As an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives.In this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index (This study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.The online version contains supplementary material available at 10.1186/s12864-023-09347-9 Malus × domestica Borkh.) has been domesticated from the wild apple Malus sieversii and hybridized with Malus sylvestris [Apple is an economically important fruit tree and has been widely grown in temperate regions around the world. The cultivated apple  and SNP arrays have been used for CNV detection in the last few years. With the development of sequencing technology, next-generation sequencing (NGS) is becoming popular for its high throughput and increasingly competitive cost . There aMdMYB10 and a 930 bp region upstream of MdMYB110a  were responsible for red-fruit-flesh phenotypes in apple [CNV plays a vital role in apple fruit development. It was reported that a tandem duplication in the upstream regulatory region of in apple , 30. To in apple . There iin apple , 32, we CNV identification in apple was performed as illustrated in the pipeline Fig. A. BrieflTo ensure the accuracy of CNV detection, we evaluated the performance of Speedseq using simulated CNV in the apple genome (see Methods). The results showed that it was able to detect simulated CNV at sequencing depth levels commonly used in genomics studies  were identified from 346 accessions . CNV was reduced in gene bodies 23.86%) compared with intergenic regions (29.59%) . Genome-wide distribution of CNVRs on each chromosome, along with SNPs, TEs and gene features was shown in Fig. For each CNVR, the nearby flanking 300 SNPs upstream and downstream were considered for linkage disequilibrium (LD) analysis. CNVRs were then grouped into three categories, Low-LD, Mid-LD and High-LD as illustrated in Methods. A great majority of CNVRs (80%) showed low LD with flanking SNPs, and 15% of CNVRs had intermediate levels of LD, while only 5% exhibited high LD and could be considered tagged by adjacent SNPs Fig. F. ExpectM. sieversii accessions located close to wild accessions while M. sylvestris accessions and cultivars formed a large branch  results of apple accessions to population structure inferred from SNPs. Although variance explained by PC1 and PC2 of CNVs (6.94%) was lower than SNPs (26.52%), the cultivars and wild relatives could be distinguished in both results Fig. C. While p = 5.72 × 10–20) and response to stress   involved in biological processes such as defense response, response to stress and response to stimulus were CN-variable , reproduction process , signal transduction  and metabolic processes. It is worth noting that several genes involved in the metabolic processes such as malate metabolic process  and sucrose metabolic process  which might be involved in the taste improvement of apple.In total, 3,621 genes completely or largely (> 50% of gene span) overlapped with CNVRs were identified in the reference genome  was calculated and genes located in top 1% MD03G1049200 (encoding NB-ARC domain-containing disease resistance protein that involved in pathogen recognition and subsequent activation of innate immune responses), has higher CN among wild relatives (mean CN = 3.92) compared with cultivars (mean CN = 2.03) . Typical resistance genes related to the specific immune response toward pathogen or external adverse stimuli such as MD15G1391000 also show a similar CN trend , which is expected as we aim to identify CNVs with high confidence. The simulation result demonstrates that our strategy is capable to detect majority of CNVs in the data of this work with high accuracy. It was reported that a 625 bp CNV in the 5’UTR of 1 genome . Corresp%, which r = 0.58; p < 2.2e-16) , and 95.92% of CNVRs overlapped with TEs, which demonstrates that CNVRs have an enrichment within TEs to some extent. The resulting CNVR map was also compared with SNPs. In contrast to the highly variable distribution of SNPs along chromosomes, the distribution of CNVR is largely flat, exhibiting a decoupling of the two variation types. The density of CNVR and SNP across the whole genome shows a significant positive correlation with local discordance  with SNPs –42. Low M. sylvestris into M. × domestica has been so intensive that cultivars now appear to be closer to M. sylvestris than to their progenitor M. sieversii. The first two principal components (PCs) based on SNPs explained 26.52% of total variance, and the population was highly structured across PC1. Compared with SNPs, the first two PCs based on CNVs explained less variance (6.94%) while the population was separated clearly along PC2. This result uncovers accessibility of CNVs in population structure inference and underlying information that may not captured by SNPs, which demonstrates significance of CNV in genome variation studies [Population genetics analysis based on CNVRs in apple was the first attempt to investigate relationships among different apple accessions to date. Population structure was largely reflected by dendrogram inferred from both CNVs and SNPs. Recent introgression from iance 6.9% while tp = 0.020) and sucrose metabolic process  which might be involved in the taste improvement of apple. For instance, MD04G1101300 and MD17G1077700 were malate degradation related genes encoding NADP-malic enzyme (NADP-ME). The rapid decrease of malate content during fruit ripening has already been attributed to its degradation by cytosolic NADP-ME in some fruits such as loquat [MD03G1248900, a gene encoding NAD(H) kinase 3, homolog of NADK3 whose up-regulation of expression coincided with fruit growth at ripening stages in tomato, might also involve in the enlargement process in the apple fruit [To better understand the roles of CNVs play, we focus on genes overlapped with CNVRs and examined their GO enrichment. These genes are mainly enriched in defense response and response to stress, which is consistent with a previous study . Among ts loquat . The higs loquat . MD03G12le fruit .M.sieversii and M.sylvestris, CN-differentiated were identified between cultivars and wild relatives, which only consisted of M.sieversii and M.sylvestris. Among 127 CN-differentiated genes, 17 of them are annotated as R genes and exhibit higher copy numbers in wild relatives than cultivars, which is consistent with the higher disease resistance in wild accessions. Higher copy number might play key roles in stronger stress resistance in wild relatives compared to cultivars. The most prominent gene is MD12G1236200 (XRN4), which was at higher CN among wild relatives (mean CN = 3.06) compared with cultivars (mean CN = 2.00) . It was reported that XRN4 involved in ethylene response and disease resistance in Arabidopsis [MD12G1236200 might also be related to the changes of similar function, which needs to be verified by wet experiments. Besides, several CN-variable genes appear to be involved in fruit developmental processes  while almost no changes were observed in cultivars (mean CN = 2.00) . High expression in flowers indicates that it may play an important role in apple growth and development [MD07G1086100 belongs to MADS-box gene family. Sequence analysis from appleMDO database (http://bioinformatics.cau.edu.cn/AppleMDO/) indicated that MdMADS110 was ubiquitously expressed in different tissues including apical bud, spur bud and flower [MD07G1086100 showing differentiated CN is likely to play possible regulatory roles in apple development. The decrease of gene copy numbers might be relevant to potential adaptability or neutrality of non-functionalization mutations, which supports the ‘less is more’ or ‘regression evolution’ hypothesis [During the process of plants domestication, CNVs are found to be related to stress tolerance, disease resistance and development . Considebidopsis . The decelopment . MD07G10d flower , 54. Stud flower . This anpothesis .In this study, we carried out comprehensive CNV analyses in wild and cultivated apples. In total, 14,839 CNVRs occupying 10.03% of the apple genome were identified, and a comprehensive map of CNVs was constructed based on 346 apple accessions. The low LD between CNVRs and SNPs indicates that CNVs are largely independent genetic variation resources. Genes overlapping with CNVRs were mainly enriched in defense response, reproduction and other metabolic processes. Some genes overlapping CNVRs are highly differentiated between cultivars and wild relatives, which appears to be related with some differences in traits between them. To confirm their genetic associations, functional verification is required in follow-up studies. These findings will provide important resources for comprehensive understanding of the genome variation and may serve as a useful reference for future genomic studies on apple and related species.https://github.com/lh3/wgsim). It was noting that we simulated reads at various levels of sequencing coverage ranging from 5 to 30X as we aimed to investigate the relationship between discovery power and coverage depth in CNV detection. Next, the reads were mapped to the unmodified apple genome and used for CNV detection following subsequent calling analysis. The true positive, false positive and false negative CNV discovery rates were calculated to evaluate performance of the method.To test the performance of the method used in this study, 200 duplications and 200 deletions were simulated in apple genome using the RSVSim (v1.28.0), an R package that can be used to simulate CNVs across a genome . The simhttps://www.rosaceae.org/species/malus/malus_x_domestica/genome_GDDH13_v1.1) [http://broadinstitute.github.io/picard/). The BAM files were further filtered based on mapping quality (reads with MAPQ < 20 were discarded) for subsequent analysis [A total of 346 accessions which have clear pedigree information were used for CNV detection. This dataset consists of 289 cultivars and 57 wild relatives. The corresponding re-sequencing data was obtained from previous studies with accession number CRA003964 in the Genome Sequence Archive (GSA) database and SRP075497 in the NCBI Sequence Read Archive . The ref13_v1.1) . Clean r13_v1.1) , 60. Potanalysis .Three available RNA-seq datasets