Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans. Here, the authors performed a large-scale in-depth cis-meQTL mapping study in 961 African Americans from the GENOA study, investigating the co-regulation of methylation and gene expression in African Americans. Although variants in noncoding regions cannot directly influence the function of a gene by disrupting protein-coding sequence6, they can influence gene expression through epigenetic regulatory mechanisms or induce epigenetic changes through the regulation of gene expression9. One important epigenetic change is DNA methylation, which influences gene expression by altering transcription factor binding capability, inducing conformational changes in the chromatin via histone modifications, and regulating microRNA expression11. Therefore, identifying genetic variants that are associated with variation in DNA methylation through methylation quantitative trait locus (meQTL) mapping becomes an important first step towards understanding the epigenetic mechanisms underlying disease associations and towards investigating the genetic architecture underlying interindividual differences in the epigenome. In recent years, meQTL mapping studies have successfully identified many cis-acting genetic variants that are associated with DNA methylation levels14. Many of these identified meQTLs are colocalized with other molecular QTLs such as eQTLs that influence gene expression, supporting the regulatory functions of meQTLs18. Importantly, some meQTLs are also associated with behavioral and disease-related complex traits such as smoking, aging, body mass index, and type 2 diabetes19, highlighting the importance of meQTL for understanding the genetic and epigenetic mechanisms underlying diseases20.Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with various diseases and disease-related complex traits. However, the majority of these disease-associated variants reside in noncoding regions and have unknown functions18. Another study collected 90 peripheral blood samples and 87 umbilical cord blood samples from African American participants21. A handful of studies have also evaluated whether meQTLs identified in European ancestry populations are replicated in African Americans. For example, results of a replication meQTL mapping study in the Framingham Heart Study (FHS)20 were investigated in 384 African Americans from the Grady Trauma Project (GTP)23. Sample sizes of African ancestry populations in these studies remain relatively small as compared to other populations. For example, the Genetics of DNA Methylation Consortium (GoDMC) included 27,750 individuals from European ancestry24, and Hawe et al. included 3799 Europeans and 3195 South Asians25 for meQTL mapping. Lack of large-scale, well-powered meQTL mapping studies in populations with African ancestry can impede our understanding of the epigenetic mechanisms underlying DNA methylation, gene expression, and disease etiology for these populations.Most existing meQTL mapping studies have thus far focused on individuals of European ancestry and are noticeably underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. However, the meQTLs observed in one population are not necessarily meQTLs in another population due to differences in allele frequencies and linkage disequilibrium patterns as well as differences in the genetic architecture underlying methylation variation across ancestries. In addition, of the few existing meQTL mapping studies performed in African ancestry participants, most had small sample sizes and reported weak associations. For example, one meQTL mapping study examined lymphoblastoid cell lines from 77 HapMap Yoruba samples26 through colocalization and mediation analysis. Overall, our results represent an important step towards revealing the shared regulatory roles of methylation and gene expression, facilitating the functional interrogation of epigenetic and gene regulatory mechanisms underlying diseases in African Americans.In this work, to fill this critical gap, we pair genotyped and methylation data from 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study to perform a comprehensive cis-meQTL mapping analysis. In addition, we integrate the meQTL mapping results with eQTL mapping results in the same sample27. The hypomethylated sites are mainly located within transcription start sites and/or CpG islands , while the hypermethylated sites are located in other regions of the genome   samples in the GENOA study  and are depleted with hypomethylated CpG sites , where hypermethylated and hypomethylated CpG sites are defined as CpG sites with a beta value above 0.7 or below 0.3, respectively29. The identified meQTLs are strongly enriched near the associated CpG sites  than those detected in both studies  in GENOA than those detected in both studies , cis window size , types of tissue used , sample sizes, as well as the applied FDR methods (permutation based20 or Holm correction21).The identified meQTLs in the present study replicate a large proportion of the meQTLs identified in previous studies of different genetic ancestries, regardless of significance thresholds used : the median PVE is 24.64% across meCpGs  and is 6.57% across non-meCpGs   across all CpG sites, with the remaining explained by trans-SNPs . As one might expect, cis-SNPs explain a higher proportion of cis-PVE in meCpGs (median = 7.29%) than in non-meCpGs (median = 0.697%)  in methylation levels that are accounted for by all SNPs, a quantity commonly referred to as SNP heritability, using the Bayesian sparse linear mixed model (BSLMM). We found that the median PVE estimate across all CpG sites is 11.24% . As one might expect, meCpGs tend to have a higher PVE than non-meCpGs (7%) Fig. .p-value < 2.23e−308). The number of independent meQTLs across meCpGs is also positively correlated with the cis-PVE of each meCpGs . They are also significantly depleted in TSS1500 , TSS200 , 5’UTR , 1st exon  and 3'UTR  , the north and south shores , different sets of enhancer regions characterized by chromHMM , as well as in weakly transcribed regions . The meCpG sites are depleted in the CpG island   and strongly transcribed regions -proximal promoter states , and enhancer states   disrupt TF binding motifs. Among the 609,992 independent meQTLs obtained from conditional analysis, 7445 (1.22%) disrupt TF binding motifs. Consequently, we would expect meQTL enrichment in the set of SNPs that directly disrupt motif binding, as is indeed observed in the present study .We carefully explored a molecular mechanism through which genetic variants may influence methylation. If a SNP disrupts a transcription factor binding site, then it has a potential to influence methylation of a neighboring CpG site either directly or indirectly through signaling cascades26. We identified the associated genes and CpG sites with each of these eQTLs to form 4854 eGene-meCpG pairs. For each eGene-meCpG pair in turn, we estimated the probability that a common SNP is associated with both gene expression and methylation through co-localization analysis with coloc39. We found that a substantial proportion of the tested eGene-meCpG pairs (46.3%) share a common SNP that influences both gene expression and methylation , more so than those with the same direction of effects , as compared to the CpG sites in all tested eGene-meCpG pairs used for colocalization analysis.We explored whether a common/shared genetic variant may influence both gene expression levels and methylation levels. To so do, we first obtained a list of 5475 primary eQTLs previously identified in the GENOA AA gene expression study42 and performed multi-trait colocalization analysis on the tested eGene-meCpG pairs. Multi-trait colocalization examines one GWAS trait at a time and investigates whether the same SNP is associated with gene expression, methylation, and the GWAS trait. A positive multi-trait colocalization signal would suggest a potentially causal SNP association underlying all three phenotypes as opposed to genetic confounding such as LD between the molecular QTLs and the GWAS association. In the analysis, we identified four multi-trait colocalization signals for four GWAS traits , the expression level of ULK4 , and DBP . rs2272007 is known to be associated with ULK4 in lymphoblastoid cell lines and with DBP48. The CpG site cg05589743 is also located in the 5’ UTR region of ULK4, which is associated with DBP in African Americans49. ULK4 is a well-known autophagy associated gene50. Excessive autophagy can eliminate cellular elements and may cause cell death, and contribute to hypertension-related heart disease51. As another example, we found that a common SNP, rs6717671, is associated with the methylation level of the CpG site cg22495460  located in the gene body, the expression level of ADCY3 , and the risk of BMI . Genetic variation in ADCY3 is associated with BMI in African Americans41. The ADCY3 gene encodes an enzyme that converts the ATP to cAMP, is involved in a large number of physiological metabolic processes52, and is an obesity-risk gene reported by previous GWAS in the African Americans53.We obtained summary statistics from GWAS conducted in African Americans for six traits We identified four meQTL-GWAS colocalization signals using coloc  DNA methylation mediates the effect of the genetic variant on gene expression  and/or (2) gene expression mediates the effect of the genetic variant on DNA methylation . At an FDR of 0.05, we identified a total of 111 pairs with significant evidence of mediation in at least one direction Fig. . Among tq value < 0.05 in the SME analysis but have an adjusted q value > 0.1 in the SEM analysis: TUBD1/cg02095219 and CHKB-CPT1B/cg00047287. In particular, the CpG cg02095219 is located in the open sea near the TUBD1 gene, and is significant in mediating the effect of rs111282327 on TUBD1  for human squamous cell carcinoma56, and overexpression of MB21D2 can facilitate cell proliferation and invasion in cancer57.We identified two eGene-meCpG pairs that have an adjusted 58, as well as 320,965 primary meQTLs and 614,195 independent meQTLs in the conditional analysis, revealing the comprehensive genetic architecture underlying methylation variation. The colocalization and mediation analyses from the present study also provide evidence supporting co-regulation of methylation and gene expression in African Americans. Overall, our results represent an important step toward revealing the co-regulation between DNA methylation and gene expression, facilitating the functional integration and interpretation of epigenetic and gene regulatory changes that influence human disease etiology in African Americans.We have presented a comprehensive meQTL mapping study on 728,578 CpG sites and 8,993,056 unique cis-SNPs in 961 African Americans. We have identified a total of 4,565,687 cis-meQTLs and 320,965 meCpG sites26, we found that the proportion of CpG sites identified to harbor meQTLs (44%) is higher than the proportion