete breeding pools. Three were described by Genotyping-by-sequencing (GBS), enables highly efficient and cost-effective simultaneous SNP discovery and genotyping  and has There were three principal objectives: (1) to confirm that foliar WSC phenotypes were significantly and directionally different amongst the populations used in the study, ensuring that subsequent genetic studies were performed on truly divergent phenotypes; (2) to establish whether selective breeding had altered WSC independently of changing leaf area in those populations; and (3) apply outlier detection and GWAS approaches to identify SNPs associated with foliar WSC accumulation.The plant material used in this study was bred and supplied by Grasslands Innovation Ltd. Selective breeding for foliar levels of water-soluble carbohydrate (WSC) in white clover was completed previously in two breeding programmes, one consisting of four cycles of recurrent selection in three breeding pools  and the Nomenclature for population names is: W = Widdup, F = Ford; NZ = New Zealand, US = United States of America; LL = large leaf, SL = small leaf; Low = low water-soluble carbohydrate (WSC), High = high WSC; P = parental generation, Mid = middle generation and End = end generation. The 25 populations were: WNZLL-Low-End, WNZLL-Low-Mid, WNZLL-Parent, WNZLL-High-Mid, WNZLL-High-End, WNZSL-Low-End, WNZSL-Low-Mid, WNZSL-Parent, WNZSL-High-Mid, WNZSL-High-End, WUSLL-Low-End, WUSLL-Low-Mid, WUSLL-Parent, WUSLL-High-Mid, WUSLL-High-End, FNZLL-Low-End, FNZLL-Low-Mid, FNZLL-Parent, FNZLL-High-Mid, FNZLL-High-End, FNZSL-Low-End, FNZSL-Low-Mid, FNZSL-Parent, FNZSL-High-Mid, and FNZSL-High-End.Approximately 100 seeds from each of the 25 populations were germinated, planted into propagation trays and grown under standard greenhouse conditions for two months. A total of 900 plants from the 25 populations (180 plants per breeding pool) were then randomly selected and transplanted into 2L pots for phenotyping. In each breeding pool, 60 plants per parent population (Parent) were randomly selected, along with 30 plants each from the middle generation low and high WSC populations  and 30 plants each from the end generation low and high WSC populations . Potted plants were kept in the greenhouse to establish for two weeks before being placed outside at Palmerston North, New Zealand , in autumn, late May 2017, in a randomised Latin square design with three replicate blocks. The block was set up on a 9 × 9 m concrete pad and plants were set at 30 cm centre-to-centre spacing.n = 600) were analysed by near infra-red spectroscopy (NIRS) at the Massey University Nutrition Laboratory,  for nutritive quality attributes including WSC concentration (g kg-1 dry matter). Two NIRS calibrations were used initially to determine WSC: (a) total soluble sugars and starch (SSS)  , the calch (SSS) .2) was estimated. The mean of four leaves was used as a proxy for average plant leaf area.Leaf area was assessed for each of the five pools. A total of 450 of the 900 plants were sampled from every second column, across all three blocks (150 plants per block). Four leaves per plant (first leaves from the stolon tip with fully opened laminae) were collected, glued to 1 mm graph paper and scanned. Scanned images were converted to binary in ImageJ , a globaemmeans” v 1.4.2 (Equation 1).Estimated Marginal Means for the WSC phenotype, using SSS, and leaf area were calculated for each population using a linear mixed model in the R package “ v 1.4.2  that accy is the phenotypic trait, and TrtC is the interaction between Pool, Generation  and Trt , high WSC and low WSC).Where: 2 scale. The Parent population was used as the baseline for comparisons between populations within each pool. Significant differences in these comparisons were investigated using “emmeans” at α = 0.05. The Low-End population was then used as the baseline for the SSS dataset so differences between the Low-End and High-End populations for each pool could be determined.Leaf area data were transformed by square root such that residuals conformed to constant variance and normality (2) values of the lines of best fit were used to initially compare the predictive abilities of leaf area for SSS. Regression analysis amongst populations was then conducted using linear mixed models constructed for each pool. Models were constructed with SSS as the dependent variable and the interaction between leaf area and population was used as the independent variable. The Parent population for each pool was used as the baseline for comparison. Adjusted r2 was reported for both types of linear models (SSS ~ leaf area and SSS ~ 0 + Population * leaf area) to avoid false inflation of r2 values. Residuals were checked for normality and assumptions were met; thus, no data transformation was required. The R scripts used to produce estimated phenotype means and perform correlation and regression analyses can be found at https://github.com/SofiePearson/White_Clover_WSC_Outlier_Detection_GWAS.Three