HOXD1 and ADAMTS12 annotated from both CHR sequences were the key candidate gene loci contributing to FHP in sheep. This study provided a novel understanding of the Mendelian genetic basis for four horns in sheep.We performed a genome-wide association study of 100 sheep genomes to investigate the hereditary basis of the four-horn phenotype (FHP) in sheep. The results revealed that a total of 3 InDels  and 14 SNPs  in CHR2 and CHR16 were significantly associated with the four-horn trait. Furthermore, p < 1.64 × 10−8) of the InDels  for FHP in the intergenic sequence (IGS) between the MTX2 and the LOC105609047 of CHR2. Moreover, 14 significant associations (corrected as p < 1.42 × 10−9) of SNPs with the FHP phenotype were identified in CHR2 and CHR16, including five  located in the intron of the ADAMTS12 gene, eight  in the IGS between MTX2 and LOC105609047, and only one  in the IGS between HOXD1 and MTX2. Obvious divergence was also observed in genotype patterns between the FHP and others (two horns and hornless) in the HOXD1 and ADAMTS12 gene regions. An extremely significant linkage also occurred between Loci I and Loci II within 100 individuals . In summary, our study indicated that the genomic sequences from CHR2 and CHR16 contributed to the FHP in sheep, specifically the key candidate genes HOXD1 and ADAMTS12. These results improved our understanding of the Mendelian genetic basis of the FHP in sheep.Horns are an important breeding trait for sheep. However, no widely recognized viewpoint on the regulatory genes and mechanisms of horns is available, and the genetic basis of the four-horn phenotype (FHP) is unclear. This work conducted a genome-wide association study with 100 sheep genomes from multiple breeds to investigate the genetic basis of the FHP. The results revealed three significant associations (corrected as  Sheep are important domestic animals that play an important role in regional economic development and meat supply security . To dateRXFP2 expression is reportedly upregulated significantly in horn buds, indicating that RXFP2 may be a key gene in the regulation of horn development [RXFP2 contributes to the hornless phenotype in multiple domestic sheep breeds [Horns are extensively considered to be an important Mendelian trait in many ruminants, and hornless herbivores accord better with the requirements of current intensive animal husbandry management . Numerouelopment . Moreovep breeds .The four-horn phenotype (FHP) of sheep is a major research topic. The genetic locus of the FHP is located on chromosome (CHR) 2 ,12. The In the present study, an association analysis of multiple breeds and large samples was performed to identify FHP-associated genes with high-depth whole-genome sequencing. Our results can help further elucidate the Mendelian genetic basis of the FHP in sheep.® Universal DNA Library Prep Kit v2.0 . Sequencing was performed using the BGI DNBSEQ-T7 platform  with > 10× per individual.A total of 88 publicly available sheep genome datasets were downloaded from the NCBI SRA Database ) was used to remove PCR duplicates. Single-nucleotide polymorphisms (SNPs) and insertion deletions (InDels) were identified and filtered using GATK (v4.2.4.1) [https://www.cog-genomics.org/plink/1.9/ (accessed on 1 June 2023)) with a minor allele frequency (MAF) < 0.05 and a call rate < 90% removed, and principal component analysis was performed with the obtained SNPs using PLINK v1.90.Fastp (v0.20.0)  was used4.2.4.1)  with cusp-values were corrected using the Bonferroni multiple test, and the significance threshold was defined as 0.05/N, where N represents the number of genome variants for SNPs or InDels. The GeneCards database (https://www.genecards.org/ (accessed on 28 September 2023)) was used to display Gene Ontology (GO) analyses for candidate genes. Genotype patterns of genes were determined with RectChr v1.36 (https://github.com/BGI-shenzhen/RectChr (accessed on 1 August 2023)). The haplotypes of selected SNPs were constructed with DnaSP 6.12.03 (http://www.ub.edu/dnasp/ (accessed on 1 June 2023)). Linkage disequilibrium and pairwise differences (STF) of haplotypes were analyzed with Arlequin v3.5.2.2 (http://cmpg.unibe.ch/software/arlequin35/ (accessed on 1 June 2023)).A genome-wide association study (GWAS) was performed using PLINK with a general linear model. All animals were divided into three categories according to horn number: four horns (4), two horns (2), and polled (0). p < 1.64 × 10−8; MTX2 and LOC105609047. In fact, MTX2 was confirmed to be related to bone development. For example, the splice site variant of the MTX2 gene (c.543 + 1G > T) can cause human mandibuloacral dysplasia, which is characterized by postnatal growth retardation, hypotonia, and dysmorphic facial features [An approximately 310.79 Gb dataset was generated from 12 sequenced individuals and uploaded to the Sequence Read Archives (PRJNA1003686). The sequencing depth of the 12 samples ranged from 10.43- to 18.04-fold . First, features ,18.p < 1.42 × 10−9; ADAMTS12 gene, one SNP  was located in the IGS between HOXD1 and MTX2, and eight SNPs  were distributed in the IGS between MTX2 and