Gossypium hirsutum L.) is an important economic crop for renewable textile fibers. However, the simultaneous improvement of yield and fiber quality in cotton is difficult as the linkage drag. Compared with breaking the linkage drag, identification of the favorable pleiotropic loci on the genome level by genome-wide association study (GWAS) provides a new way to improve the yield and fiber quality simultaneously. In our study restriction-site-associated DNA sequencing (RAD-seq) was used to genotype 316 cotton accessions. Eight major traits in three categories including yield, fiber quality and maturation were investigated in nine environments (3 sites × 3 years). 231 SNPs associated with these eight traits (− log10(P) > 5.27) were identified, located in 27 genomic regions respectively by linkage disequilibrium analysis. Further analysis showed that four genomic regions  held favorable pleiotropic loci and 6 candidate genes were identified. Through genotyping, 14 elite accessions carrying the favorable loci on four pleiotropic regions were identified. These favorable pleiotropic loci and elite genotypes identified in this study will be utilized to improve the yield and fiber quality simultaneously in future cotton breeding.Upland cotton ( Gossypium hirsutum L.) is one of the most important economic crops, contributing 90% of global cotton fiber production, and is considered the main source of renewable textile fibers1. The cotton industry is an important component of the world economy2. Yield and fiber quality, which are closely linked with cotton fiber production, are the most important traits of cotton6. In early cotton breeding, yield and fiber quality were negatively associated due to the introduction of Beasley’s triple hybrid7. Pleiotropic loci or linkage drag were the main reasons for this negative correlation9.Upland cotton  resistance gene resulted in a yield decrease in tobacco because of pleiotropy of the gene and/or linkage drag19. This phenomenon has also been found in other crops. In rice, linkage drag between disease-resistance and yield was detected on chromosome 620. Linkage drag between root development and heading date was found in wheat21. Additionally, in tomato, genes underlying fruit chemistry can affect metabolic quality, as a result of linkage drag. Linkage drag has limited the improvement of crops via conventional breeding. For example, in tomato, linkage drags affect nearly 200 Mb genome regions (25.6% of the assembled genome)22.Linkage drag is often observed in crop breeding23. Among cotton species, Gossypium barbadense has excellent fiber quality, while the yield of G. hirsutum is high26. Linkage drag not only makes it difficult to transfer favorable traits from G. barbadense to G. hirsutum4, but also significantly limits the simultaneous improvement of yield and fiber quality in G. hirsutum9. With the development of crop breeding, the linkage drag of yield and fiber quality was disrupted by the work of Culp et al.27. Although the relationship between fiber quality and yield has changed from linkage drag to favorable linkage, the molecular mechanism underlying this change has never been reported.Linkage drag is equally serious in cotton breeding28. Many regions or genes associated with multiple traits have been identified by GWAS. For example, two pleiotropic regions were identified by GWAS in rice. One region was located on chromosome 11 and was associated with plant height and panicle length; the other region was associated with panicle number, spikelet number and leaf blade width, and was located on chromosome 530. In soybean, the Ln gene for four-seed pods and leaf shape was identified by GWAS31. In cotton, four QTL regions associated with multiple traits affecting yield and fiber quality were identified using the CottonSNP63K array32. It was also reported that 4820 genes were associated with different traits simultaneously in upland cotton by resequencing and GWAS33. Some regions or genes associated with more than one yield trait have been identified, but they nearly all had no effect on fiber quality4. Few loci both benefited to fiber quality and yield have been reported.The identification of favorable pleiotropic loci for fiber yield and quality is very important for cotton breeding. With the development of sequencing technologies, single nucleotide polymorphism (SNP)-based genome-wide association study (GWAS) has become an effective method to identify favorable pleiotropic loci across the whole genome in plantOur work aimed to identify favorable pleiotropic loci associated with fiber quality and yield, and accessions carrying these pleiotropic loci. In our experiment, RAD-seq and GWAS were combined to identify favorable pleiotropic loci and related genes in cotton. Phenotypic data were obtained from three locations over three years (3 × 3), which help us to explain the relationship between fiber quality and yield. Four regions  and 14 elite accessions were picked as carrying favorable pleiotropic loci, could be utilized in future cotton breeding directly.Data for the eight traits under nine environments, including yield components-, fiber quality- and maturity-related traits of upland cotton are list  result of each trait under nine environments were used as input data , SI (0.15), FL (0.18), and FS (0.28), while negative correlations were found between FD and LP (− 0.18) and FE (− 0.34), indicating a later FD contributes to better yield and fiber quality. The relationship of fiber yield, quality and FD may facilitate the GWAS identification of pleiotropic regions and genes.The next correlation analyses of each traits under the nine environments showed that some traits were stable under different environment including FL, FS, BW, SI and LP, while some traits were not, including FE and FD . Trait data from different environments were calculated independently to compare environmental effects and screen common loci with strong signals. In total, 231 key SNPs (− log10(P) > 5.27) were identified, and 54 common SNPs can also be identified by multi-locus GWAS  = 8.56).We performed genome-wide association analysis based on 93,687 high-quality SNPs and eight traits using the single-locus GWAS methods (EMMAX)Based on the LD block analysis, all key SNPs were categorized into 27 regions, including 200 genes  were identified as pleiotropic regions, located on chromosomes A06, A07 and D11  = 4.65  and better fiber quality (FL and FS) than the group hapA07_2_b . This indicated the hapA07_2_a was favorable rare haploid type for better fiber quality and yield. In total, 18 genes were located in the LD block 12 region  and better fiber quality (FL and FS) , compared with the group hapA07_1_b  Fig. k–n. The 1_b Fig. j, indictD11_a and hapD11_b by the phylogenetic tree  than the group hapD11_b  and better fiber quality (FL and FS)  Fig.  and S10.A06_a and hapA06_b. The group hapA06_a had more accessions (218) than the group hapA06_b  and later maturity, compared with hapA06_b  were identified by GWAS5. In particular, Ma et al. identified 7,383 unique SNPs and 4,820 genes associated with fiber-related traits33, and Du et al. identified 98 significant peak associations for 11 agronomically important traits in G. arboretum35.GWAS has been used to identify trait-related regions recently, and many such regions have been reported39. Some are associated with more than one trait, but all the trait belongs to yield trait and no effect on the fiber quality5. Few loci associated with both fiber quality and yield have been reported32. Our work identified four pleiotropic regions associated with fiber quality and yield, and 14 accessions carrying these pleiotropic regions. Among the four LD blocks we identified, LD blocks 6 and 8 were located on chromosome A07. Compared with other reports32, LD block 8 was smaller and was associated with more traits in our experiment. Additionally, LD block 6 has not been reported previously. The region from 24.02 to 24.09 Mb on chromosome D11 was reported to be associated with fiber length only23. In our study, LD block 31 (23.2–24.0 Mb) on chromosome D11 was considered as a pleiotropic region associated with BW and FL. LD block 1 on chromosome A06 was first identified to be associated with LP and FD in our work.Many GWAS have been done, and many loci and related genes have been identified, but nearly all of these loci are associated with one traitIn contrast to other works, we also aimed to identify elite accessions carrying the four LD blocks . Consequently, we identified 14 accessions belonging to group class_1 and 65 accessions belonging to group class_2 with better fiber quality and higher yield.Thus, GWAS on a natural population can identify many trait-related regions and elite accessions and is an efficient way to identify pleiotropic regions.7. Thus, the simultaneous improvement of yield and fiber quality has long been the goal of cotton breeding9. Culp et al. showed that the linkage drag of yield and fiber quality was disturbed27. However, the molecular mechanism underlying this linkage drag has not been reported yet.As previously reported, yield and fiber quality, which are the most important traits of cotton, were negatively correlated with each other for a long time after the introduction of Beasley’s triple hybrid to improve fiber strengthAs a result of the development of cotton breeding, their relationship of fiber quality and yield showed a positive correlation in our study. We analyzed 316 accessions, including many elite germplasm resources. The phenotypic data analysis showed that yield and fiber quality have become positively correlated. Significant positive correlations were found between BW and FL (0.30), BW and FS (0.37), and LP and MIC (0.35). Some pleiotropic