The power of genome-wide association studies (GWAS) is often limited by the sample size available for the analysis. Milk fatty acid (FA) traits are scarcely recorded due to expensive and time-consuming analytical techniques. Combining multi-population datasets can enhance the power of GWAS enabling detection of genomic region explaining medium to low proportions of the genetic variation. GWAS often detect broader genomic regions containing several positional candidate genes making it difficult to untangle the causative candidates. Post-GWAS analyses with data on pathways, ontology and tissue-specific gene expression status might allow prioritization among positional candidate genes.OSBPL6 and AGPS on Bos taurus autosome (BTA) 2, PRLH on BTA 3, SLC51B on BTA 10, ABCG5/8 on BTA 11 and ALG5 on BTA 12. Novel genes in previously known regions, such as FABP4 on BTA 14, APOA1/5/7 on BTA 15 and MGST2 on BTA 17, are also linked to important FA metabolic processes.Multi-population GWAS for 16 FA traits quantified using gas chromatography (GC) in sample populations of the Chinese, Danish and Dutch Holstein with high-density (HD) genotypes detects 56 genomic regions significantly associated to at least one of the studied FAs; some of which have not been previously reported. Pathways and gene ontology (GO) analyses suggest promising candidate genes on the novel regions including Integration of multi-population GWAS and enrichment analyses enabled detection of several novel genomic regions, explaining relatively smaller fractions of the genetic variation, and revealed highly likely candidate genes underlying the effects. Detection of such regions and candidate genes will be crucial in understanding the complex genetic control of FA metabolism. The findings can also be used to augment genomic prediction models with regions collectively capturing most of the genetic variation in the milk FA traits.The online version of this article (10.1186/s12864-019-5573-9) contains supplementary material, which is available to authorized users. FAs in milk can originate either through direct transport from the rumen to the mammary gland via the blood, or from de novo synthesis in the mammary gland from acetate, beta-hydroxybutyrate [DGAT1 and SCD1 [Several fatty acids (FAs) of varying carbon chain length , Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, mammary gland gene expression status and information in the mammalian phenotype ontology database .Table Our multi-population GWAS resulted in the detection of 56 genomic regions containing single nucleotide polymorphisms (SNPs) significantly associated with at least one of the studied FA traits Table . Signifip-value) across FA traits ranged from a –log 10 p-value of 6.9 for C18:0 to a –log 10 p-value of 126 for C14 index. Figs. p-value = 11.39), C15:0 , C16:0 , C16:1 , C18:1c9 , C18:2n6 , C18:3n3 , CLA  and C18 index  was observed at two variants on BTA 14 (ARS-BFGL-NGS-4939 and BovineHD1400000216). This region (14a) was significantly associated with all studied FA traits except C12:0. The lead SNP in this region explained up to 34% of the genetic variation in C18:1c9 and C18:2n6. Two other regions on BTA 14 remained significantly associated with multiple FA traits after accounting for the fixed effect of the lead SNP from region 14a (ARS-BFGL-NGS-4939). The second region (14b) was also significantly associated with most FA traits except C12:0. The third region on BTA 14 (14c), was significantly associated with C14:1, C16:1, C14 index and C18 index. The lead SNP in this region explained 2.7% of the genetic variation in C18 index and 1.6% in C14 index.Peak sizes , C12:0  and C14:0 (24.2) was detected with two variants on BTA 19 (BovineHD1900014372 and BovineHD1900014348). Significant associations were also detected for C8:0, C16:0, C18:1c9, C14 index and C18 index with SNPs located between 37.3 to 61.3 Mbp on chromosome 19. Particularly for C14:0, 22.3% of the genetic variation was explained by the lead SNP in this region.Strongest association for C10:0 , C14 index  and C16 index  was found with SNPs on chromosome 26 (BovineHD2600005461). Significant associations were also detected for C8:0, C10:0, C12:0, C14:0, C16:0, C16:1, C18:0 and C18 index. The lead SNP in this region explained 39.0% of the genetic variation in C14:1.The strongest association for C14:1 , which resulted in different significantly enriched GO terms and KEGG pathways relevant to FA related mechanisms , enriching the GO terms of FA metabolic process (GO:0006631) and lipid binding activities (GO:0008289). A study by Nafikov et al. [FABP4 haplotype negatively associated with saturated milk FAs and the ratio between saturated and unsaturated FAs while having positive effects on the unsaturated FAs. Marchitelli et al. [FABP4 affected the ratio of monounsaturated/saturated FA in milk. Additionally, variation in FABP4 is reported to affect other milk production traits such as milk yield [FABP4 as the most likely candidate gene.The third region on BTA 14 (44.7–49.9 Mbp) was exclusively associated with C14:1 and C16:1 as well as C14 index and C18 index. This region was previously reported for significant associations with C16:1 . The regv et al.  reportedi et al.  also reps GO:00089. A studFASN on BTA 19 [SCD1 on BTA 26 [ACLY, STAT5a, PRKAA1, GH on BTA 19 and ELOVL3, ACLS5 on BTA 26. Significant associations were previously reported between variants within some of these genes and some milk FA traits [Broader regions were detected on BTA 19 (37.3–61.3 Mbp) and BTA 26 (2.9–43.0 Mbp). The genes n BTA 19  and SCD1n BTA 26  have preA traits , 31.DGAT1 and SCD1 regions than previous GWAS using different parts of the multi-population dataset used in the current analysis [DGAT1 (ARS-BFGL-NGS-4939) and SCD1 (BovineHD2600005461) loci were similar in direction and highly correlated between the three populations but estimated effects in the Chinese sample were consistently lower across the FAs compared to the Dutch and Danish Holstein samples.In our study, more FA traits have been found to have significant associations with the analysis –11, 14. MGST1 was suggested as the most likely candidate gene [MGST1 gene. However, our enrichment analysis did not establish any connection to MGST1 with significantly enriched FA related GO terms and pathways. Additionally, PLBD1 and LRP6 genes were connected to several pathways including lipid localization (GO:0010876) and transport (UP_KEYWORDS) suggesting that the significant association observed in the region with 10 FA traits might not be limited to the MGST1 effect.The three regions detected on BTA 5 overlap with previously reported regions for milk FA traits , 9, 32. ACSS2 as the highly likely candidate gene. Meanwhile, using infrared (IR) predicted phenotypes for the de novo FAs, Olsen et al. [NCOA6, not the ACSS2, is responsible for significant associations in the region. Our enrichment analysis however links ACSS2 with several significantly enriched pathways while no such links were established for the NCOA6 gene.The region on BTA 13 was previously detected in the Dutch Holstein population , 11 and n et al.  suggesteAPOA1, APOA4, APOA5, and DPAGT1. The apolipoproteins APOA1/4/5 enriched glycerolipid metabolic process (GO:0046486), fat digestion and absorption (bta04975) as well as negative regulation of FA biosynthetic process (GO:0045717) while the DPGAT1 was involved in lipid biosynthetic process (GO:0008610). The strongest associations observed in the region were between C18.0 and variants within the alipoprotein genes, which showed opposite direction of effects on C10:0 and C14:0. Although effects were not significant, the lead SNP in the region also showed moderate effects on the other de novo FAs including C8:0  and C12:0  with direction of effects similar to C10:0 and C14:0. The alipoproteins APOA1/4/5 are thus collectively suggested as the candidates underlying the strong effect on C18:0 observed in the region. The opposing effects on the de novo FAs might be directly through involvement of the alipoproteins in negative regulation of FA biosynthesis or indirectly through the effect on C18:0, which suppresses de novo synthesis.Similarly, the first region on BTA 15 (27.2–31.2 Mbp) has been reported in previous studies including a joint Chinese-Danish Holstein population . SeveralMGST2 significantly enriched GO terms that included FA metabolic (GO:0006631) and biosynthetic (GO:0006633) processes. The MGST2 is previously linked to intramuscular FA composition in pigs [MGST2 is suggested as the likely candidate gene underlying effects on the first region of BTA 17. Using a subset of the dataset used in the current study to fine map BTA 17, Duchemin et al. [LARP1B as a primary candidate gene in the second region (17b). However, our enrichment analysis did not result in significant enrichment of any of the FA pathways and ontology terms for genes in the region.The two regions detected on BTA 17 are also in agreement with previous findings. The regions detected by Bouwman et al. (2012)   family, a group of intracellular lipid receptors, is shown to be involved in lipid binding (GO:0008289) and transport (UP_KEYWORDS) processes. The OSBPL6 gene is found to be expressed in the human mammary gland during several stages of lactation [OSBPL6 gene is also shown to have a binding site for miR-33a/b [AGPS) also enriched GO terms related to FA synthesis including lipid biosynthesis process (GO:0008610). In the mammalian phenotype database, mutation in the AGPS gene in mice has been linked to abnormal lipid levels (MP:0001547), which is a rather broad term in the database referring to any anomaly in the concentrations of fat-soluble substances in the body, including circulating triglyceride and free FAs. Thus, our enrichment analysis indicate that both the OSBPL6 and AGPS might have roles on de novo synthesis of FAs. Pattern of SNP effects in the region is also in agreement with enrichment analysis such that strongest association was estimated with C8:0 and C10:0 while moderate, but not significant effect was measured for C12:0 and C14:0 . Opposing direction of the lead SNP effect were also observed for the de novo synthesized FAs, except C15:0, on the one hand and most of the long chain FAs on the other  was shown to be involved in FA metabolic process (GO:0006631). Mutations on the PRLH gene in mice have been associated with increased circulating triglyceride levels (MP:0001552) and increased total body fat amount (MP:0010024) in the mammalian phenotype database. In mammals, the PRLH gene is known to stimulate prolactin release and regulate its expression. Prolactin, which is a polypeptide hormone, has been shown to induce lipogenesis in many tissues [PRLH gene, through regulation of prolactin release might have effects on milk yield. The pattern of SNP effects in the region suggest a connection with the poly-unsaturated fatty acids (PUFAs) with strongest associations observed for C18:3n3 and CLA. The direction of effects of the lead SNP was similar for all unsaturated FAs as well as all the desaturation indexes, while opposing effects were estimated for the de novo synthesized FAs and C16:0  genewhich is implicated in lipid transport (UP_KEYWORDS) and localization (GO:0010876) processes. Pattern of effects in the region show strong effect on C14:1 and moderate effects on with C14 index  and C18 index  in direction opposite to the strong effect on C18:0  and ABCG8 enriched several pathways and processes including fat digestion and absorption pathway (KEGG ~bta04975) and the GO terms of lipid localization (GO:0010876) and transport. In the mammalian phenotype database, the ABCG5 and ABCG8 genes are linked to increased circulating triglyceride level (MP:0001552), abnormal lipid homeostasis (MP:0002118) and abnormal phospholipid level (MP:0004777). In humans, mutations in ABCG5/8 have been linked to conditions characterized by abnormal accumulation of sterols in blood and tissues [ABCG5 and ABCG8 genes in bovine liver, mammary gland, digestive tract and blood samples. Expression of ABCG5/8 in bovine mammary gland might indicate that apart from absorption and transport of lipids from the digestive tract, ABCG5/8 might also be involved in the secretion of lipids from the mammary gland into the milk. Significant association in the region was limited to C16:0. Although not significant, this region was also associated with C16:1  and CLA , with directions of effects on CLA opposite to the effects on C16:0, C16:1, and C16 index  gene located on the second region of BTA 12 was shown to enrich the lipid biosynthesis process (GO:0008610) and