corresponding to accessions with sample number C77, W249 and W412  was obtained from GSA database (CRA003991) under accession number CRR274993, CRR274994, CRR274995, CRR274999, CRR275000, CRR275001, CRR275002, CRR275003, CRR275004. Each sample consisted of three biological replicates. We processed these data as described previously  and usedP-value of 0.05. Finally, only deletions and duplications supported by RD, SR and RP analysis simultaneously and longer than 50 bp were retained for further analysis.SpeedSeq, a software package that integrates RP, SR and RD method, was used for CNV detection in this analysis . It incoThe CNV region (CNVR) is defined as a combined region of overlapping CNVs in the apple genome after aggregating and filtering out the ones existed in less than ten accessions. Here, CNVR were merged from different accessions with at least 90% stringently reciprocal overlap by extending the boundaries of the overlapping CNVs. Then they were further classified as “gain” (i.e. CNVRs merged from duplications across different accessions), “loss” (i.e. CNVRs merged from deletions across different accessions) and “both” (i.e. CNVRs merged from both duplications and deletions within the same regions across different accessions) events according to their types. Only CNVR presented in at least ten individuals were used for subsequent functional and population genetics analysis, thus minimizing the bias caused by uniformity of sequence coverage depth and impact of rare CNVR.In order to facilitate subsequent analysis, all CNVRs were recoded manually by converting a loss event into “0/1”, a neutral event into “0/0” and a gain event into “1/1”, thus we converted the file into VCF-format file and filtered the CNVRs with MAF < 0.05 to keep consistent with criterion used in SNPs filtration for comparison with SNPs in population structure analysis.https://bigd.big.ac.cn/) with accession number GVM000128. Accessions with both CNV and SNP information were selected for analysis, thus 343 samples were retained. For each CNVR, the nearest 300 upstream and 300 downstream SNPs with a minor allele frequency greater than 5% were selected. Pairwise genotype correlations  for all SNP-SNP and SNP-CNVR pairs were calculated by PLINK (v1.90p) [r2 values ordered by decreasing rank and a median SNP-SNP rank value was calculated. According to the number of times (N) CNVR rank over the SNP-SNP median rank, each CNVR was classified as Low-LD, Mid-LD and High-LD to SNP as follows:Transposable element (TE) was shown as a source of novel genetic diversity in Arabidopsis because of large extent not in linkage disequilibrium (LD) with nearby SNPs . As TE a(v1.90p) . Then, rTo explore the population structure and convergence of accessions, admixture analysis was performed ADMIXTURE (v1.3.0) with 500 replicates and cross-validation error (CV) procedure was run . NeighboP-value was below 0.05.Gene contents encompassed by CNVR were assessed by comparing coordinates between them. Only genes covered by CNVR more than 50% were considered for functional analysis. GO enrichment analysis was carried out on these genes using the R package topGO (v2.28.0) . SignifiM. sieversii and M. sylvestris, only wild relatives belonging to these two species were considered in this analysis [Because cultivars have been domesticated mainly from analysis . The staectively . \documeAdditional file 1: Fig. S1. Test of the Speedseq using simulated CNVs in the GDDH13 genome, for a range of sequencing coverage levels. Deletions (A) and duplications (B). TP, True positives; FN, False negatives; FP, False positives.Fig. S2. Examples of CNVs identified. (Upper) A 630bp deletion in the 5’UTR of MdCBF2 (MD01G1196100). (Lower) MD15G139100 was fully overlapped by a duplication on the Chromosome 15. Fig. S3. SNP versus CNV densities in 1Mb sliding windows with 500kb step size across the genome. The local discordance was marked with a red circle. Fig. S4. Neighbor-joining clustering for all accessions with each color of branch corresponds to a single pedigree as noted. Fig. S5. 17 R genes with differentiated CN profiles (top 1% Vst) between cultivars and wild relatives. Fig. S6. Boxplot of CN profiles of genes related with various biological functions. Whiskers extend to the highest and lowest values no greater than 1.5 times the inner quartile range. For both comparisons, groups were significantly different - Wilcoxon rank sum test with continuity correction, p < 0.0001.Additional file 2: TableS1. Summary of 346 apple accessions and resequencing data used in the CNV analysis. Table S2. Summary of CNV events in different accessions. Table S3. Extent of apple genome features impacted by non-overlapping CNVs. Table S4. List of all CNVRs and CN estimated by CNVnator in the apple genomes. Table S5. The genes completely or partially inside (50% overlap) of the identified CNVRs in the apple genome. Table S6. GO Biological Process enrichment for all CNVR genes. Table S7. Population-differentiated CNV-Genes between cultivars and wild relatives. .


Groat protein content (GPC) is a key quality trait attribute in oat. Understanding the variation of GPC in oat germplasms and identifying genomic regions associated with GPC are essential for improving this trait. In this study, the GPC of 174 diverse oat accessions was evaluated in three field trials. The results showed a wide variation in GPC, ranging from 6.97% to 22.24% in this panel. Hulless oats displayed a significantly higher GPC compared to hulled oats across all environments. A GWAS analysis was performed based on 38,313 high-quality SNPs, which detected 27 non-redundant QTLs with 41 SNPs significantly associated with GPC. Two QTLs on chromosome 6C (QTL16) and 4D (QTL11) were consistently detected in multiple environments, with QTL16 being the most significant and explaining the highest proportion of the phenotypical variation in all tested environments except in CZ20. Haplotype analysis showed that the favorable haplotypes for GPC are more prevalent in hulless oats. These findings provide a foundation for future efforts to incorporate favorable alleles into new cultivars through introgression, fine mapping, and cloning of promising QTLs. Avena sativa L.), belonging to the genus Avena L. of the grass family is the sixth most important cereal crop globally, after rice, wheat, maize, barley, and sorghum [Oat  analysis for oat GPC was conducted by Zhu et al.  using thDespite some genomic regions associated with GPC in oats having been identified previously, most of the genetic maps used in these studies have been generated using diverse DNA markers. The paucity of common markers among these maps has made the comparisons of the positions of QTLs challenging . MoreoveAssociation mapping is a powerful alternative to traditional QTL mapping approaches. It identifies QTLs by utilizing the majority of recombination events of a set of diverse germplasms that have occurred in the evolutionary history, resulting in higher mapping resolution compared to linkage mapping . SeveralRecent advancements in oat genomics have led to the generation of some high-quality and fully annotated oat genomes ,28. ThesThe GPC of 174 diverse oat accessions was evaluated on a dry groat weight basis in three field trials. A wide variation of GPCs, ranging from 6.97% to 22.24% across three trials, was observed, which is in line with the segregation of displaying quantitative traits a–d. The p = 0.36). This trend held true in each environment except in WJ20, where the mean GPC of cultivars was slightly higher than that of landraces (11.89% vs. 11.85%) (p < 0.001) and in each individual environment (p > 0.05) were found between modern cultivars and landraces of either hulled or hulless oats in the testing environments  . However 11.85%) a. The 60ironment . Except ronments c,d.The screening of the 174 diverse oat accessions for consistent high (top 20) and low (bottom 20) GPC in at least two environments identified 11 accessions with high GPC and eight accessions with low GPC . Of the p < 1 × 10−3) with GPC based on phenotypic data in individual environments and BLUP values across three environments  were detected in at least two environments including BLUP . QTL11 wThe chromosomal locations of previously reported QTLs ,18,19,20p < 0.001) in all the test environments (The major QTLs detected in the panel of 174 diverse oat accessions for GPC had two haplotypes: one favorable and another unfavorable for GPC. Haplotype G for QTL16 (QTL16-HAP-G) was present in 64 accessions with an average GPC ranging from 13.69% to 16.30% in different environments , whereasronments a. Hence,ronments b. Therefronments c,d. Of tronments c. Similaronments d. In concytochrome P450-like gene, MYB domain-containing proteins, and disease-resistance genes. The SNP, S6C_285259972 on 6C which was found to be significantly associated with GPC in all tested environments and was located near the gene A.satnudSFS6C01G004265, while the three SNPs on 4C that were significantly associated with GPC in WJ20 and in the multi-environment model were situated close to gene A.satnudSFS4D01G000779. However, the functions of both genes have not yet been annotated.The significant SNPs associated with GPC were used to pinpoint potential candidate genes using the recently annotated “Sanfensan” reference genome, as shown in Groat protein content (GPC) is a key characteristic in oats and thoroughly investigating the phenotypic variation present in the existing oat germplasms is crucial for improving this trait. In the current study, the GPC of 174 diverse oat accessions from 42 countries or regions was evaluated through three field trials. These accessions were selected from a larger pool of 659 diverse oats (Diverse Oat Panel) that had been genotyped by GBS technology . The selPrevious studies have shown that hulless oats have higher GPC than hulled oats ,30. HoweMany studies have reported that GPC is influenced by the environment to a fairly high level ,32,33. T2 < 15%, This study conducted a GWAS for oat GPC using phenotypic data in individual environments and BLUP values. A total of 27 non-redundant QTLs were identified . Most ofIn this study, most of the identified QTLs were