regions were identified, providing an explanation for the molecular mechanism underlying the simultaneous improvement of yield and fiber quality.Our genotyping showed that few accessions carried the pleotropic regions LD blocks 6 and 8 , 47 from the United States (US), 162 from the Yellow River (YR), 52 from the Yangtze River (YZR) and 20 from other places (OTH) . FD was calculated from the sowing day to the day that 50% of the plants had appeared the first flower in per plot. Thirty naturally mature bolls from each accession were hand-harvested to calculate BW and to gin the fiber. The SI was obtained by weighing 100 cotton seeds. Fiber samples were separately weighed for calculating LP, MIC, FL, FS, FE. The average of the three replicates in the same environment were calculated as the phenotypic data for each accession for phenotypic analysis33.All trait measurements followed the standards described in the Descriptors and Data standard for cotton (http://www.graphpad.com). Pearson’s correlation coefficient between pairwise phenotypes was obtained with the R software40, using the package “psych”41.All data statistics, including Student’s two-tailed t test and two-way analysis of variance (ANOVA), were calculated by GraphPad Prism 9 . The genomic DNA in each well was digested with 1 μl Fast Digest TaqI (Fermentas) for 10 min at 65 °C in a volume of 30 μl. For ligation reactions, 1 μl of barcoded adapters (10 μM) were added to each well, along with T4 DNA ligase (Fermentas), in a total volume of 40 μl. The ligation reaction was incubated for 1 h at 22 °C and heat-inactivated at 65 °C for 20 min. The ligation products for 24 different samples were pooled into a single tube, and 2 µl chloroform was added to inactivate the restriction enzyme. The mixtures were centrifuged at 12,000×g for 1 min and the supernatant was transferred to a new tube. DNA fragments between 400 and 700 bp were selected on a 2% agarose gel (Amresco) and purified using a QIA quick Gel Extraction Kit (Qiagen). The samples were resuspended in 50 elution buffer and amplified with 10 cycles of PCR. The PCR reaction included 8 μl of library DNA, 25 μl of Phusion Master Mix (Finnzymes), 1 μl common primer (10 μM), 1 μl index primer (10 μM) and 15 μl water. The amplified library was purified using a QIA quick PCR Purification Kit (Qiagen), further quantified on the Agilent 2100 Bioanalyzer and sequenced on an Illumina Hiseq 2000 instrument.In 2012, we planted seeds of all 316 accessions in small pots in a greenhouse for DNA extraction, five full seeds for each accession. After the two cotyledons spread, the seedlings were collected and frozen at − 80 °C immediately5 using the software BWA v0.5.943. Samtools v0.1.1844 was used to generate consensus sequences for each individual and prepare input data for SNP calling with realSFS based on Bayesian estimation. SNPs matching the following criteria were removed: (1) Raw SNPs with sequencing depth > 6500× or < 60 ×  . (2) When the length between two adjacent SNP loci was < 5 bp. (3) SNPs with a call rate < 70% in the whole population. (4) Minor allele frequency (MAF) was > 0.05. (5) The proportion of heterozygous genotypes was > 30%. SNP locations in the genome were categorized into different regions according genomic annotations, including noncoding, CDS, intron, UTR and 1000 bp up/downstream of gene.The paired end reads from each individual were identified by their barcodes and aligned against the reference genome45. Population structure was determined using the software Admixture v1.30 with default settings46. Association analysis was conducted using the EMMA eXpedited (EMMAX) software package34. The EMMAX package was downloaded from http://csg.sph.umich.edu//kang/emmax/download/index.html. A total of 93,687 high-quality SNPs  in 316 G. hirsutum accessions were used to perform GWAS for eight traits under ten environments  to verify the SNPs identified by single-locus GWASP value was used to define the threshold signals of the GWAS, for which we used a significance cutoff of 5.34e−6 35.In this study, the 54 and RNA extraction followed the instructions of RNA Isolation Kit (Tiangen Biotech).Two accessions were selected for RNA-Seq, J02-508 with higher yield and better fiber quality compared with Zhong870203, sampled at root, steam, leaf, − 3, 0, + 3, + 5, + 10, + 15, + 20, and + 25 DPA. Sample collection was performed as described in Zou’s research5, using HISAT2 (ver. 2.2.0). Gene expression levels (FPKM) were calculated using StringTie (ver. 2.13)39.After be qualified, the RNA sample was used to construct RNA sequencing library. The main process was as follows: First, rRNA was eliminated by the Ribo-Zero rRNA Removal Kit (Epicentre); second, rRNA-depleted RNA was broken with the help of Fragmentation Buffer; third, cDNA Synthesis; and fourth, end repair, add 3ʹ A tail, ligate adapters, PCR, sequencing was carried out on Illumina HiSeq X Ten platform. After filtering adapter and low-quality reads, the clean data was got and used to mapped onto the genome of Upland cotton10(P) > 5.27) were calculated on the whole genome level by the Haploview software, with the ‘Solid Spine of LD’ algorithm55. If the LD block contained SNPs associated with multi-trait, then it was defined as the pleiotropic loci. The expression patterns of the genes in the LD blocks were obtained from the data of Zhang56 and our own RNA sequencing. Genes with specific expression patterns in specific tissues or development stages were supposed to affect related traits32.As a confidence interval, linkage disequilibrium (LD) decay was used to identify LD blocks and candidate genes. LD blocks of all the significance SNPs .The genotype of the SNP locating in the LD block were filtered and considered as input data in the TASSEL 5 (ver. 20200709). By constructing phylogenetic tree, 316 accessions were classified into two groups, hapSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens.The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced . A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP – SNP and DUP – SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays.The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.The online version contains supplementary material available at 10.1186/s12864-022-08418-7. A type of genomic variation that affects large regions of the genome is caused by structural variants (SV). SVs can alter the total genome size by deleting , duplicating  or inserting  longer stretches of DNA . Those SVs are often referred to as copy number variations (CNV). In contrast, inversions (INV) and translocations (TRA) do not affect the length of the genome  . EspeciaThe power for detection of SVs of certain types and sizes, however, is highly technology-dependent in various aspects . During Other than for SVs, the use of SNPs has become routine over the last two decades. Therefore, large whole-genome-sequencing (WGS) reference panels , 17 and LD between two variants can be measured using a variety of estimators (reviewed e.g. by Qanbari ), of whivariants . The ovevariants . LD ther. [By now, a bunch of studies has addressed the question of LD between SVs and surrounding SNPs in humans with contrasting results. Generally, common DELs were shown to be in good LD to SNPs by most of the studies , 21–24, .  found veReduced LD between SNPs and SVs can have diverse reasons. A main factor is the increased possibility of the occurrence of recurrent mutations in regions of low sequence complexity by non-allelic homologous recombination (NAHR) . SVs fro. [. [. [For livestock, results on SV – SNP LD are very rare, even though a high number of publications targeted SV. Based on a GWAS on 26,362 Holstein dairy cattle 50 k genotypes, Xu et al.  found a . [.  who inve. [. [.  includedIn chickens, a variety of studies investigated CNVs on a quantitative basis. The studies either used aCGH –39, utilThis is the first study that assessed SV – SNP LD in chickens to investigate the usefulness of SNP markers in capturing SV-based genomic variance. We, therefore, identified SVs from paired-end short-read sequences in three commercial chicken populations , thoroughly described the SV callset, and assessed the strength of LD between those SVs and SNPs. We also identified major reasons for some existing differences to SNP – SNP LD and evaluated the performance of four available SNP arrays to tag SVs.. [For the study, paired-end short-read sequences of 90 chickens from three populations  were used. The raw data was first published by Qanbari et al.  who desc. . SVs wer.  for eachFigure p = 1.00), while DELs  and INVs  showed a significant bias towards larger chromosomes. DUPs  and BNDs  also showed a numerical bias towards larger chromosomes, which, however, was not significant. Note that the R2 value of the model was comparably small with 0.39.We further checked for chromosome-wise differences in the number of called variants by regressing the relative number of called variants per chromosome on the relative chromosome length Fig. S. SNPs diDistributions of minor allele frequencies  . HoweverTheoretically, one SNP in strong LD to the variant of interest would be enough to serve as a marker that (partly) captures the effect of the variant for, e.g., GWAS or genomic selection as tag SNP. We, therefore, investigated the presence of potential tag SNPs close to the variants of interest. The used measure was the maximum observed kb Fig. S. Mean \dons Fig. . Only mence Fig. . Mean \dNPs Fig. .Fig. 5MeWe additionally defined a variant as tagged if entclass1pt{minimaIn practice, the interest of researchers and breeding companies may not be the taggability of SVs by WGS SNPs, but by array SNPs. Those come with a different allele frequency spectrum and