glycerolipid metabolic process (GO:0046486). The ALG5 gene has previously been shown to be differentially expressed during the different stages of lactation in bovine [p-value = 3.07) and C18:0  where opposite direction of effects were observed for C18:0 . MiRNAs are small RNAs that regulate the expression of complementary messenger RNAs . Severaland SCD1  as well and SCD1  and showand SCD1 . Among tand SCD1 . Wang etand SCD1  showed tOSBPL6 and AGPS on BTA 2, PRLH on BTA 3, SLC51B on BTA 10, ABCG5/8 on BTA 11 and ALG5 on BTA 12. Post-GWAS analyses using multiple data sources on pathways, ontology terms and tissue-specific gene expression status enabled prioritization of highly likely causative candidate genes among several positional candidates on detected regions. Use of such data in combination in combination with analysis of patterns of effects across the milk FA spectrum allowed linking some of the candidates to specific FA synthesis mechanisms. Detection of several novel regions and candidate genes will be contribute to the knowledge base on genetics underlying the bovine milk FA composition.Multi-population GWAS for GC-quantified FA traits resulted in the detection of 56 genomic regions significantly associated to at least one of the studied FAs, including novel regions explaining relatively smaller fractions of the genetic variation. Enrichment analysis of genes harbored in detected regions reveals promising candidate genes some of which have not been previously linked to milk FA traits, including Test-day milk samples were obtained from 784 Chinese, 675 Danish and 1566 Dutch Holstein cows sampled from 18 herds in China, 22 herds across Denmark and 398 herds in the Netherlands. Stages of lactation of sampled cows ranged from 3 to 700 days in milk in the Chinese population, 9 to 481 days in milk in the Danish population and 60 to 278 days in milk in the Dutch Holstein cows. To standardize the samples from the three countries, only cows at days-in-milk of 60 and above were considered for the multi-population GWAS. Thus, 700 Chinese, 614 Danish and 1566 Dutch samples were available for the analysis. The reason to standardize the dataset by lactation stage is that the genetic determination of milk fat composition traits might be different in the early stages of lactation. There is evidence that effects of genes in early lactation differ from those later in lactation . ExcludiFA traits, including C8:0, C10:0, C12:0, C14:0, C14:1, C15:0, C16:0, C16:1, C18:0, C18:1c9, C18:2n6, C18:3n3 and C18:2 cis-9,trans-11 (CLA), were analyzed using the GC method. Details of the sample preparation for the GC analyses are as described by Li et al.  for DaniHigh-density (HD) genotypes, real or imputed, were available for all cows used in the analyses. All cows in the Chinese dataset were initially genotyped using the BovineSNP50 Beadchip . A sample population of 96 Chinese Holstein bulls, genotyped using the BovineHD Beadchip (777 K), was available as reference to impute the 50 K genotypes of the cows to HD.Part of the Danish cows were genotyped using the BovineSNP50 Beadchip, while the remaining Danish cows were genotyped using the BovineHD Beadchip and used as reference to impute the 50 K genotypes of the first part of the Danish cows to HD as described in Gebreyesus et al. . The DutA single-SNP association test was implemented using a mixed linear model in the GCTA program . Associaijkl is the phenotype of cow l; μ is the fixed effect of mean; parityi and herdj are the fixed effects of parity and herd, respectively; b1 is the regression coefficient for DIM, DIMijkl is a covariate of days in milk; b2 is the allele substitution effect for SNP, SNPk is a covariate indicating the number of copies of a specific allele  of the SNP; and animal is the random additive genetic effect. Animal effects were assumed to be distributed as G is the genomic relationship matrix constructed using all HD genotypes but excluding the SNPs on the chromosome on which SNP k is located. Residuals were assumed to be distributed as: Where yp-value = 3.4 to –log10 p-value = 5.0. We used a –log10 p-value of 5.0 as the genome-wide significance threshold for all FA composition traits.Significance thresholds were determined using a false discovery rate (FDR). Significance thresholds corresponding to FDR of 5% ranged for different FA from –log10 p-values were employed. In this approach, the SNP with the highest –log 10 p-value for the studied FA trait was considered as the lead SNP. The allelic dosage of such a lead SNP was then fitted as fixed effect for a second round of chromosome-wide analyses. If other SNPs, also significantly associated in the first round GWAS, were still found to have -log 10-pvalue > 5 in the second round analysis, the SNP with the highest –log 10 p-value in the second analysis was taken as the second lead SNP and its allelic dosage fitted as fixed effect for a third round of analysis. This procedure was iterated until no further SNP with -log 10-pvalue > 5 was observed. The SNPs that showed significant association in a round of GWAS but showed –log 10 p-value < 5 upon fitting the allelic dosage of the lead SNP were then considered as part of a region around that lead SNP. The position of the first and last such SNP before and after the lead SNP were considered as the boundaries of the region.To determine if a region harbored one or more QTL, iterative approaches fitting the effect of SNPs with the highest –log 10 2pqα2, where p and q are the allele frequencies and α is the allele substitution effect [Genetic variance explained by the lead SNP in a region was calculated from the GWAS summary as: n effect . The proG constructed using all HD SNPs. Computation of genetic variance explained by SNPs from a GWAS summery might lead to overestimation of SNP effects [2) estimates were computed as:where  effects  speciallhttps://www.ensembl.org/biomart/martview). The DAVID functional annotation tool (https://david.ncifcrf.gov) was then used to analyze overrepresented GO biological terms, which included the terms cellular component (CC), molecular function (MF), biological process (BP) and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Ontologies in the mammalian phenotype database were accessed and searched for genes connected to abnormalities relevant to FA metabolism using the Mouse Genome Informatics (MGI) web platform (http://www.informatics.jax.org/batch).Genes found within detected genomic regions were retrieved from the ensemble database using the BioMart web interface based on the UMD 3.1 bovine genome assembly Effects of the lead SNPs in all the detected genomic regions. SNP regression coefficients (SNP effects) in all the FA traits studied, along with the standard errors and –log 10 Additional file 2:List of genes identified as enriched using the DAVID based enrichment analysis. Gene names and the positions in the genome  clustered to at least one of the significantly enriched pathways and GO terms. (XLSX 24 kb)Additional file 3:Micro-RNAs located within detected genomic regions. List of micro-RNAs (names and ensemble stable IDs) located in detected genomic regions and their positions in the genome . (XLSX 11 kb)


The importance of Indian germplasm as origin and primary center of diversity of cultivated melon is widely accepted. Genetic diversity of several collections from Indian has been studied previously, although an integrated analysis of these collections in a global diversity perspective has not been possible. In this study, a sample of Indian collections together with a selection of world-wide cultivars to analyze the genetic diversity structure based on Genotype by Sequence data.A set of 6158 informative Single Nucleotide Polymorphism (SNP) in 175 melon accessions was generated. Melon germplasm could be classified into six major groups, in concordance with horticultural groups. Indian group was in the center of the diversity plot, with the highest genetic diversity. No strict genetic differentiation between wild and cultivated accessions was appreciated in this group. Genomic regions likely involved in the process of diversification were also found. Interestingly, some SNPs differentiating inodorus and cantalupensis groups are linked to Quantitiative Trait Loci involved in ripening behavior (a major characteristic that differentiate those groups). Linkage disequilibrium was found to be low (17 kb), with more rapid decay in euchromatic (8 kb) than heterochromatic (30 kb) regions, demonstrating that recombination events do occur within heterochromatn, although at lower frequency than in euchromatin. Concomitantly, haplotype blocks were relatively small (59 kb). Some of those haplotype blocks were found fixed in different melon groups, being therefore candidate regions that are involved in the diversification of melon cultivars.The results support the hypothesis that India is the primary center of diversity of melon, Occidental and Far-East cultivars have been developed by divergent selection. Indian germplasm is genetically distinct from African germplasm, supporting independent domestication events. The current set of traditional Indian accessions may be considered as a population rather than a standard collection of fixed landraces with high intercrossing between cultivated and wild melons.The online version of this article (10.1186/s12864-019-5784-0) contains supplementary material, which is available to authorized users. Cucumis melo L.) is one of the most important fruit crop species belonging to the Cucurbitaceae family. Nowadays, melon is cultivated worldwide and extensive phenotypic variability is found among modern and traditional cultivars, landraces, feral and wild plants. Several intraspecific classifications have been proposed based on morphological traits [Melon  markers in four collections from different regions of India: landraces North India , East IGenotyping-by-Sequencing (GBS) approaches  have becThe GBS analysis of the 78 new IBMCP melon accessions  analysis was carried out to investigate the genetic structure of the current melon germplasm. A preliminary analysis was performed to verify the absence of bias due to GBS data merging. We found that accessions from the same horticultural group  from the two data sets plotted in the same MDS space region, indicating that the possible bias for merging was negligible , however, for K > 3 the group seems to be a mixture of several populations , except three of them that were ungrouped and ‘Jenny Lind’ which grouped with the cantaloupe group. This group, despite of being classified initially as ces Fig. a. STRUCTIndian and African groups were located in the middle of the plot, although slightly separated, and clearly differentiated from the other groups  and six groups , based on MDS results. The highest percentage of variation, 58.49%, was due to the diversity within groups (Table C. melo agrestis vs melo) was 22.29%, and the variation among groups within subspecies was 19.23% of the total variance. Variation among groups according to Fst was 41% of the total genetic variation. The AMOVA done only with the groups also reflected that around 60% of the genetic diversity was within the defined groups  were detected to be scattered through the genome in all chromosomes .The average Ho was very low (average Ho = 0.08) in most groups, except for the Indian group where it was equal to He (He = Ho = 0.22)  distribution showed a reverse pattern.On the other hand, HThe ratio between the frequency of high polymorphic SNPs (MAF > 0.4) and low polymorphic SNPs (MAF < 0.05) was also calculated (MAF0.4/MAF0.05) to obtain further details on the distribution of genetic diversity within the groups. MAF0.4/MAF0.05 ratios were low in all the groups (even nearly 0 for the conomon group), except for the Indian group where MAF0.4/MAF0.05 was high. The frequency of SNPs with different MAF also varied drastically among the groups. For the whole collection, SNP frequency at different MAF showed low variability, ranging from 0.15 to 0.22  explained 22.29% of the genetic variation between melons from the two subspecies, C. melo subsp. melo and C. melo subsp. agrestis. There was a considerable variation of Fct across the genome, nevertheless, 39 SNPs showed high levels of Fct (> 0.70) with loci located throughout all the chromosomes except for chromosome12  even at very close distances (< 1 kb) and it decreased steadily up to 40 kb approximately. The 95th percentile of the r2 among unlinked SNP was 0.35, corresponding to a physical distance of 17 kb  reached 49 and 97 Kb for inodurus-related and cantaloupe