found on the C and D chromosomes, while only one QTL was located on chromosome 4A. This indicates that the C and D subgenomes possess a greater number of loci controlling the accumulation of groat content compared to the A-subgenome. This outcome is in accordance with previous studies that have shown that the C genome diploids and the CD genome tetraploid species tend to have higher levels of GPC than the A genome diploids and the AB genome tetraploids ,35.The availability of the high-quality oat reference genomes ,28 enablcytochrome P450 and disease-resistance genes, which have also been recognized as potential candidate genes for GPC in wheat [Candidate gene predictions revealed potential associations between GPC and several important genes . For exain wheat . Howeverin wheat  also limThis study found that the combination of the two superior haplotypes of the two major QTLs had a significantly higher GPC compared to the combination of the two inferior haplotypes. These findings suggest new varieties with high GPC can be developed by pyramiding these superior alleles. While previous studies have suggested a negative correlation between grain yield and GPC in cereal crops ,38, someIn a previous study, a diverse panel of 659 oat accessions was collected and sequenced using the GBS strategy. This oat panel, referred to as the Diverse Oat Panel, contains 128 hulless oats from China, 145 oat lines nominated by breeders from North America and Europe, and 371 diverse accessions collected from 52 countries or regions [Oat accessions were planted at the Weijiang experimental farm of Sichuan Agricultural University in Wenjiang, Chengdu  over two years (2019 and 2020), and at the Chongzhou Experimental Station of Sichuan Agricultural University in Chongzhou  in 2020, referred to as WJ2019, WJ2020, and CZ2020, respectively. The experiments followed a randomized complete block design without replication at each location. Each plot comprised two 1.5 m rows with 30 cm inter-row spacing and 10 cm inter-plant spacing. Nitrogen and superphosphate fertilizers were applied one week prior to sowing at a ratio of 80 kg/ha. Field management, disease, pest, and weed control were carried out as needed. After maturity, the seeds from each oat accession were harvested separately by rows and dried in a forced-air oven at 50 °C to constant weight. Equal amounts of seed samples from each row were bulked to represent a single balanced sampling per line. Subsequently, seeds with similar sizes were selected for the measurement of crude protein content. Crude GPC was determined using a Kjeldahl 8400 nitrogen analyzer  and expressed as a percentage of dry weight. To make a comparison of the GPC between hulled and hulless oats, the hulls of the hulled oats were removed manually before analyses. Each sample from the three environments was analyzed three times to generate technical replicates, and the mean values among the technical replicates were used for further analysis.https://github.com/taiyun/corrplot, accessed on 12 January 2023) to determine the consistency of GPC in different environments.A frequency map was generated to display the distribution of GPC among the oat accessions in each environment. Descriptive statistics, including range, mean, standard deviation, and coefficient of variation, were summarized in R (v4.05) . The sigIn a prior study, 49,702 high-quality SNPs were identified after aligning the GBS reads from the 659 oat accessions in the Diverse Oat Panel to the “Sanfensan” reference genome . The 49,p-value threshold of 0.001 (−log10 p = 3.0) was used to avoid removing true positive associations. Annotations for the associated markers for GPC were assigned based on the closest annotated gene identified near the SNP physical location using the “Sanfensan” reference genome, and protein functions were further explored through literature mining of annotated information.Marker-trait associations were carried out for each trial separately, and also considering a multi-environment model. For multi-environment model analysis, best linear unbiased predictions (BLUPs) were estimated by the lme4 R package for use as the phenotypic input for the subsequent association analysis. The mixed linear model (MLM) method implemented in TASSEL 5.0 software was used to detect the association between markers and GPC, and the population structure and kinship information (K matrix) were taken care of to minimize false-positive associations. The population structure was represented by the first three principal components, which explained 34.77, 7.64, and 3.80% of the total variance, respectively, while the K matrix was estimated by using the centered identity-by-state method. Quantile-Quantile (Q-Q) plots and Manhattan plots were drawn using the R package “CMplot” . Given te-value < 10−20 were considered as their chromosomal locations of the flanking marker on the reference genome.Several previous studies ,18,19,20t-test.Haplotype analysis was carried out for the major QTLs detected in at least two environments using the LDBlockShow program . The “faThis study examined the GPC of 174 diverse oat accessions and revealed a wide range of variability in GPC. Hulless oats had a higher mean GPC compared to hulled oats in all field trials. A GWAS study identified 27 unique QTLs on 12 chromosomes that were associated with oat GPC. Two of them were stably detected in multiple environments. Genotypes with favorable alleles at these two major loci exhibited significantly higher GPC. In summary, this study highlights the genetic diversity and potential targets for enhancing the GPC in oats.


As pre-cut and pre-packaged chilled meat becomes increasingly popular, integrating the carcass-cutting process into the pig industry chain has become a trend. Identifying quantitative trait loci (QTLs) of pork cuts would facilitate the selection of pigs with a higher overall value. However, previous studies solely focused on evaluating the phenotypic and genetic parameters of pork cuts, neglecting the investigation of QTLs influencing these traits. This study involved 17 pork cuts and 12 morphology traits from 2,012 pigs across four populations genotyped using CC1 PorcineSNP50 BeadChips. Our aim was to identify QTLs and evaluate the accuracy of genomic estimated breed values (GEBVs) for pork cuts.HMGA1, VRTN and BMP2 were associated with body length and weight. Subsequent analysis revealed that HMGA1 primarily affects the size of fore leg bones, VRTN primarily affects the number of vertebrates, and BMP2 primarily affects the length of vertebrae and the size of hind leg bones. The prediction accuracy was defined as the correlation between the adjusted phenotype and GEBVs in the validation population, divided by the square root of the trait's heritability. The prediction accuracy of GEBVs for pork cuts varied from 0.342 to 0.693. Notably, ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones exhibited prediction accuracies exceeding 0.600. Employing better models, increasing marker density through genotype imputation, and pre-selecting markers significantly improved the prediction accuracy of GEBVs.We identified 14 QTLs and 112 QTLs for 17 pork cuts by GWAS using haplotype and imputation genotypes, respectively. Specifically, we found that We performed the first study to dissect the genetic mechanism of pork cuts and identified a large number of significant QTLs and potential candidate genes. These findings carry significant implications for the breeding of pork cuts through marker-assisted and genomic selection. Additionally, we have constructed the first reference populations for genomic selection of pork cuts in pigs.The online version contains supplementary material available at 10.1186/s40104-023-00914-4. Pig carcass cutting is a process of decomposing the post-mortem carcass into various cuts with different sizes and weights according to the tissue structure of different anatomical parts, followed by trimming, cooling, packaging, and preservation. The economic value of pork cuts varies depending on their quantity and quality. Different pork cuts also require diverse cooking and processing methods –3. In reIn this study, 17 pork cuts and 12 carcass morphology traits were measured on 2,012 pigs from four populations genotyped using the CC1 PorcineSNP50K BeadChip (CC1 Chip) . The aimA total of 2,012 pigs were randomly sampled from Muyuan Food Co., Ltd.  for pork cut evaluation, as described by Xei et al. , at an aTwelve carcass morphology traits were measured for all individuals, including carcass straight length (SL), oblique length (OL), thoracic number (THN), lumbar number (LUN), thoracic length (THL), lumbar length (LUL), single lumbar length (SLUL), shoulder backfat depth (SBD), 6th_7th rib backfat depth (RBD), waist backfat depth (WBD), hip backfat depth (HBD), and the mean of backfat depth (MBD). Additionally, the carcass was cut into 17 pork cuts as shown in Fig. P < 10−5) were filtered out using PLINK (v1.90b6.24) [Genomic DNA was extracted from the muscle tissue of each animal using the routine phenol/chloroform extraction method. Individuals were genotyped using the CC1 PorcineSNP50 BeadChip 51,368 SNPs) , 8 accor SNPs , 890b6.24) . After qn ≥ 3) with an average sequencing depth of 17.1 X. The detailed imputation process was described in our previous study [P < 10E −5) by PLINK (v1.90b6.24) [R2) between original genotypes from the target panel and imputed genotypes were calculated as imputation accuracies. The accuracies (Mean R2/concordance rate) of the imputed genotypes for the experimental population were 0.89/99.16%, which implied a high quality of the imputed genotypes.Genotype imputation for the experimental population was performed using IMPUTE5  from a h90b6.24) . The hap90b6.24)  and PHAS90b6.24) . Then, g90b6.24) . The impn is the number of phenotypic individuals. The vector M is the allele frequency matrix of centered genotypes with dimensions equal to the number of individuals and the number of SNPs and pi is the frequency of the reference allele at the i-th SNP. Using Bonferroni corrections of 0.05 divided by the number of SNPs to correct multiple comparisons would result in an overly stringent threshold in our study, as many SNPs are highly correlated. Pe'er et al. [−8 could be used in human GWAS based on independent haplotype blocks in an African population structure. We used the same genome-wide threshold in our study based on the assumption that an equal number of independent haplotype segments exist between pigs and humans. The chromosome-wide significance threshold of 1 × 10−6 was used as the suggestive significance threshold [Single locus association analysis was conducted using the GEMMA software (version 0.98.1)  with a ls method  as: \docr et al.  and Johnr et al.  proposedhreshold , 25.X is the incidence matrices of the ancestral haplotypes rather than SNP genotypes. The haplotype effects were fitted as random effects. G is the kinship matrix calculated from the SNP genotypes using VanRaden's method.Haplotypes of the SNP genotypes were constructed by PHASEBOOK . It assu1 is a vector of ones, a follows a normal distribution of N, which was calculated using VanRaden's method [Two genomic selection models were implemented to evaluated the genomic accuracy of pork cuts. (1) Genomic best linear unbiased prediction (GBLUP) , which is method , and thes method . Then \dns (MME) . In thisns (MME) , and the,n is the number of phenotypic individuals. i-th SNP genotype of the individual .