lower resolution than WGS SNPs, which influences the LD patterns . HoweverThe availability of variable SNPs close to the variants of interest was strongly dependent on the resolution of the arrays. While the 600 k array had a variable array SNP within 15 kb for more than 90% of the variants in all three populations, the 60 k and the 55 k array came with a slight shift of this dependency of having a variable array SNP for > 80% of the variants at 50 kb and > 90% at 100 kb Fig. S. The 10 75 Fig. S. 40% of Strong LD between genomic markers and causal genomic variants is the fundamental requirement of methods like genomic prediction  and GWASThe median sequencing coverage of the samples (5 – 17 X) was comparably low for SV discovery. Despite the fact that the sequencing depth differed between layers and broilers, results were similar for all three populations. An effect of the sequencing depth on the results is therefore unlikely, as the results could be repeated across sequencing depths.The SV calling approach was intended to return highly accurate variant calls, therefore prioritizing precision over sensitivity. This especially required the exclusion of regions with unusually high coverage, as they may be artefacts of inaccurate read mapping in regions of low sequence complexity . As thosWe further identified a lack of homozygous calls of DUPs, INVs, and BNDs with regard to HWE Figs. , S12. OnThe overall levels of SNP – SNP LD within the populations reflect the knowledge from the literature , 68 and The DEL – SNP LD, all in all, was on the same level as SNP – SNP LD. This implies good predictability of DEL effects by SNP call sets and is in accordance with the majority of the existing studies , 21–24. . [The level of DUP – SNP LD was strongly reduced compared to SNP – SNP LD and DEL – SNP LD, which is in accordance with the existing studies , 28, 32.mentclass2pt{minimA second cause for reduced DUP – SNP LD could be a higher rate of genotyping errors in DUP. In fact, we identified a significant reduction of homozygous DUP calls compared to HWE Figs. , S12 as A further possibility of reduced DUP – SNP LD may be the occurrence of multi-copy CNVs (mCNVs) , 30 in o. [We also evaluated the linkage between SNPs and INV/ BND and found low levels of LD (26.8% and 18.5% of SNP – SNP LD). The reduced LD in our study is again partly due to local allele frequency differences , of whiWe evaluated LD patterns between a comprehensive SV callset and surrounding SNPs in three commercial chicken populations. We found DEL – SNP LD to be on the same level as SNP – SNP LD, while DUP – SNP, INV – SNP, and BND – SNP LD were strongly reduced. This was in accordance with the availability of tag SNPs for a high share of SNPs and DELs, while tag SNPs for DUPs were rare and mostly missing for INVs and BNDs. Different arrays came with a density-dependent ability to tag WGS SNPs and SVs but did not show strong systematic differences compared with taggability by WGS SNPs. The main reason for existing differences in SNP – SNP and DUP/INV/BND – SNP LD in our study was due to local MAF differences. Those accounted for ~ 50% of this difference in the strength of LD. This implies that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.. [The study used WGS data of 25 white layers, 25 brown layers, and 40 broiler chickens. The raw data was first published by Qanbari et al. , which c. .Population integrity was controlled using principal component analysis in plink 1.9 . The SNPAlignment on the reference genome galGal6/ GRGC6a and SNP calling were conducted in a previous study  followinSV calling was conducted following a consensus calling approach. SVs were first separately called per individual and then genotyped on population-level by running Delly 0.8.5 , Manta 1Additionally, read depth profiles for all samples in 100 bp windows were generated using Mosdepth 0.2.9  and SVs Caller overlap: At least two of the three callers needed to support the variant.Genotype concordance: The genotype that was supported by two out of the three callers was considered as the consensus genotype. Genotypes without the necessary support were set to missing for later re-imputation. If more than two samples did not have the necessary genotype concordance support for an SV, the complete SV was removed from the data set.Removal of high coverage regions: Local coverage was extracted by Mosdepth 0.2.9  in 100 bDifference to flanking coverage: DELs and DUPs calls were checked for non-consistent coverage changes relative to the flanking coverage by evaluating the Duphold Flanking Fold Change (DHFFC) . DELs weSupport by SNP calls on DELs: SNP calls need to be homozygous on heterozygous DELs and missing on homozygous DELs. We, therefore, calculated for each DEL genotype the relative number of wrong SNP genotypes . If the sum of those error rates across samples exceeded two or 50% of the number of samples that were at least heterozygous for the DEL, the DEL was filtered. Otherwise, the putatively wrong DEL genotypes were set to missing for later re-imputation.The merged callset was then filtered based on the following parameters:https://github.com/Nestak2/image-sorter2). The SVs needed to be scored as ‘pass’ by each of the two observers to be further used . No INS remained, as Lumpy does not call INS and there was no overlap between Delly and Manta. Samplot 1.0.19  was thenThe final SV callset  was then merged with the SNP callset . The samples were phased and missing genotypes were imputed by beagle 5.0  with defLD between two loci with a maximum distance of 100 kb was initially estimated from phased haplotypes as follows:where osenberg . As we rosenberg  as a meamentclass2pt{minimBonferroni corrected bootstrap confidence intervals for the LD decay were estimated by resampling the A tag SNP was defined as the SNP with the highest ng array , a 60 k ng array , a 55 k ng array . The annng array  liftOverhttps://doi.org/10.5281/zenodo.5770348).The complete pipeline was set up in snakemake 5.3.0  and the Additional file 1. Supplementary results, tables and figures.Additional file 2. MultiQC report.Additional file 3. Observer concordance of the visual filtering step.


Body conformation traits are economically important in the goat meat industry. Good growth performance in goats, including an accelerated growth rate, can improve carcass weight and meat yield. The identification of genetic variants associated with these traits provides a basis for the genetic improvement of growth performance. In this study, we measured six body conformation traits, including body height, body length, cannon circumference, chest depth, chest width, and heart girth. By a genome-wide association study of a Chinese meat goat breed, 53 significant single nucleotide polymorphisms and 42 candidate genes associated with these traits were detected. These findings improve our understanding of the genetic basis of body conformation traits in goats.PSTPIP2, C7orf57, CCL19, FGF9, SGCG, FIGN, and SIPA1L) were identified as components of the genetic architecture underlying body conformation traits. Our results provide useful biological information for the improvement of growth performance and have practical applications for genomic selection in goats.Identifying associations between genetic markers and economic traits has practical benefits for the meat goat industry. To better understand the genomic regions and biological pathways contributing to body conformation traits of meat goats, a genome-wide association study was performed using Dazu black goats (DBGs), a Chinese indigenous goat breed. In particular, 150 DBGs were genotyped by whole-genome sequencing, and six body conformation traits, including body height (BH), body length (BL), cannon circumference (CC), chest depth (CD), chest width (CW), and heart girth (HG), were examined. In total, 53 potential SNPs were associated with these body conformation traits. A bioinformatics analysis was performed to evaluate the genes located close to the significant SNPs. Finally, 42 candidate genes (e.g.,  As one of the oldest domesticated livestock, goats provide abundant meat, milk, and fiber . Most ofPRDM6 gene influences growth-related traits, such as CC, CD, and CW, in the early growth and development of goats [PITX2 is significantly associated with body height and body length in Guanzhong dairy goats and Hainan black goats [PRLR are significantly correlated with BL, BH, CD, HG, and CC [Growth performance is an economic trait in the meat goat industry. Body conformation traits of goats directly reflect the body size, structure, and development, which are closely related to physiological function and production performance. Body height (BH), body length (BL), heart girth (HG), chest width (CW), chest depth (CD), and cannon circumference (CC) are the most frequently used body conformation traits . Howeverof goats . PITX2 ick goats . Two ind, and CC .Genome-wide association studies (GWAS) are a popular approach to detect genome-wide single-nucleotide polymorphisms (SNPs) related to phenotypic traits . To dateThe Chongqing Key Laboratory of Forage & Herbivore approved this experiment. All goat experiments followed the Southwest University Institutional Animal Care and Use Committee regulations , and no animals were anesthetized or euthanized during the study.A total of 150 female DBGs weighing 35–40 kg from Tengda Farm  were used in this study. According to the China National Standard (NY/T 1236-2006) , our teaThe integrity and concentration of the genomic DNA were assessed by agarose gel electrophoresis and a NanoDrop spectrophotometer , respectively. Then, the sequencing of each sample was performed on the DNBSEQ-T7 platform . According to the standard protocol, the DNA