of genes identified to harbor eQTLs (31.08%). The number of independent meQTLs per meCpG site  is also higher than the number of independent eQTLs per eGene . In addition, the identified meQTLs explain a slightly higher proportion of cis-SNP heritability than that by eQTLs. Specifically, the primary meQTLs explain a median of 68.62% cis-heritability of methylation while the primary eQTLs explain a median of 64.52% cis-heritability of expression. Both primary and conditional meQTLs explain a median of 87.35% cis-heritability of methylation while primary and conditional eQTLs explain a median of 77.83% cis-heritability of expression. These results suggest that methylation likely has stronger genetic determinant as compared to gene expression. Nevertheless, we acknowledge that the identified primary meQTLs do not explain all cis-SNP heritability in meCpGs, and neither do the additional conditional meQTLs identified through the conditional analysis. In addition, the cis-SNP heritability only represents a small proportion of total SNP heritability, suggesting that a large fraction of SNP heritability remains largely unidentified. Future studies with larger sample sizes are necessary to fully capture the polygenic genetic architecture underlying DNA methylation variation24. While the sample sizes of the eQTL study (N = 1032) and the meQTL study (N = 961) are similar, we acknowledge that many other factors may influence the comparison of results and contribute to the observed differences. First, the tissues for the samples in the expression and methylation data are different: the expression data are collected from lymphoblastoid cell lines (LCLs), while the methylation data are collected from peripheral blood leukocytes. Second, the technologies used for the measurement are different. The expression was measured using the Affymetrix Human Transcriptome Array 2.0, which surveys the entire transcriptome. In contrast, the methylation was assessed using the Illumina Infinium HumanMethylationEPIC BeadChip, which covers only 30% of the human methylome with a particular focus on regulatory regions59. The limited methylome coverage should be taken into consideration when generalizing the meQTL findings to the whole genome38.Comparing to the eQTL mapping study in the same cohort60. The FHS and BEST meQTL studies were conducted using the 450 K array that interrogates a much broader array of CpG types across the epigenome. We also note that we did not perform tests on all of the significant SNP-CpG pairs obtained from other studies. This is because we selected different cis-window sizes for the SNP-CpG pairs and used different methylation platforms.Comparing to the previous meQTL studies, we found a higher replication rate in African ancestry than other ancestries. For the PB and CBA samples from African ancestry, the replication rate is 0.98 in GENOA. In contrast, for the European ancestry samples from GoDMC, FHS, and Hawe et al., the replication rate in GENOA is 0.77, 0.82, and 0.93, respectively. In the South Asian ancestry samples from BEST and Hawe et al., the replication rate is 0.91 and 0.93, respectively. This may be in part because the PB and CBA samples have a more similar ancestral background and LD structure than the FHS and BEST samples when compared with GENOA samples. Also, the FHS study has a much larger sample size, which allowed them to detect meQTLs with small effects that are not detectable by other studies. It also may be a direct result of the methylation platforms used, as the meQTL studies in African ancestry samples were conducted using the 27 K BeadChip, which is comprised almost exclusively of methylation sites in gene promoters and CpG islands63, could be beneficial as those methods can account for the correlations in methylation among CpG sites and the correlations among SNPs due to LD. Second, the methylation data in the present study are collected from peripheral blood leukocytes, which consist of a set of closely related cell types such as neutrophils, monocytes, eosinophils, basophils, and lymphocytes. Consequently, deconvolution of the methylation data in the future could enable cell-type specific meQTL mapping analysis for the identification of cell-type specific meQTLs. Third, extending the analysis beyond cis-meQTL mapping by conducting trans-meQTL mapping could help comprehensively characterize the genetic architecture underlying methylation31. Unfortunately, due to the extremely heavy computation burden resulted from the very large number of SNP-CpG pairs to be examined, we were unable to perform trans-meQTL mapping in the present study  from the initial study population returned for the second examination (Phase II: 2001–2005). Demographic information, medical history, clinical characteristics, lifestyle factors, and blood samples were collected in each phase. Written informed consent was obtained from all subjects and approval was granted by participating institutional review boards .The Genetic Epidemiology Network of Arteriopathy (GENOA) study is a community-based study of hypertensive sibships that was designed to investigate the genetics of hypertension and target organ damage. The study includes African Americans (AA) from Jackson. In the initial phase of GENOA (Phase I: 1996–2001), all members of sibships containing at least 2 individuals with essential hypertension clinically diagnosed before age 60 were invited to participate, including both hypertensive and normotensive siblings. The exclusion criteria for GENOA included secondary hypertension, alcoholism or drug abuse, pregnancy, insulin-dependent diabetes mellitus, or active malignancy. Eighty percent of AA (64), version v2.r and IMPUTE version 265 using the 1000 Genomes project Phase I integrated variant set release (v3) in NCBI build 37 (hg19) coordinates (released on March 2012). Since genotyping was performed on multiple platforms, imputation was performed separately by platform and then the imputed data were combined. After imputation, SNPs with minor allele frequency (MAF) ≤ 0.01 or imputation quality score (info score) ≤ 0.4 in any platform-based imputation were removed, leaving 30,022,375 markers covering both SNPs and SNVs/INDELs. After removing the SNVs/INDELs with PLINK66, we retained 28,681,763 SNPs. The GENESIS package in R was used to infer population structure67. We used the PC-AiR function to extract the first five genotype PCs and used GEMMA68 to estimate an individual relatedness matrix. We controlled for both PCs and the relatedness matrix in the meQTL mapping analysis.AA blood samples were genotyped using either the Affymetrix Genome-wide Human SNP Array 6.0 platform or the Illumina Human1M-DUO BeadChip. For each platform, participants were excluded if they had an overall SNP call rate <95% or sex mismatch between genotype and self-report. SNPs were excluded if they had a call rate <95%. Principal component analysis was performed to identify and remove samples whose genotype profile appeared to be different from all other samples (outliers). After removing outliers, there were 1599 AA samples with available genotype data. Imputation was performed using the Segmented HAPlotype Estimation & Imputation Tool (SHAPEITN = 1106) or Phase 2 (N = 304) using AutoGen FlexStar . Bisulfite conversion was performed with the EZ DNA Methylation Kit , and methylation was assessed using the Illumina Infinium HumanMethylationEPIC BeadChip. The shinyMethyl R package was used to generate the density plot of the raw intensity data to identify sex mismatches or sample outliers. Samples with incomplete bisulfite conversion were identified and removed using the QCinfo function in the ENmix R package. Sample identity was checked using the 59 SNP probes implemented in the EPIC chip, and mismatched samples were removed. Next, the Minfi R package was used to perform background correction and normalization using Noob. The regression on correlated probes (RCP) method was used to adjust probe-type bias. Individual probes with detection p-value <1e−16 were considered successfully detected. Samples and probes with detection rate <10% were removed. Following these steps, 857,121 probes for 1100 samples at Phase I and 294 samples at Phase II remained for analysis. Next, we removed CpG sites on the X and Y chromosomes. We then followed15 to further remove 36,860 cross-reactive probes59 and 24,495 probes with a SNP at the target CpG site or within a single-base extension, resulting in a final set of 771,134 CpG sites. White blood cell counts were estimated using Houseman method. The extracted methylation data were then converted from β-values to M-values69 through Genomic DNA was extracted from stored peripheral blood leukocytes that was collected at Phase 1 (N = 961). For each CpG site in turn, we followed21 to extract cis-SNPs that are +/−50 kb of the CpG site. We performed a sensitivity analysis on chromosome 22 using cis-SNPs that reside +/−50 kb, +/−500 kb, or +/−1 Mb of the CpG site for cis-meQTL mapping and heritability estimation and found similar results across different window sizes; thus, we used +/−50 kb for subsequent analyses. In the meQTL mapping analysis, we focused on a set of 728,578 CpG sites that contain at least one cis-SNP and genotype information for 8,993,056 imputed cis-SNPs on 961 AA individuals that also have methylation data. The median number of cis-SNPs per analyzed CpG site is 300 , with range varying from 1 to 2298. For each analyzed CpG site, we examined one cis-SNP at a time and applied a linear mixed model implemented in GEMMA68 for meQTL mapping.The meQTL mapping analysis was performed using individuals with both genotype and methylation data , the top five genotype PCs from PC-AiR; x is the n by 1 vector of genotypes for the genetic variant of interest; n by 1 vector of u is a random effects term to control for individual relatedness and other sources of population structure, where the n by n matrix e, an n by 1 vector, and MVN denotes the multivariate normal distribution. We used the command “gemma -bfile cpgname -maf 0.01 -r2 0.9999999 -hwe 0 -n phenotype -k relatedness -lmm 1 -o output” to fit the model in GEMMA.For each variant, we fitted the following linear mixed model:p-value for each CpG site as the candidate meQTL and used its p-value as the CpG site-level significance measure. We permuted the sample labels 10 times and applied the same meQTL mapping procedure to obtain an empirical null distribution of CpG site-level p-values72. As a sensitivity analysis, we also evaluated constructing the empirical significance cutoff using 2, 5, 10, 50, 100, 200, 300, 400, and 500 permutations on chromosome 22; however, since the results were similar, we used 10 permutations for our subsequent analyses. With the empirical null distribution, we computed the false discovery rate (FDR) associated with each p-value threshold following refs. 71 and obtained the p-value threshold that corresponded to a 5% FDR control, which is 2.267195e−4 in the present