groups, respectively  were larger than 1 Mb, 40% smaller than 1 kb, being the median 4.5 kb  and most of them were located in euchromatic regions Table  Three haIn the current report, data from two independent GBS experiments were merged. The final number of common SNPs after filtering (6169 SNPs) was significantly lower than the SNPs detected in each individual experiment and in previous melon GBS reports –21. A prPrevious SNP-based genetic diversity analysis of melon germplasm focused mainly on occidental germplasm  with a relatively small representation of Far-East  and, especially, Indian germplasm , 19–23. The main novelty of the current report is the inclusion of Indian germplasm that had not been studied previously in a global array of melon accessions. MDS analysis showed that the Indian germplasm is located in the center of the MDS space as a compact but wide group. African germplasm is closely related, but STRUCTURE analysis supports its differentiation from Indian germplasm. The fact that cultivated and wild accessions are grouped by origin (African and India) indicates at least two domestication events occurring independently in the two continents, as proposed previously , 12. Indis among groups. Fis is high in all the groups except in the Indian group, in which it is nearly 0. High Fis has been reported often in inodorus, cantalupensis and conomon (including chinensis and makuwa) accessions [is has been recently reported in landraces collected directly in fields cultivated by traditional farmers, as snapmelons from North India [Another interesting result is the difference in Fcessions , 13, 25.th India  and snakth India . Severalst was found in three pairwise group comparisons . The largest number of SNPs with a high Fst value was found in the cantaloupe vs Indian comparison. Interestingly, the SNPs with the highest Fst value (Fst > 0.75) between inodorus-related and Indian groups also showed a high Fst between cantaloupe and Indian groups . These are therefore good candidates to mark genomic regions that were fixed (either by selection or by drift) during the diversification from India to the Mediterranean Basin. It is remarkable that an important proportion of SNPs with a high Fst value were found on chromosome 11. Estimation of Fst among melon groups have been investigated previously in few reports [st values for SNPs between cantaloupe and inodorus collections were also reported by Esteras et al. [st among the groups, which supports the hypothesis that those regions may be involved in the differentiation of the groups of cultivars. Moreover, one of these SNPs (fr14p22) is located in the CmNAC-NOR gene, a member of the NAC-domain TF family, that is the gene underlying the QTL for climacteric ripening ETHQV6.3 [nodorus and cantalupensis varieties have different ripening behaviors: non-climacteric and climacteric, respectively. An SNP was also found on chromosome 3  differentiating those groups located in the region of other climacteric QTL: ETHQB3.5 [A relatively large number of SNPs with high F reports . Given tETHQV6.3 , 30. FruETHQB3.5 . These cPrevious reports on LD in melon have shown that LD decays rapidly within a few kb , 20, 23.LD in heterochromatin was higher, but still, it was low. Heterochromatic regions show no recombination in melon biparental populations , but theConcomitantly, haplotype blocks were generally small, with a large proportion of them located in heterochromatic regions, as found previously by . NeverthThus, LD in the melon genome can be considered as relatively low. The extent of LD depends on the genome region under study due to the recombination suppression, for instance, in the centromeric regions. Anyway, the LD in the centromeric regions can also be considered low (r2 ≈ 0.3), indicating that recombination has occurred in those regions during the history of melon cultivation, even though recombinants usually are not found in the common mapping populations .Given the low LD observed in the present study , other regions carrying genes also involved in cultivar diversification may have been missed. Higher SNP density would be necessary to obtain a full picture of the dynamics of melon genome during the diversification, which, for example, could be obtained by resequencing.C. melo genetic variability space. Looking with more detail into the distribution of the genetic variability among Indian accessions, this group of accessions can be separated more clearly by geographical origin than cultivated/wild types, which indicates that probably there is no strict genetic differentiation between wild and cultivated melons in India. Indian germplasm is the group with the highest genetic diversity, indicated by high SNP variability, high proportion of SNPs with high MAF and low haplotype fixation. Its heterozygosity level was as expected under H-W equilibrium, whereas in the other groups it was very low, probably as a consequence of farmer selection to maintain cultivar homogeneity for desired traits. In fact, the low proportion of SNPs with high MAF in the other groups confirms that rare alleles are fixed in few cultivars/landraces, not shared among other cultivars, and maintained as consequence of farmer selection of specific cultivars. The situation in Indian group is radically different, the high proportion of SNPs with high MAF (in cultivated and wild melons) supports that the genetic variability is not fixed within landraces, but it is shared among them. Thus, the Indian germplasm represented here may not be a standard collection of landraces , but a population of plants with extensive intercrossing in the field among wild and cultivated melons that would maintain the high genetic diversity. An interesting question is why did not Indian traditional farmers caused gene fixation in landraces, whereas high values of the fixation index are relatively common in landraces from both extremes of the melon geographical distribution. One possible factor for this may be the sex determination of the female flower, being strictly female in Indian landraces (and therefore obligatory out-crossers) and hermaphrodite in the most of rest, which facilitates selfing. The co-existence of wild and cultivated melons in Indian fields would produce frequent intercrosses and making difficult the fixation of alleles in specific landraces.Accessions representing several Indian collections that have been previously investigated separately have been studied to have a better picture of the genetic diversity distribution in these collections using GBS-based SNP markers. In the current report, those accessions have been integrated and contextualized using a truly representative sample of global melon germplasm. Indian germplasm appears as a group of related accessions, clearly separated from other international accessions, and it has a central position in the In summary, our results confirm the crucial importance of India as a center of melon diversity. Germplasm origination in India differs from African germplasm. Mediterranean and Far-East traditional cultivars would have developed from Indian germplasm by a divergent diversification. Melons cultivated by local Indian communities might be in the early steps of domestication with genetic exchange with wild melons as there is low evidence of genetic differentiation from the wild melons and the low degree of fixation. These results suggest that the germplasm may be considered genetically as a population of plants, rather than as a standard group of landraces.A total of 175 melon accessions were analyzed in two independent studies at West Virginia State University  and at inodorus, ameri and flexuosus botanical groups), and 14 accessions without a clear category that were classified as “ungrouped” , cantaloupe (51 accessions), conomon (14 accessions), dudaim (4 accessions), Indian (37 accessions), inodorus-related . The GBS was performed by LGC Genomics GmbH (Germany) following the procedure reported by Elshire et al. . Brieflyhttps://bitbucket.org/tasseladmin/tassel-5-source/wiki/Tassel5GBSv2Pipeline). Only SNPs, not indels, with a minimum read depth of three were retained. Chromosomal assignment and position of SNPs on the physical map were deduced from the melon reference genome (version 3.5.1) at https://www.melonomics.net/ [SNPs were extracted using TASSEL-GBS Discovery/Production pipeline (ics.net/ . SNPs wewww.maizegenetics.net) [https://ics.hutton.ac.uk/curlywhirly/). For STRUCTURE analysis, 20 independent runs for each K value ranging from 2 to 10 were performed with a burn-in length of 500,000 and 1 million iterations. The optimal subpopulation was calculated from the second order rate of change of likelihood (ΔK method) [Before structure analysis, SNP matrix was pruned for linkage disequilibrium to avoid possible bias due to the variability associated to large haplotype blocks. Tag SNPs were defined with SNPAnalyzer 2.0  The geneics.net) and STRUCics.net)  analysis method) .C. melo subsp. agrestis or subsp. melo) and the groups were defined after MDS analysis. Distribution of genetic variability among and within groups was calculated by Analysis Molecular of Variance (AMOVA) with two classification levels: subspecies and groups and also with only the MDS grouping classification. Expected (He), observed (Ho) heterozygosity and MAF were calculated for each group, as well as the Wright’s F statistic [ct (differentiation among subspecies) Fst (genetic differentiation among groups), and Fis (inbreeding coefficient). The ratio between high and low polymorphic SNPs was obtained by dividing the number of SNPs with MAF > 0.4 divided by the number of SNPS with MAF < 0.05 (MAF0.04/MAF0.05) for the whole collection and each group.The genetic diversity was investigated with Arlequin ver. 3.1 . Accessitatistic  Fct . The r2 statistical threshold was established as the 95th percentile of the r2 distribution for unlinked SNPs. LD was analyzed for different subdata sets: whole collection, only SNPs in euchromatin, only SNPs in heterocromatin , inHaplotype blocks were defined with SNPAnalyzer 2.0 . HaplotyAdditional file 1:Table S1. Details of the accessions analysed in the current study. Group, subspecies and type are according passport data. MDS group is according MDS analysis . The assay indicates the origin of the data: WVSU , IBMCP (current work). (XLSX 20 kb)Additional file 2:Table S2. Single Nucleotide Polymorphsim genotypes after merging WVSU  and IBMCP (current work) data. (XLSX 5578 kb)Additional file 3:Figure S1. MDS analysis including the whole germplasm collection. The origin of the GBS data is indicated by the dot color: blue for WVSU [for WVSU  and red Additional file 4:Figure S2. STRUCTURE results for K = 2 to 5. The five groups defined by Multi Dimensional Scaling (MDS) analysis are indicated at the right with their respective colors. K = 5 showed the Delta K peak, defining five populations. Wild accessions are highlighted with a star with the color of their STRUCTURE populations. (PPTX 480 kb)Additional file 5:Figure S3. Expected (He) heterozygosity for SNPs across the melon genome in the groups Inodorus-related, Cantaloupe and Indian. (PPTX 765 kb)Additional file 6:Figure S4. Histograms of the distribution of SNPs with different Minor Allele Frequencies (MAF) among Multidimensional scaling groups and the whole collection . (PPTX 83 kb)Additional file 7:Table S3. Fst values for SNP loci for Inodurus-related-cantaloupe and Indian pairwise group comparisons (XLSX 283 kb)Additional file 8:Figure S5. Linkage disequilibrium (r2) versus physical distance. (A) Whole germplasm analysing euchromatin and heterochromatin independently. (B) Inodorus-related and cantaloupe groups. Dashed lines indicate the false discovery rate at p < 0.05 based on the 95th percentile of the r2 distribution of unlinked SNPs. Curves were fitted by second-degree LOESS. (PPTX 158 kb)Additional file 9:Table S4. Haplotypes blocks defined by SNPanalyzer 2.0 (XLSX 57 kb)Additional file 10:Figure S6. Genomic location of LD blocks on the melon genome. Blocks fixed in C. melo ssp. melo (M) and cantaloupe group (C) are indicated. Centromeric regions in the chromosomes are marked in black. (PPTX 164 kb)