hundred plants common to the sets used for evaluation of WSC and leaf area were used for phenotypic correlation analysis (Pearson correlation). This dataset was then split further into five datasets corresponding to the five pools  within which correlations between the two variables were investigated. Prior to correlation analysis, leaf area and SSS data were evaluated for normality using the Shapiro-Wilk Normality Test implemented in R . Data thPstI and MspI, with some modifications (see Supplementary Methods). Each library was then sequenced in parallel on two lanes of a flow cell on an Illumina HiSeq 2500  at Invermay Agricultural Centre .Genomic DNA was extracted from 1,536 white clover individuals  using the freeze-dried tissue protocol described in Raw data FASTQ files containing sequence reads were processed for SNP identification using Trait Analysis by aSSociation, Evolution and Linkage (TASSEL) v 5.0 , using dAfter SNP calling, all filtering was performed using VCFtools v 0.1.16 . To provadegenet” v 2.1.1 , with 800 principal components retained, accounting for approximately 90% of the total genetic variation. The optimal clustering solution corresponded with the lowest Bayesian Information Criterion (BIC). Individual assignment from the a priori grouping to the K-means determined clusters was visualised and compared using the R package “ade4” v 1.7-15  , impleme v 2.1.1  for R so v 2.1.1 . The anav 1.7-15 .xvalDapc” function, and was run with 100 replicates from 1 – 50 principal components (PCs) with individuals grouped based on the K-means determined number of clusters. This cross-validation method determined 6 PCs should be used, hence the final “dacp” was run with “n.pca = 6” and 6 discriminant functions (DFs) retained. A scatter plot of individuals grouped by K-means on DFs was created using “adegenet”  with 9,999 permutations (a priori of K = 24 and the DAPC-determined grouping of K = 11) was also computed using the R package “hierfstat” v 0.04-22  implemented with “ v 2.9.1  in R. Thutations . A matri 0.04-22  using th4-22   . Missingpcadapt” v 4.0.3 was used to detect loci driving variation on the principal components  with the best fit to the data was determined using the scree test  separating high and low WSC populations, each of the five pools was analysed in PCAdapt separately. Scree plots were produced  were visualised in a Venn diagram using the R package “VennDiagram” v 1.6.20 (https://github.com/SofiePearson/White_Clover_WSC_Outlier_Detection_GWAS.The filtered VCF file was converted to a Reference Alternative file using the KGD ad depth . The fivv 1.6.20 . The scrrrBLUP” (n = 605) possessing both genotypic and phenotypic information  was used to determine the position of the SNP in the protein and whether there was a synonymous or non-synonymous change. For outlier SNPs that occurred outside of genes, a maximum distance of 10 Kbp either side of each SNP were recorded. When considering potential candidate genes, the upstream and downstream regulatory elements of the gene, including the promoter region, were also considered. If intergenic outlier SNPs were located less than 1 Kbp away from the start codon of a gene, they were also classified as putatively in linkage disequilibrium with the gene due to the proximity to a promoter. White clover genome annotations , and therefore subsequent analyses focused principally on SSS alone. Population fitted values for SSS are presented in p < 0.01) for all comparisons, with the low WSC populations lower for SSS than the Parent, and the high WSC populations higher for SSS content than the Parent (p < 0.05) differences between the Low-End and High-End populations (mean difference of 78.3 g kg-1 DM) (Water-soluble carbohydrate (WSC) content was measured using two NIRS calibrations , which were found to be highly correlated within the sample set (re Parent Table 1.2) are presented in p < 0.05)  and WUSLL  pools showed a significant decrease in leaf area from the Parent to Low-End population. In the WNZSL pool there was a significant increase in leaf area in both the High-Mid and High-End populations relative to the Parent population .Population fitted values for mean leaf area (cm < 0.05) Table 1.p < 0.05 except for FNZSL (p = 0.73). Pearson’s coefficients of determination for WNZLL (r2 = 0.13), WNZSL (0.33), WUSLL (0.26), FNZLL (0.09), FNZSL (-0.015) and the combined dataset (0.14), indicated between 1.5 – 33% of the observed SSS phenotypic variation was accounted for by leaf area in each of the pools. These low r2 values demonstrate that the basic linear model (SSS the dependent variable and leaf area the independent variable) provided a poor to average fit to the data.Correlation analysis was used to measure the strength of relationship between SSS and leaf area for each pool and for the combined pool dataset , with the exception of WUSLL-Parent , FNZLL-Low-End  and FNZLL-High-End . Because all populations, except