(2) Bayesian sparse linear mixed model (BSLMM), which assumes that the effects of markers follow a mixture of two normal distributions . It assu 0.98.1)  uses theThe accuracy of genomic predictions was evaluated using the fivefold cross-validation method and leave-one-out method. In the fivefold cross-validation method, the population  were divided into five equal groups. For each test, one group of individuals served as the validation dataset, while the other four groups constituted the reference dataset. The test was repeated until all individuals had predicted GEBV, and then the prediction accuracy was calculated. In leave-one-out method, the main step is to take one individual out as the verification group each time, and the remaining individuals as the reference group. The test was repeated to circularly predict the GEBV of all individuals, and calculate the prediction accuracy.p and Aw denote, respectively, the prediction accuracy of GEBV for the proportion and weight of pork cuts. To further investigate the genomic prediction accuracy impacted by pre-selection of SNPs, we perform GWAS analysis on the reference dataset and selected SNPs which significantly associated with the phenotype to predict the GEBV of individuals in the validation dataset. In the GWAS based on genotype imputation data, we selected the SNPs with P-values < 0.01 to predict GEBV. Considering that the SNPs of microarray genotyping are much less than the imputation data, we selected SNPs with P-value < 0.05 to predict GEBV in SNP Chip data. The prediction accuracy was calculated using the formula proposed by Hayes et al. , the forentclass1pt{minimaP = 4.03 × 10−9) associated with the proportion of FLB located at 31,161,760 bp of Sus scrofa chromosome (SSC) 7. This QTL region contains genes  associated with body height and limb bone length [P = 1.05 × 10−16) associated with the RI proportion located at 97,732,109 bp of SSC7. This QTL region contains the VRTN gene, which has been identified and functionally validated as a causative gene affecting the number of thoracic vertebrae and ribs [P = 6.25 × 10−11) associated with the HLB weight located 188,108 bp downstream of BMP2 gene on SSC17. In carcass morphology traits, we identified 14 QTLs significantly associated with carcass length , length and number of vertebrae . The two major candidate genes identified in carcass morphology traits affecting carcass length and vertebral length were VRTN and BMP2.In haplotype-based association studies, we identified a total of 14 QTLs significantly associated with pork cuts and 14 QTLs significantly associated with carcass morphology traits (Table e length –35. In mand ribs . In leg P = 1.57 × 10−7) located at 161,408,832 bp and a QTL on SSC5 associated with LC weight, with the most significant SNP  located at the position of 81,315,221 bp, 460,749 bp away from IGF1 gene.Additionally, we detected new QTLs significantly associated with pork cuts, such as a QTL on SSC1 significantly associated with the weight and proportion of BF, with the most significant SNP , located near rs17_15644200, showed a significant association with CB proportion, with a P-value of 2.18 × 10−7. Both SNPs are situated upstream of the BMP2 gene. Similarly, the SNPs at positions 97, 130, 183 bp and 97,576,486 bp on SSC7 are top SNPs affecting CB weight and proportion, with P-values of 2.88 × 10−7 and 3.92 × 10−9, respectively. These SNPs are located upstream of the VRTN gene at positions 484,524 bp and 38,221 bp. Additionally, two QTLs  containing causative gene of VRTN affecting vertebra number also significantly affected the weight and proportion of RI. In leg cuts, a total of 14 and 13 QTLs were identified affecting the weight and proportion of the pork cuts respectively. Among them, the greatest number of QTLs that affect the weight and proportion of TL were identified. The most significant SNP affecting the weight of TL was located at 68,490,542 bp on SSC10, with a P-value of 8.07 × 10−8, located in the intronic region of the WDR37. Furthermore, the QTLs significantly associated with HLB weight and proportion were located on SSC17 at 14,621,182–19,590,143 bp and 149,33,905–17,042,539 bp, covering BMP2.In shoulder cuts, a total of 25 QTLs were identified for the weight of pork cuts and 26 QTLs for the proportion of pork cuts Table . NotablyVRTN on SSC7 and BMP2 on SSC17 were found to be the major QTLs affecting carcass length, vertebrae length, and number of vertebrae , followed by BPS , and the lowest prediction accuracy was TPB . In carcass morphology traits, the highest prediction accuracy was THN (A = 0.882), followed by LHN (A = 0.749), and the lowest prediction accuracy was LUN (A = 0.373)  and THL (A = 0.579), respectively (Table P = 9.54 × 10−8)   with the phenotype, and these loci may be unfavorable to the prediction of GEBV. Previous studies have found that the accuracy of GEBV prediction can be improved by excluding markers that have no effect on traits or have inconsistent effects among different populations [P-value of 0.027, in the combined populations. However, the prediction accuracy of genotype imputation data was significantly higher than that of chip data in the YK and LY populations  in VRTN was found to be significantly correlated with thoracic vertebra number in our previous studies, and was confirmed by a series of biochemical experiments [VRTN, which was significantly associated with the weight and proportion of RI and CB, SL, OL, THL and LUL. Li et al. [BMP2 was the strongest candidate affecting carcass length. However, this study found that the QTL upstream of the BMP2 was associated with weight and proportion of FLB, HLB and RI, SL, OL, THL, LUL and SLUL. And, Zhang et al. [HMGA1 and PPARD as candidate for limb bone length in pigs in the Large White × Minzhu intercross population. Furthermore, other studies have reported that HMGA1 is a strong candidate gene affecting pig body size [HMGA1 gene was significantly associated with the proportion of FLB. Overall, VRTN, BMP2, and HMGA1 are prominent candidate genes influencing pig body size and play crucial roles in bone development.We identified three candidate genes associated with skeletal development, namely eriments . In thisi et al.  found thg et al.  identifiody size , 41, 42.Based on previous research, we know that several factors can influence the accuracy of predicting genomic estimated breeding values (GEBVs). These include the selection and size of the reference population , 44, marAs we all know, the most important thing in animal breeding is to select elite individuals and those are identified as the candidates with high EBVs. One of the widely used molecular breeding methods is marker-assisted selection, which involves identifying QTLs associated with traits of interest and then using models incorporating these QTLs to predict EBV in individuals . In thisHMGA1 as a candidate gene that affects the size of the fore leg bones, VRTN as a causal gene that affects the number of vertebral and rib bones and BMP2 as candidate gene that affects the size of both hind leg bones and fore leg bones, as well as the length of a single vertebral bone. The QTLs and candidate genes we identified have important implications for marker-assisted selection and genome selection. Moreover, we conducted genomic selection of pork cuts and carcass morphology traits in different populations. We found that the prediction accuracy of GEBVs for pork cuts ranged from 0.342 to 0.693, and that the predictive accuracy of several traits, including ribs, boneless picnic shoulder, tenderloin, hind leg bones, and scapula bones, exceeded 0.6. We also found that genomic selection strategy of using BSLMM model, with higher density of effective markers and pre-selecting markers can improve the accuracy of GEBVs. Furthermore, we constructed the first reference populations for genome selection of pork cuts in pigs. These reference populations contain the genetic information of main commercial breeds of Landrace, Yorkshire, and Duroc, which can be directly used for genome selection for most of the commercial pig companies. Overall, our study provides valuable insights into the genetics of pork cuts in pigs and lays a foundation for improving the efficiency of pig breeding programs.In this study, we identified 14 QTLs and 112 QTLs associated with 17 pork cuts, as well as candidate genes, using HGWAS and IGWAS for the first time. Our results suggest the independent regulation of skeletal development by several genes across different body parts. Specifically, we identified Additional file 1: Table S1. Summary information for four populations.Additional file 2: Table S2. Estimates of heritabilities with their standard error (SE) for pork cuts and carcass morphology traits in the combined populations.Additional file 3: Table S3. Significant loci associated with pork cuts and carcass morphology traits by GWAS after genotype imputation.