Meat production from the commercial crossbred Duroc × (Landrace × Yorkshire) (DLY) pig is predominant in the pork industry, but its meat quality is often impaired by low ultimate pH (pHu). Muscle glycogen level at slaughter is closely associated with pHu and meat technological quality, but its genetic basis remains elusive. The aim of this study was to identify genes and/or causative mutations associated with muscle glycogen level and other meat quality traits by performing a genome-wide association study (GWAS) and additional analyses in a population of 610 DLY pigs.P = 2.54e−11) quantitative trait locus (QTL) on SSC15 (SSC for Sus scrofa chromosome) for the level of residual glycogen and glucose (RG) in the longissimus muscle at 45 min post-mortem. Then, we demonstrated that a low-frequency  R200Q missense mutation in the PRKAG3 (RN) gene caused this major QTL effect on RG. Moreover, we showed that the 200Q (RN–) allele was introgressed from the Hampshire breed into more than one of the parental breeds of the DLY pigs. After conditioning on R200Q, re-association analysis revealed three additional QTL for RG on SSC3 and 4, and on an unmapped scaffold (AEMK02000452.1). The SSC3 QTL was most likely caused by a splice mutation (g.8283C>A) in the PHKG1 gene that we had previously identified. Based on functional annotation, the genes TMCO1 on SSC4 and CKB on the scaffold represent promising candidate genes for the other two QTL. There were significant interaction effects of the GWAS tag SNPs at those two loci with PRKAG3 R200Q on RG. In addition, a number of common variants with potentially smaller effects on RG (P < 10−4) were uncovered by a second conditional GWAS after adjusting for the two causal SNPs, R200Q and g.8283C>A.Our initial GWAS identified a genome-wide significant , along with many minor genes.We found that the The online version of this article (10.1186/s12711-019-0488-0) contains supplementary material, which is available to authorized users. Pork quality is an economically important trait in the pig industry and its improvement has been one of the major goals in modern pig breeding –4. Howevhttp://www.animalgenome.org/cgi-bin/QTLdb/SS/index), which provide valuable insights into the genetic architecture of these traits. However, these GWAS are typically designed to provide a survey of common variants with a minor allele frequency (MAF) higher than 0.05 and, thus, examine only a portion of the genomic landscape of complex traits. Until now, low-frequency (0.01 ≤ MAF < 0.05) and rare (MAF < 0.01) variations that affect pork quality have been more challenging to access. In addition, although GWAS can be used to discover significant association signals for a trait, uncovering the specific genetic variants and pathways that cause these associations is still a major challenge in the post-GWAS era. To date, only a limited number of studies have investigated the molecular mechanism of GWAS hits with fine-mapping [FTO (fat mass and obesity-associated protein), via integrative analyses of epigenomic data, allelic activity, motif conservation, gene regulator expression and gene co-expression patterns, and target genome editing verification [PHKG1 gene in the regulation of muscle glycogen level, pH, and drip loss in Duroc crosses [Following the advent of genome-wide association studies (GWAS) and larger-scale consortia meta-analyses of GWAS, about 9900 variants associated with pork quality traits have been identified  missense mutation is responsible for excess glycogen and low ultimate pH in skeletal muscle, which results in “acid meat”, which has lower water holding capacity and poor processing yield [PRKAG3 gene, T30N (p.Thr30Asn), G52S (p.Gly52Ser), and I199V , were also suggested to affect meat quality traits in a wider range of commercial pig breeds, e.g. Large White, Duroc, and Landrace [To our knowledge, y traits . This geng yield –13. ThreLandrace . The I19Landrace .Several GWAS have been performed on meat quality traits, such as pH and colour, but there have been relatively few studies on the genetic basis of muscle glycogen and lactate. Since the concentrations of these metabolites can be regarded as intermediate phenotypes that affect meat quality, identifying overlapping quantitative trait loci (QTL) for muscle glycogen, pH, and other meat quality traits will facilitate the discovery of candidate genes and the underlying molecular mechanisms.In China, more than 70% of the pork produced originates from three-way crossbred Duroc × (Large White × Yorkshire) (DLY) pigs, but the high incidence rate of PSE , DFD , and acid meat incurs consumer dissatisfaction and significant economic loss. Thus, it is important to investigate the genetic architecture of meat quality traits in DLY pigs. Previously, we reported the detection of genetic variants that are significantly associated with multiple meat quality traits but not for GP-related compounds traits . Thus, oThis study involved two cohorts of DLY commercial pigs: (1) a discovery cohort that consisted of 306 males and 304 females raised on a farm of the Jiangxi Guohong Group Co. Ltd in Jiujiang city (Jiangxi Province), where the DLY piglets were produced by crossing ~ 20 adult Duroc boars with ~ 800 L × Y sows; and (2) a replication cohort that comprised 150 DLY pigs, which came from a farm of the Guangdong Wen’s Food Group Co. Ltd in Guangzhou city (Guangdong Province). All experimental individuals were raised under a standardized environment and slaughtered at local commercial abattoirs when they reached a live weight of ~ 110 kg. To constitute these cohorts, 30 to 40 individuals were randomly sampled from groups of 150 or more finishing pigs that were sent for slaughter at the same time. Thus, the cohorts were completely unrelated and the kinship between animals in each cohort was very low.PRKAG3 R200Q locus and had no direct relationship with the above-mentioned DLY cohorts.In addition, 179 Duroc, 192 Landrace, and 190 Yorkshire pigs from various farms in Yongchun country (Fujian Province) and Qingyuan city (Guangdong Province) were also included in the study. These individuals were genotyped for the The levels of residual glycogen and glucose (RG) and of lactate in the longissimus muscle (LM) were determined for the two DLY cohorts. The standard equation to calculate the glycolytic potential (GP) of muscle is GP (μmol/g of wet muscle) = 2 × (glycogen + glucose + glucose-6-phosphate) + lactate. However, based on our observations that the level of glucose-6-phosphate is much lower than that of other components in the post-mortem muscle, we ignored it and used the reduced equation to: GP = 2 × RG + lactate. After slaughter, LM samples were collected between the tenth rib and the first lumbar vertebra from the left side of each carcass within 45 min post-mortem. We estimated the levels of RG and lactate in the LM with the muscle glycogen assay (Item No: A043) and the lactic acid assay (A019-2) kits, respectively, which were produced by the Nanjing Jiancheng Bioengineering Institute. In addition, pH, drip loss, and colour were measured on LM at 36 h post-mortem in the discovery cohort, as described previously .−5) were removed, and individuals for which the call rate was lower than 0.90 and the Mendelian inheritance error rate was higher than 0.05 were also removed from the dataset. After quality control, 39,369 SNPs and all individuals remained for subsequent statistical analyses. The chromosomal positions of SNPs were based on the Sscrofa11.1 pig genome assembly.Pig genomic DNA was isolated from ear tissue by standard phenol/chloroform extraction. To fulfil the requirements for genotyping, all extracted DNA samples were standardized to a final concentration of 50 ng/µl and quality controlled. In total, 610 animals from the Guohong DLY cohort were genotyped for 61,565 SNPs using the IlluminaPorcineSNP60K Beadchip according to the standard manufacturer’s protocol. Quality control procedures were performed by PLINK v1.07 . BrieflyPreviously, we used this DLY cohort to detect QTL for many meat quality traits except GP-related traits by performing a GWAS that was based on the Sscrofa10.2 reference genome . Recentlpolygenic function of GenABEL v1.7 [Associations between each SNP and the studied phenotypic traits were obtained by using a general linear mixed model . The genBEL v1.7 . The modutosomes , and \doatch N =  effects,ironment , 21. ThePopulation stratification can lead to false positive results in GWAS, thus it was assessed by examining the distribution of the test statistics that were generated from the thousands of association tests and their deviation from the null distribution in quantile–quantile (Q–Q) plots , which w2 was estimated for all pairs of SNPs within the QTL region by using PLINK v1.07 [We analyzed the patterns of LD blocks for the chromosomal regions that contained multiple significant SNPs clustered around the top SNP. The LD block patterns were built by using the Haploview version 4.2 software with default settings . The LD NK v1.07 .Sus scrofa chromosome), which is close to the PRKAG3 gene (at 120.85–120.87 Mb) and overlapped coincidently with other significant loci for RG-related traits (from 114.8 to 121.4 Mb), we attempted to identify the causative variant(s) in this gene. First, a comparative analysis of the complete PRKAG3 sequence including its promoter [for information on primers  mutation was detected by the PCR–RFLP method, as described previously [PRKAG3 that were identified by sequencing and the 42 surrounding SNPs that were identified from swine genome databases, were genotyped for the whole DLY population using a Sequenom Massarray system at Shanghai Genesky Biotechnologies Inc. On SSC3, a splice mutation g.8283C>A in the PHKG1 gene that may explain the effect of another identified locus affecting RG and meat quality traits was also genotyped, as described by Ma et al. [Because the most significant GWAS top SNP rs326377357  for RG was detected in a region on SSC15  analyses. These analyses involved 52 pigs, including 17 DLY, five Duroc, 10 Landrace, 16 Yorkshire, and 4 Hampshire pigs, among which the 17 DLY and 3 of the Hampshire pigs were heterozygous for the PRKAG3 R200Q variant, whereas the remaining Hampshire pig was homozygous. Since only 60 K SNPs and the 73 additional markers that were located in the vicinity of PRKAG3 were genotyped for the DLY pigs, the same SNPs were extracted from publicly available or our own genome-sequence data for the 35 selected purebred pigs. At the target regions, haplotypes were phased using PHASEBOOK [For the HASEBOOK . MaximumHASEBOOK .PRKAG3 gene  or for haplotypes that contained linked markers  in this gene. Associations of a SNP (or haplotype) with meat quality traits were examined using the least square means method of the GLM  procedure in the R software (version 3.5.0), with a model that included the fixed effects of sex, slaughter batch, and the effect of a SNP (or a haplotype), along with a residual error as a random effect. The SNP–SNP (or QTL–QTL) interaction analysis for three GWAS tag SNPs  was performed in R with a model that was identical to that used for SNP association analysis, except that interaction effects between SNPs were included. The significance values for interaction effects of pair-wise and of three SNPs on RG were calculated by two-way and three-way ANOVA, respectively. Plots for the main effects of the three SNPs and their two-way interactions were generated by using the interaction2wt function of HH package in the R software.Association analyses were performed either for individual SNPs within the L*  of 1.003 was found for GP, RG, lactate, and other meat quality traits in the DLY population, which indicates no evidence of population stratification. The Q–Q plots of the test statistics for each separate GWAS are in Additional file LRAD1, which encodes the LRAT domain containing 1 protein. On SSC4, the top SNP rs319599168 (at 84.98 Mb) is an intronic variant in the transmembrane and coiled-coil domains 1 (TMCO1) gene. The most significant SNP  for RG level was on SSC15, rs326377357 , which was surrounded by seven other significant SNPs. Intriguingly, the region between 114.8 and 121.4 Mb on SSC15 also contained many SNPs that were significantly associated with most other RG-related traits, e.g. pH 36 h, drip loss, color score, and three color parameters, L*, a* and b* (Table PRKAG3 gene (at 120.86 Mb), which has been shown to impact these meat quality traits in Hampshire pigs. Thus, PRKAG3 is the most likely causative gene for the SSC15 QTL based on its position and functional annotations.Our initial GWAS on the DLY population demonstrated that 10 SNPs, representing three genomic loci, were significantly associated with RG Table , no signPRKAG3 gene, we sequenced it using DNA samples from six DLY animals that comprised the three different genotypes at the top SNP rs326377357 , among which the alternate homozygous individuals had high versus low RG levels. We detected 53 SNPs within