samples were tested, amplified, and purified, and libraries were constructed. During library construction, the genomic DNA was randomly broken into fragments of about 350 bp by the fragmentation instrument. After end repair, the sequencing connectors were added for sequencing. Since the raw data contained reads with junctions or low quality, filtering was performed as follows: (1) reads with adapters were removed, (2) paired reads were removed when the N content exceeded 1% of bases in a read, and (3) paired reads were removed when the quality score ≤ 5.http://broadinstitute.github.io/picard, accessed on 24 February 2015) and Samtools  [p < 1 × 10−6. After quality control, all individuals and 16,776,605 SNPs were retained for subsequent analyses.Clean data were mapped to the goat genome (ARS1)  using Buer 2019)  were useer 2019) . The detA principal components analysis (PCA) was applied to estimate the population structure of 150 goats using the FactoMineR package in R (v4.0.4). The top PCs explaining the largest proportion of variance were extracted to draw scatter plots and to evaluate the population structure . Heat may is a phenotype, Xβ is the population structure effect and fixed effects, such as year and season, kγkZ is the marker effect to be tested; ξ~N  is the polygenic effect, e~N  is the residual effect, and K is the kinship matrix inferred from the SNPs. Then, we used GEMMA [p-value threshold was 6.01 × 10−8, with a corresponding −log p-value of 7.22. Significant SNPs were visualized by Manhattan plots, and p-value distributions  were visualized by a quantile–quantile (QQ) plot.Since GWAS does not allow missing or unknown SNPs, we used Beagle 5.0  to infered GEMMA  to obtaiCandidate genes were identified according to their physical positions and functions based on the ARS1 reference genome assembly. Significant SNPs were annotated to their nearest genes using ANNOVAR. Gene Ontology (GO) is an international standardized gene function classification system based on GO terms in three broad categories: molecular function (MF), cellular component (CC), and biological process (BP). Kyoto Encyclopedia of Genes and Genomes (KEGG) is the main public database to identify pathways that are significantly enriched for candidate genes compared to reference genes. Gene Ontology (GO) and KEGG pathway analyses of the candidate genes were performed using the clusterProfiler package in R (v4.0.4) .The descriptive statistics for six body conformation traits  are presented in After library construction and sequencing with the DNBSEQ-T7 system, 17.83 GB of raw data were produced for each individual. After the filtering processes, sequence data for 150 DBGs were mapped to the reference genome to a mean depth of 6×, with mapping rates of 99.06% on average . A totalThe population structure was evaluated based on the top three PCs by 3D plots B. In thep-values from the GWAS. Most SNPs did not deviate from the expected p-values, suggesting that the models for GWAS were reasonable. After filtering and obtaining adjusted phenotypes, GWAS was performed on the SNPs. Based on stringent thresholds, we found 53 genome-wide significant SNPs  for BH, BL, CC, and CD and no significant SNPs for CW and HG  and chromosome 12 (FGF9 and SGCG). Additionally, 11 significant SNPs associated with CD were located on chromosomes 4, 20, and 22. The genes nearest to these SNPs were SUN3, C7orf57, MANEA, ENSCHIG00000020259, DOK5, CBLN4, ENSCHIG00000022146, CDH9, ENSCHIG00000008494, CDKAL1, SOX4, and EDEM1. ; seasonal reproduction ; and metabolism  [JAK2, KCNQ1, PDE5A, PDLIM5, and TBX5) directly associated with muscle development were obtained. Wang et al. [Lhx2, FGF9, Wnt2, MC1R, and FGF5 may be related to cashmere production traits. Along with the decreasing cost of next-generation sequencing, the application of WGS is becoming increasingly popular [In 2012, a female Yunnan black goat was used to construct a reference genome for the first time by combining Illumina next-generation sequencing and whole-genome mapping techniques . Whole-gERPINBs) . In addiERPINBs)  used whog et al.  performeLRP1B and GRID2 showed significantly different patterns of linkage disequilibrium [LOC108633278, PPP1R12A, and YIPF4) between the high-yield and low-yield groups. There was a CNV in the CBLB gene identified at the individual level. Guan et al. identified some candidate regions based on allele frequency differences in the DBGs and Inner Mongolia cashmere goats. Hormone activity and signaling pathways such as neurohypophyseal hormone activity and adipocytokine signaling pathway [There have been several genomic studies of DBGs to date; however, these studies are limited by small sample sizes. Genome-wide selection signatures in 31 DBGs from two groups with differences in fecundity revealed that ilibrium . In addiilibrium  classifi pathway  may affePSTPIP2, which was located on chromosome 24, was significantly associated with BL. According to Lukens et al. [PSTPIP2 negatively regulates IL-1β, which plays an important role in osteomyelitis. This gene is highly expressed in synovial cells, suppresses the inflammatory response, and reduces the number of osteoclasts by inhibiting the function of fibroblastic synovial cells. In addition, PSTPIP2 possesses the ability to control the bone resorption process by regulating the assembly of the podosome [In the GWAS, s et al.  and Yao s et al. , PSTPIP2podosome . Our GO C7orf57, EDEM1, SUN3, MANEA, CDKAL1, CDH9, CBLN4, and SOX4. Among these, Chio et al. found an association between C7orf57 and sporadic amyotrophic lateral sclerosis based on 553 patients [CDKAL1 was identified as an essential gene for the maintenance of normal mitochondrial morphology and function in adipose tissue [CDKAL1 affects the transcription of growth hormone genes [SOX4 regulates apoptosis in cardiomyocytes and controls embryonic and cardiovascular development [In the GWAS of CD, the following candidate genes were identified: patients . CDKAL1 e tissue . In addine genes . SOX4 reelopment . KEGG anCCL19 on chromosome 8 promotes ligament ossification [UBAP1 leads to neurological disorders via the ESCRT pathway, such as hereditary spastic paraplegia [FGF9 and SGCG, located on chromosome 12, are noteworthy candidates. FGF9 maintains the osteogenic progenitor cell population by the activation of Akt signaling [SGCG can cause malnutrition and reduce muscle function [FIGN, which can affect mammalian development [SIPA1L1, which upregulates osteoblast development via miR-617/smad3 [We detected many significant SNPs associated with CC, especially on chromosomes 8 and 12. For example, fication . A lack raplegia ,51. Addiignaling ,53. In cfunction . Additioelopment , and SIP17/smad3 , are potWe identified many genetic markers that may affect the growth performance of goats. These significant regions have been reported in other studies, supporting the validity of our results. Information on the genomic regions found in this study can facilitate the identification of candidate genes for body conformation traits. However, we did not detect any significant associations for CW and HG, and these results may be due to imprecise phenotypic determinations or an inadequate sample size. In addition to technical factors, the polygenic basis of the morphological traits makes it difficult to detect genetic factors with small phenotypic effects. Ultimately, these results provide guidance for genomic selection in the DBG population for highly efficient genetic improvement.PSTPIP2, C7orf57, CCL19, FGF9, SGCG, FIGN, and SIPA1L likely contribute to the regulation of bone and muscle development. This was the first large-scale genome survey of DBG, and the results are expected to contribute to genomic selection. Furthermore, our approach can be used for the discovery of genetic markers in goats.In this study, genomic and phenotypic data for 150 DBGs were collected, and genome-wide association studies were carried out. An analysis revealed that


Escherichia coli. Based on the combination of the 15-sequence primer sets, each SNP site encompassing forward and reverse primer sequences (620–919 bp) were aligned and an SNP-based marker was designed. Each SNP marker exists in at least two SNP sites at the 3′ end of each primer; one natural and the other artificially created by transition or transversion mutation. Thus, 12 sets of SNP primers (225–488 bp) were developed for validation by amplifying the target E. coli. Finally, a temperature gradient triplex PCR kit was designed to detect target E. coli strains. The selected primers were amplified in three genes , with fragment sizes of 401, 337, and 232 bp for E. coli O157:H7, E. coli, and E. coli O145:H28, respectively. This allele-specific SNP-based triplex primer assay provides serotype-specific detection of E. coli strains in one reaction tube. The developed marker would be used to diagnose, investigate, and control food-borne E. coli outbreaks.Single-nucleotide polymorphisms (SNPs) are one of the most common forms of genetic variation and as such are powerful tools for the identification of bacterial strains, their genetic diversity, phylogenetic analysis, and outbreak surveillance. In this study, we used 15 sets of SNP-containing primers to amplify and sequence the target  Escherichia coli bacteria [−9 changes per nucleotide per generation, are important biologically informative markers in bacteria [Escherichia [Salmonella [Brucella [Bacillus species [E. coli [E. coli compared to other recognized molecular techniques [E. coli [E. coli genes may be useful markers for the development of rapid surveillance of food-borne diseases, outbreak tracing [Salmonella [Single-nucleotide polymorphisms (SNPs) are single-base differences in DNA between