study. We refer to any CpG site that passes the FDR threshold of 5% as an meCpG and refer to the SNP with the lowest p-value in each meCpG site as the primary meQTL. Following refs. 71, we also refer to any cis-SNP with a significant association with a meCpG site as an meQTL.After mixed model analysis, we selected the SNP with the lowest 73. To do so, we refer to the primary meQTLs as E1 SNPs. In the forward stages, for each CpG in turn, we performed association analysis conditional on the E1 SNP and identified the strongest SNP association among the remaining SNPs. We refer to the identified SNP as an E2 SNP if its conditional p-value is below the genome-wide significance threshold established above (2.267195e−4). Next, we performed further association analysis conditional on both E1 and E2 SNPs to identify E3 SNPs. We repeated this process until the smallest p-value among the remaining SNPs no longer exceeded the genome-wide significance threshold. In the backward stages, we tested each variant that is already included in the model while controlling for all the other included variants for a CpG site and dropped the variant if it was not significant.For each meCpG in turn, we performed conditional analysis using forward and backward stepwise selection to identify additional conditional meQTLs following refs. 26. The previous eQTL mapping analysis focused on expression measurements for 17,616 protein-coding genes and genotype information for 30,022,375 imputed SNPs from 1032 AA individuals. Among the 1032 AA individuals used in eQTL mapping, 800 overlapped with the 961 individuals used in the present meQTL mapping analysis. In the previous eQTL analysis, the gene expression data were processed with Combat74 to remove batch effects or other technical covariates. The cis-SNPs within 100 kb of each gene were extracted and the linear mixed models implemented in GEMMA68 were used for eQTL mapping. The eQTL mapping adjusted for age, gender, the top five genetic principal components (PCs), as well as a genetic relatedness matrix to control for familial relationships. We identified 5475 primary eQTLs and used them in the colocalization analysis described in the following section.We obtained eQTL mapping results on GENOA AA samples from a previous study24, Hawe et al.25, Framingham Heart Study (FHS)20, a study on adult peripheral blood (PB) samples, and umbilical cord blood at birth (CBA) samples76, and the Bangladesh Vitamin E and Selenium Trial (BEST) study15. We selected these cis-meQTL studies for comparison because they are blood-based genome-wide meQTL studies with sample size greater than 100 from diverse ancestries. We did not compare with the HapMap meQTL study18 due to its small sample size and low detection power for cis-meQTLs.We compared our cis-meQTL mapping results to six previous cis-meQTL mapping studies that include the Genetics of DNA Methylation Consortium (GoDMC)24 measured DNA methylation levels in whole blood using the Illumina HumanMethylation 450 K BeadChip. Cis-meQTL mapping analysis in the GoDMC study was performed on 27,750 European participants, with 420,509 CpG sites and 10 million SNPs. Among the 71,616,458 cis-SNP-CpG pairs separated by <1 Mb, the GoDMC study identified 169,370 significant meCpGs with p-values below 1e−8. The GoDMC study made SNP-CpG pairs with nominal significant p-values in at least one study publicly available. In the comparison, we matched CpG sites between the two studies using CpG site IDs and matched SNPs using base pair positions. Among the 71,616,458 SNP-CpG pairs available in European population, the SNPs and CpG sites are separated by <50 kb in 21,023,205 pairs, and 9,010,077 of them were tested in GENOA AA samples.The GoDMC study25 measured DNA methylation levels in peripheral blood using the Illumina HumanMethylation 450 K BeadChip. Cis-meQTL mapping analysis in Hawe et al. was performed on 3799 European and 3195 South Asian participants. Among the cis-SNP-CpG pairs separated by <1 Mb, Hawe et al. identified 41,608 significant meCpGs in Europeans and 69,495 significant meCpGs in South Asians with p-values below 1e−14. The Hawe et al. study made SNP-CpG pairs with significant p-values in at least one study publicly available. In the comparison, we matched CpG sites between the two studies using CpG site IDs and matched SNPs using base pair positions. Among the 11,165,559 SNP-CpG pairs available in European and South Asian population, the SNPs and CpG sites are separated by <50 kb in 2,507,048 (European) and 4,292,518 (South Asian) pairs, 1,338,027 and 1,223,921 of them were tested in GENOA AA samples.Hawe et al.20 measured DNA methylation levels using the Illumina Infinium Human Methylation450 BeadChip (450 K) in the whole blood buffy coat samples for 4170 European ancestry participants. The cis-meQTL mapping analysis in FHS was performed using 415,318 CpG sites and 8.5 million SNPs. SNPs and CpG sites separated by <1 Mb were tested for association. This study identified 4,447,327 unique meQTLs at the Bonferroni-corrected p-value threshold of 0.05, along with 121,599 meCpGs and 27,235,697 significant SNP-CpG pairs. The FHS study only made available the significant SNP-CpG pairs at the Bonferroni-corrected p-value threshold of 0.05. We obtained the significant SNP-CpG pairs from FHS and compared them with our meQTL mapping results. In the comparison, we matched CpG sites between the two studies using CpG site IDs and matched SNPs using base pair positions. Among the 27,235,697 significant SNP-CpG pairs available in the FHS study, the SNPs and CpG sites are separated by <50 kb in 10,894,656 pairs, and 4,951,955 of them were tested in GENOA AA samples.The FHS study76 and CBA samples75  were performed on individuals with African ancestry, with detailed analysis in ref. 21. Briefly, the PB study contains n = 90 samples and the CBA study contains n = 87 samples, with a common set of 222,888 SNPs and 20,093 CpG sites measured in both studies using the HumanMethylation27 BeadChip (Illumina). The cis-meQTL mapping analyses in both studies were performed on 529,224 unique SNP-CpG pairs that were separated by <50 kb. The cis-meQTL mapping analysis detected 724 cis-meQTLs in PB and 629 cis-meQTLs in CBA with a conservative Holm adjustment for multiple testing, with these significant SNP-CpG pairs publicly available. We obtained the significant SNP-CpG pairs from both studies and compared them with our results. In the comparison, we matched CpG sites between the two studies using the CpG site IDs and matched SNPs using base pair positions. Among the 724 significant SNP-CpG pairs available in PB, 517 of them were tested in GENOA AA samples. Among the 629 significant SNP-CpG pairs available in CBA, 531 of them were tested in GENOA AA samples.The meQTL mapping studies on PB15 measured DNA methylation levels in whole blood using Illumina HumanMethylation 450 K BeadChip kit (Illumina). Cis-meQTL mapping analysis in the BEST study was performed on 337 Bangladesh samples, with 423,604 CpG sites and 8,639,940 SNPs. Among the 994,862,964 SNP-CpG pairs separated by <500 kb, the BEST study identified 77,664 significant meCpGs with empirical p-values below the FDR threshold of 0.01. The BEST study made all SNP-CpG pairs with p-value <0.05 publicly available. We obtained the SNP-CpG pairs, ranked the meCpGs based on the smallest p-value in each meCpG, extracted the top 10,507,576 significant SNP-CpG pairs that correspond to the 77,664 significant meCpGs, and compared them with our results. In the comparison, we matched CpG sites between the two studies using CpG site IDs and matched SNPs using base pair positions. Among the 94,168,979 SNP-CpG pairs available in the BEST study, the SNPs and CpG sites are separated by <50 kb in 17,862,650 pairs, and 8,395,396 of them were tested in GENOA AA samples.Finally, the BEST studyp value distribution in GENOA78. Therefore, the replication rate effectively captures the proportion of signals in the previous study that is replicated in the present study. We were unable to calculate p-value <2e−11. The Hawe et al. 2022 (EU and SA) only provided SNP-CpG pairs significant (p < 1e−14) in at least one population. The GoDMC (EU) only provided SNP-CpG pairs with p-value < 1e−5 in at least one cohort and provided association statistics from meta-analysis. The BEST study (SA) only provided SNP-CpG pairs with p-value <0.05, and did not provide MAF or allele information. The PB and CBA studies (African) only provided significant pairs with p-value <0.05 after Holm adjustment. Consequently, when some meQTLs identified in the present study were not replicated in the previous studies, we often do not know whether the previous studies did not identify these meQTLs or simply did not test these SNP-CpG pairs.We compared the meQTL mapping results from each of the above studies. In each comparison, we calculated the replication rate  a p-value threshold of 1e−5 for both studies, and (3) the Bonferroni corrected p-value threshold of 0.05 for the tested SNP-CpG pairs in both studies. In each comparison, we also separated the meQTLs from the present study into two sets: the ones that replicate previous study and the ones that do not. We then compared SNP effect sizes and allele frequencies between the two sets.Because previous studies used different significance thresholds, we examined three different thresholds in the comparison: (1) the 79, we also used BSLMM80 to partition the methylation level variance into a cis-component and a trans-component. The cis-component represents the proportion of methylation level variance explained by cis-SNPs while a trans-component represents the proportion of methylation level variance explained by trans-SNPs. The cis-SNPs were defined as those SNPs that reside within 50 kb of the CpG site as described above while the trans-SNPs were defined as all other SNPs.For each CpG site, we estimated the proportion of variance in methylation level explained by all SNPs using the Bayesian sparse linear mixed model (BSLMM) implemented in GEMMA. Following ref. 81. Specifically, CpG shores are defined as the 2 kb of sequence flanking of a CpG island. CpG shelves are defined as the 2 kb of sequence further flanking CpG shores. While the remaining regions outside of CpG island/shore/shelf are denoted as open sea81.We examined whether the identified meCpG sites are enriched in functional genomic regions defined based on four sets of genomic annotations. The first set of genomic annotations are obtained from the R package “IlluminaHumanMethylationEPICanno.ilm10b2.hg19”. In particular, the R package annotated the analyzed CpG sites based on their relation to CpG islands, which are short genomic regions with high CpG density, into the following six regions: CpG islands, north and south shores, north and south shelves, and open sea81. Following82, if a CpG site has multiple annotations, we select one according to the following criteria: TSS200 > TSS1500 > 5′UTR > 1st Exon > Body > 3′UTR > Intergenic.The second set of genomic annotations are obtained based on the relative position of CpG sites with respect to genes. It includes seven genomic annotations: TSS1500 , TSS200 (if the CpG site resides 0–200 bases upstream of the TSS of a gene), 5′UTR , 1st exon (if the CpG site resides in the first exon of a gene), gene body (if the CpG site resides between the ATG and the stop codon), 3′UTR (if the CpG site resides between the stop codon and poly A tail), and intergenic regions83 and ENCODE85. Specifically, we downloaded 111 Roadmap reference epigenomes83 and 16 additional epigenomes from ENCODE85 for a wide range of human cell types and tissue types. We focused on 25 blood-related epigenomes  and used the ChromHMM model to define 15 epigenetic states: (1) active TSS (TssA), (2) flanking active TSS (TssAFlnk), (3) transcription at gene 5′ and 3′ (TxFlnk), (4) strong transcription (Tx), (5) weak transcription (TxWk), (6) genic enhancers (EnhG), (7) enhancers (Enh), (8) zinc finger genes and repeats (ZNF/Rpts), (9) hetero-chromatin (Het), (10) bivalent/poised TSS (TssBiv), (11) flanking bivalent TSS/enhancers (BivFlnk) (12) bivalent enhancers (EnhBiv), (13) repressed Polycomb (ReprPC), (14) weak repressed Polycomb (ReprPCWk), and (15) quiescent/low (Quies).The third set of genomic annotations are based on the epigenetic states inferred from ChromHMM using the functional genomic data from Roadmap reference epigenomes86 (http://motifmap.ics.uci.edu) and annotated 79,331 SNPs in the present study to be within the motif binding sites.The last set of genomic annotations are based on the positions of regulatory motifs. Specifically, we downloaded 3,961,042 predicted human regulatory motif sites (hg19) for 607 motifs from the motifmap37, which models the meQTL signal landscape and multiple annotations jointly.For each of the above functional genomic or motif annotations in turn, we performed Fisher’s exact test to examine whether the meQTLs are enriched with the annotation. In addition, we performed enrichment analysis using Torus87, we followed refs. 88 and pruned the list of meCpG sites. Specifically, if a primary eQTL is associated with multiple meCpG sites, we only retained the meCpG site whose primary meQTL had the highest LD with the primary eQTL. We then extracted the retained meCpG site, paired it with the eGene, with the eQTL being the link, to obtain a total of 4854 eGene-meCpG pairs. The 4854 eGene-meCpG pairs were used for down-stream co-localization and mediation analyses.We obtained a list of eGene-meCpG pairs for co-localization analysis to examine whether a common causal variant may influence both gene expression and methylation , methylation (p2), or both (p12). We set p1 + p12 at 0.00084 as we identified 5406 unique primary eQTLs among the 6,432,684 examined cis-SNPs in the eQTL analysis. We set p2 + p12 to be 0.03 as we identified 254,113 unique primary meQTLs among 8,993,056 cis-SNPs in the meQTL analysis. Following the recommendation of ref. 15, we examined six choices of p12 , corresponding to the probability that an eQTL is also an meQTL being either 5%, 10%, 25%, 50%, 75%, or 90%. Also following ref. 15, we evaluated the validity of the six p12 choices using internal empirical calibration89, which assesses the similarity between the prior and posterior expectations from the colocalization analysis. With this criterion, we selected 6.3e−04 as the best choice for p12  were included in the analysis. In addition, for the GWAS-meQTL colocalization analysis, in addition to using only the CpGs in the eGene-meCpG pairs, we also performed a genome-wide scanning of GWAS-meQTL colocalization signals. To do so, we followed36 and split the GWAS summary statistics into 2583 approximately linkage disequilibrium (LD) independent regions90. We only included GWAS hit loci that encompassed a GWAS significant (p < 5e−8 and p < 1e−5) signal and with at least 50 SNPs in common between the SNPs in GWAS and SNPs tested in an meCpG.For each GWAS trait in turn, we performed colocalization analysis using both the default setting of coloc and the Susie version of coloc to estimate the posterior probability that the same variant is responsible for GWAS-meQTL or for GWAS-eQTL. Specifically, among the eGene-meCpG pairs used in co-localization analysis in the previous paragraph, we extracted eQTL and meQTL association results for SNPs that reside within 50 kb of the lead-eQTL and intersected these SNPs with the GWAS SNPs based on SNP positions to arrive at a common set of SNPs. Only pairs with at least one GWAS association signal that surpassed a relaxed significance threshold  for SBP, DBP, and BMI, as we did not have access to HTN, PP, and the phecode of T2D in our UKBB application. For each of the three traits in turn, we obtained a random subsample of individuals of white British ancestry to match the sample size of the African ancestry GWAS studies. We then performed GWAS analysis where we included age, sex, and top 20 genetic PCs as covariates to obtain summary statistics. Afterwards, we performed colocalization analysis with coloc  and performed multi-traits colocalization analysis with moloc , using the GWAS summary statistics from UKBB.40. For BMI, we obtained the GWAS summary statistics from the African Ancestry Anthropometry Genetics Consortium (AAAGC) with 52,895 individuals41. For the four blood pressure traits, we obtained the GWAS summary statistics from COGENT-BP meta-analyses with a total of 31,968 individuals from 21 African ancestry cohorts42.We obtained GWAS summary statistics for six complex traits that include type 2 diabetes (T2D), body mass index (BMI), and four blood pressure traits including pulse pressure (PP), diastolic blood pressure (DBP), systolic blood pressure (SBP), and hypertension (HTN). All GWAS were conducted in African ancestry samples. For T2D, we obtained the GWAS summary statistics from the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium study, which is a meta-analysis of 17 GWASs with a total of 8284 cases and 15,543 controls91 to estimate the posterior probability that the same variant is shared across GWAS traits, eGenes and meCpGs. To do so, in addition to the eQTL and meQTL summary statistics from the GENOA study that were used in the coloc analysis, we included the additional GWAS summary statistics in the moloc analysis. Specifically, on each eGene-meCpG pair, we extracted association results for all SNPs that reside within 100 kb of the eQTL from the eQTL and meQTL analyses. We intersected these SNPs with the GWAS SNPs based on SNP IDs to arrive at a common set of SNPs shared between datasets. We retained eGene-meCpG pairs with association results for at least 10 SNPs in the moloc analysis. In moloc, 15 configurations of possible variant sharing schemes were calculated across the GWAS trait, gene expression, and methylation91. We used the posterior probability of association (PPAs) threshold of 0.8 as evidence for multi-trait co-localization. We followed moloc recommendation91 and set the prior probabilities to be 1e−4, 1e−6, and 1e−7 for the association of one, two, or three traits, respectively.For each GWAS trait in turn, we performed multi-trait colocalization analysis using moloc74 to remove batch effects, and adjusted for age, gender, the top five genotype PCs, and then took the residuals and quantile normalized them. We then re-estimate the exposure-outcome association adjusting for the mediator in the following regression:X is the exposure, M is the mediator, Y is the outcome, We performed mediation analysis on the eGene-meCpG pairs used in the co-localization analysis using 800 individuals who have gene expression, methylation, and genotype data. We conducted the mediation analysis in two directions. The first is the SNP-Methylation-Expression (SME) direction, where the SNP genotype is treated as the exposure, methylation level for the CpG site is treated as the mediator, and gene expression is treated as the outcome. The second is the SNP-Expression-Methylation (SEM) direction, where the SNP genotype is treated as the exposure, gene expression is treated as the mediator, and methylation is treated as the outcome. In both types of mediation analyses, for methylation, we adjusted for gender, age, top 5 genetics PCs, white blood cell type proportions, and batch effects using the lmer function in R, and then took the residuals and quantile normalized them. For gene expression, we used ComBat method in the sva R packageWe also estimated the exposure on mediator effect through the following regression:p-values based on a standard normal distribution. We declared significance at an FDR of 0.05.We then obtained the Sobel test statistics for testing the mediation effect in the form of92, we also calculated the mediation proportion, which represents the proportion of the total effect of the exposure on the outcome mediated through the mediator, in the form of:FollowingFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Glycine max [L.] Merr.). However, assessing the quality of variation is essential because the published variants were collected using different bioinformatic methods and parameters. Furthermore, despite the enhanced genome contiguity and more efficient filling of “N” stretches in the new reference genome, there remains a dearth of endeavors to verify the caliber of variations present in it. The primary goal of this research was to discern a dependable set of SNPs that can withstand reconciliation across multiple reference genomes. Additionally, the investigation aimed to reconfirm the variations through the utilization of numerous whole genome sequencing data obtained from publicly available databases. Based on the result, we created datasets that comprised the thoroughly verified SNP coordinates between the reference assemblies. The resulting “SoyDBean” database is now publicly accessible through the following URL: http://soydbean.plantprofile.net/.Due to the development of sequence technology and decreased cost, many whole genome sequences have been obtained. As a result, extensive genetic variations have been discovered from many populations and germplasms to understand the genetic diversity of soybean ( Glycine max [L.] Merr.) is a legume that is one of the most important crops for animals and humans, owing to its high protein and oil content. It can also hold atmospheric nitrogen through symbiosis with microorganisms1. G. max is known to have been domesticated from East Asia around 7000–9000 years ago2. With the development of Mendel’s laws of heredity and the advancement of our understanding of plant genetics, molecular breeding techniques became popular3. Multi-omics breeding methods are now used in many experimental breeding programs, along with recent improvements in the methods of phenotypic and molecular observation on soybean plants4.Soybean  were generated through various studies that aimed to compare genetic variation in wild and cultivated soybeans and identify allele diversity