the PRKAG3 gene  Hampshire pigs [RQ and RR). In fact, highly significant differences were observed between these two genotypes for all tested traits, including RG level, GP, pH 36 h, drip loss, and three color parameters of meat   and a twofold greater drip loss (P = 2.93e−12) than the wild-type RR animals, which confirms that the dominant R200Q (RN–) mutation has a strong influence on meat quality of DLY pigs.It is known that the ire pigs , thus we*) Table . NotablyPRKAG3 may have significant effects on meat quality in pigs [PRKAG3 and neighboring genes. Based on the genotyping data for the six DLY pigs mentioned above, we identified nine haplotypes in the PRKAG3 region  for further analysis. In addition to the R200Q variant, we genotyped 72 SNPs that were located in 11 genes  significant associations with the meat quality traits studied here than any other tested SNP , the association between the PRKAG3 locus and the meat quality traits was no longer significant, even at a significance level of 0.01. These results indicate that the R200Q substitution accounts for most, if not all, of the effects of the QTL on SSC15.Some studies have suggested that, in addition to the R200Q variant, other variants and/or genes in the region flanking  in pigs , 27–29. PRKAG3 that have been reported to influence muscle glycogen level [P < 0.05) associated with several meat quality traits (Table P = 2.54e−11) and R200Q (P = 6.23e−49). Moreover, we found that, in the DLY replication cohort, the R200Q and L53P mutations also had a stronger effect on RG level than the G52S and I199V mutations -52G(G)-53L(T)-193A(C)-194L(C)-199V(G)-200Q(A)” (denoted as Hap9) contained the 200Q alleles, and its effects on RG level, pH 36 h, and drip loss differed significantly from those of the other haplotypes  in en level , also seHap9 haplotype detected in the DLY population is exactly the same as the RN– haplotype that was found exclusively in the Hampshire breed by Milan et al. [RN– allele has probably been introgressed from Hampshire pigs into the parental lines of the DLY pigs. To validate this, we constructed two maximum likelihood (ML) trees for haplotypes for the PRKAG3 region (120.84–120.86 Mb) and for a more extensive region around PRKAG3 (105.83–135.83 Mb), using the genotyping data for carriers of the RN– allele in DLY and Hampshire, and non-carriers of the RN– allele in Duroc, Landrace, and Yorkshire pigs. As expected, both ML trees showed that almost all 200Q-haplotypes from DLY pigs clustered with the RN– haplotype of the Hampshire breed  of the DLY discovery cohort were sampled for DNA, they were the only ones genotyped for the RN− mutation. We found that the 200R allele was fixed in these individuals, which suggests that the DLY pigs inherited this allele only from their mothers (L × Y sows). This is consistent with the results of the phylogenetic analysis that showed that the majority of the 200R (rn+)-haplotypes for the 30-Mb region around PRKAG3 in the DLY pigs were more closely related to the Duroc’s haplotypes than to Landrace and Yorkshire haplotypes  of our DLY population several or many generations ago, we assumed that the DLY heterozygous individuals contained recombinant chromosomes that combined the classical RN− haplotype with haplotype fragments that originated from the Landrace or Yorkshire lines. This hypothesis was confirmed by the detection of a ~ 2.38-Mb (between 117.70 and 120.08 Mb) haplotype fragment of Yorkshire origin that was located just upstream of the RN− haplotype in three of the heterozygous DLY individuals  clustered with Landrace haplotypes   near LRATD1 disappeared, while rs81244397  became a prominent SNP . Thus, we included PHKG1g.8283C>A in the conditional GWAS to evaluate its potential contribution to the QTL signal. As expected, this previously detected causal variant showed a highly significant association with RG level (P = 1.85e−9) and drip loss (P = 1.90e−3), and its minor allele “A” (MAF = 0.049) increased RG level and drip loss, with additive effects of 5.82 μmol/g and 0.67%, respectively  associations with RG level  between SNP rs319599168 and PRKGA3 R200Q, because the difference in mean RG level between the AG and GG genotypes at rs319599168 (or R200Q) was much larger in heterozygous individuals at R200Q (or rs319599168) than in homozygous individuals at the same locus. The interaction between rs81347579 and R200Q was also significant (P = 0.02), but the interaction between rs81347579 and rs319599168 was not (P = 0.20)  and of the above-mentioned two-SNP interactions , as well as of the three-SNP interaction (P = 5.63e−6).Intriguingly, the tag SNPs rs81347579 and rs319599168 had significance levels before and after adjusting for P<5e−8 and P < 1e−5, respectively [P value thresholds (1.27e−6 and 2.54e−5) in the initial GWAS and the first conditional GWAS. However, for the second conditional GWAS, after controlling for the two causal variants (PRKAG3 R200Q and PHKG1g.8283C>A), we preferred to consider P < 1e−4 as the suggestive threshold for the detection of additional QTL for RG level. Based on this P value, we identified seven chromosomal regions that harbored suggestive SNPs with a P < 1e−4, including one previously identified SNP, rs81347579, close to the CKB gene. Notably, nearby the LRATD1 gene on SSC3, the top SNP, rs81240897 (Table 2 = 0.17) and had markedly different MAF, which suggests that they may represent distinct QTL. Those suggestive SNPs detected by the conditional GWAS individually explained 0.91 to 2.84% of the phenotypic variance, and their MAF were in the range of 0.14 to 0.45 . In contrast, we found a slightly stronger and significant association between GP and pH 36 h in all the DLY pigs  as well as in DLY pigs that do not carry the 200Q mutation . This discrepancy may in part be due to the effect of other PRKAG3 mutations or other genes in our population. Importantly, our results showed that RG level at an early stage postmortem is more likely to impact meat quality than GP level, because its associations with pH 36 h, drip loss, and meat color parameters were all significant at P < 0.001 and at a higher level than the associations of GP level with those same meat quality characteristics  plays a critical role in the progression of colon cancer [TMCO1 gene encodes an endoplasmic reticulum transmembrane protein that forms a functional calcium-selective channel that prevents Ca2+ stores from overfilling [RYR1, which encodes a ryanodine receptor that is present in skeletal muscle and mediates the release of Ca2+ from the sarcoplasmic reticulum into the cytoplasm, also contains mutations that cause porcine malignant hyperthermia susceptibility and pale, soft, and exudative meat [CKB gene , was indeed in stronger LD with R200Q (r2 = 0.053) than other SNPs that passed quality control. However, when the number of typed SNPs within the candidate region increased, several new SNPs were identified, less than 1.4 kb away from R200Q, which were more tightly linked with R200Q and more significantly associated with RG level than rs326377357 . The frequency of the RN−) allele increased RG level and GP by approximately fourfold and 87%, and markedly decreased pH 36 h and water-holding capacity (Table RN−/rn+ or RN−/RN− with high accuracy (> 95%). However, this simple criterion to infer the genotype at R200Q may not be suitable for DLY pigs, because only 65% (11 out of 17) of the RN− carriers in the DLY population met these criteria, while the other seven DLY individuals that had such RG levels and GP values were homozygous for the wild-type genotype (rn+/rn+). This discrepancy may be due to distinct genetic backgrounds, genetic heterogeneity and/or interactions of R200Q with other loci.Our results demonstrated that the 200Q (ty Table . AlthougRN−) allele has been confirmed, since it was shown to be present in a very high percentage of Hampshire pigs, but not in other breeds [RN− Hampshire pigs was present in DLY pigs. Based on the finding that all Duroc boars that contributed to the DLY population analyzed are normal (rn+/rn+ genotype), and based on results of the evolutionary tree analysis and the haplotype-sharing analysis of five commercial breeds, we deduced that the 200Q allele was introgressed from the Hampshire breed to the purebred parental lines, including both Landrace and Yorkshire. Surprisingly, we did not identify a Landrace or Yorkshire pig that carries RN− allele, probably because of its low frequency, but we did identify carriers of this allele in a Duroc line. Thus, our study provides evidence for the first time that the RN− allele may already be segregating in all parental breeds of DLY.The Hampshire origin of the 200Q  and PRKAG3-CKB (R200Q × rs81347579) genes to be significantly associated with RG level, which confers an increased risk for the RN− phenotype. Indeed, we found that the additive effect of the minor allele (A) at rs319599168 explained only ~ 3.0 μmol/g  and at least eight minor QTL (including 7 in Tables TMCO1 on SSC4) for RG level. The former two loci belong to low-frequency variants in the DLY population and together explained nearly 70% of the phenotypic variation in RG level in this population. By contrast, the effect of the minor and common QTL variants (MAF > 5%) was only 3 to 10% of the effect of PRKAG3 R200Q on RG level and each of them explained 1 to 3% of the phenotypic variance. These results suggest that, in DLY pigs, muscle glycogen level and meat quality traits are regulated by a limited number of major genes and many other minor genes.In this study, we detected two major QTL  plots of the SNP distribution after quality control in GWAS for all tested meat quality traits. Datasets include 39,369 SNPs that were genotyped in the DLY population. In the Q–Q plots, -log10 (P) values of observed association statistics on the y-axis are compared to those expected under the hypothesis of no association on the x-axis. The solid line represents concordance between observed and expected values. The shaded region shows the 95% confidence interval based on the beta distribution. Genomic inflation factor, λ, is shown for each dataset.Additional file 4: Table S2. Screening for mutations in the PRKAG3 gene in six pigs with different genotypes  at the GWAS tag SNP rs326377357. Fifty-three SNPs in PRKAG3 were identified by comparative sequence analysis. Their positions on the Sscrofa11.1 pig genome assembly are shown. The grey lines highlight the 31 markers that were used to type the 610 DLY pigs.Additional file 5: Figure S3. Haplotype diagram of 53 SNPs in six DLY individuals. By sequencing the PRKAG3 gene in six DLY animals, 53 SNPs and nine haplotypes were identified, 31 of these SNPs indicated by solid triangles were selected and genotyped for the whole DLY population. The other 22 SNPs indicated by the empty triangles were not genotyped, but they were in complete linkage disequilibrium (LD) with selected markers of the same colour. The upper and lower triangles indicate the two alleles of a SNP. All alleles of haplotype 1 are represented by both the letters and upper triangles.Additional file 6: Table S3. Information about the 73 SNPs selected and genotyped on the whole DLY population. Of the 73 genotyped markers, 31 marked with a grey line were derived from PRKAG3 re-sequencing data, and the other 42 SNPs adjacent to PRKAG3 were screened from online databases , the literature and our own whole-genome sequence data of different European commercial pig breeds.Additional file 7: Figure S4. Manhattans plots of GWAS for seven meat quality traits with increased marker density in the SSC15 QTL region. These traits include GP, pH, drip loss, Minolta L*, a*, b* and color score. The PRKAG3 R200Q mutation was the most significant SNP across all seven meat traits.Additional file 8: Table S4. Effects of four missense SNPs in PRKAG3 on RG level in the replication cohort. The effects of PRKAG3 G52S, I199V, L53P and R200Q mutations on RG level were assessed. G52S and I199V were not significantly associated with RG level , whereas R200Q and L53P showed the same significant (P = 2.77 × 10−9) association with this trait.Additional file 9: Table S5. Identification of parental origin (Landrace or Yorkshire) of the RN− allele in DLY pigs. A haplotype-sharing analysis was conducted on 10 individuals in the 3.2-Mb region (117.6–120.8 Mb) upstream of the RN− locus. The classical RN− haplotypes are highlighted in blue. Obviously, the haplotypic fragments marked in yellow are shared between three DLY pigs and two Yorkshire pigs, while those marked in green are shared between DLY0800 and Landrace2612.