WUSLL-Parent, had non-significant p-values (p > 0.05) for both the intercept and slope, we were unable to reject the null hypothesis, allowing the conclusion that there was no relationship between SSS and leaf area for all but one population (WUSLL-Parent).The data were then split into populations for each pool and regression analysis was used to test if leaf area was significantly predictive of SSS for each pool at the population level. Linear models were constructed with SSS as the dependent variable and the interaction between leaf area and population was used as the independent variable. Including populations in the linear model increased the adjusted r-1 were selected . Bioanalyzer evaluation of the 13 pooled GBS libraries, prior to sequencing, showed that small adapter dimers present in the pre-size selection libraries (88 bp) were removed successfully post-size selection and that library fragment sizes were limited to the targeted 193 – 313 bp range.High molecular weight (> 15 Kbp) genomic DNA and free from RNA contamination, was isolated successfully from 1,536 plants. For GBS library construction, 47 individuals per population with DNA concentration > 10 ng µLA total of 191,484 SNPs were called initially across all samples. After filtering for depth, multiallelic loci, missing and minor allele frequency, a total of 14,743 SNPs were retained for 1,113 samples. A total of 109 samples were removed across all populations, most of which were population WNZSL-Parent, due to a high proportion of missing data (> 80%). A further 15 samples from amongst other populations were removed due to high missing data (> 80%). Positive control samples, a single genotype repeated in all 13 GBS libraries, were at first retained to check for consistency across GBS libraries using a principal component analysis (PCA). Duplicated GBS data from 47 individuals were also removed as they were derived from duplicated technical replicates included for quality control and were not required for subsequent analyses.T. occidentale-derived (TrTo) subgenome, and pseudomolecules 9 to 16 belong to the T. pallescens-derived (TrTp) subgenome , except for nine individuals from the WUSLL-Parent population that grouped with WUSLL-H. One to two samples from the FNZLL-Low-End, WUSLL-High-Mid and WUSLL-Low-End also grouped with the PARENT cluster.Discriminant analysis of principal components (DAPC) was conducted to determine the number of clusters described by the data and to validate the pre-defined genetic clusters . The lowest Bayesian information criterion (BIC) value from the “grouping Figure 3xvalDapc” function determined the optimal number of PCs to retain was 43 (root mean square error = 0.0033). However, both the root mean square error and mean successful assignment plateaued at 6 PCs (root mean square error = 0.0107 and mean successful assignment = 0.9928) with very little change thereafter (K-means and the two axes representing the first two discriminant functions (DFs) of the DAPC analysis , separation occurred on both the first and second DF. For WNZSL and WUSLL, Low and High population clusters showed very little separation on the first two DF and in fact clear separation was not observed on any DF (data not presented). The FNZLL-L and FNZLL-H clusters only showed clear separation on the fifth DF.The “analysis Figure 4ST) was calculated both for the a priori population grouping (K = 24) and for the grouping identified above (K = 11). For biallelic marker systems ST values from 0 – 0.05 indicate little differentiation, 0.05 – 0.15 moderate differentiation, 0.15 – 0.25 great differentiation, and values above 0.25 indicate high differentiation (a priori population grouping (K = 24), the WUSLL-Parent population showed moderate genetic differentiation from the NZ Parent populations (0.06 – 0.08), while FST values were low among the three NZ Parent populations (0.03 – 0.04). Pairwise comparisons of the high WSC populations (Mid and End) within each pool and of the low WSC populations (Mid and End) within each pool showed low to moderate differentiation, with FST values ranging from 0.03 – 0.09. Divergent low and high WSC pairs within a pool  showed greater differentiation as indicated by an FST range of 0.12 – 0.22  , and the remainder partitioned among populations (19.3%). A hierarchical AMOVA for the 11 genetic clusters determined by K-means revealed that 15.4% of the variance was distributed among clusters, and only 5.4% was distributed among populations within clusters. Approximately similar variance was found within populations or clusters , indicating that genetic variation is mainly distributed within populations  for each pool can be grouped together and the two low WSC populations (Low-Mid and Low-End) for each pool can be grouped together. Population groupings for subsequent outlier locus detection were based on this K = 10 grouping (Parent populations were excluded).A total of 342 loci with missing values less than 