specific to wild soybeans7, identify genetic information available for soybean breeding8, study the genetic diversity and structural properties of soybeans10, construct a haplotype map using whole genome sequence data11, and etc.The utilization of next-generation sequencing (NGS) technology has revolutionized the acquisition of complete genome sequences and the generation of high-throughput sequencing data for researchers, making it a cost-effective and effortless process. Moreover, the availability of vast amounts of Next-Generation Sequencing (NGS) data enables high-depth observations for each base, ensuring the accuracy of the data, and allowing for the identification of sequence variations at the single base level12. However, integrating individual research variants data into these large datasets is not straightforward due to the differences in variant calling progress, tools, and data filtering employed.The creation of these large datasets was driven by the fact that a consolidated matrix of accumulated variants encompassing a broad spectrum of genetic diversity in both wild and cultivated soybeans would provide immensely informative insights into understanding allelic diversity that may be linked to useful phenotype13. Since there is no gold standard for this pipeline, various pipelines, and filtering criteria have been employed to create different published datasets  > 30, the mapping quality > 30, the minor allele frequency (MAF) > 0.02, and the fraction of missing genotype < 0.05 and stored in Variant Call Format (VCF). Through 183 soybean accessions (102 G. max and 81 G. soja), we detected 12,447,986 SNPs in Glyma 1.01 and 12,473,679 SNPs in Wm82.a2.v1. To determine whether a sufficient number of SNPs had been identified, we examined the increase in the number of SNPs with the addition of accessions cultivated soybean and wild soybean separately. We observed that the number of SNPs did not significantly increase beyond 90 accessions for G. max and 50 accessions for G. soja, indicating that an adequate number of SNPs had been discovered .The SNP datasets used in the SoyDBean database consist of subpopulations of 95 cultivated soybeans (G. max) and 72 wild soybeans (G. soja), collected from six countries  to obtain a vast amount of variant data  and Bootstrap . Users can upload a VCF file of versions Glyma 1.01 and Wm82.a2.v1, and the web interface will select and update input SNPs to the reliable SNPs contained in the database , VCFs written in Wm82.a2.v1 . This enabled us to generate a matrix with 4,135,736 SNPs from 1309 accessions , excluding SNPs for which genotype information was missing more than 5% of 1309 accessions in the VCFs integration process. Firstly, we grouped the SNPs screened from our datasets, and the remaining SNPs (which passed the mapping stage but were excluded during genotype match rates filtering), and examined their genotype match rates in 1309 accessions consolidated matrix  from different reference genomes that had already been published and obtained a large and robust dataset that can be utilized for further molecular market-based studies28. In addition, we ascertained that the composition of the reference sequence is vital for the reliability of SNPs, such as the extent to which the quantity of K-mers containing "C" and "G" is high or low. It has been reported that high GC-content and repeat-rich regions are associated with assembly errors, especially in short reads29, which can potentially affect the reliability of genotype calls. As reference genomes have improved, they cover a greater portion of the genome, but challenges still remain in SNP validation. Therefore, we adopted a conservative approach and generated a reliable SNP dataset through mutual comparisons with each reference genome. We conducted a comparison between our reliable SNP dataset and the 170,223 SNPs that had been previously selected for the creation of the 180K AXIOM® SoyaSNP array30. This analysis revealed that 73% of these SNPs  were found within our reliable SNP dataset. By refining the SoyaSNP array using our dataset, we can enhance the reliability of SNP array analyses. Furthermore, we plan to incorporate the updated soybean reference genome (Wm82.a4.v1) into our efforts to further update the set of reliable SNPs.As sequencing technology continues to advance and costs decrease, there is a growing abundance of whole genome variant calls for G. max SNP dataset that researchers can employ in the BaseRecalibrator step of the GATK pipeline for SNP validation tools. This allows for the calibration of the sequencing data, improving the accuracy and reliability of downstream analyses. By incorporating the reliable SNPs dataset in the calibration process, researcher can reduce systematic errors in the data and improve the quality of our analysis results. This dataset can be accessed at http://soydbean.plantprofile.net/downloads/. Our approach will facilitate the analysis and integration of published variant data, thereby providing researchers with access to a wider range of variation information from various populations. This is expected to increase the likelihood of identifying genomic locations that are associated with desired characteristics, thereby aiding the genome-based breeding process.Currently, our database was created using 167 accessions of cultivated and wild soybeans. However, as we continue to add more datasets to our pipeline, the number of reliable SNPs is expected to increase. We are currently incorporating additional datasets and plan to update our pipeline on a quarterly basis to ensure the inclusion of the latest available data. The “SoyDBean” database has been created to enable researchers to filter and process data that has been built using different pipelines and reference genomes, into set of comparable and reliable SNPs. Furthermore, our database offers a Supplementary Information 1.Supplementary Information 2.


Reliable and high-throughput genotyping platforms are of immense importance for identifying and dissecting genomic regions controlling important phenotypes, supporting selection processes in breeding programs, and managing wild populations and germplasm collections. Amongst available genotyping tools, single nucleotide polymorphism arrays have been shown to be comparatively easy to use and generate highly accurate genotypic data. Single-species arrays are the most commonly used type so far; however, some multi-species arrays have been developed for closely related species that share single nucleotide polymorphism markers, exploiting inter-species cross-amplification. In this study, the suitability of a multiplexed plant–animal single nucleotide polymorphism array, including both closely and distantly related species, was explored. The performance of the single nucleotide polymorphism array across species for diverse applications, ranging from intra-species diversity assessments to parentage analysis, was assessed. Moreover, the value of genotyping pooled DNA of distantly related species on the single nucleotide polymorphism array as a technique to further reduce costs was evaluated. Single nucleotide polymorphism performance was generally high, and species-specific single nucleotide polymorphisms proved suitable for diverse applications. The multi-species single nucleotide polymorphism array approach reported here could be transferred to other species to achieve cost savings resulting from the increased throughput when several projects use the same array, and the pooling technique adds another highly promising advancement to additionally decrease genotyping costs by half. The development of medium-density genotyping tools for the inexpensive, rapid, and reliable screening of hundreds of samples is of utmost importance in molecular breeding programs and for the management of wild populations and germplasm collections. Nowadays, there are several high-throughput genotyping technologies and platforms, each with advantages and disadvantages. Whole genome sequencing (WGS) is increasingly being used; however, it can be a costly approach for the routine screening of large numbers of samples, particularly in species with large genome sizes. While reduced-representation genotyping-by-sequencing [GBS ] is moreMalus domestica) and pear (Pyrus communis) Illumina Infinium II 9K SNP array (Crassostrea gigas) and European oysters (Ostrea edulis) Axiom 60K SNP array (Dicentrarchus labrax) and gilthead seabream (Sparus aurata) (Piaractus mesopotamicus) and tambaqui (Colossoma macropomum) , with densities ranging from a few hundred to several thousand markers (Rubus genus of the Rosaceae family comprises red (Rubus idaeus) and black  raspberries, as well as blackberries (Rubus subgenus Rubus). Simple sequence repeat markers were used to confirm identity and assess diversity at the Agricultural Research Service of United States Department of Agriculture (USDA-ARS)—National Clonal Germplasm Repository (NCGR) , is an undomesticated species supporting the production of honeys with unique high antimicrobial properties, which attract premium prices. The recent publication of a reference genome for L. scoparium  and silver trevally  are two candidate species for aquaculture in NZ. Selective breeding programs for both these finfish species have recently been initiated to diversify the aquaculture sector  and silver trevally  was examined.This study describes the design of a multi-species plant–animal 60K SNP array that combines 13K SNP markers for Rubus spp.), Different sequencing datasets were available for each of the four organisms targeted in this study and the variant calling and filtering analyses performed are described in As raspberry variants were generated from GBS performed in bi-parental populations, genotypic data were imported into JoinMap v5.0  to ident−5 -perc_identity 0.8) to fish DNA, and vice versa, using the reference genomes for all four species, to avoid cross-hybridization between fish and plant DNA. Finally, SNPs that were too close to the start of the chromosome/scaffold (<60 bp), and for which flanking sequences could not be extracted, were eliminated.For the selected SNPs for each of the species, 60 bp up- and down-stream flanking sequences were extracted from the respective reference genomes and submitted to Thermo Fisher Scientific for quality scoring. Only SNPs for which at least one probe was recommended  were kept. BLASTn (v2.6.0) analysis was then performed to remove plant SNPs with flanking sequences that showed high similarity  were checked, and those that had either no hits or more than one mismatch were discarded. This quality check was necessary because the SNPs were designed on the R. occidentalis reference genome, the only raspberry genome publicly available at this time, while target populations for application of this SNP array are mainly derived from R. idaeus.For the raspberry SNPs, alignments to a Finally, probes for all selected SNPs were tiled on an Applied Biosystems Axiom myDesign