Flavobacterium columnare is the pathogen agent of columnaris disease, a major emerging disease that affects rainbow trout aquaculture. Selective breeding using genomic selection has potential to achieve cumulative improvement of the host resistance. However, genomic selection is expensive partly because of the cost of genotyping large numbers of animals using high-density single nucleotide polymorphism (SNP) arrays. The objective of this study was to assess the efficiency of genomic selection for resistance to F. columnare using in silico low-density (LD) panels combined with imputation. After a natural outbreak of columnaris disease, 2874 challenged fish and 469 fish from the parental generation (n = 81 parents) were genotyped with 27,907 SNPs. The efficiency of genomic prediction using LD panels was assessed for 10 panels of different densities, which were created in silico using two sampling methods, random and equally spaced. All LD panels were also imputed to the full 28K HD panel using the parental generation as the reference population, and genomic predictions were re-evaluated. The potential of prioritizing SNPs that are associated with resistance to F. columnare was also tested for the six lower-density panels.The accuracies of both imputation and genomic predictions were similar with random and equally-spaced sampling of SNPs. Using LD panels of at least 3000 SNPs or lower-density panels (as low as 300 SNPs) combined with imputation resulted in accuracies that were comparable to those of the 28K HD panel and were 11% higher than the pedigree-based predictions.Compared to using the commercial HD panel, LD panels combined with imputation may provide a more affordable approach to genomic prediction of breeding values, which supports a more widespread adoption of genomic selection in aquaculture breeding programmes.The online version contains supplementary material available at 10.1186/s12711-023-00832-z. Aquaculture production has increased substantially over the past decades and is now supplying more aquatic products than fisheries . CompareThe late implementation of genomic selection in aquaculture breeding programmes compared to terrestrial livestock species is partly due to the lack of high-throughput genotyping platforms for most species and due to the significant cost of genotyping the large number of individuals required for efficient genomic selection. Therefore, to date, genomic selection has only been implemented for a handful of aquaculture species that have the largest production value, and typically by the largest companies. Several strategies have been investigated to reduce genotyping costs and make genomic selection more affordable for small- and medium-scale breeding programmes, such as genotyping only a proportion of the individuals , 13, pooTo date, many studies have investigated the potential of using LD SNP panels for genomic selection in various aquaculture species and they concluded that LD panels containing between 1000 to 2000 SNPs , 18 and Flavobacterium columnare has become a major concern for rainbow trout farming in Finland. Flavobacterium columnare is a bacterium distributed worldwide that affects fresh water fish under warm water conditions, usually when temperatures are above 18–20 °C, but it has also been reported to affect salmonids in cooler water conditions  of fish were computed using the following mixed linear best linear unbiased prediction (BLUP) animal model based on pedigree only (PBLUP) or genomic only (GBLUP) information using the BLUPF90 software :\documenwhere 2 = 0.21 as estimated in Fraslin et al. [The performance of genomic prediction was assessed by estimating the accuracy of genomic prediction and the bias . The accn et al. ). The bin et al. .Accuracies of the PBLUP and HD-GBLUP were previously reported in , i.e. thDecreasing the number of SNPs decreased the accuracy of genomic prediction Fig. , and no Variation among the 10 replicates was greater for lower densities with a bigger standard deviation  was significantly higher than the accuracy obtained with panels of the same density when the SNPs were selected randomly or equally-spaced, except for EquaLD vs TopLD at 700 SNPs for which the difference was non-significant . For higher densities (3K and 5K), prioritising the SNPs based on the GWAS significantly decreased the accuracy of genomic prediction compared to RandLD or EquaLD panels of the same densities .The accuracy and bias of genomic prediction obtained with the TopLD panels are in Table Values are the mean accuracy and mean bias obtained as average of the 100 replicates (5 groups * 20 replicates).For all the TopLD panels, the GEBV obtained were highly biased with on average a bias of 0.575, which represents an over-dispersion of the breeding values. In contrast, the RandLD and EquaLD panels showed very little bias .After imputation, for all starting SNP densities, the accuracy of genomic prediction for both SNP selection methods ranged from 0.63 to 0.65 with a plateau at 0.65 from the 3000-SNP density and above.At the lowest densities  for a breeding population of 8000 offspring and 200 parents, which reflects the Finnish breeding programmes of rainbow trout. The prices used are estimated for both LD panels since these are not currently available on the market. For the high-density SNP array (57K SNPs), genotyping costs approximately 20€ per sample when genotyping 8200 fish resulting in a total cost of ~ 164K €, which can be highly prohibitive for most breeding programmes. Under the assumption that a 3K SNP panel could be used to genotype all 8200 fish at a cost of 15€ per sample, this would represent a 25% reduction in genotyping cost for a 3% decrease in accuracy compared to the full HD panel. Another possible scenario is that all offspring are genotyped for a very low-density SNP panel (300 SNPs) at a cost of 7.5€ per sample, with parents genotyped for the existing 57K array at a higher cost of 30€ per sample (the price depends largely on the number of samples genotyped); in this scenario genotyping would cost 66K€. This reduces the genotyping cost by 60% compared to the price of the 57K SNP panel for only a 4% decrease in accuracy using the imputation approach. Moreover, F. columnare in this Finnish rainbow trout population .Another possible explanation is the existence of long-range linkage disequilibrium that has been previously characterised in salmonids species. In this rainbow trout population from LUKE, we estimated that the linkage disequilibrium between two SNPs separated by ~ 1 Mb is on average 0.11 (± 0.16) , i.e. loP. salmonis in a population of 1938 rainbow trout was similar to the accuracy obtained with the HD panel of 27K SNPs using a Bayes C approach. Two in silico studies on five fish species  [Finally, the optimal density panel to obtain near maximum accuracy without imputation varies slightly depending on the species, the size of the full-sib families and the architecture of the trait. However, for most aquaculture species and traits, a LD panel of 3000 SNPs was sufficient to reach an accuracy similar to the HD panel  with ref salmon) , 18 comp salmon) . For Eur salmon) –19, 55,  salmon) , 55 wher salmon) . In rainOur results confirm that for rainbow trout, accurate genomic prediction can be achieved with a low marker density ranging from 3000 to 7000. The long-range linkage disequilibrium and low recombination rate that exist in salmonids and other aquaculture species along with the structure of the breeding programmes that rely on large families and close relationships between the training and validation populations are likely the main drivers for the good performance of the LD panels .F. columnare in this rainbow trout population, which increased the accuracy of genomic prediction when it was included in a GBLUP approach in which SNPs were weighted by their allele substitution effect. In the current study, we wanted to test the effect of including SNPs that are significantly associated with resistance to F. columnare in the LD panels (TopLD panels). This strategy significantly increased the accuracy of genomic prediction compared to the RandLD and EquaLD panels for densities of 1000 SNPs and lower. This was expected as the TopLD panels included the SNPs that were the most significantly associated with resistance, and thus the highest effect. Similarly, Al-Tobasei et al. [F. psychrophilum [Previously, Fraslin et al.  detectedi et al.  reportedrophilum , 72 usedrophilum , and shorophilum , which hFlavobacterium columnare was oligogenic, with the largest QTL on trout chromosome Omy3 and several minor QTL and a polygenic effect [2 = 0.77 on average). This high linkage is responsible for a relatively strong effect of all the SNPs in this 55-Mb region and, as the density of the TopLD panels increases, more SNPs on chromosome Omy5 with redundant information are sampled, which do not contribute to the accuracy of genomic prediction since no or very little new information is added. The overrepresentation of SNPs from QTL in the TopLD panels led to highly biased prediction (on average 0.55). Furthermore, creating those LD panels based on GWAS results would not be applicable in practice. Indeed, not only does this analysis require that the GWAS be performed on the training population, but also it requires the development of a new LD panel for each population (and trait) since the QTL might not be shared between populations (and traits). For resistance to CD, it has been reported that some QTL are shared between two close Finnish populations [In the current study, for densities of 3000 and 5000 SNPs, RandLD or EquaLD performed significantly better than TopLD to predict the value of the fish in the validation set. In our population, the genetic architecture of resistance to c effect . As the c effect  on two rulations  but not ulations , 74. TheThe accuracy of imputation increased rapidly as the number of SNPs in the LD panel increased and from a 3000-SNP density upwards, the imputation accuracy ranged from 0.84 to 0.86 (± 0.001 − 0.003) for RandLD and EquaLD, and remained below 0.90 even when 20,000 SNPs were included in the LD panel (i.e. only about 8000 missing SNPs to impute). Those values are within the range of those previously reported for Atlantic salmon by Tsai et al. , and lowF. psychrophilum. They imputed a LD panel of 7K SNPs to a high-density of 32K SNPs and reported a lower accuracy of genomic prediction after imputation. However, since the actual genotyping was performed with 7K SNPs, the accuracy of imputation could not be estimated and this decrease could not be linked to imputation errors.Interestingly, in our study, the accuracy of genomic prediction post-imputation for both RandLD and EquaLD panels was quite stable, regardless of the starting density before imputation. The accuracy of genomic prediction did not seem to be affected by lower imputation accuracies, as observed for the lowest densities (300–700 SNPs). In a simulation study on rainbow trout, Dufflocq et al.  in theirIn order to better understand what could cause this decrease in