individual organisms ,2,3,4. Tbacteria ,6. SNPs bacteria ,8, widerbacteria , and thebacteria ,11,12. Nbacteria ,14. In abacteria . Howeverherichia ,17, Salmlmonella , BrucellBrucella ,20, and  species . To date[E. coli ,23. Howechniques . Using d[E. coli ,24,25,26[E. coli . SNPs wi tracing  and typilmonella . This aplmonella ,28,30,31E. coli (STEC), E. coli O157 has been a major food-borne pathogen since the early 1980s; however, early and accurate diagnosis presents preventive measures to minimize the risk of food-borne pathogens and their outbreaks [E. coli O157:H7 has been reported in several outbreaks worldwide, in both developing and developed countries, including in the USA [E. coli, such as the PulseNet [E. coli [E. coli, but it has limitations in the development of a universal panel for MLVA [E. coli that are difficult to distinguish using conventional typing techniques [E. coli. In this study, we altered a mismatched nucleotide within the three bases closest to the 3′ end (SNP site) in each primer sequence to detect serotype-specific E. coli. The aim of this study was to investigate the interrogation of informative SNPs in genes of WGS sequences of E. coli, along with the development of SNP marker-based detection.Shiga toxin-producing utbreaks ,33. E. c the USA , Canada  the USA , and Eur the USA . CurrentPulseNet ,38, MultPulseNet , and MulPulseNet  techniquPulseNet ,20,38. TPulseNet ,41. Simi[E. coli ,23; howe[E. coli . Multilofor MLVA . Neverthchniques . To overE. coli strains, including three O157 and non-O157 sequences, was downloaded from GenBank. From these, E. coli str. K-12 contained the most known genome sequences and was therefore selected as the reference strain, whereas the remaining five E. coli strains were considered for comparison. The accession numbers of all six E. coli strains are provided in A total of six E.coli genome sequences were downloaded and compared to the reference genome sequence. A total of 2160 SNPs were identified from the alignment of E. coli WGS  (E. coli (NC_000913) are shown in The six coli WGS . From thnd leuD) . The ambE. coli genomes via the bioinformatics software analysis. Within the SNP markers, SNP sites, bold ambiguous codes, and the positioning of the amino acid codes in each gene of the reference E. coli genome and with the respective genes are marked in E. coli strains  of the four target E. coli strains.During the first PCR amplification, seven primer sets  were amplified on the target genes of the tested  strains , whereas strains  and thre strains . MoreoveE. coli strains are shown in n = 38 out of 60) were submitted to the NCBI database with the following accession numbers (OL589326–OL589363). The chimeric, off-target sequences with non-specific genes and dimer sequences (n = 22) were not used for further analysis of the SNP-based primer design (ileS gene in reference non-O157 E. coli str. K-12 (NC_000913)  where at least one natural SNP was present  . The amp present .E. coli strains. Two examples of these primers are shown in thrB) genes of four E. coli strains were aligned. It was found that the number of the natural SNP position was 342 and the artificial transition (purine to purine)-mutated SNP position number was 340 on the aligned gene sequences. Thus, we designed the SNP based marker ‘O-thrB-3-F/R’ to include at least two SNPs, e.g., the forward primer length was 18 bp from 325 to 342  and the reverse primer length was 20 bp from 642 to 661 . The amplified target product size was 337 bp . Thus, in each primer sequence of the SNP sites, we altered an artificial base, except for O-polB-4-F, so that SNP-based primers improved their ability to bind the PCR template sequence, thereby improving the allele-specific detection of target E. coli strains  with three markers  were selected to detect three target E. coli strains  produced the target band, whereas the other two markers (O.ileS2-3 and O.caiB-3) produced the off-target bands  is a good candidate for E. coli O157:H7. However, if we want to know the serotypes of an unknown E. coli, we have to PCR-amplify each of 12 primer pairs, which is a limitation of the developed SNP-based (n = 12) marker. We overcame the cost and time involved in repeated PCR amplification of the SNP-based marker through the development of a triplex SNP marker (the developed triplex PCR marker is indicated as “O1”)  .E. coli strains were detected in a single reaction with the developed assay (E. coli (KVCC-BA1800069) and E. coli O145:H28 (KVCC-BA1800090), respectively  and the tested E. coli (lane no. 2–7) was matched with E. coli O157:H7. Lanes no. 9–11 were matched to the target E. coli . Lanes no. 14–19 were matched to the target E. coli O145:H28 . Five of the isolates (lanes no. 20–25) were not exactly matched to the three target E. coli strains. They might have originated from the animal fecal samples as different E. coli strains . For more validation, 23 identified  strains . SometimE. coli, for instance, PCR bands [E. coli [To date, several different methodological approaches have been used to detect CR bands , PFGE [4CR bands , phage tCR bands , and MLSCR bands ,23. Ther[E. coli ,27,28,48[E. coli ,26 and t[E. coli , to deve[E. coli .E. coli genome  at the 3′ end SNP sites , but all the isolates were not exactly matched to the three target E. coli serotype strains . Sixty SNPs were selected for re-sequencing, and 20 of the confirmed SNPs in 11 genes of AIEC strains were used for the identification of E. coli [E. coli strains, among which the SNP-flanking regions of aligned sequences on 11 genes were selected for further sequencing and finally only three SNPs containing three genes  were confirmed for the SNP-based triplex PCR marker. However, Moorhead et al. speculated that PCR enzymes are capable of three to five proofreading activities to correct an artificial mismatch [E. coli genome  SNP triplex PCR method.Recently, software algorithms and parameters have been used to search for SNP positioning from raw or assembled genome sequences ,57. SNP- strains ,30,31,58 strains . Nonethe primers . Current primers . Desphandatabase . In our mismatch  but DNA mismatch . The simi genome . TherefoE. coli strains, including three E. coli O157 and three non-O157 strains, were retrieved from the GenBank database and each was aligned with the respective reference (K-12 substr. MG1655). (ii) We used the NUCmer program [https://vcftools.github.io/index.html, accessed on 29 December 2021), and a filtered SNP matrix was constructed from the output VCF files [E. coli were sequenced (E. coli O157 and non-O157). (iv) Then, E. coli primers were designed based on the four aligned E. coli sequences. (v) E. coli O157 and non-O157 specific-SNP markers were used for PCR amplification and evaluation. (vi) Finally, the SNP marker-based triplex PCR kit was designed. A schematic flow diagram of the development of the SNP-based marker for E. coli detection is shown in There were six steps involved in the development of SNP-based markers. They were as follows: (i) the whole-genome sequences of six  program  to searcCF files . (iii) TE. coli strains, including three different serotype strains, were selected for DNA isolation, PCR amplification (with the designed SNP-encompassing primers), and sequencing. The two E. coli O157 strains were O157:H7  and the non-O157 strains were E. coli (KVCC-BA1800069) and E. coli O145:H2 (KVCC-BA1800090). The target bacterial colonies were streaked onto nutrient agar media, and a single colony was collected using a sterilized toothpick. The colonies were incubated at 35 °C for 18 h in a 5 mL lactose broth (LB) solution. Genomic DNA was extracted from 1 mL of LB culture fluid using a DNeasy Blood and Tissue Kit, according to the manufacturer’s instructions .The four E. coli based on NGS data were downloaded from GenBank , including one reference strain of the most studied and best-annotated genome (E. coli str. K-12), which was used as a reference genome. E. coli str. K-12 substr. MG1655 (NC-000913.3) was downloaded along with five query strains, E. coli IAI39 (NC_011750), E. coli O104:H4 str. 2011C-3493 (NC-018658), E. coli O157:H7 str. Sakai (NC-002695), E. coli O83:H1 str. NRG 857C (NC-017634), and E. coli UMN026 (NC-011751). Among them, three were E. coli O157 and three were non-O157. We selected the most suitable SNPs based on the allelic diversity of the aligned sequences with SNP sites encompassing a primer compared to the reference and query sequences of E. coli  to determine the SNP positions on WGS alignments and to design primers both upstream and downstream of SNP sites [https://vcftools.github.io/index.html, accessed on 15 July 2021), and variant annotation was performed using SnpEff v.4.3.0 . The final output of the workflow was a filtered SNP matrix. SNP positions were inferred using show-snp programs [thrB, threonine synthase-thrC, transcriptional activator protein-nhaR, isoleucine-tRNA ligase-ileS, 4-hydroxy-tetrahydrodipicolinate-dapB, carbamoyl-phosphate synthase-carB, DNA polymerase-polB, ribulokinase-araB, inner membrane protein-yabI, thiamine transport system permease protein-thiP, and 3-isopropylmalate dehydratase small subunit-leuD) were amplified with four E. coli strains, which were selected as targets for sequence analysis for the desired SNPs . Multiple primer sets were produced but the suitable primer sets were selected based on corresponding SNP positions and amplicon length (approximately 750–1000 bp).We used the NUCmer software of MUMer v4.0.0  solution. Genomic DNA was extracted from 1 mL of LB culture fluid using a DNeasy Blood and Tissue Kit, according to the manufacturer’s instructions . Then the target E. coli strains were selected for re-sequencing . Information on the whole genome of