array (Thermo Fisher Scientific). A set of 8,000 Dish Quality Control (DQC) probes (200 for each of the four organisms) were also included for quality control at genotyping. These probes were designed from non-polymorphic genome locations, determined by examining the sequences of the individuals used for variant calling.Rubus and mānuka leaf samples were collected by a consortium of different institutes and DNA extracted in the respective laboratories. Rubus DNA extractions were performed in 96-well plates from freeze-dried tissue using commercial kits [e.g. Macherey-Nagel Nucleospin kit was used at Plant & Food Research (PFR)]. Mānuka samples were collected in natural stands in remote locations using silica beads in 2 mL screw cap tubes as described in μg. DNA from one fish and one plant sample were then pooled and processed for genotyping.SNP array genotyping reactions were performed by multiplexing one fish and one plant DNA sample in equal quantities, as specified below. Rubus only samples, 511 mānuka only, 214 Australasian snapper only, 322 silver trevally only, 2,908 Rubus + snapper pooled samples, 39 Rubus + Japanese red seabream, 225 Rubus + trevally, 23 Rubus + kingfish, 1,122 mānuka + snapper, and 656 mānuka + trevally  and analyzed separately in the Axiom Analysis Suite v5.1.1 software . The data were quality-filtered using a DQC threshold of 0.82 (default) and a QC call rate threshold of 95, and genotypes were called with the default parameters. The OffTargetVariants (OTV) caller was run on the OTV, i.e. SNPs that might contain null alleles.The SNP data were automatically split for the four sets of SNPs (PolyHighResolution (PHR) SNPs and NoMinorHom (NMH)] were used for these analyses. These are SNPs that show two (NMH) or three (PHR) clear and well-separated clusters for each genotypic class (AA/AB/BB). The methods applied in each species are described in Rubus spp.), The effectiveness of the SNP array was evaluated by verifying if the expected population structure could be depicted in subsets of samples for all four organisms. Only the higher quality SNPs [T-test (or Welch T-test) in the R stats v4.0.0 package. Correlations between quality values of plant and fish samples from the same reaction were depicted in scatter plots. All samples submitted for genotyping were used for these comparisons.The quality parameters DQC, QC call rate, and call rate were compared among species using boxplots. The effect of DNA pooling on the quality of genotyping was also assessed for each of the four main species by generating boxplots, calculating descriptive statistics such as mean, standard deviation (SD), and median, and running an unequal variance (independent) L. scoparium “Crimson Glory” , MAF, multi-allelic and A/T and C/G SNPs resulted in a total of 2,419,846 SNPs. After linkage disequilibrium (LD) pruning, 26,719 SNPs were left, of which 13 were removed because they either aligned to the raspberry or mānuka genomes, or because they were too close to the terminal of the scaffold. Finally, 22,238 SNPs were recommended by Thermo Fisher Scientific scoring, and 18,489 randomly selected SNPs were included in the array. It was noted that 13,204 of these SNPs were in coding regions, according to the male and female gene annotation for the The initial SNP calling performed by R. idaeus, R. subgenus Rubus, L. scoparium, C. auratus, and P. georgianus . All replicates had identity-by-state (IBS) >0.97 except for “Wakefield”, which grouped into two sets of duplicated samples each with IBS >0.97. Two of the “Wakefield” samples that were identical to each other but different from the other three were later ascertained to be sampling errors. However, since they were technical replicates from the same sample, they were included in the analysis. Over all the PHR and NMH SNPs verified, 6,885 (93.7%) had no genotyping inconsistencies and were considered robust. Of these, 6,235 were raspberry SNPs and 650 blackberry SNPs. Principal component analysis (PCA) showed three main clusters along the PC1 (25.44% of variation explained), with almost all samples from the NCGR grouping together into one cluster . These included 2,899 raspberry and 1,489 blackberry SNPs. Two main clusters could be observed on the PC1 (29.90% of variation explained) vs PC2 (7.65%) plot, with several samples remaining ungrouped (ngrouped . As for MonoHighResolution), 761 had call rate below the threshold, and the majority  had poor clustering  were classified as PHR and 941 (5.1%) as NMH, while the others were monomorphic, OTV, or of poor quality. In the second batch, numbers were similar, with 11,601 (62.8%) PHR and 1,000 (5.4%) NMH. A total of 10,692 and 472 SNPs were classified as PHR and NMH in both batches, respectively , and werSTF between the two populations was estimated to be 0.19.Since only 48 samples were submitted in the first batch, genotypes for these were called together with all trevally samples from the second batch, and 1,072 out of 1,203 passed all QC filters. Only 10 samples failed the DQC filter, and 121 did not pass the QC call rate threshold of 95. All kingfish samples, which were analyzed together with the trevally, failed at DQC. Of the 20,234 SNPs included in the array, 10,157 were classified as PHR, 1,556 as NMH, 2,521 as OTV, 146 were monomorphic, 1,100 had call rate below the threshold, and 4,754 exhibited poor clustering . PHR andT-test, P-value < 0.05); however, differences were more marked in the plant than in the fish samples   . Finallyaluated) , while Qaluated) .Our work demonstrates the successful development of a multi-species plant–animal SNP array and the application of this array in selective breeding programs and the management of natural populations and plant germplasm.Rubus spp., mānuka, snapper, and trevally. When looking at the numbers of high-quality SNPs (PHR and NMH), this array had conversion rates of 58%, 56%, 61%, and 58% for each genus, respectively  by the indigenous Māori people , reproduction occurs via mass spawning of selected broodstock lines, and subsequently collected fertilized eggs and reared for on-growing. This means that parental relationships are unknown in these species; hence, genetic tools are necessary for offspring parental assignment. Additionally, high-throughput genotyping with this SNP array will enable quantitative genetic studies, QTL mapping, as well as the estimate of breeding values and inbreeding rates. Routine genetic screening of new wild broodstock lines introduced in captivity, as well as the offspring generated, would also help maintain high genetic diversity in the breeding program and maximize genetic gain.The SNP array data generated in this study allowed the discrimination of two different wild trevally populations from Australia and NZ. Even though further work (e.g. mtDNA sequencing) is necessary to confidently resolve whether these represent two distantly related trevally populations or that a different species was instead caught in Australia, this finding indicates that the SNP array can assist in both wild population management and fishery assessments. Furthermore, the application of the SNP array to inform selective breeding of trevally , as demoRubus and mānuka than in snapper and trevally, which had higher and more uniform DQC values  and is expected to be close to 1 for high-quality samples; and the QC call rate, which is the genotyping call rate across a subset of arrayed SNPs selected by Thermo Fisher Scientific. With no previous indication about the quality of the arrayed SNPs, as is normal in new designs like in this study, the QC call rate was not very reliable in determining sample quality. Therefore, we also looked at the call rate over all the SNPs; however, it should be noted that this value is only returned for the samples that are successfully genotyped, i.e. those samples that already passed QC and are therefore considered of higher quality. When looking at the DQC, differences in sample quality were observed between plant and fish samples. A greater failure rate was reported in both C values . It is iobserved , which iobserved ; howeveric calls . A similabolites . Often Di method  or the pRubus, where a larger variability was also observed  were combined, and genotyping was successfully executed for all the species screened. Several samples were genotyped from non-pooled reactions, allowing us to assess the effect of DNA pooling on the subsequent quality of the results. Overall, samples that were not pooled had a higher success rate than those that came from a mixed reaction; however, differences were observed between fish and plant samples. In snapper and trevally, the differences in DQC between pooled and non-pooled samples were significant but not as marked as in mānuka and observed . Interesobserved . Pooled observed ; althougn = 39) and yellowtail kingfish (n = 23) DNA samples were screened over the array. The objective was to evaluate the performance of snapper and silver trevally SNPs on two closely related species within the same family, as it has been performed successfully in other taxa  and 61% (snapper), which enabled a genotyping density suitable for applications in both breeding and conservation strategies. The robust and highly polymorphic SNP markers developed here for each species might be used in the future to design higher efficiency multi-species arrays, as well as being supplemented with new markers.jkad170_Supplementary_DataClick here for additional data file.