LOC105609047.Furthermore, a total of 35,120,641 SNPs were obtained after filtering from all animals for further GWAS analysis. The GWAS results revealed that 14 SNPs exceeded the Bonferroni significance threshold  induces a change from alanine to serine and thus causes an abnormal limb phenotype in mice with macrodactyly [HOXD13 [Obviously, the SNP  with the highest associated signal value was located in the region between findings ,12. Neard others F. HOXD1  progeny . In part progeny ,12,20. Icomotion . HOXD11  mammals . HOXD11  of mice . An SNP odactyly . Severe  [HOXD13 .ADAMTS12 gene. Throughout the whole-genotype patterns, distinct differences existed in two regions of ADAMTS12 between four-horn breeds and other breeds  [ADAMTS12 and PTHrP can inhibit chondrocyte differentiation by inhibiting chondrocyte differentiation marker genes, such as Col II and Sox9 [Additionally, several SNPs located on CHR 16 with the highest associated signal values were annotated to the elopment ,27. Inteelopment . ADAMTS1gulation . The rol (PTHrP) . Other eand Sox9 . The proand Sox9 ,32.ADAMTS12 was enriched in several GO terms . Eight haplotypes were reconstructed from Loci I and Loci II  in CHR 16. The discrepancy could be due to the different genetic composition of the four-horn sheep group. Indeed, a private population’s genetic background can severely influence the identification of target-trait-associated markers when a single breed represents the target trait in a GWAS. Accordingly, different associated candidate genomic regions of goat wattle traits were identified from previous reports with various breeds [A previous study showed that the candidate genes of FHP in sheep are located in CHR2 and CHR10 . Coincids breeds ,49. Furts breeds ,51. HereGWAS was performed to identify the molecular markers and genomic regions associated with the FHP in sheep. The results confirmed that the genomic sequences from CHR2 and CHR16 contribute to this trait. This study can further elucidate the Mendelian genetic basis of the FHP in sheep.


Lactuca sativa) from accessions of its presumed wild progenitor (L. serriola), revealing a total of six genetic subgroups that reflected a differentiation based on cultivar typology. Phylogenetic relationships and principal component analysis revealed a clustering of butterhead types and a general differentiation between germplasm originating from Western and Eastern Europe. To determine the potentiality of SPET for gene discovery, we performed genome-wide association analysis for main agricultural traits in L. sativa using six models  to compare their strength and power for association detection. Robust associations were detected for seed color on chromosome 7 at 50 Mbp. Colocalization of association signals was found for outer leaf color and leaf anthocyanin content on chromosome 9 at 152 Mbp and on chromosome 5 at 86 Mbp. The association for bolting time was detected with the GLM, BLINK, and FarmCPU models on chromosome 7 at 164 Mbp. Associations were detected in chromosomal regions previously reported to harbor candidate genes for these traits, thus confirming the effectiveness of SPET for GWAS. Our findings illustrated the strength of SPET for discovering thousands of variable sites toward the dissection of the genomic diversity of germplasm collections, thus allowing a better characterization of lettuce collections.Single primer enrichment technology (SPET) is a novel high-throughput genotyping method based on short-read sequencing of specific genomic regions harboring polymorphisms. SPET provides an efficient and reproducible method for genotyping target loci, overcoming the limits associated with other reduced representation library sequencing methods that are based on a random sampling of genomic loci. The possibility to sequence regions surrounding a target SNP allows the discovery of thousands of closely linked, novel SNPs. In this work, we report the design and application of the first SPET panel in lettuce, consisting of 41,547 probes spanning the whole genome and designed to target both coding (~96%) and intergenic (~4%) regions. A total of 81,531 SNPs were surveyed in 160 lettuce accessions originating from a total of 10 countries in Europe, America, and Asia and representing 10 horticultural types. Model ancestry population structure clearly separated the cultivated accessions ( The avaThe possibility to curtail the complexity of genomes and apply NGS, increasing read depth in determined genomic regions, enabled the development of reduced-representation library based-methods (RRL) . Among tZea mays L.), black poplar (Populus nigra L.) (Elaeis guineensis Jacq.) (Solanum spp.) (Prunus armeniaca L.)  developed single primer enrichment technology  , a noveligra L.) , oil pals Jacq.) , cultivaum spp.) , and peaiaca L.) , showingLactuca sativa L.) is a commercially important crop belonging to the Compositae (Asteraceae), one of the largest angiosperm families comprising over 1,800 genera and 24,000 species  Lettuce Network (L. serriola (5) . A detailed list with all available information on the assayed accessions is provided in Plant materials consisted of 155 accessions of  Network . Plant m Network Figure 12.2L. sativa accessions  and its annotation were retrieved from https://lgr.genomecenter.ucdavis.edu/Home.php and all gene coordinates were extended by 5,000 bp upstream and 1,000 bp downstream. All selected genomic variants were intersected with these gene coordinates and labeled as gene-space variants. A panel of 50k target sites was then built by imposing a minimum distance of 3,000 bp for variants on the gene-space and a minimum distance of 200,000 bp in the intergenic regions. After two rounds of design, a final panel of 41,547 targets were successfully identified by unique probes. Each probe consisted of a 40-bp sequence. SNP calling was enabled 460 bp downstream of the probe.For probe design, a dataset including whole-genome resequencing data of 131 cessions  was conscessions . Variant2.3Genomic DNA was isolated from young leaves of a single individual per accession using a NucleoSpin Plant II Mini kit . Libraries were prepared using the “Allegro Targeted Genotyping” protocol from NuGEN Technologies , using 10 ng/μl of DNA as input and following the manufacturer’s instructions. Libraries were quantified using the Qubit 2.0 Fluorometer, and their size was checked using the High-Sensitivity DNA assay from Bioanalyzer  or the High-Sensitivity DNA assay from Caliper LabChip GX . Libraries were quantified through qPCR using the CFX96 Touch Real-Time PCR Detection System  and sequenced on the Illumina NovaSeq 6000 .2.4L. sativa cv Salinas V8  were performed with the Illumina bcl2fastq2 Conversion Software v2.20 . Read quality check and adapter trimming were carried out using ERNE v1.4.6  and Cutalinas V8  was donelinas V8  with deflinas V8  followinAll analyses were implemented in GATK Best Practices v4.1.2.0  and incl2.5H) was calculated according to the formulaGenetic diversity summary of the SNP matrix was performed by the Geno Summary tool implemented in Tassel v5.2.15 . Considep and q each represent the frequency of the different alleles for each SNP.where The polymorphic information content (PIC) was calculated according to the formula K value. Individuals were considered to belong to a specific K population if its membership coefficient (qi) was ≥0.5, whereas the genotypes with qi lower than 0.5 at each assigned K were considered as admixed. A neighbor-joining phylogenetic tree was built using the Jones–Taylor–Thornton (JTT) model with 1,000 bootstraps. Analyses were conducted in MEGA X software  during the 2020–2022 spring seasons. Plants were grown in a randomized block design with three replicates. Field trials were conducted using the standard agricultural practices for the local area of cultivation. Four traits were assayed including (i) seed color , (ii) outer leaf color before bolting stage , (iii) leaf anthocyanin content before bolting stage , and (iv) bolting time (number of days from sowing to bolting).2.7L. sativa genotypes. Six models were used including the general linear model (GLM) . GLM and CMLM were computed in Tassel v 5.2.82 (https://ritchielab.org/software/phenogram). Significant association signals were checked for their physical position on the L. sativa  V8 genome. The information about predicted genes was downloaded from the Lettuce genome browser v8.0 (https://phytozome-next.jgi.doe.gov/jbrowse/). Underlying genes and their functions were determined according to Genome-wide association analysis was performed in 155 el (GLM) , the mixel (GLM) , the mulel (GLM) , the comel (GLM) , the fixel (GLM) , and theel (GLM) . All modv 5.2.82 . MLM, MLv 5.2.82 . Manhatt33.1L. sativa cv Salinas V8, 41,547 probes were designed, of which 1,707 (4.1%) were localized in intergenic regions and 39,840 (95.9%) within genes  (An admixture-based clustering model implemented in the software ADMIXTURE  was usedK) Figure 3Lactuca serriola accessions were grouped separately from the rest in a distinct group (K = 5). Thirty-two accessions belonging to 8 out of the 10 considered cultivar types were classified as admixed, as they showed values for the highest cluster membership coefficient (qi) lower than 0.5. The Fixation Index (STF) values, measuring the population (K) differentiation based on SNP data, are reported in The subpopulations reflected to some extent a differentiation based on cultivar typology rather than country of provenance  mostly comprising the same type of cultivars. Considering the average q-value at K = 6  genotypes were grouped closely together according to the cluster K5. The distribution of the accessions in the PCA bi-plot graph corroborated population structure analysis highlighting, among L. sativa accessions, a clustering of butterhead types compared to the rest  (Atp-dependent rna helicase DEAH5. The percentage of phenotypic variation explained (PVE%) by each locus ranged from 0.03% to 45.26%. Only with the CMLM model was the highest peak found 147 kb downstream to the previous one  and in correspondence to Cytokinin dehydrogenase 3. For leaf anthocyanin content, five models detected a robust association on chromosome 5 at 86.12 Mbp in correspondence to Phototropin-2 with a PVE% ranging from 4.55% to 15.26%. In addition, all models