E. coli is provided in Taq DNA polymerase (Qiagen), and 21.7 μL distilled water to a final volume 30 μL. The first and second cycles of PCR consisted of amplification at 95 °C for 5 min, followed by 30–35 cycles of denaturation for 30 s, annealing at 55 °C for the 1st PCR cycle and 50 °C for the 2nd PCR cycle with 30 s, polymerization at 72 °C for 1 min 30 s, and a final elongation at 72 °C for 10 min. The amplified product sequences are provided in E. coli. The amplified PCR products of the target E. coli were re-sequenced . Thus, all the designed primers were further analyzed using NetPrimer  to select the optimal primer pairs. The validated polymorphisms are referred to as “confirmed SNPs”.The target quencing  using neequenced  and aligE. coli strains. When SNPs were found with ambiguous or overlapping peaks, they were removed from further analysis. Finally, it was possible to confirm efficient SNP-based primers with the expected target bands of E. coli strains. Therefore, allele-specific primers were designed to discriminate single base changes through experimental optimization [Standard PCR primers targeting numerous SNP locations were initially designed through an in silico approach, and the primers were optimized via repeated PCR testing. To improve detection efficiency, we introduced a mismatched base within the triplet base of the 3′ end primer of each primer sequence. It was observed that there were no non-specific bands during PCR amplification . Optimizmization ,55.E. coli strain-specific SNP markers in a single reaction, a temperature gradient SNP-based triplex PCR kit was developed. Each PCR reaction consisted of 1 μL (5 ng/μL) of template DNA, 3 μL each of forward and reverse primers, DSbio Hot Start Taq mixture 10 μL, and 6 μL distill water to a final volume 20 μL.  was tested with the triplex PCR marker (Staphylococcus aureus (NCCP-14780); Bacillus cereus (NCCP-14579) and the Gram-negative bacteria were Salmonella enterica (NCCP-15756), E. coli (NCCP-14034), Pseudomonas aeruginosa (NCCP-16099), Shigella dysenteriae (NCCP-14746), Klebsiella pneumoniae (NCCP-14631), and Enterobacter cloacae (NCCP-14621). These bacteria were checked with the target bacteria  during the triplex PCR assay, and cross-reactivity was observed via PCR amplification with the specific length of the target band.Wild-animal fecal samples were collected from various agricultural regions across South Korea (unpublished data). From these fecal samples, eviously . For effR marker . In addiE. coli but also in a wide variety of other bacterial species. SNP-based markers represent a target for PCR methods to easily and rapidly differentiate between different E. coli strains [E. coli. In addition, the mutations can serve as phylogenetic markers for strain classification. Despite the importance of SNPs in our understanding of the diversity of E. coli populations, the research community is currently lacking a comprehensive database, even though multiple frontline laboratories in the USA and Canada have applied SNP analysis for the typing of STEC for clinical public health purposes [E. coli. Therefore, SNP-based studies promote food-borne disease outbreak monitoring and prevention, and the analysis and control of pathogenic E. coli. In this study, we used only E. coli obtained from wild-animal fecal samples for the evaluation of detection efficiency. However, further analysis and investigation should be conducted with a number of variable sources of E. coli for the evaluation of the efficiency of the developed SNP-based triplex marker assay.SNPs are the most common form of genetic variation, not only in  strains ,66. STECpurposes . SNP-basEscherichia coli (E. coli O157:H7). Kangwon National University, Korea. Patent application No HP-17-076.Yung Chul Park, M.M. Rahman, and S.J. Lim. Development of multiplex PCR kit and detection of pathogenic


LCORL and identified a further significant peak on ECA 1. Using a single-step approach with a combined relationship matrix, the estimated heritability for withers height was 0.31 (SE = 0.08) and the corresponding genomic breeding values ranged from − 2.94 to 2.96 cm. A mean reliability of 0.38 was realized for these breeding values. The analyses of withers height showed that compiling a reference population across breeds is a suitable strategy for German Warmblood horses. The single-step method is an appealing approach for practical genomic prediction in horses, because not many genotypes are available yet and animals without genotypes can by this way directly contribute to the estimation system.Reliability of genomic predictions is influenced by the size and genetic composition of the reference population. For German Warmblood horses, compilation of a reference population has been enabled through the cooperation of five German breeding associations. In this study, preliminary data from this joint reference population were used to genetically and genomically characterize withers height and to apply single-step methodology for estimating genomic breeding values for withers height. Using data on 2113 mares and their genomic information considering about 62,000 single nucleotide polymorphisms (SNPs), analysis of the genomic relationship revealed substructures reflecting breed origin and different breeding goals of the contributing breeding associations. A genome-wide association study confirmed a known quantitative trait locus (QTL) for withers height on equine chromosome (ECA) 3 close to The online version contains supplementary material available at 10.1007/s13353-021-00681-w. Genomic selection was first proposed 20 years ago by Meuwissen et al. . About 1LCORL (ligand-dependent nuclear receptor corepressor-like) gene has later been confirmed in other studies and horse populations , 453 from Oldenburg (OL), 100 from Oldenburg International (OS), 427 from Trakehner (TRAK), and 176 from Westfalian (WESTF) studbook. The two Oldenburg breeding associations were considered separately, as they have a different focus with OS predominantly concentrating on the breeding of show jumping horses and OL putting more emphasis on dressage ability. Selection of horses contributing to the reference population was based upon two criteria: (i) a low level of preselection  and (ii) the current genetics of German riding and sport horses should be represented as comprehensive as possible. Therefore, from a larger number of broodmares, those with the lowest pedigree relationships were selected. Genomic data were made available through molecular genetic analyses of genome-wide single nucleotide polymorphisms (SNPs) using commercially available SNP arrays. The GGP Equine 70 k BeadChip® (Neogen/Illumina) containing 65,157 SNPs was used for genotyping of 193 mares, in the following referred to as the first cohort. The remaining 1964 mares (second cohort) were genotyped using the GGP Equine Plus BeadChip® (Neogen/Illumina) which became available more recently and included 6790 additional SNPs, so in total 71,947 SNPs.p(χ2) < 0.001) were discarded. Additionally, duplicate SNPs (n = 55) were removed. After quality control, the numbers of remaining SNPs were 58,562 for the first cohort and 62,070 for the second cohort. To generate a homogeneous SNP dataset, all animals were imputed to a uniform level using the program Beagle 5.0 , OL (n = 444), OS (n = 98), TRAK (n = 416), and WESTF (n = 173)) and 62,070 SNPs. Pedigree information contained three ancestral generations, resulting in a total pedigree size of 14,632 individuals. In particular, the selected mares descended from 608 sires with 1 to 82 offspring (mean 3.5) and from 2006 mares with 1 to 3 offspring . Table The final data set used for further analyses included 2113 mares ; μ is the overall mean; age classi represents the fixed effect of the ith age class at measurement , the first age class contains mares younger than 3 years, the second class includes mares between 3 and 4 years old, the third between 4 and 5 years old, the fourth between 5 and 6 years old, and the fifth group includes all mares older than 6 years old; β is the effect of the candidate SNP; SNPj represents the SNP genotype indicator variable ; g− is the additive genetic effect based on a genomic relationship matrix calculated using all SNPs except those on the chromosome where the candidate SNP is located; and eijk is the random residual effect. To partition the variance onto different chromosomes and the SNPs identified as significant, a genomic relationship matrix was built separately for the 31 autosomes and the significant SNPs in the GCTA software  of the realized genomic relationship matrix across the different subpopulations was conducted using GCTA 1.91.6 beta1 . First, the numerator relationship matrix (A) based on the pedigree information  and the realized genomic relationship matrix (G) based on the genotyped animals (n = 2113) were built and used to generate a combined relationship matrix (H) for all individuals  and the second group with genotyped horses (n = 2113). The inverse of the combined matrix can be computed as ; X is a design matrix for the effects of age class; b is the corresponding vector of fixed effects; Z is a design matrix for the random animal effects; a is the corresponding vector of additive genetic random effects; and e is the random residual effect. To improve the computation speed, the matrix H can be transformed such that the random animal effects become independent  and allele frequencies underlined the results described above, as the breeds within the clusters showed similar values. This is exemplarily shown for the top associated SNPs in Supplementary table The first two eigenvectors of the genomic relationship matrix were used to visualize the relationship at the