5% were used for the analysis of molecular variance (AMOVA). The AMOVA for PCK value, the number of PCs to investigate, in all four pools was PCK = 1. The first PC captures the distinction between high and low WSC populations in all pools  larger than 5.34 were retained from each pool and identified as putative outliers. To reduce the risk of detecting SNPs associated with population structure, SNPs identified as outliers were subjected to another criterion: they had to be present as outliers in two or more pools. Of 643 total outlier SNPs detected using PCAdapt, 36 were found in common amongst two or more pools based on PC1. A total of 329 outliers were detected using BayeScan at α = 0.05, with 27 common to two or more pools  had the most SNPs in common (n = 22) whereas PCAdapt and BayeScan only had two SNPs in common and PCAdapt and KGD-FST had 13 SNPs in common.PCAdapt was used to identify SNPs corresponding to PCs that differentiate low and high WSC populations, within each pool. Cattell’s scree test  > 3. By the same criterion two additional SNPs on pseudomolecules 1 and 5 were ranked highly for the WSC-NIRS trait but not SSS (VPS35B (Vacuolar protein sorting-associated protein 35B) gene, the second located to the intron of a CBP (chlorophyll a-b binding protein) gene, and the third located to the coding region of a La-related protein 7-like gene. The two markers on pseudomolecule 3 located to the intron of a transcription regulating protein (PKS-NRPS hybrid synthetase CHGG_01239-like), and the marker on pseudomolecule 4 was located 305bp upstream of the start codon of a gene with unknown function. The SNP on pseudomolecule 5 was located 855 bp before a glgC (glucose-1-phosphate adenylyltransferase) gene, the SNP on pseudomolecule 6 was intergenic, the SNP on pseudomolecule 8 located to the intron of COG8 (conserved oligomeric Golgi complex subunit 8), the SNPs on pseudomolecule 9 were located in the coding region of UPL6 (E3 ubiquitin-protein ligase) gene and on the intron of a mixed-amyrin synthase gene. The SNP on pseudomolecule 11 located to the coding region of a clustered mitochondria protein gene. Two SNPs located in coding regions conferred non-synonymous mutations, as both altered the first base of a codon and subsequently the encoded amino acid. The SNP located in VPS35B caused an isoleucine to valine change, while the SNP in UPL6 caused a glutamine to glutamic acid change. The SNP in the coding region for the clustered mitochondrial protein gene and the SNP for the La-related protein 7-like gene both conferred synonymous mutations. The SNP on pseudomolecule 5 was located near a gene (glgC) that may be considered a prime candidate for a role in WSC accumulation, based on its inferred function. Significant SNPs for other phenotypic traits are found in A genome-wide association study (GWAS) was carried out using 24 white clover populations that had both genotype and WSC phenotype data. No SNPs were found to be significantly associated with SSS after correction for multiple testing  within each pool coalesced to a single cluster, as did the two low WSC populations (Low-Mid and Low-End). By contrast, the Parent populations for all pools formed a single cluster. AMOVA on this reduced genetic cluster set showed greater variation among clusters (15.4%) than among populations within clusters (5.4%), indicating the populations within clusters were very similar. This is supported by FST values (FST 0.03 – 0.09) representing low genetic differentiation (ST 0.12 – 0.22) between Low WSC and High WSC clusters, indicating selection was successful in driving genetic separation between the high and low WSC populations. Selection within the first 2 – 3 generations (Parent to Mid), therefore, produced the greatest genetic change, with less change occurring in the next 2 – 3 generations (Mid to End). This pattern is reflected in both WSC phenotypes  should be used. One SNP associated with movement of sugars showed this pattern  pool was slightly more genetically distinct from the New Zealand (NZ) cultivars (FST 0.06 – 0.08), than the NZ Parent populations were among themselves (FST 0.03 – 0.04)  , with pa studies . It is,  ‘SRVR’; ) and NZ  ‘SRVR’; ). As theAnalysis of molecular variance (AMOVA) revealed that the genetic variation within each of the 24 white clover populations accounted for a mean 81% of the total variation, whereas 19% of the variation occurred among populations . The higST-derived methods: BayeScan and KGD-FST. However, BayeScan FST values were higher than those estimated by KGD-FST, for all pairwise comparisons. For example, in the WNZLL pool, the minimum FST value determined for a SNP locus by BayeScan was 0.21 with a maximum of 0.69 and mean of 0.23. In the equivalent KGD-FST analysis the minimum FST value was 0.0, maximum of 0.99, and the mean was 0.04. Both sets of FST values fitted χ2 distributions (data not presented), but the mean BayeScan FST values were higher. BayeScan is relatively