Catalpa fargesii Bur is a valuable commercial tree in China and identifying SNPs that associate with wood property would make a foundation of the marker-assisted breeding in the future. However, related work has not been reported yet.Association study is a powerful means for identifying molecular markers, such as single-nucleotide polymorphisms (SNPs) associated with important traits in forest trees. SUS) gene from the genome of C. fargesii, which is a key enzyme in sucrose metabolism and also associated to wood formation in trees, coding 806 amino acids that expressed mainly in young branches, xylem, and leaves according to real-time quantitative PCR. Then we identified allelic variations of CfSUS associated with nine wood quality associated traits in Catalpa fargesii Bur. Totally, 135 SNPs were identified through cloning and sequencing the CfSUS locus from a mapping population  and 47 of which were genotyped as common SNPs  in the association population that comprised of 125 unrelated individuals collected from main distribution area. Nucleotide diversity and linkage disequilibrium (LD) analysis showed CfSUS has a relative low SNP diversity (πT = 0.0034) and low LD (r2 dropped below 0.1 within 1600 bp). Using the association analysis, we found 11 common SNPs and 14 haplotypes were significantly associated with the traits , explaining 3.21–12.41% of the phenotypic variance. These results provide molecular markers above associated with wood basic density, pore rate, and six other traits of wood, which have potential applications in breeding of Catalpa fargesii Bur.We cloned a 2887 bp long sucrose synthase  contains supplementary material, which is available to authorized users. Catalpa fargesii Bur. is a valuable tree native to China. Its timber not only exhibits good mechanical properties, such as stiffness and ultimate stress, but also has good chemical properties and high corrosion resistance [C. fargesii breeding. Although the mechanisms influencing wood properties are unclear, some researchers have speculated that the specific structure, content, arrangement, and interaction of macromolecules in secondary cell walls may confer unique properties, making wood better suited for different applications [sistance . The proications . This suications , 3.PtoPsbW gene and five wood quality traits have been identified in Populus tomentosa [Populus deltoides significantly influenced eight composite traits that associated with wood properties, including lignin percentage, lignin syringyl/guaiacyl ratio, lignin structure, and the growth of plants [Molecular marker-assisted selection has been proven effective in resolving the complex quantitative traits of genetic components to improve and accelerate traditional tree breeding. In particular, association mapping, is an effective way for elucidating the potential relationship between allelic variation and complex quantitative trait variations in natural populations . Previouomentosa . In addif plants .SUS participates in providing UDP-glucose for cellulose biosynthesis and directly associated with cellulose synthase complexes [SUS may help identify molecular markers associated with wood quality traits to guide C. fargesii breeding.Sucrose is the main source of carbon for compounding cellulose, one of the major components of secondary cell walls, and wood vascular tissue in the stem comprises highly active sink cells that use sucrose for cellulose synthesis . In thesomplexes . TherefoC. fargesii (CfSUS). Real-time quantitative PCR (RT-qPCR) was used to identify the gene expression in six different organs. Subsequently, nucleotide diversity and linkage disequilibrium decay within the CfSUS were assessed in a mapping population (n = 93). Finally, single-marker- and haplotype-based association tests were performed to examine the putative effects of allelic variations on nine wood quality traits in an association population comprising 125 C. fargesii individuals. This is the first time that association analysis about CfSUS were studied to identify molecular markers probably affecting wood property, which could support the genetic improvement of C. fargesii and other tree species.In this study, we first cloned and identified a SUS gene family member in C. fargesii individuals that collected from all of the provenances throughout its whole natural distribution range in China, for the initial SNP association mapping. The distribution zone from which these individuals were collected, could be divided into four geographic regions: Fenhe River Valley, Jinghe River Valley, Jialingjiang River Valley, and Yellow River Valley. In the year 2009, branch segments of 125 native C. fargesii individuals were collected from eight cities in four provinces and grafted, and the population was grown in Xiaolongshan National Nature Reserve, Gansu Province, China . The clonal plantation was established using a randomised complete block design with two plants per clone in each block (row spacing is 2 m and plant spacing is 2 m) and totally six replicates. Within the association population, 93 individuals were randomly selected  to identify SNPs within the gene via PCR amplification and sequencing.We used a population consisting of 125 unrelated The 125 individuals of the association population were sampled in 2012 to characterise their wood quality traits. Cores from bark to pith were collected in the south-facing direction of the original stems to measure wood density and other wood properties with an increment borer (7 mm) at breast height (1.3 m above the ground). The wood samples were fixed in formalin–acetic acid–alcohol (FAA) after collection, and nine wood property traits were measured in 2013: wood basic density (WBD), pore rate, cell wall percentage , cell wall thickness, radial lumen diameter, chordwise lumen diameter, radial fibre central cavity diameter, chordwise fibre central cavity diameter, and average fibre central cavity diameter.2/(W1 − W2 + W2/ρcw), where W1, W2 and ρcw represent the water-saturated weight, oven dry weight, and wood cell wall component density, respectively. We used the constant 1.53 g/cm3 for ρcw [WBD was measured according to the formula WBD = W for ρcw , 12. The for ρcw : Cores f for ρcw . The phe for ρcw .The meanCfSUS, including a 2418-bp open reading frame (ORF) from a previous RNA sequencing data. We amplified CfSUS cDNA using SUS-CDS-specific primers  according to the manufacturer’s instructions. First-strand cDNA was synthesised from 2 g RNA using PrimeScript™ 1st Strand cDNA Synthesis Kit . We obtained the 2887-bp complete coding sequence(CDS) of CfSUS based on the cDNA sequence on the conserved domain . Finally, 5067 bp genomic DNA sequences of CfSUS, including a 1394-bp 5′UTR, 3406-bp coding region, and 267-bp 3′UTR were obtained. The entire DNA sequence of CfSUS was verified using the SUS-e primers . Four specific primers  were designed to sequence the introns in CfSUS was used for BLAST in the GenBank database and multiple SUS proteins from other species were selected for phylogenetic tree construction using MEGA software and alignment with DNAMAN software. We analysed the phylogenetic relationship of CfSUS with the amino acid sequences of SUS from other species identified from NCBI (http://www.ncbi.nlm.nih.gov) using BLAST : Orobanche ramosa (AEN79500.1), Nicotiana tabacum (AHL84158.1), Solanum tuberosum (NP_001274911.1 and NP_001275286.1), Solanum lycopersicum (CAA09681.1 and ADM47608.1), Cichorium intybus (ABD61653.1), Actinidia chinensis (AFO84090.1), Camellia sinensis (AHL29281.1), Gossypium hirsutum (ADY68848.1), Gossypium barbadense (ADY68844.1), Arachis hypogaea var. vulgaris (AEF56625.1), Jatropha curcas (AGH29112.1), Gossypium aridum (AEN71079.1), Gossypium tomentosum (AEN71067.1), Eucalyptus grandis (ABB53602.1), Populus tomentosa (ADW80558.1), Manihot esculenta (ABD96570.1), Hevea brasiliensis , Hordeum rulgare (CAA46701.1 and CAA49551.1), Lolium perenne (BAE79815.1), Triticum aestivum (CAA04543.1 and CAA03935.1), Bambusa oldhamii , Oryza sativa , Saccharum officinarum (AAF85966.1), Zea mays (AAA33514.1), and Tulipa gesneriana (CAA65639.1). Phylogenetic tree was conducted using MEGA ver. 5(maximum likelihood method) [Amino acid sequences of cum AHL848.1, SolaCfSUS in different tissues using RT-qPCR.Total RNA was extracted from six different tissues, including leaf, bark, phloem, xylem, flower, and young branch, from three 11-year-old “Xianhuiqiu” clone (as three times repetition) using RNeasy Kits  and reverse-transcribed into cDNA using PrimeScript™ 1st Strand cDNA Synthesis Kit . The cDNA samples were used to analysis the expression of −ΔΔCT method.RT-qPCR was performed with a LightCycler 480 System  using the SYBR Premix Ex Taq Kit  with the recommended amplification system by the manual. The primers for amplification , DNA sequences including 53-bp 3′UTR and 3406-bp coding region, was cloned, sequenced, and analysed in 93 randomly selected individuals from the C. fargesii association population. To ensure the sequencing accuracy, four pairs of primers  were used to amplify four fragments (800–1500 bp) of the whole sequence   and DnaSCfSUS was estimated by linear regression analysis of LD using DnaSP v4.90.1. The LD level between the 47 common SNP markers were valued as r2  using the HAPLOVIEW (https://www.broadinstitute.org/haploview/haploview), where the interval of the parameter varies from 0 to 1. The significance of r2  for all the SNP sites were calculated using 100,000 permutations. Genotypic data of CfSUS identified in this population were showed in Additional file The decay of LD with the increase of physical distance between SNPs within the candidate region of n = 125), 47 common SNPs and nine wood properties associated traits were considered, and a mixed linear model (MLM) was selected to fit each SNP-phenotype combination using TASSEL v2.0.1 [Qυ + Zu + e. In this formula, “y”, “μ”, “υ”, “u” and “e” respectively represent a vector of phenotype observation, (a vector of) intercepts, (a vector of) population effects, (a vector of) random polygene background effects and (a vector of) random experimental errors. Q is a matrix used to define the population structure by STRUCTURE and Z is a matrix relating y to u. Var(u) = G = σa2κ with σa2 is the unknown additive genetic variance and κ is the kinship matrix. In this model, the Q and K represent the estimated membership probability and pairwise kinship, respectively. The Q matrix was identified based on the population structure pattern (K = 3) within the association population , which was assessed by STRUCTURE v2.3.1 [R2) explained by each SNP was calculated use the formula R2 = SSt/SST, where SSt and SST represented the variance between genotypes and the total variance, respectively [The associated mapping was carried on according to the method of Wang et al. . In the L v2.0.1 . We usedE v2.3.1 . The K mE v2.3.1  and usedHaplotype frequencies and haplotype association tests were evaluated and performed using Haplotype Trend Regression software  on a