genomic level. Figure p-values (-log10(p) values; Fig. LCORL gene and had a minor allele frequency of 0.49. On ECA 1, the SNP BIEC2_23633  reached the significance level of P ≤ 0.05. Analyses of the SNP effects indicated that a few SNPs are strongly associated with the expression of withers height in horses while many SNPs can only explain a minor part of the genetic variance for the trait. The three SNPs on ECA 3 mentioned above together explained 22.8% of the variance. By including the SNP BIEC2_808543 as a fixed effect in the LOCO analysis, the significant signal on ECA 3 dropped completely, only the peak on ECA 1 was above the significant threshold  105,547,002 bp), BIEC2_808500 , and BIEC2_808466  showing the highest negative decadic logarithms of the empirical r = 0.97 for the 2113 mares  for the genomic breeding values are shown in Table Variance component estimation was done for all 14,632 horses in the pedigree using the transformed matrix H, resulting in a value of 3.95 for the additive genetic variance and a value of 8.62 for the residual variance, and, thus, a heritability of 0.31 with a standard error (SE) of 0.08. The genomic breeding values estimated in this study ranged from − 2.94 to 2.96, with the majority of horses having a value close to 0. The genomic breeding values and the underlying phenotype showed a correlation of In this study, 2113 mares with 62,070 SNPs and data for withers height were available. Data was contributed by five different breeding associations which were not equally represented in the present reference population. Instead, breeding associations with a large total number of broodmares were stronger represented in the reference population than breeding associations with only a small number of broodmares.The data set consisted of data from five different horse breeding associations, all of them being subpopulations of the German Warmblood horse. In Germany, conventional breeding values for dressage and jumping performance are routinely estimated jointly for all Warmblood horse breeds. Thus, a joint reference population is the obvious solution to guarantee a fast implementation of genomic selection, especially because most of the subpopulations are much too small to build their own reference population. In dairy cattle breeding, several studies have shown that establishing a joint reference population across related breeds can increase the reliability of genomic breeding values, especially for small breeds  for the 2113 mares when using the combined relationship matrix, whereas the SNP-based heritability was higher  and more similar to the estimates reported in the literature. Signer-Hasler et al.  estimateThe objective of this study was to quantify the reliability for first genomic breeding values for the reference trait withers height. For this purpose, a single-step procedure was used. In horse breeding, this is the method of choice because the information from non-genotyped horses can also be incorporated directly and also information from genotyped horses (which are not phenotyped). Especially in the initial phase of genomic selection, this is an advantage, because not many genotypes of horses are available yet Below is the link to the electronic supplementary material.


GBP4 for which an increased copy number has been negatively associated with cattle stature, and the BoLA gene family which has been linked to the immune response and adaption of cattle. The ARS map file of the GGP Bovine 150K Bead Chip maps the genomic position of more SNPs with increased accuracy compared to the UMD map file. Comparison of the CNVRs identified between the two reference assemblies suggests the newly released ARS reference assembly is better for CNV detection. In spite of this, different CNV detection methods can complement each other to generate a larger number of CNVRs than using a single approach and can highlight more genes of interest.Copy number variants (CNVs), which are a class of structural variant, can be important in relating genomic variation to phenotype. The primary aims of this study were to discover the common CNV regions (CNVRs) in the dual-purpose XinJiang-Brown cattle population and to detect differences between CNVs inferred using the ARS-UCD 1.2 (ARS) or the UMD 3.1 (UMD) genome assemblies based on the 150K SNP (Single Nucleotide Polymorphisms) Chip. PennCNV and CNVPartition methods were applied to calculate the deviation of the standardized signal intensity of SNPs markers to detect CNV status. Following the discovery of CNVs, we used the R package HandyCNV to generate and visualize CNVRs, compare CNVs and CNVRs between genome assemblies, and identify consensus genes using annotation resources. We identified 38 consensus CNVRs using the ARS assembly with 1.95% whole genome coverage, and 33 consensus CNVRs using the UMD assembly with 1.46% whole genome coverage using PennCNV and CNVPartition. We identified 37 genes that intersected 13 common CNVs (>5% frequency), these included functionally interesting genes such as  Copy number variants are structural variants caused by insertions, deletions, duplications, and translocations of DNA fragments . The lenXinjiang-Brown (XJ-Brown) cattle are a dairy and beef dual-purpose breed and are a valuable germplasm resource in the field of cattle breeding. The breed is often used for crossbreeding with local cattle breeds and has a large number of hybrid progeny in China . While mXinjiang-Brown (XJ-Brown) cattle is a dual-purpose composite breed with ancestral introductions from Kazakh cattle, Brown Swiss cattle, Alatau cattle and Kostroma cattle . This stAll 403 animals in the study were genotyped on the GGP Bovine 150K SNP BeadChip using the iScan System. SNP genotyping was performed using GenomeStudio  based onCNV detection can be impacted by genotyping batch effects. To reduce the batch effect in CNV detection, two custom cluster files of the UMD and ARS versions were trained on the genotypes of XJ-Brown cattle as instructed in an Illumina technical note . InitialWe used the newly trained custom cluster files to call genotypes, and this was followed by another quality control step. We removed samples with a call rate less than 0.95 leaving 397 individuals, and removed SNPs with call rate less than 0.90. This resulted in 137,945 SNPs remaining in the UMD version, and 138,331 SNPs remained in the ARS version. The map, genotype, Log R Ratio and B Allele Frequency information for these remaining individuals and SNP were exported into the Final Report for further analysis.PennCNV  and CNVPCNVPartition  is a pluPennCNV  softwarecnv_clean function was used to convert the CNV results into a standard format and make basic summaries. The argument “drop_length = 5” was used to discard the CNVs larger than 5 Mb. The cnv_summarise_plot function was used to make the CNV distribution, frequency and length group plots. The call_cnvr function was used to generate CNVRs for all results, this method merges CNVs that overlap by at least one bp into a CNVR and reports the frequency of each CNVR. Then, the CNVR distribution map was created using the cnvr_plot function. A gain CNVR indicates the region only contains duplicated CNVs which comprise more than two copies, a loss CNVR indicates the region only contains deleted CNVs of zero copies or one copy, and a mixed CNVR indicates that the region contains at least one duplicated CNV and at least one deleted CNV.The R package HandyCNV  was usedcompare_cnv and compare_cnvr in HandyCNV . Then, the compare_gene function was used to produce a comparison plot for the consensus genes.The  website  . The cacnv_visual and plot_cnvr_panorama were used to visualize CNVs and high frequency CNVRs. To explore the results, we used these functions to customize the CNV plot by chromosome, sample, region of interest and target gene.CNV identification methods detect a deviation of BAF and LRR to distinguish different types of copy number, visualization of CNV via the BAF and LRR plots are a direct way to check and validate the CNVs. The functions To compare these results to known cattle CNV databases, a total of nine studies on CNVs of other cattle breeds were downloaded . Among them, eight CNV results were downloaded from DGVa database  and one After quality control, 137,945 SNPs and 138,331 SNPs remained in UMD and ARS maps, respectively. In comparing the two maps, there were 122,963 SNPs (89.1%) located on the same chromosome, 264 SNPs (0.2%) had an unknown position in both UMD and ARS maps, 13,769 SNPs (10.0%) had an unknown position in the UMD map, and 545 SNPs (0.4%) had an unknown position in the ARS map . The numFour sets of CNV results were produced in further analysis. After quality control, 3,686 CNVs were identified from PennCNV with the ARS map (Penn-ARS), 1,293 CNVs from CNVPartition with the ARS map (Part-ARS), 3,200 CNVs from PennCNV with the UMD map (Penn-UMD), and 1,239 CNVs from CNVPartition with the UMD map (Part-UMD). The results from using an ARS map detected more CNVs than using the UMD map. The average number of CNVs per individual were 9.50, 3.42, 8.27, and 3.39 , and theAll methods detected more deletions than duplications, and all methods identified more three-copy CNVs than four-copy CNVs. PennCNV detected more one-copy than zero-copy deletions whereas the CNVPartition detected more zero-copy than one-copy deletions . PennCNVThere were 931, 279, 821 and 246 CNVRs with total lengths of 150.1 Mb (about 5.5% of the genome coverage), 211.3 Mb (about 7.8% of the genome coverage), 135.0 Mb (about 5.0% of the genome coverage), and 199.2 Mb (about 7.3% of the genome coverage) from the Penn-ARS, Part-ARS, Penn-UMD, and Part-UMD results, respectively . There wSome 954 and 841 CNVRs with a total length of 