robust against confounding demographic processes, but strong selection, hierarchical structure, population bottlenecks and recent migration can impact this method and artificially inflate FST  ≤ 2 indicates decisive evidence for selection and corresponds to large positive alpha values. Therefore, not only do the SNPs need to have high FST values, but also large alpha values to be indicative of adaptive selection. Although the inflated FST values technically indicate all the SNPs are under selection, as FST values greater than 0.25 indicate high genetic differentiation  and should be used to make decisions  monosaccharide vacuole transporter, ERD6-like 4 (At1g19450), was physically associated with SNP 16_32428574, detected in both the WNZLL and FNZSL pools. ERD6-like transporters are involved in energy-independent sugar efflux from the vacuole (ERD6-like 4 as being highly homologous to the sugar beet (Beta vulgaris) hexose transporter U43629 (SWEET) proteins were discovered  approach to reduce Type I error, whereby SNPs that are the highest ranking and have -log10 ≥ 3.0 are reported  3.0 threshold and two SNPs in the WSC plot exceeded the log10 3.0 threshold. Seven candidate genes in close proximity to these highly ranked SNPs were investigated further for their potential association with genes.GWAS failed to identify SNPs significantly associated with variation in leaf WSC levels, after accounting for population structure. The false discovery rate applied was controlled by Bonferroni’s multiple testing correction method, which has been suggested to be too stringent  and may reported . ApplyinglgC, encoding the small subunit of ADP-glucose pyrophosphorylase, which is involved in starch biosynthesis and in turn may affect glucose and sucrose concentrations (glgC (Glucose-1-phosphate adenylyltransferase) to be associated with drought resistance in Medicago sativa. This suggests that glgC may also play a role in osmotic adjustment or osmoprotection, affecting the capacity of cells to accumulate solutes and lowering water potential during periods of osmotic stress. Low molecular weight sugars are often involved in osmotic adjustment which adds to the confidence of associating variation in glgC with WSC accumulation in white clover.SNP 5_47903593 is associated with trations . Zhang eUPL6 gene, which is involved in protein post-translational modification, mediating the addition of ubiquitin groups to target proteins for subsequent proteasomal degradation. Increased protein degradation due to environmental stress has been observed in plants as a way to mobilise nitrogen or eliminate damaged proteins  occur in genes associated with regulation of transcription, while SNP 11_7689073 is associated with translation initiation. Another SNP (9_23070656) was associated with the proteins . This maproteins . A sixth sorting  and is iERD6-like 4 and glgC. SNPs associated with a range of other candidates were also identified, which are involved in numerous aspects of plant development, membrane transport, post-translational processing, cell division and pathogen response. The clear phenotypic separation of the high and low WSC populations provides a robust platform for further investigation of foliar WSC accumulation in white clover, using transcriptomics and proteomics.The genetic control of foliar WSC accumulation in white clover was examined for the first time, by examining genetic changes in five breeding pools subject to divergent selection. Breeding for divergent foliar WSC was successful in all pools, with significant differences in WSC between low and high WSC populations achieved at the conclusion of the recurrent selection programmes, and these differences were not attributable to changes in leaf area. Outlier analyses identified GBS SNP markers that differentiate low and high WSC populations and, from these and from GWAS, two strong candidate genes were identified: https://github.com/SofiePearson/White_Clover_WSC_Outlier_Detection_GWAS.The code can be found publicly available on GitHub. The original contributions presented in the study are publicly available. The DNA sequence data have been deposited in the NCBI SRA database with links from BioProject accession number PRJNA915069. The phenotype data used in the study are provided in SP contributed to experimental design, analysed phenotypic data, conducted laboratory work, undertook SNP filtering and genetic analyses, and contributed to the writing of the manuscript. MF and AG conceived and designed the study and contributed to the writing of the manuscript. CM developed the experimental design and PPM and PM completed statistical analyses of data. AL and SH completed laboratory work supporting GBS, and RJ performed raw sequence processing and SNP calling. JF bred the white clover populations used in the experiment. JT and PL contributed to writing the final version of the manuscript. All authors contributed to the article and approved the submitted version.