accuracy post-imputation for LD panels with densities higher than 5K, we first imputed the HD panel to get a SNP call rate of 100% (as done in previous studies) , 59 and In aquaculture, selection for improved resistance to a pathogen is usually performed through a controlled infectious challenge , 10, 76.F. columnaris detected in our previous study [F. psychrophilum [Bishop and Woolliams  introducus study  and withrophilum , which arophilum  bacteriarophilum , 80, 81,F. psychrophilum in rainbow trout in an injection challenge, an immersion challenge and a natural outbreak in a farm. The genetic correlation between resistance to an experimental challenge and resistance under farm conditions has been evaluated in a small number of studies. High correlations have been reported in Atlantic salmon for resistance to A. salmonicida [L. salmonis [F. psychrophilum using an injection challenge increased the resistance after a natural outbreak. However, more recently, two studies on resistance to the amoebic gill disease (AGD) in Atlantic salmon [In our previous report on the same dataset , we discmonicida , L. salmsalmonis  and for salmonis , 85 a disalmonis . In rainsalmonis  showed tc salmon , 89 estiSparus aurata and Dicentrarchus labrax  compared to the use of a high-density panel for a considerable reduction in cost (about 25%). However, the price of 15€ per sample for genotyping using a 3K SNP panel is hypothetical as such panels do not exist for rainbow trout, and in reality they could be more expensive than estimated, thus reducing the interest of low-density genotyping. One solution would be to incorporate those 3K SNPs on a multispecies SNP panel that would be produced in larger numbers and thus be less expensive. Such multi-species panels have been developed for various aquaculture species . An intotamicus . The carotamicus , or all otamicus . In the In the current study, we analysed the relevance of using low-density panels for genomic selection as a way to reduce the genotyping cost with marginal loss in accuracy, which would allow low- and medium scale aquaculture breeding programmes to implement genomic selection. The use of low-density panels is also interesting for larger companies because for the same genotyping budget, it would allow to genotype more individuals. These additional genotyped individuals could be used to increase the training population size, which in turn could increase the accuracy of prediction , 67, 68,In conclusion, the use of low-density SNP panels may reduce the costs of genomic selection in rainbow trout without a major reduction in the prediction accuracy of breeding values. Using low-density SNP panels (about 3000 SNPs) or very low-density SNP panels (about 300 SNPs) combined with imputation using HD-genotyped parents would result in a decrease of prediction accuracy of only 3–4% compared to a HD-genotyped population, which corresponds to an increase of 10.5–11% compared to a pedigree-based prediction. The good performance of such low-density panels might be potentially valid for most aquaculture species with long-range linkage disequilibrium, low recombination rates and breeding programmes that rely on sib-testing with large family size. Our findings suggest that a cost-effective genomic evaluation to improve the accuracy of selective breeding in rainbow trout is feasible and low-density genotyping combined with imputation could be a way to speed-up the implementation of genomic selection in low- or medium-scale breeding programmes.Additional file 1: Table S1. Number of SNPs in the low-density panels. RandLD = random sampling of SNPs in the low-density panels. EquiLD = equidistant sampling of SNPs in the low-density panels. Table S2. Values of accuracy and bias of genomic prediction obtained for the random, equidistant and top LD-Panels before and after imputation. RandLD = random sampling of SNPs in the low-density panels. EquiLD = equidistant sampling of SNPs in the low-density panels. TOP-LD = top SNP low-density panels. Mean of accuracy ± sd across all 100 values. Table S3. Proportion of increase or decrease of the accuracy of genomic prediction obtained for the random, equidistant and top LD-panels before and after imputation compared to pedigree and HD-panels. RandLD = random sampling of SNPs in the low-density panels. EquiLD = equidistant sampling of SNPs in the low-density panels. TOP-LD = top SNP low-density panels. Mean of accuracy ± sd across all 100 values. PBLUP = pedigree-based BLUP. HD-GBLUP = genomic based BLUP obtained with the high-density (HD) panelAdditional file 2: Figure S1. Accuracy of genomic prediction for resistance to F. columnare in rainbow trout, obtained with SNP panels of different densities, before and after imputation and before or after re-setting genotype missing in the HD-panel as missing after imputation. The red dotted line is the average accuracy for the HD-GBLUP (28K) prediction (0.68), the blue dotted line is the average accuracy for the pedigree-based BLUP prediction (0.59). The LD panels were created with random SNP sampling (RandLD). The blue line is the accuracy value obtained with the LD-panels (RandLD) and the dark blue line is the accuracy value obtained after imputation for those panels. The orange line is the accuracy obtained after imputation of those panels and after re-setting all the missing genotype from the HD-panel as missing in the imputed-LD-panels.


The contemporary effective population sizes were 59, 98, 102, and 113 for Turkmen, Caspian, Persian Arabian, and Kurdish breeds, respectively. By analysis of the population genetic structure, we classified the north breeds (Caspian and Turkmen) and west/southwest breeds (Persian Arabian and Kurdish) into two phylogeographic clades reflecting their geographic origin. Using the de-correlated composite of multiple selection signal statistics based on pairwise comparisons, we detected a different number of significant SNPs under putative selection from 13 to 28 for the six pairwise comparisons (FDR < 0.05). The identified SNPs under putative selection coincided with genes previously associated with known QTLs for morphological, adaptation, and fitness traits. Our results showed HMGA2 and LLPH as strong candidate genes for height variation between Caspian horses with a small size and the other studied breeds with a medium size. Using the results of studies on human height retrieved from the GWAS catalog, we suggested 38 new putative candidate genes under selection. These results provide a genomewide map of selection signatures in the studied breeds, which represent valuable information for formulating genetic conservation and improved breeding strategies for the breeds.Indigenous Iranian horse breeds were evolutionarily affected by natural and artificial selection in distinct phylogeographic clades, which shaped their genomes in several unique ways. The aims of this study were to evaluate the genetic diversity and genomewide selection signatures in four indigenous Iranian horse breeds. We evaluated 169 horses from Caspian ( Different agro-ecological conditions across Iran have resulted in the development of distinct indigenous horse breeds. Iran has five officially registered horse breeds, including the Persian Arabian, Caspian, Turkmen , Dareshuri, and Kurdish, with long-standing husbandry, cultural, and historical importance Fotovati . UnfortuThe Arabian horse is distributed worldwide, with a population size of >1 million . However, this breed is divided into relatively small breeding populations in many countries in the Middle East, including Egypt, Saudi Arabia, Syria, and Iran, each with very different phenotypic attributes Forbis . Based oFST . Horses in the ASRI data set were selected based on pedigree information, without crossbreeding in their pedigree, and those passed microsatellite parentage testing. These samples were collected from four Iranian provinces . One Persian Arabian (collected from Tehran) and two Turkmen (collected from Isfahan and Tehran) samples were present in the ASRI data set used in this study. A total of 86 samples from three breeds, including Turkmen (n = 34), Caspian (n = 22), and Kurdish (n = 30), were collected by the National Animal Breeding Center (ABC) from six provinces . This data set was collected between 2014 and 2015. For each breed, horses were sampled based on their pedigree. To extend and include Persian Arabian horses in our samples, we merged our data set with the data from two studies: 96 samples from Sadeghi et al. , with the main focus on establishing genomic information for the Kurdish breed  based on the EquCab3.0 assembly . Then, samples were genotyped at Neogen GeneSeek Inc.  using the GGP Equine 70K SNP BeadChip array . Samples from the two other data sets were genotyped with the 670K Equine SNP chip as described previously  less than 0.25. The --genome option in PLINK was implemented to calculate the IBD values. The number of removed SNPs and samples after each step of QC are shown in Fig. Ne) can have a major effect on the error rate for imputation in Beagle, wherein the Markov chain uses the genetic data set to initialize a haplotype cluster and then initializes the Hidden Markov Model by identifying the most likely path through the haplotype cluster. Based on the default setting, this works well for imputation in outbred human populations, but the algorithm can be adjusted to the specific genetic structure of the respective data set by changing the Ne parameter. Pedigrees in Iranian horse breeds have much less genetic diversity than in human populations, which results in a smaller Ne. To deal with this, we predicted the Ne for each breed using NeEstimator V2.1 .The genotype data from different data sets were merged, keeping the loci that overlapped between the chips using PLINK V1.9  ≤ 0.5. For admixture and relatedness analysis, SNPs were pruned using the same sliding window and SNPs shifted per step, while the r2 threshold was 0.3. After LD-based SNP pruning, 28,387 and 22,523 SNPs remained for PCA, and admixture and relatedness analysis, respectively.As LD-based pruning reduces the effect of ascertainment biases on diversity estimates  estimates to display the magnitude of differentiation  of the R statistical software  = 500 kbps. The detected ROHs were divided into five categories, including 0 to < 4 Mb, 4 to < 8 Mb, 8 to < 16 Mb, 16 to < 32 Mb, and > 32 Mb. For each of the ROH length categories, the number of runs in each breed was calculated by averaging the sum of all ROHs per animal within the breeds. To identify the position of a ROH peak, we used a threshold of 0.7, which indicates the ROH shared in more than 70% of the individuals within each population. The genomic inbreeding coefficient using ROH information (FROH) was calculated as follows:LROHj is the length of the jth  ROH detected in an individual, and Lgenome is the total length of the genome that was used.ROH, the indicator of genomic autozygosity due to a long stretch of homozygous SNPs that may have been inherited from a recent common ancestor, were studied to increase our understanding