Abigar cattle, native to southwestern Ethiopia’s hot and humid environment, are recognized for their adaptability and vital contribution to local livelihoods and the livestock value chain. Investigating their genetic basis for adaptive traits is crucial for sustainable use. However, there is a paucity of studies on genomic diversity, population structure, and selection signatures of Abigar cattle. This study introduces the first whole-genome sequencing of Abigar cattle, revealing genes linked to heat tolerance, immune response, and stress resilience in tropical conditions. These findings offer essential genomic insights for future Abigar cattle breeding.STF) selection scan methods, we identified 83 shared genes that are likely associated with tropical adaption. The functional annotation analysis revealed that some of these genes are potentially linked to heat tolerance , immune response , and oxidative stress response (SLC23A1). Given the wider spreading impacts of climate change on cattle production, understanding the genetic mechanisms of adaptation of local breeds becomes crucial to better respond to climate and environmental changes. In this context, our finding establishes a foundation for further research into the mechanisms underpinning cattle adaptation to tropical environments.Over time, indigenous cattle breeds have developed disease resistance, heat tolerance, and adaptability to harsh environments. Deciphering the genetic mechanisms underlying adaptive traits is crucial for their improvement and sustainable utilization. For the first time, we performed whole-genome sequencing to unveil the genomic diversity, population structure, and selection signatures of Abigar cattle living in a tropical environment. The population structure analysis revealed that Abigar cattle exhibit high nucleotide diversity and heterozygosity, with low runs of homozygosity and linkage disequilibrium, suggesting a genetic landscape less constrained by inbreeding and enriched by diversity. Using nucleotide diversity (Pi) and population differentiation ( African cattle have been playing an indispensable role in shaping the development and interdependence of societies, economies, and ecosystems across the continent for generations. As a fundamental pillar of African agriculture, these magnificent animals hold immense significance in the lives of millions, contributing to livelihoods, food security, cultural heritage, and economic growth ,2. AfricIn the continent, Ethiopia has broad and diverse bovine genetic resources, with over 28 indigenous cattle breeds inhabiting the diverse geographic and climate conditions . These ihttps://en.tiangen.com/content/details_43_4224.html, accessed on 8 June 2022). The paired-end libraries with an average insert size of 500 bp were constructed for each individual, with an average read length of 150 bp. The whole-genome sequencing (WGS) was performed using the MGI-SEQ 2000 platform by Frasergen Bioinformatics Co., Ltd. . To investigate the genetic relationship between Ethiopian cattle and other cattle breeds, we retrieved additional WGS samples of African and European taurine and African Sanga cattle breeds from the NCBI database (Whole-blood samples (10 mL) were collected from 10 Abigar and 10 Barka (Begait) cattle breeds in their natural breeding tracts. DNA extraction was carried out at the Institute of Biotechnology, Addis Ababa University, Ethiopia, using a Tiangen genomic DNA extraction kit following the manufacturer’s protocols . Then, the raw sequences were subjected to quality control and trimmed using the Trimomatic v0.39 [Bos taurus reference genome (ARS-UCD1.2) [https://broadinstitute.github.io/picard/, accessed on 31 August 2023). The identification of short variants (SNPs) was performed following the GATK 4.3.0 best practice protocol . The protocol involved utilizing the HaplotypeCaller function on individual samples to detect variants, followed by the consolidation of individual samples and joint genotyping using the GenotypeGVCFs function. Furthermore, a two-step machine learning model called variant quality score recalibration (VQSR) was used to enhance the quality and reliability of variant calls. A set of high-quality variants that underwent careful curation and validation were utilized to train the VQSR model. After filtering against poor-quality sequence and non-variant data, a total of 33,522,977 biallelic, autosomal SNPs were retained for downstream analysis. Furthermore, the extent of missing genetic data within the individual sample and the interrelationship among the other samples were evaluated using “--missing-indv” and “--relatedness”, respectively, provided by the vcftools v0.1.15 [To confirm the quality of the raw sequencing data, we performed a per-base sequence quality check using the fastQC v0.11.8 software (ic v0.39  using de-UCD1.2)  using BW-UCD1.2) . SAMtool-UCD1.2) . To acco v0.1.15 . FortunaSTF) within 100 kb windows, with a step size of 50 kb, along the bovine autosomes. The observed heterozygosity was determined as the fraction of total heterozygous SNPs to the total number of sites considered within each genome, using the ARS-UCD1.2 taurine cattle reference genome as the basis for analysis. The observed and expected heterozygosity of each population were computed by employing the “--het” option in PLINK v1.9 [After variant calling and obtaining the variant call set, we performed genomic diversity analysis to explore the patterns of genetic variation within and between populations. The vcftools v0.1.15  was emplINK v1.9 . SubsequPrincipal component analysis (PCA) and admixture analysis were employed to infer population structure and admixture levels. These analyses were started with a set of high-quality autosomal SNPs, and then SNPs with minor allele frequency (MAF) less than 0.05 were filtered out, resulting in 23,963,154 SNPs. We further pruned the filtered SNPs for high levels of pairwise linkage disequilibrium (LD) using Plink 1.9  with theThe decay of linkage disequilibrium (LD) with the physical distance between SNPs was calculated and displayed using the PopLDdecay software ver. 3.41  with itsSTF) [http://useast.ensembl.org/index.html, accessed on 1 August 2023), employing the ARS-UCD1.2 bovine reference genome [https://david.ncifcrf.gov/content.jsp?file=release.html, accessed on 1 August 2023) was employed to unravel the gene ontology (GO), pathways, and enriched terms of the candidate genes [To elucidate the positive signatures of selection in Abigar cattle, a within-population genomic scan method  was employed. Additionally, the population differentiation between the Abigar and Holstein cattle breeds was estimated by Weir and Cockerham statistics (FST) . To implFST) . The tope genome . The Datte genes .To better understand genomic variations in Ethiopian indigenous cattle populations, we performed whole-genome sequencing of ten Abigar (ABI) and ten Barca (BAR) cattle and compared them with publicly available genomic data from Ankole (ANK), NDama (NDA), and Holstein (HOL) cattle breeds . The cleWe performed Principal Component (PCA), admixture, and neighbor-joining (NJ) analyses to explore the genetic relationships among Ethiopian and reference cattle breeds, including African Sanga (Ankole), African taurine (NDama), and the commercial cattle breed (Holstein) . The PCASTF) between African zebu and taurine breeds revealed significant genetic divergences. Conversely, moderate differentiation was found between the African Sanga and zebu breeds (u breeds . These mu breeds c. As expu breeds c. The obu breeds d.STF) selection scan methods were employed to uncover selection sweeps associated with adaptive traits in Abigar cattle. In both methodologies, we designated the top 0.5% of genomic regions as candidates under selection. A total of 310 and 323 genes were detected by the Pi and STF methods, respectively , oxidative stress response (SLC23A1), and heat tolerance genes   and population differentiation (ectively . Of thesectively . Interesectively . The ided RXFP2) . The genp ≤ 0.05, Bonferroni correction) enriched biological processes  are defined as contiguous stretches of the genome where an individual inherits identical alleles from both parents . In our Indigenous African cattle breeds’ exhibit unique resilience and resistance to a range of challenging environmental pressures compared to their commercial counterparts. This resilience not only highlights the adaptive capabilities of these breeds but also emphasizes the potential value they hold for sustainable livestock management and breeding programs, especially in the face of changing and unpredictable environmental circumstances. In this analysis, we have identified several candidate genes that shed light on the adaptive significance of Abigar cattle in hot and humid tropical environments marked by multiple environmental stressors, disease prevalence, limited feed resources, and parasitic challenges. Interestingly, the genes found within the identified candidate regions in our current investigation exhibit functions closely tied to traits encompassing heat tolerance, immune response, and the ability to counteract oxidative stress .ASIP, DNAJC18, HOXC13, HOXC12, HSF4, and RXFP2 , a key player in pigmentation. ASIP reduces eumelanin production by downregulating the melanocortin 1 receptor (MC1R) while simultaneously promoting pheomelanin production [ASIP gene might account for white pigmentation in goats [ASIP expression led to elevated eumelanin production and, subsequently, a darker coat color [ASIP gene’s coding region frequently results in a recessive black coat color in rabbits [Interestingly, Abigar cattle are characterized by their predominant coat colors of white and grey ,14. The oduction . Beyond in goats  and sheein goats . Additioat color . Similar rabbits .RXFP2 gene (BTA12: 29.21–29.27 Mb), previously reported for its pleiotropic effects encompassing both reproductive and horn development functions [We have also identified the unctions ,47,48. Tunctions . Similarunctions .HOXC12 and HOXC13) are integral developmental regulators intricately involved in shaping essential morphological traits that actively drive hair follicle differentiation, growth, and overall development [HOXC13 gene is a key determinant impacting skin thickness. The combined factors of skin thickness and the abundance of hair follicles play a pivotal role in finely tuned body temperature regulation. For instance, animals with thicker skin, such as the heat-tolerant Bos indicus breeds of cattle, exhibit improved thermoregulation compared to their heat-sensitive counterparts like the Bos taurus breeds [DNAJC18 gene encodes for heat shock proteins that play vital roles in facilitating protein folding, directing misfolded proteins towards degradation pathways, and upholding protein equilibrium within the cellular environment. The positive selection signals around this region are further confirmed by significantly lower Pi and high STF values , exhibiting a robust positive selection signal in Abigar cattle  signaling pathway, tasked with the regulation of immune responses. Specifically, IRAK3 operates as a negative regulator of TLR signaling, adjusting the innate host defense mechanisms to modulate the extent and duration of excessive inflammation [For generations, the African continent has faced multitudes of selection pressures, including diseases and parasites. This prolonged exposure has nurtured potent innate and acquired immune responses, empowering the ability to combat an extensive range of diseases and parasitic challenges. For instance, we identified a highly significant genomic region on BTA7: 50.64–50.74 Mbp encompassing three immune response genes   , primariIndigenous African cattle breeds exhibit pronounced resilience and resistance to environmental pressures, holding immense potential for sustainable livestock management. This study unveiled genes that have undergone positive selection in Abigar cattle. These genes contribute to various biological and cellular functions, collectively shaping the adaptive characteristics of this cattle breed within tropical environments. The genes are mainly associated with heat tolerance, immune response, and oxidative stress counteraction and have previously been elucidated in other African cattle breeds. The shared African cattle-specific adaptation genes underscore their potential value for future breeding programs and sustainable livestock management in the face of climate change and breeding objectives.