three-marker sliding window. The significance of the associations was tested using 1000 permutation. Only haplotypes with a frequency not less than 1% were selected and positive FDR (Q ≤ 0.1) was used to correct the multiple test.d) to additive(a) effects(|d/a|) estimated from the least-square means for each genotypic class. The algorithm and formulas used for calculating dominance (d) and additive(a) effects were described by Du et al. [d/a| ≤ 0.5 was defined as additive effects, whereas partial or complete dominance was defined as the values within the range 0.50 < |d/a| < 1.25, and values of |d/a| ≥ 1.25 were regarded as overdominance.The modes of gene action were defined as the ratio of dominant , a 202-bp 5′UTR sequence, and a 267-bp 3′UTR sequence. The full length of the CfSUS DNA sequence was 5067 bp, containing a 3406-bp coding region flanked by a 1394-bp 5′UTR sequence and 267-bp 3′UTR sequence  ranged from 0 (exons 2 and 12) to 0.0103 (intron 3), while θw ranged from 0 (exons 2 and 12) to 0.0241 (intron 3) in coding regions.To characterise the nucleotide diversity and linkage disequilibrium of 2 value dropped to 0.1 in less than 1600 bp .The degree of LD showed a linear regression at a relatively rapid rate; the r bp Fig. , indicatP < 0.05 and Q < 0.10) . In this case, the value of |d/a| was 0.189 and appeared to be additive effect (Table 2 = 6.39%) and the mode of gene action was appeared to dominant effects  using MLM to identify the single-SNP-based associations. In total, 17 significant associations with eight phenotypic traits  were identified , were significantly associated with seven traits  , the nucleotide diversity (πT = 0.0034) of CfSUS was similar to that of CfC3h , suggesting that these two genes share a similar pattern of genetic variance in natural C. fargesii populations. However, the nucleotide diversity of CfSUS was lower than that of PtSUS1 (πT = 0.0092) and PtSUS2 (πT = 0.0109), which had been proved as potential homologous gene in present study in Populus tomentosa [SUS gene may be more conserved in C. fargesii than P. tomentosa.SNP-based association analyses are important for elucidating the SNP distribution and frequency patterns within a candidate gene . In thisCfSUS was rapidly decayed within 1600 bp , which indicated that low LD may also existed in other genes of C. fargesii. However, few studies have assessed the LD in C. fargesii, and the degree of LD in other genes or the whole genome of C. fargesii is unknown. In future studies, we will estimate LD decay in more genes and longer genomic fragments and even explore the haplotype variability on the whole-genome level.Understanding LD patterns is indispensable for association mapping. In our study, LD in omentosa , Eucalyps nitens , Pinus slvestris , loblolllvestris , and Doulvestris , which slvestris . MoreoveCfSUS gene , which indicated that CfSUS have a potential utility of a gene-based association mapping method in developing SNP markers in C. fargesii. Finally, we identified 11 SNPs and 14 haplotypes were significantly associated with wood property by association analysis. These findings imply a functional role of CfSUS in mediating wood properties and providing SNP markers associated with wood property, suggesting the potential application in marker-assisted breeding of C. fargesii in the future.In this study, we cloned a Additional file 1Table S1. Phenotype of associated population. (XLSX 19 kb)Additional file 2Figure S1. The distribution pattern for each trait measured in the entire population (n = 125) of Catalpa fargesii. (JPG 7743 kb)Additional file 3Table S2. The mean values, range of variation (RV), standard error (SE) and coefficient of phenotypic variation (CV (%)) for each wood property trait measured in association population. (DOCX 18 kb)Additional file 4Table S3. Primers used in this paper. (DOCX 15 kb)Additional file 5Table S4. Genotypic data of CfSUS identified in the associated population. (XLSX 30 kb)Additional file 6Figure S2. Genomic organization of CfSUS. Positions of 47 common SNP markers are shown as vertical lines. (JPG 1318 kb)Additional file 7Figure S3. Phenotypes of different genotypes of significant SNPs associated with wood property traits. (JPG 6446 kb)Additional file 8Figure S4. An unrooted phylogenetic tree of SUS members from Arabidopsis thaliana, Populus trichocarpa and Catalpa fargesii. Arabidopsis thaliana: ATSUS1(AT5G20830.1), ATSUS2(AT5G49190), ATSUS3(AT4G02280), ATSUS4(AT3G43190), ATSUS5(AT5G37180) and ATSUS6(AT1G73370); Populus trichocarpa: PtrSUS1(Potri.018G063500.1), PtrSUS2(Potri.006G136700.1), PtrSUS3(Potri.002G202300.1), PtrSUS4(Potri.015G029100.1), PtrSUS5(Potri.012G037200.1), PtrSUS6(Potri.004G081300.1), PtrSUS7(Potri.017G139100.1); Catalpa fargesii Bur.:CfSUS. (JPG 1588 kb)


The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately. The availability of genome-wide single nucleotide polymorphism (SNP) panels has enabled the implementation of genomic prediction in many livestock species. . FurtherDevelopments in sequencing technologies provide new opportunities to advance the fine mapping of QTLs. Given the relatively high cost of sequencing, there is a trend in which genotyped animals (mostly genotyped for SNP densities between 10 and 700K) are imputed up to whole genome sequence (WGS), and the resulting GWAS has more power and precision to detect significant QTL . With thKorean cattle (Hanwoo) are categorized into three sub-groups based on coat color: brown, brindle, and black. The Brown Hanwoo is the largest and most common subgroup, and intensive selection has occurred to improve meat quality and quantity traits . Korean All phenotypic and genotypic data were recorded during standard production protocols at Brown Hanwoo Experimental Station, National Institute of Animal Science (NIAS), Rural Development Administration, South Korea. Animal health and welfare issues were followed according to approved guidelines of the Animal Care and Use Committee (NIAS) and the ethics committee approval number was 2015-150. Phenotypic data from 2,110 Hanwoo steers for the traits; marbling score, meat texture, meat color and fat color  were used in this study. These individuals were produced from 252 sires and 2,064 dams. All of the data was collected at The Hanwoo Improvement Centre, South Korea during a progeny-testing program between the years 2000 and 2013. The steer progeny  were slaughtered at the same age (24 months), and phenotypic measurements were taken on the chilled carcass. The full details of feeding, management practices and traits measurements are reported elsewhere .All meat quality traits were recorded using the Korean beef carcass grading system (BCGS). This system was established in 1992, and nationwide implementation occurred in 1999 . MarblinAll animals with phenotypic data were genotyped with the 50k SNP Chip . These animals were subsequently imputed to higher densities using a large reference dataset of genotyped individuals. This reference dataset included a total of 4,887 Hanwoo animals genotyped with the 50K SNP chip  and 928 animals genotyped with the 777K SNP chip  and 203 reference animals sequenced with an average depth of 25.6X. In the reference dataset, 655 animals were genotyped with both the 50K and 777K chips. Similarly, 140 animals with sequence data were also genotyped with the 50K chip. There were no animals genotyped for both 777K and whole-genome sequence. The imputation process was done in multiple stages. In the first stage, individuals were imputed from the 50K SNP chip up to 777K, followed by an imputation step from 777K up to whole genome sequence level. The phasing and imputation was undertaken, one chromosome at a time, using Eagle version 2.3.2 for phasing and Minimac3 for the imputation . The accIn the current study, variance components and the resulting heritability for each trait were estimated using bivariate linear mixed effects models implemented in the the GREML module of GCTA . Prior tWhere y is a vector of phenotypes for the traits; b is a vector of fixed effects, u is a vector of additive genetic effects and it was assumed to be distributed as WGS data , and σg2The GWAS analysis was undertaken using the MLMA module from GCTA for each trait. The previous model was expanded to include each SNP as a fixed effect and can be represented as:Where, q is the vector of allele substitution effects and, W is the matrix of genotype codes  for each SNP. As in the previous model, u is a vector of additive genetic effects and it was assumed to be distributed as ing SNPs . All othip and iq are the allele frequencies for the thi SNP, iα is the estimated additive effect of the thi SNP on meat quality traits, Where lgorithms. The Mixlgorithms. This me2 = 0.5) using the software PLINK v1.07 (−6) that is less stringent than the Bonferroni threshold to avoid false negative and to control false positive due to multiple testing.Often false discovery rate (FDR) of significant SNP is a problem in GWAS. To manage FDR, it is important to check the distribution of p-values. In the current study, the p-values were continuously distributed which showed that the FDR is being controlled. Another problem is often SNP effects are often inflated due to multiple testing of SNP adjacent to each other. A common way to manage multiple testing is using a Bonferroni correction help to decide what SNP are significant or not. However, the Bonferroni multiple testing procedure is commonly perceived as being too stringent in large-scale simultaneous testing situations such as those that arise in the microarray (imputed) data analysis . TherefoNK v1.07 . The pliBos taurus) UMD3.1 assembly was used for the annotation of significant SNPs. Finally, LocusZoom standalone version (The online database (Ensembl) under Cow , respectively. Conversely, the genetic correlation between marbling score and meat texture was high (−0.97 ± 0.02) and the genetic correlation between meat and fat color was moderate and positive (0.43 ± 0.15). Meat color showed moderate genetic correlation (0.66 ± 0.13) with meat texture and, fat color also showed moderate genetic correlations with meat texture (0.52 ± 0.15). Estimated variance components are reported in −6) p-value threshold, located on 14 selected chromosomes (one region per selected chromosome). For each trait, only the most significant regions were selected for further downstream analysis. For the trait of marbling score, the GWAS distinguished four significant regions across four chromosomes comprising of 31 significant SNPs that passed the alternative p-value threshold (p < 1×10−6). The additive genetic variance explained by each significant SNP varied from 2 to 3.6%. The GWAS for meat texture identified two significant regions which comprised of 13 SNP, on two chromosomes, that passed the alternative p-value threshold (p < 1×10−6). The additive genetic variance explained by each significant SNP varied from 3 to 3.6%. The three most significant regions (p < 1×10−6) were considered for meat color. These regions