of the genetic diversity status of the four native horse populations. ROH segments along the genome were calculated based on the sliding window method using the R package detectRUNS  Turkmen vs. Caspian, (ii) Turkmen vs. Kurdish, (iii) Turkmen vs. Persian Arabian, (iv) Caspian vs. Kurdish, (v) Caspian vs. Persian Arabian, and (vi) Persian Arabian vs. Kurdish, to determine the genetic regions under selection using T Wright , FLK ) was computed for all SNPs using the scale function in R software.The FST on comparing the observed and expected variances of FST across loci. FLK accounts for complex demographic structures and differences in the effective population size using a phylogenetic estimation of the population’s kinship (F) matrix to consider historical branching and heterogeneity of the genetic drift  for each locus as described in Yurchenko et al. , where the dcms is a vector of the raw DCMS values) of the MASS package  or including ± 40 kbps at both sides to control the regulatory regions. We selected 40 kbps extension based on the LD decay result from all breeds, where the average r2 was >0.2 (see the section “Population genetic structure and linkage disequilibrium”). The gene ontology (GO) pathway enrichment analysis was performed by the enricher function from the clusterProfiler R package , which includes 2605 QTL from 104 publications on 64 different traits. To detect the putative QTL under selection, we considered the QTLs that were located in genomic regions, including consecutive SNPs with a q-value lower than 0.05 or extended up to ± 40 kbps at both sides. We extracted the associated recorded genes for height in humans accessible on the genomewide association study (GWAS) catalog database (https://www.ebi.ac.uk/gwas)  information from the horse genome assembly build Equcab3.0 were downloaded from Biomart in November 2021 Kasprzyk . To idenA total of 275 genotyped horse samples using two types of SNP assays from four data sets were included in this study  and the second highest Ne in the older generations (≥54 generations ago)  Fig. . The KurNe was estimated with various thresholds used to screen out rare alleles  and southwest and/or west of Iran (Persian Arabian and Kurdish) Fig. . PC1 alsThe relationships indicated in the relatedness analysis using identity-by-descent measures Fig.  were supAdmixture analyses were performed by assuming up to 7 clusters (K) to identify the most likely number of ancestral populations as determined by the cross-validation (CV) algorithm. The results are shown in Fig. r2 in all breeds dropped rapidly up to approximately 100 kbps  was estimated for each population separately  included LLP homolog (LLPH) (chr6: 82.8 mega base pairs (Mbps)), high mobility group AT-hook 2 (HMGA2) (chr6: 82.5 Mbps), and Musashi RNA Binding Protein 2 (MSI2) (chr11: 32.2 Mbps), previously shown to be associated with human height based on the GWAS catalog database , alternate gaits (n = 1), chronic progressive lymphedema (n = 2), and osteochondrosis QTL (n = 1).We identified 13 significant SNPs located near to 12 genes  on 7 different chromosomes (chr)   with a DCMS value of 14.86 , wither height (n = 4), hair density (n = 1), alternate gaits (n = 1), and navicular bone morphology (n = 1). Of those, two suggested QTLs under selection for wither height located on chr11 (with QTL IDs 166105 and 165767) did not show selection pressure in the two other Caspian pairwise comparisons. No GO terms were significantly overrepresented in this comparison.A total of 27 significant SNPs were identified in this comparison , wither height (n = 2), alternate gaits (n = 2), equine sarcoids (n = 1), and immunoglobulin G level (n = 1).We identified 24 significant SNPs in this comparison, with the most significant SNP on chr16 (71.8 Mbps) with a DCMS value of 14.59. Gene annotation analysis of the significant SNPs detected 28  candidate genes. Of them, 9 candidate genes were found in the GWAS catalog list. The results showed 19 significantly overrepresented GO terms for the Caspian vs. Persian Arabian comparison. The most significant GO term was GO:0016790 (thiolester hydrolase activity). Among the significant SNPs, 4 loci were located close to 7 QTLs, which were classified into 5 QTL types, including immunoglobulin E level , with the most significant SNPs on chr10 (32.7 Mbps). Gene annotation analysis of these significant SNPs resulted in 28 candidate genes . Of those, 11 candidate genes were located in the GWAS catalog database for human height. Gene enrichment analysis of the candidate genes detected 2 significantly overrepresented terms that were classified as belonging to the cellular component domain. The comparison detected only 1 significant SNP close to 2 QTLs that was classified as type 1, navicular bone morphology.n = 1), osteochondritis dissecans (n = 1), and osteochondrosis (n = 1).We identified a total of 13 significant SNPs on chr2, chr5, chr14, chr16, chr19, and chr27. The most significant SNP based on the DCMS method was located on chr27 (37.0 Mbps). Gene annotation analysis of these significant SNPs found 15 candidate genes for the pairwise comparison. Of them, 7 genes were found in the GWAS catalog list for human height. Gene enrichment analysis of the candidate genes detected 29 significantly overrepresented terms in the comparison, while the four top significant terms were related to the molecular function domain. In the comparison, 2 of 13 significant loci were located close to 3 QTLs, which were classified into 3 types, including immunoglobulin E level . A total of 31 candidate genes was detected in the gene annotation analysis in the comparison. Of them, 9 genes were located in the GWAS catalog list. One of these, TGFB2 (chr30: 143 Mbps), has been noted as a candidate for human height in 10 different GWAS studies  (chr9: 81.6 Mbps). The other 7 genes were ABRAXAS2 (chr1: 8.3 Mbps), GABPB1 (chr1: 140.9), NREP (chr14: 59.7 Mbps), STARD4 (chr14: 59.8 Mbps), RORB (chr23: 17.1 Mbps), Cub and Sushi Multiple Domains 1 (CSMD1) (chr27: 37.0 Mbps), and CAMK1D (chr29: 23.1 Mbps), which have also been associated with human height. Gene enrichment analysis of candidate genes from Kurdish vs. Persian Arabian comparison found two significantly overrepresented terms in the molecular function domain . The results showed that 7 of 28 significant loci were located close to at least one QTL. These nearby QTLs had one of three classifications, including Guttural pouch tympany , wither height , and navicular bone morphology .Here, 29 SNPs with q-values less than 0.05 on chr5 (n = 1) and chr27 (n = 2) in both Persian Arabian vs. Kurdish and Persian Arabian vs. Turkmen comparisons.We found 10 significant SNPs that were common in two comparisons, including Caspian vs. Kurdish and Caspian vs. Persian Arabian. Of these 10, 4 SNPs were also significant in the Turkmen vs. Caspian comparison, located on chr6 from 82.4 to 82.8 Mbps , which was common in the pairwise comparisons between Caspian and both Kurdish and Persian Arabian populations. There were 6 and 14 additional candidate genes for the pairwise comparisons between Caspian and both Persian Arabian and Kurdish populations, respectively, which were located in the GWAS catalog list for human height , overrepresented in the comparisons between Turkmen and both Caspian and Kurdish populations, which is an ortholog for a protein-coding gene in humans and is a major component of the interphotoreceptor matrix (IPM). The IPM is suggested to have many important functions related to growth factors, such as regulation of retinoid transport, participation in cytoskeletal organization in the surrounding cells, and regulation of oxygen and nutrient transport . U6 was associated with human height in 16 different GWASs  (chr8: 25.9 Mbps), DDX55 (chr8: 25.9 Mbps), eukaryotic initiation factor 2B 1 (EIF2B1) (chr8: 25.9 Mbps), and GTF2H3 (chr8: 25.9 Mbps), in the comparisons between Kurdish and both Turkmen and Caspian breeds. We also detected one gene, trafficking protein particle complex 9 (TRAPPC9) (chr9: 81.6 Mbps), that was common in the comparisons between Kurdish and both Persian Arabian and Caspian breeds.There were 5 common genes, including ENSECAG00000006191 (chr8: 25.9 Mbps), CSMD1 (chr27: 35.8 Mbps), listed in the GWAS catalog database, in the comparisons between Persian Arabian and both Turkmen and Kurdish breeds. CSMD1 is a large transmembrane complement inhibitor with expression in many tissues such as testis, brain, lung, colon, thyroid gland, breast, and pancreas , kyphoscoliosis peptidase (Ky) (chr16: 71.8 Mbps), EPHB1 (chr16: 71.8 Mbps), and U6, which were listed in the GWAS catalog for human height.The common SNPs resulted in identification of 5 nearby genes in the comparisons between Persian Arabian and both Turkmen and Caspian breeds. There was also 1 common candidate gene, Some of the previously identified QTLs were found in the suggestive regions of selection in each of the comparisons  was present in these two comparisons . This information can be applied in mating systems with the aim of minimizing inbreeding depression, especially in the Persian Arabian horses, which showed the highest estimated genomic inbreeding value compared with the other Iranian breeds.VanRaden et al.  showed tADGRV1 (chr14: 76.8) was under selective pressure in the Arabian and Kurdish comparison. This gene is suggested to have highly differentiated nonsynonymous alleles, which leads to an excess of the adrenocorticotropic hormone in Yakutian horses, which are known to be highly adapted to subarctic environments  using the GGP equine SNP70 BeadChip detected HMGA2 as a major QTL for this trait (Frischknecht et al. HMGA2. This gene has a well-known role in height determination in other species, including humans (Buysse et al. HMGA2 and the human height trait (Supplementary file, Table LLPH, previously associated with height in human studies based on the GWAS catalog database. A GWAS in Duroc pigs showed a significant association between the average daily gain and a haplotype block containing LLPH (Quan et al. HMGA2 and LLPH as strong candidates for the height variation between Caspian and the other Iranian breeds. Beyond these genes, we suggest 38 new putative candidate genes under selective pressure that are related to variation in height traits in humans.The Caspian horse is thought to be an ancient breed native to the north of Iran. This horse is considered as a small horse with height ranging from 9 to 10 hands (Shahsavarani and Rahimi-Mianji Each Iranian horse breed has its own historical background and differs in usage. Finding selection signatures close to QTLs indicates a relationship between the selection for traits and the effects of variation at a locus. Accordingly, we investigated the candidate QTLs and biological pathways within the candidate regions under selection to identify pathways that might have shaped the Iranian horse genome. Some of the SNPs identified under selection were located close to previously reported QTLs related to morphological and behavioral traits in horses, such as wither height, hair density, navicular bone morphology, and alternate gaits. Some of them were located near to QTLs related to adaptation traits such as immunoglobulin level (Smith et al. HMGA2 and LLPH as strong candidates. In addition, the identified candidate sweep regions provide a genomewide map of selection signatures in the studied breeds, including genes and QTLs for known morphological, adaptation, and fitness traits. This information may be valuable in formulating genetic conservation and improved breeding strategies for these populations.In general, exploring the genetic variation between Iranian horse populations evolved under diverse ecological conditions with prominent phenotypic variation suggested a clear genetic division among the breeds, which largely reflects their geographic origins. The use of information obtained from extensive studies in humans, retrieved from the GWAS catalog, can help to better interpret the results of selection signature studies. This information helped us to introduce 38 new candidate genes for height variation in Iranian horse populations in addition to the well-known regions that contain Supplementary file