Crassostrea virginica), which is a species of significant aquaculture production and restoration efforts throughout its native range. SNP discovery was performed using low-coverage whole-genome sequencing of 435 F1 oysters from families from 11 founder populations in New Brunswick, Canada. An Affymetrix Axiom Custom array was created with 219,447 SNPs meeting stringent selection criteria and validated by genotyping more than 4,000 oysters across 2 generations. In total, 144,570 SNPs had a call rate >90%, most of which (96%) were polymorphic and were distributed across the Eastern oyster reference genome, with similar levels of genetic diversity observed in both generations. Linkage disequilibrium was low (maximum r2 ∼0.32) and decayed moderately with increasing distance between SNP pairs. Taking advantage of our intergenerational data set, we quantified Mendelian inheritance errors to validate SNP selection. Although most of SNPs exhibited low Mendelian inheritance error rates overall, with 72% of called SNPs having an error rate of <1%, many loci had elevated Mendelian inheritance error rates, potentially indicating the presence of null alleles. This SNP panel provides a necessary tool to enable routine application of genomic approaches, including genomic selection, in C. virginica selective breeding programs. As demand for production increases, this resource will be essential for accelerating production and sustaining the Canadian oyster aquaculture industry.Dense single nucleotide polymorphism (SNP) arrays are essential tools for rapid high-throughput genotyping for many genetic analyses, including genomic selection and high-resolution population genomic assessments. We present a high-density (200 K) SNP array developed for the Eastern oyster ( Crassostrea gigas) is the most important farmed species, with introductions in 66 countries for cultivation outside of its native range , with a value of ∼$31 million (CAD) in 2017 (an increase of 25% from 2016). Aquaculture production has also increased elsewhere C. virginica's natural range, which extends from Northern New Brunswick in Canada, to the Gulf of Mexico in the United States with the adoption of floating aquaculture techniques. C. virginica is a particularly interesting species for aquaculture production due to its long shelf life and broad range of environmental tolerances  farming demonstrated substantial increases in prediction accuracy when using genomic estimated breeding values (gEBVs) and accelerated rates of genetic gain for key production traits  with clear economic benefits (Genomic selection (e.g. genomic-BLUP or G-BLUP) provides a valuable alternative by allowing the incorporation of genomic information into selective breeding programs of farmed species to accelerate genetic progress  and tambaqui (Colossoma macropomum)  has been released (Ostrea edulis)  . For theC. virginica) in Canada. This chip will enable rapid and cost-effective high-throughput genotyping for a broad range of applications, including genome-wide association studies, population genetic analyses, monitoring of genetic diversity in wild and farmed populations, and genomic selection to accelerate genetic progress. We also demonstrate the power of cost-effective low-coverage whole-genome sequencing (lcWGS), which has been shown to be an optimal approach for accurately inferring allele frequencies , we were able to map SNPs onto the genome, allowing for the targeted selection of variants with an even distribution along the genome. The development of our high-density SNP chip will complement the resources developed for US populations by extending the availability of genomic tools to cover the northern limits of the species' distribution. This resource will provide a much-needed opportunity to accelerate Eastern oyster production in Canada by enabling genomic predictions, as well as encouraging the incorporation of genomic tools for monitoring breeding populations  and facilitating further genomic research on wild and cultured populations.The objective of this study was to design and validate the first high-density (200 K) SNP array specific to the Eastern oyster  in Shippagan, New Brunswick, Canada. This strain was founded from 464 oysters from 11 wild populations sampled in 2014 across New Brunswick and bred in a series of interpopulation crosses in winter 2015; a second generation was produced in 2018. The wild founder populations have been previously shown to exhibit high levels of within-population diversity and between-population differentiation . Briefly, raw sequencing reads were trimmed and filtered for quality using fastp with default parameters (https://www.ncbi.nlm.nih.gov/genome/398] using BWA-MEM with default parameters (bam format) using the GATK model (-GL 2) in ANGSD v0.923  minimum MAF (0.05 and 0.1); (2) size of neighboring regions around each SNP of interest ; and (3) maximum number of SNPs permitted in the neighboring regions . The best SNPs for the purpose of the Affymetrix array  would have a MAF ≥ 0.1 and 0 SNPs in the neighboring 30 bp on each side of the SNP of interest. With this approach, multiple subsets of SNPs were generated with different degrees of filtering stringency; SNPs that passed the most stringent combination of criteria were present in all subsets. For a given SNP, we counted the number of subsets to which it belonged and ranked all SNPs by the number of sets in which they were present (i.e. SNPs that were present in more sets passed more stringent criteria and were therefore assigned a higher ranking). SNPs were given a priority number starting from 1 for the highest priority and increasing as priority got lower, as is required by Affymetrix for SNP evaluation. This list and associated information, including chromosome name and position, the flanking sequences, and central SNP alleles, and the priority number was sent to Affymetrix for evaluation. Based on the scores received and our own priority numbers, we selected the best SNPs for printing on the Affymetrix Axiom® myDesign Custom Array including ∼200 K SNPs, while also ensuring that the distribution of SNPs retained was even across all chromosomes after correcting for chromosome length , including the 435 oysters that were sequenced for SNP discovery. The F1 broodstock oysters were subsequently bred in 82 crosses. A total of 3,000 F2 oysters were sampled randomly in November 2020 and muscle tissues were preserved in 95% ethanol. In addition, ∼1,000 F2 oysters were selected as broodstock based on measured traits . Muscle tissue samples were taken from these breeders during spawning in January 2021. DNA extractions were performed on all 4,000 F2 oysters as described above (see “DNA Extraction and Sequencing”) and genotyped on the SNP chip. Genotyping was carried out at the Génome Québec Centre d'expertise et de services  following standard protocols for the Axiom Affymetrix platform. Quality control (QC) analysis and genotype calling were performed using the Axiom® Analysis Suite Software .o and He, respectively), across the 2 cohorts using VCFTools (IS) was estimated across loci as (He—Ho)/He and significant departures from Hardy–Weinberg equilibrium (HWE) were evaluated using Plink, with a Bonferroni adjustment for multiple comparisons (α = 0.05). LD decay was computed for each cohort independently up to a maximum distance of 500 kb between SNPs using PopLDdecay v.3.41  and observed and expected heterozygosity  per SNP were estimated using Plink v.1.90 (Kinship was reconstructed using the R package “Sequoia”  with 545Low-coverage whole-genome sequencing (lcWGS) of 435 oysters yielded an average of 19.65 million reads per sample. This represented an average of 2.64 Gbp per sample, which translates to an average coverage of 3.86 × (sd 1.33X). Following all data cleaning and preparation steps with the WGS sample preparation pipeline , 85.2% of the raw reads were mapped to single positions on the genome and were used for calling SNPs. After applying filters and calling genotypes in ANGSD, we retrieved a total of 54,945,771 SNPs.p-convert value > 0.6  minimum MAF, (2) size of neighboring region around each SNP of interest, and (3) maximum number of SNPs allowed in each neighboring region, which generated 18 sets of SNPs with more or less stringent parameters. A total of 159,800 SNPs passed the most stringent combination of criteria (MAF > 0.1 and 0 SNPs in neighboring regions of 30 bp on each side of the SNP of interest). Across all 18 sets, a list of 896,289 SNPs was retained, with an average rate of missing SNP genotypes of 20% across the entire data set. These SNPs were ranked by priority and sent to Affymetrix for evaluation. Of these candidate SNPs, a total of 793,104 were categorized by Affymetrix as “recommended” or “neutral”, 91% of which had a ue > 0.6 . From thes > 0.6 . Based oes > 0.6 . All of p-convert value of 0.7; transition SNPs (A/G and C/T) comprised about 58% of the successfully converted SNPs  across both groups  at the shortest inter-SNP distance (30 bp) was ∼0.32 for both groups and showed a moderate to low decay of LD with increasing distance between SNP pairs  and to < 0.1 at a distance of approximately 3.5 and 15 kb, respectively  value > 0.856. A total of 144,570 SNPs had a minimum call rate of 90%, with a median minor allele frequency (MAF) of 0.2 . Out of ted SNPs . The corh groups . MoreoveNP pairs , Table 3ectively .IS), which revealed an increase in MIE rate with increasingly positive FIS values , whereas for loci with an error rate > 0.01, there was a strong correlation (r = 0.78) , corresponding to a total of 230 full-sib families, 115 maternal half-sib families, and 161 paternal half-sib families, were reconstructed for full parentage (sire and dam). The resulting pedigree was used to estimate Mendelian inheritance errors (MIEs) across individual oysters and across all genotyped SNPs that passed quality control (∼144 K). At the individual level, the average MIE rate was 0.015 (+- 0.002), with a maximum error count for an individual of 6,270 . MIEs were distributed across each of the 10 chromosomes . The med = 0.78) . This he = 0.78) .C. virginica in Canada to date and will be an essential asset for the oyster aquaculture industry. The recent assembly of a high-quality chromosome level reference genome for the Eastern oyster [https://www.ncbi.nlm.nih.gov/genome/398] enabled us to achieve an even distribution of SNPs across all ten Eastern oyster chromosomes, ensuring genome-wide coverage of genetic variation represented on the chip. After genotyping more than 4,000 individual oysters spanning 2 generations, the majority of the successfully called SNPs (144 K) were polymorphic (96%), yielding a total of 139 K high-quality SNPs that were successfully genotyped on the chip . A similar conversion rate was observed on the high-density SNP panel of similar size developed for the Pacific oyster (C. gigas), in which 133 K SNPs out of 190 K (70%) were successfully genotyped  and genomic selection. Additional sampling and genotyping of oysters from populations outside of the geographic area sampled for this study will be needed to evaluate marker conversion rate and commercial applicability in populations that were not used for SNP array design.The 200 K SNP array designed and validated in this study represents the first SNP panel characterized for C. virginica genome, a large proportion of the 144 K SNPs with called genotypes exhibited low error rates (median MIE rate = 0.002), with 72% of these SNPs having an error rate lower than 1%, suggesting that the majority of called SNPs segregate under Mendelian inheritance. However, a nontrivial number of SNPs exhibited high error rates, up to a maximum MIE rate of 60%. While we acknowledge that the MIEs detected in this study are contingent on the results of the performed kinship reconstruction and assumes that parents are correctly genotyped, the protocol we used minimizes the risks of generating wrong assignments , and complies with the recommendations in both the Sequoia manual and literature (C. virginica (C. virginica genome (IS) and deviations from Mendelian segregation, which cannot be induced by selective forces.Although MIEs were observed across the r2 declined at a moderate rate with increasing physical distance. Relatively low levels of LD are expected for C. virginica given the high recombination rates characteristic of oyster genomes  in Canada, a species for which aquaculture production is increasing rapidly and genomic tools to sustain this growth are needed. The low-coverage whole-genome sequencing approach used here offered a low-cost and effective method for the discovery of a large number of highly polymorphic SNPs for panel development. Integration of the C. virginica reference genome allowed for the selection of SNPs that were evenly spaced along the entire genome. Indeed, validation of the SNP chip by genotyping >4,000 oysters across 2 generations of crosses showed that the 144 K successfully converted SNPs were evenly distributed across all 10 chromosomes. The relative persistence of LD with increasing physical distance between markers and the high density of SNPs targeted suggest that this panel is suitable for achieving high prediction accuracy. Analysis of MIEs revealed a large proportion of SNPs on the chip that segregate under Mendelian inheritance. The detection of elevated rates of unexpected genotypes at some SNP loci may be attributed to heterozygote deficiencies resulting from null alleles and highlight candidates for further investigation of copy-number variation.This manuscript presents the first high-density (∼200 K) SNP array designed specifically for the Eastern oyster  and to unravel the genomic architecture of economically important traits, evaluate genomic breeding values, establish genomic prediction protocols, assess the adequate number of markers required to optimize resources, perform population genomic assessments, and conduct low-cost genomic selection for accelerated genetic gains.The availability of this SNP array extends the coverage of genomic resources for jkad071_Supplementary_DataClick here for additional data file.