included 22 SNPs on three separate chromosomes, and each SNP explained 3 to 6% of the additive genetic variance. Five significant regions comprising of 41 SNPs across five chromosomes were identified for fat color (p < 1×10−6), among the 41 SNPs, five SNPs reached the Bonferroni threshold. The additive genetic variance for fat color explained by each significant SNP varied from 3 to 9%. Manhattan plots illustrating the results from the GWAS for marbling score  and meat texture  are shown in The quantile–quantile (QQ) plot for each trait showed that the model (MLM) fitted the data well. Genomic inflation factor  obtained from the QQ plot indicated low bias and ranged between 1 and 1.04 for all studied traits and shown in −7) SNP (rs43287038) explained 3.4% of the genetic variance. Among the five significant SNPs located at 23.38 Mb on BTA12, the most significant SNP was rs208621284 with the p-value of 1.17×10−7 and explained 3.7% of the genetic variance. These five SNPs were in high LD with each other  database, these significant SNPs are annotated as intron variants (56%), and 44% of those SNPs are annotated as intergenic variants. Intergenic regions were defined as regions more than 5 Kb distant from genes whereas regulatory regions were defined as regions located 5 Kb upstream and 5 Kb downstream of genes. Detail description of selected top significant SNPs including effect size of each SNP and adjacent genes to the significant SNPs on 14 selected chromosomes  were identified close to the most significant SNP that was associated with marbling score and meat texture, particularly the top significant SNP (rs208621284) was found in the transcribed strand region of the FREM2 gene. LocusZoom plots for the top significant SNPs that were associated with marbling score  and texture  are shown in The GWAS for marbling score identified four significant regions (−6) four SNPs on BTA12 and seven SNPs on BTA29 were detected. The four most significant (p = 5.81×10−7) SNPs  were located at 23.4 Mb on BTA12. This peak consisted of the same QTL region that was identified for marbling score. Interestingly, another significant region was identified on BTA29, with the most significant SNP  located at 19.9 Mb and explained 4.8% of the total genetic variance. All significant SNPs from both chromosomes  were annotated as 45% intron, 36% intergenic, and 18% downstream gene using VEP in Ensembl database. Detail description of selected top significant SNPs including effect size of each SNP and adjacent genes to significant SNPs on BTA12 and 29 (The GWAS for meat texture identified significant regions on BTA12 and BTA29 (−7) SNP found on BTA2 was rs210985952 and located at 9.57 Mb. Eight significant SNPs were located on BTA14 at the similar genomic position of 83.35 Mb, and the most significant SNP (p = 1.6×10−7) on this chromosome was rs137372673, and it explained 4.5% of the genetic variance. Similarly, eight significant SNPs spanned between 51–53 Mb on BTA24 and the most significant SNP (p = 8.5×10−7) on BTA24 was rs110365059, and it explained 4.3% of the genetic variance. All SNPs that were associated with meat color located on BTA2 were annotated as an intron variant in VEP/Ensembl database while all SNPs that were located on BTA14 and 24 were annotated as intergenic variants. Detail description of selected top significant SNPs including effect size of each SNP and candidate genes that were associated with significant SNPs on chromosome 2, 14, and 24 (2 0.955. Within this genomic region (0.5 Mb), seven candidate genes  were identified, particularly the putative variant (rs137372673) was found in the transcribed strand region of the ENPP2 gene. LocusZoom plots for the top significant SNPs that were associated with meat color  and fat color  are shown in The GWAS identified three major regions strongly associated with meat color on chromosome 2, 14, and 24 (−10) were located on BTA21 at 21.77 Mb. The other 20 significantly associated SNPs were located on BTA21 spanning a region of 21.38–21.77 Mb. Apart from BTA21, the next most significant (p = 7.45×10−9) SNP (rs210647091) was located on BTA7 at the genome position of 6.1 Mb. The three significant (p < 1.8×10−7) SNPs that were found on BTA10 were located at 89.6 Mb. On BTA16, the most significant (p = 1.24×10−8) SNP (BTA16:69913004) was located at genome position of 69.91 Mb. Similarly, the most significant SNPs  was located at 46.59 Mb. There were also a number of single SNP across multiple chromosomes that showed association with fat color; however, these were not considered for further QTL region analysis. According to VEP (Ensembl), all significant SNPs that were associated with fat color were categorized as 42% intergenic, 31% upstream gene, 23% intron, and 4% downstream gene variants. Detail description of selected significant SNPs including effect size of each SNP and adjacent genes to the significant SNPs on chromosome 7, 10, 12, 16, and 21(2 0.94. Within this genomic region (0.5 Mb), four candidate genes  were identified, particularly the putative variant (rs210647091) was located within the CPAMD8 gene region , and this estimate is very similar to the one estimated by A major reason for the slight differences between the results obtained in the current study and those previously reported are that the data used in this study was from a controlled progeny test whereas data in another study  was fromSFT2D3 gene was the nearest gene to the most significant SNP. According to SFT2D3 gene was associated with the synthesis of lipoproteins. Lipoproteins are a core component of fat and cholesterol cells and have been linked with lipid cell membrane development. This provides further evidence that a QTL for marbling is located in this region.The GWAS detected 31 significant SNPs associated with marbling score. Significant regions on BTA2 have previously been reported by several authors in many beef cattle populations . AlexandFREM2 gene  is located close to the most significant SNP. According to Potential QTL regions associated with marbling score in beef cattle have been previously identified on BTA16 by GALR1 gene was a candidate gene located close to the most significant SNP on BTA24 and it is responsible for neuropeptide and peptide hormone bindings (GALR1 gene has been linked to the synthesis of bioactive lipids (FREM2) (ANO5) was identified on BTA29 that was potentially associated with meat texture and the gene (ANO5) was known for its involvement in the formation of amino acids  in Hanwoo was 0.23 for pairwise distances of less than 25 kb, and LD dropped to 0.1 for 40 to 60 kb. These estimates of LD concur with LD estimates from the Hanwoo cattle used in the current study (result not shown). Similarly, 2 = 0.1) and high heterozygosity (0.4) in brown Hanwoo cattle compared to other Asian cattle. Furthermore, The most significant SNP (rs208621284) associated with meat texture showed high LD with the adjacent significant SNPs , the ext−8). Other regions have also previously detected on chromosome 14 and 24. Recently, The GWAS for meat color identified 23 significant SNPs on BTA2, 14 and 24. Similar QTL regions have been reported in previous studies on similar traits . This stENPP2 gene was responsible for lipid degradation and metabolism in human muscle. ENPP2 gene was also associated with pigmentation in the cell. Furthermore, FAM171B gene was located near to the most significant SNP (rs210985952) on BTA2 that was associated with meat color, however, the function of this gene is not well known in livestock. Similarly, POLI was a candidate gene found on BTA24 near to the most significant SNP (rs380354915) that was associated with meat color and it has been shown to be involved in DNA replication in human, although it has not been explicitly linked to meat or cell color . Among those candidate genes, ENPP2 gene was located near to the most significant SNP (rs137372673) located on BTA14, and this SNP showed high LD (0.95) with the adjacent SNPs  for fat color was found close to the most significant SNP on BTA 7. It has no prior GWAS evidence of association with fat color, but it has been linked to the fat composition in Human. Other genes, CPAMD8 and RHCG have been linked to various processes involved in lipid cell structure and development but have not been previously associated with fat color (RHCG was a nearby gene to those significant SNPs associated with fat color on BTA21 and the gene involved in ammonium transmembrane transport in the cell (The candidate gene (at color . Furtherthe cell .−6) and Bonferroni threshold along with downstream gene analysis to identify regions that are likely to be linked to QTL for the traits discussed. As stated previously, many researchers advocate alternative thresholds under appropriate circumstances. The Bonferroni threshold has been widely used for traditional GWAS; however, this threshold may no longer be appropriate for methods that utilise the mixed linear model (MLM) procedures as done in GCTA. The Bonferroni threshold would likely increases false negatives and therefore discards significant observations in the MLM model  which helped in the identification of potential genes relating to the discussed traits. Some variation between our results and those previously described could also be due to the choice of significance level/p-value threshold used to identify significant SNPs. Some conjecture still remains regarding definitions of significance. In this study, we have used a combination of both an alternative  and ENPP2 (meat color). The significant regions identified in this study may provide valuable biological information to improve genomic selection accuracy in Hanwoo beef cattle breeding programs.This study has estimated genetic parameters for meat quality traits in Korean indigenous beef cattle (brown Hanwoo) using imputed WGS data. It provides valuable estimates of genetic parameters and key insight into regions that underpin variation for the traits discussed. In this study, 107 significant SNPs that were highly associated with meat quality traits were identified across 14 chromosomes. These regions were in close proximity to the genes The datasets in this manuscript are available at the National Agriculture of Biotechnology Information Center (NABIC) (accession number NV-0544-000001).The animal study was reviewed and approved by the Animal Care and Use Committee (NIAS) and the ethics committee approval number was 2015-150.MB, CG, SC and JW conceived and designed the study. MB performed data analysis (GWAS) and drafted the manuscript. SC, ND and JW had a contribution in data analysis and imaging. SC, JW, ND and DL were responsible for editing the manuscript. CG was responsible for imputation of 50K and 777K genotype data to sequence level. DL, BP, MP and RH were responsible for phenotypic data collection, genotyping and in quality control of sequence data. SC was responsible for overall supervision task . All authors read and agreed on the contents of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.