The compact size of iiSNP amplicons compared to short tandem repeat (STR) markers increases the likelihood of successful amplification with degraded DNA samples. Allele frequencies and relevant forensic statistics were calculated for each population group as well as the aggregate population sample. Examination of sequence data in the regions flanking the targeted SNPs identified additional variants, which can be combined with the target SNPs to form microhaplotypes (multiple phased SNPs within a short-read sequence). Comparison of iiSNP performance with and without flanking SNP variation identified four amplicons containing microhaplotypes with observed heterozygosity increases of greater than 15% over the targeted SNP alone. For this set of 1036 samples, comparison of average match probabilities from iiSNPs with the 20 CODIS core STR markers yielded an estimate of 1.7 × 10 When highly compromised biological samples are encountered, DNA template molecules may be too short for efficient amplification of any region beyond 100 to 200 bases in length using standard polymerase chain reaction (PCR) amplification. The largest PCR amplicons are most adversely affected by fragmented or inhibited samples, leading to a characteristic downward trend in detection intensity with increasing amplicon size. For the most highly compromised DNA samples, few or no STR markers will amplify in a typical human identification (HID) multiplex. In such cases, markers with smaller PCR amplicons are better suited to HID applications ,4. SinglPrior to the introduction of next-generation sequencing methods at the turn of the millennium, obtaining DNA sequences was laborious. For forensic applications, DNA sequences could be analyzed individually using Sanger sequencing , or smalWe present the sequencing results and allele frequencies for these 94 iiSNPs, and additional variation contained in amplified regions flanking the targeted SNPs, in four sets of samples representative of the US population . This work facilitates statistical calculations in investigative DNA identification efforts where the populations characterized here are relevant.Anonymous liquid blood samples with self-reported ancestries were purchased from Interstate Blood Bank  and Millennium Biotech, Inc.  or provided by DNA Diagnostics Center  as buccal swabs from paternity testing samples anonymous to NIST. A total of 1036 samples were included in this study, the same samples previously reported by our group ,29,30, dSamples were analyzed as previously described  with thePrimary data analysis was performed using Universal Analysis Software (UAS) version 1.3 (Verogen) . SNP genhttps://github.com/Ahhgust/STRaitRazor, accessed on 26 August 2022). Allele calling thresholds used with UAS data (above) were also applied to the results of this in-house analysis.As an alternate approach to the UAS, an in-house method incorporating STRait Razor v3.0 (SR3)  was usedThe finalized SNP call set was used as input for STRAF 2.0  for calc6 Markov chain steps and 1 × 105 dememorization steps on a locus-by-locus basis.Arlequin 3.5  was usede = 1/(p2 + q2), where p and q are the frequencies of the two allelic states for a biallelic SNP. For each locus, Ae was calculated for each population tested using population-specific allele frequencies from the dataset.The number of effective alleles for each locus was calculated using the equation: ARandom-Match Probabilities for individual SNP profiles were calculated using frequency data from the 1000 Genomes Project  for the A total of 97,384 genotype calls can be made for a population of 1036 samples at 94 loci. In the current study, 96,889 genotypes are reported after manual curation of the UAS genotype calls and comparison with an orthogonal analysis method  for an oACR was generally consistent across loci , with a Locus rs338882 exhibited lower average coverage for the reference allele [C] (73.45× ± 41.82×) versus the alternate allele [T] (146.82× ± 81.47×), with an average ACR of 0.51 ± 0.13 , Polymorphism Information Content (PIC), Probability of Matching (PM), Power of Discrimination (PD), Observed Heterozygosity (Hobs), Power of Exclusion (PE), and Typical Paternity Index (TPI)) calculated in STRAF  are presp < 0.05). When performing numerous pairwise comparisons, it is expected that some level of type-1 errors  will be observed due to random chance when using a significance threshold suitable for discovering linkage disequilibrium indicators. This is exemplified by the observation that, of the 913 SNP pairs with t-test p-values < 0.05, only 36 pairs are physically located on the same chromosome (p < 0.0001 only in the Caucasian category); these loci are physically separated by 190 Mb of DNA sequence on Chromosome 3.Testing for Linkage Disequilibrium in Arlequin returned 913 SNP pairs with exact T-test values below Arlequin’s default significance level  with s < 0.05 . Again, e values for all populations. The original literature from the SNPforID Consortium recommending these three loci for identity-informative purposes [e values reflect slight allele frequency variation among populations from different regions of the globe. This highlights the importance of using appropriate population frequency data for weight-of-evidence calculations.The number of effective alleles per locus is shown in purposes  characteComparison of UAS genotype calls with those generated by an in-house analysis method incorporating SR3 yielded 97,222 concordant genotype calls from 97,384 possible genotypes (99.8% concordant). Genotypes that did not satisfy minimum thresholds by both analysis pipelines were considered concordant with the UAS calls . The remaining 162 discordant genotypes were mostly no-calls by the SR3, where the presence of errors at various positions within sequencing reads excluded those reads from the count of reads corresponding to the ‘correct’ genotype call. This mechanism caused the read count to drop below allele calling thresholds in 154 cases. In eight instances, the SR3 analysis recovered genotypes that did not satisfy minimum thresholds in the UAS call set.Close inspection of the genotypes from the two analyses revealed an incorrect UAS genotype call for one sample at SNP locus rs10092491. This sample was typed by the UAS as homozygous T, whereas the sequence analyzed by the in-house analysis resulted in a heterozygous T/C genotype. The UAS interpreted 109 of 196 reads (>50%) as deletions , which dThe region containing SNP rs10092491 with an adjacent deletion represents a microhaplotype  obtained with the in-house analysis method. Further examination of the output of genotype calls from the in-house analysis indicated MHs of one or more flanking SNPs (plus the UAS target SNP) in 74 of the 94 iiSNP amplicons in the ForenSeq DNA Signature Prep Kit . Of thesobs (15% to 26%) as compared to the target SNPs alone, consistent with the published literature [obs from between 5% and 15%, while the remaining 62 loci with microhaplotypes exhibited low (<5%) or no increase in Hobs. Some microhaplotypes were observed at low frequency; 36 loci had singleton observations  representing private variation which had negligible impact on Hobs in this study.Of the 74 loci with microhaplotypes identified, four microhaplotypes associated with target SNP loci rs876724, rs1109037, rs10776839, and rs2830795  exhibiteterature ,21. Anot−38) were approximately four orders of magnitude lower (more discriminating) than the RMP values calculated from the 20 CODIS core sequence-based STRs (mean = 2.36 × 10−34). Further, the 94 iiSNPs and sequence-based STRs outperformed length-based STR RMP calculations (mean = 1.02 × 10−28) by ten and six orders of magnitude, respectively . The assay exhibited mostly uniform coverage for heterozygous genotype calls (ACR). Two loci had frequently imbalanced allele coverage: rs6955448 and rs338882.e) in all populations in the study. Analysis of Ae values  in the short homopolymer adjacent to the target SNP locus rs10092491. This ostensibly caused misalignment by the UAS analysis algorithm, resulting in an incorrect homozygous genotype call. While this low-frequency deletion was observed in only one sample in this population study of 1036 individuals, the presence of a high number of reads interpreted as deletions could serve as an indicator of this or similar artifacts, which may not be automatically flagged by the UAS.Flanking region variation comprising microhaplotypes within the iiSNP amplicons in the ForenSeq DNA Signature Prep Kit suggests the possibility of extracting more information from the kit’s SNP content by analysis of the full sequence string. Microhaplotypes associated with target SNP loci rs876724, rs1109037, rs10776839, and rs2830795, are likely to routinely provide additional information. Some flanking variation seen at low frequency may be of benefit to kinship analysis. The full complement of 94 biallelic iiSNPs produces extremely low random-match probabilities, making complex additional analyses beyond the UAS optional. The iiSNP random-match probabilities are comparable to values calculated for sequence-based alleles of STRs, both of which outperformed length-based STRs by several orders of magnitude in this study.The NIST 1036 sample set was used here to characterize performance of the iiSNP content of the ForenSeq DNA Signature Prep Kit in terms of technical performance, population genetic parameters, and direct comparison with RMP values from STR markers by length and sequence. In the absence of corroborating genotype data for the 94 iiSNP loci, we used a parallel analysis method to improve the accuracy of genotype curation and to provide frequency information for sequence variation in flanking regions of the amplicons. The use of the NIST 1036 sample set in this study adds to the extensive forensic marker information available for these samples and maintains their relevance in facilitating technology transition.