312.0 Mb (about 11.7% genome coverage) and 291.2 Mb (about 10.9% genome coverage) were in the combined ARS and UMD CNVR lists. The genome coverage of CNVRs on BTA 13, 18 and 26 was less than 5%, the coverage of CNVRs on BTA 27 and 28 was greater than 20% in this sample. There were 38 and 33 CNVRs that passed the consensus frequency threshold (sample size > 20) in the ARS and UMD lists, the total length was 52.84 Mb (about 1.95% genome coverage) and 38.99 Mb (about 1.46% genome coverage), respectively . The CNVThe comparisons were made on both individual and population levels between each pair of CNV results. All results showed that the overlap rate at the population level was higher than that at the individual level.In the ARS reference genome results, the percentage of overlapped CNVs was 17.3% of Penn-ARS compared to 47.3% of Part-ARS on the individual level , and 54.Only the results on UMD could be compared with the known datasets, therefore, all positions mentioned in this section are UMD coordinates. A total of 9,934 CNVRs were generated from CNV lists in the DGVa database , with a We compared our results with those identified in Brown Swiss cattle and found 141 CNVRs in XJ-Brown cattle overlapped with 215 CNVRs in Brown Swiss cattle with a total length of 37.5 Mbp, accounting for 12.9% of the CNVR length in this experiment and 65.6% of Brown Swiss cattle’s CNVR length. Among the 276 CNVRs that had no intersection with DGVa datasets . only niCNVRs with relatively higher frequency are presented in Some CNVRs could be cross-validated between the four detection methods, however, we observed that the samples in which CNVs are detected may differ between CNV detection methods and between genome assemblies. The differences between reference genomes may be due to the different number and order of SNPs that were used to detect the CNVRs. We present a CNVR on BTA 15 as an example to illustrate these differences . This CNThe SNP differences between different reference genomes could also be observed through this example. In this CNVR on BTA15, there was one SNP with unknown position in the ARS map but six SNPs in the UMD map. In addition, two SNPs had a different order in the two genome assemblies. When we ignored the missing SNPs in this region, the BovineHD1500023413 was located on the fifth loci in ARS map, but on the ninth loci in the UMD map; Another SNP, BovineHD1500023509 was located on the 11th loci in the ARS map, but on the 80th position in the UMD map .In view of the two observations above, the samples detected in each result were also different. In example, a total of 86 samples were detected by Penn-ARS, 60 samples were detected by Part-ARS, 56 samples were detected by Penn-UMD and only 32 samples were detected by Part-UMD. When Penn-ARS and Part-ARS results were combined, a total of 106 non-duplicated samples were found, and when Penn-UMD and Part-UMD results were combined, a total of 63 independent samples were found. When all the results are combined, a total of 120 unique samples were obtained . It showBLA-DQB, LOC100848815 and BOLA-DRB3 genes.The ARS union set (a combined set of Penn-ARS and Part-ARS results) had 1428 genes annotated in 463 CNVRs, and among these, 277 genes were annotated in 24 consensus CNVRs. After filtering these genes by frequency (>5%), a total of 26 genes (about 9.4%) remained in the ARS union set (Supplementary Table 2). A total of 1,370 genes were annotated in 417 CNVRs in the UMD union-set. Of these genes, 272 were annotated in 18 consensus CNVRs (Supplementary Table 3). After filtering the genes by their frequency, only 31 genes (about 11.4%) remained in the UMD union set. Combining results between assemblies resulted in 37 unique genes, of which 20 genes had common frequency in both the ARS and UMD results , six genSNP intensity based CNV detection methods are heavily influenced by SNP density and the order of the SNPs on each chromosome. In this study, two main differences were observed between SNPs on the UMD and ARS maps that affected CNV detection on the Bovine GGP 150k Beadchip. The first difference is the total number of SNPs mapped to chromosomes. Approximately 10% of the SNPs in the UMD SNP manifest file had no physical position on the UMD map and were discarded before CNV detection. Many of these missing SNPs did have a physical position on the ARS SNP manifest file resulting in 13,769 additional SNPs available for CNV detection. This difference in SNP density between reference genomes likely influenced the differences we observed in CNV detection on the same CNV regions. This demonstrates the importance of checking how many SNPs are available before CNV detection, if there are many missing SNPs, then it may be better to try to use a well-assembled map file or an up-to-date reference assembly.The second difference between genome assemblies is how SNP positions differed between ARS and UMD maps of the SNP chip. The SNP order on chromosomes differed between the UMD and ARS maps, as observed in Figure 1, this is likely due to the relative assembly quality of the reference genomes. CNV detection methods required at least three continuous SNPs with the same status to detect a CNV, therefore, the detection of the CNVs may be interrupted by the order of SNPs on a chromosome . Due to In this study, the consensus CNVRs from the two methods for ARS and UMD were reported. While, the ARS reference genome has become the main reference genome in recent studies , the offThe two methods PennCNV  and CNVPCNVs can be compared at the individual level or the population level. The comparison between individuals can detect the similarity between different methods, while the comparison at the population level can verify the repeatability of CNVs . CNVR isThe consensus CNVRs were defined as the CNVRs in the same reference genome that passed the common frequency threshold (Frequency ≥ 0.05) in the union sets of CNVs from the two detection methods. There are two advantages to get consensus CNVR in this way. First, combining multiple CNV results in the same reference genome together to then generate CNVRs will generate the complementary CNVRs list directly. Second, the common thresholds will exclude some low frequency CNVRs and keep the most reliable results.The CNV results collected from the DGVa database are on the UMD reference genome. Therefore, this study only used the UMD union set to compare with it. The comparison results of this study showed that CNVRs with a detection rate greater than 5% of the sample size were also observed in either the two detection methods or the previously reported studies. After comparing the results of this study with the DGVa or Brown Swiss datasets, we identified a large number of population-specific CNVR, but we could not confirm that these regions were XJ-Brown specific CNVs. This may be because the reference data cited is limited, or because the reliability of low frequency CNVRs is unknown. Even with a high frequency CNVR, there is no way to confirm whether a region is breed-specific, because accurate genotyping of CNVs cannot be obtained from SNP chip data. However, the SNP chip has low cost advantages so large sample sizes can be obtained, and it can still provide some valuable information to infer copy number variation. According to the results of this study, the higher the frequency of CNVR, the higher the accuracy of the results. Therefore, it is suggested that the regions with higher population frequency should be preferentially selected for subsequent analysis when using the PennCNV or CNVPartition methods to detect CNV.CNVs can delete or duplicate genes and those changes may impact gene expression and protein function , therefoGBP4 (Guanylate binding protein 4) gene. GBP4 is an immune related gene, and structural variants intersecting GBP4 have been reported at common frequencies in multiple cattle populations  genes located at BTA 23: 25,560,755–25,730,370 bp (ARS) are members of bovine leukocyte antigen (BoLA) gene families and have been associated with immune response and adaption in cattle studies . They have been reported as being related to circulating IGF-binding protein-3 (IGFBP-3) concentration in a genome-wide meta-analysis in human studies (LOC782061 (Synonyms: AKR1C4) gene on BTA 13: 43,322,783–43,433,183 (ARS) which is one of the aldo-keto reductase family members and has been shown to relate to expression level during the Bovine estrous cycle (NOSTRIN, CERS6, MIR2353 and STK39 genes. CERS6 is involved in lipid and sphingolipid metabolism pathways, and is potentially involved in endocrine control of lactation in dairy cows (We identified 37 consensus genes which were predicted to be impacted by a CNVR in at least one of our analyses. Several genes have been reported to intersect CNVs in previous cattle studies, such as the ulations . An incr stature . BLA-DQB studies . Severalenotypes . The MAP studies . Structuus cycle  and in kus cycle . The coniry cows . The ideIn summary, two CNV detection methods were employed in this study to infer CNVs on each of the ARS-UCD 1.2 and UMD 3.1 genome assemblies. The detailed comparisons of CNVs, CNVRs and annotated genes presented are the most complete CNV results reported in XJ-Brown cattle. A significant proportion of SNP are missing their location in the default UMD 3.1 map file of the GGP Bovine 150k Beadchip, which suggests that improved detection of CNV can be obtained by using the ARS-UCD 1.2 reference genome. A total of 44 common consensus CNVRs were identified with high agreement between reference genomes, and 37 consensus genes were annotated in all CNVRs list, and these results will be helpful for the subsequent experimental design of functional verification of genes impacted by CNVs.