detected the strongest STA on chromosome 9 at 152.91 Mbp within an MLO like protein 11. Also, for outer leaf color, the strongest associations were in both chromosome 5 and 9, at ~27 kbp distance from those identified for leaf anthocyanin content. For leaf color, a Signal peptidase complex subunit 3B was the candidate gene identified with GLM, CMLM, and FarmCPU on chromosome 5 at 86.15 Mbp. The three models exhibited a PVE% ranging from 2.78 to 20.32. Furthermore, all models detected the strong STA at 152.88 Mbp on chromosome 9 in correspondence to a General transcription factor 3C polypeptide 6, with a PVE% ranging from 10.46% to 47.65%.In order to narrow down to potential GWAS hotspots, we considered the top-ranked SNPs within each model Table 3.FAR1-related sequence 10 located on chromosome 7 at 164.43 Mbp. The three models exhibited a PVE% ranging from 18.71% to 48.65%.For bolting time, only GLM, BLINK, and FarmCPU revealed the strongest STA in an intergenic region at 1.77 kbp from 44.1In this work, we investigated the effectiveness of SPET as a tool for high-throughput genotyping in lettuce. This method has been developed recently, but so far, very little information about how well it performs in plants is reported. To that end, we developed and validated a novel SNP panel enriched of intraspecific SNPs from 131 resequenced genomes and consisting of over 40,000 probes across the lettuce genome. The potentialities of SPET rely on the high-efficiency enrichment of targeted loci and the high scalability of up to thousands of probes in a single reaction . In addiL. sativa and L. serriola. Indeed, the accessions of the two species were clearly separated. This evidence promotes the potentiality of SPET for phylogenetic studies, as already observed in aubergine and tomato  and ANS (Anthocyanin Synthase) that encode key enzymes for anthocyanin biosynthesis. We found four main candidate genes. Phototropin 2 and mlo like protein 11 both play a key role in leaf development and physiology. Phototropin 2 is primarily involved in the reception of light direction in the blade and has been demonstrated to promote leaf expansion and flattening  was found to be involved in outer leaf color on chromosome 9. In plants, transcription factors regulate secondary metabolism , a component of the phytochrome A and putatively involved in regulating light control during the developmental stage  and FRF (FRS-Related Factor) genes  genes . The varThe creation of a novel SPET assay in lettuce was described in this work, and its potential for genetic diversity and GWAS research was demonstrated by comparing the results with earlier discoveries using different genotyping technologies. Additional research could weigh the benefits and drawbacks of SPET in comparison to whole-genome short and long read sequencing.5Here, we presented SPET as an efficient method combining the properties of random complexity reduction techniques and arrays, allowing us to choose a set of gene-associated targeted markers for the accurate characterization of lettuce germplasm. The combination of population ancestry and phylogenetic approaches proved to be effective to better understand the genomic structure of lettuce genotypes. It is evident that the observed diversity patterns reflect the varietal composition of the collection and, to a minor extent, the geographical origin, which can be assumed primary factors underlying the diversification. Given the high marker density, the SPET panel has been used as a proof of concept for genome-wide association analysis to identify genomic regions underpinning the variation of main agronomic traits in lettuce. We confirmed previous findings, refined the genomic position of trait loci, and demonstrated the power of SPET for GWAS. These results will be useful for breeding and selection in lettuce. Further applications may include analysis of genetic relationships among species, management of genebank collections, and genetic fingerprinting for plant variety protection as well as GWAS for other additional important traits in lettuce.The original contributions presented in the study are included in the article/SG and MB conceived and coordinated the project. PT analyzed genomic data and prepared the draft of the manuscript. MB, DP, IK, CV, AL, GB, CA, and TZ performed phenotyping trials. DS, MB, RvT, and SG jointly designed the SPET array. DS and PT performed bioinformatic analysis. All authors contributed to the article and approved the submitted version.


Introduction: Body measurement traits are integral in cattle production, serving as pivotal criteria for breeding selection. Wenshan cattle, a local breed in China’s Yunnan province, exhibit remarkable genetic diversity. However, the molecular mechanisms regulating body measurement traits in Wenshan cattle remain unexplored.Methods: In this study, we performed a genome-wide association method to identify genetic architecture for body height body length hip height back height (BAH), waist height and ischial tuberosity height using the Bovine 50 K single nucleotide polymorphism Array in 1060 Wenshan cattles.Results: This analysis reveals 8 significant SNPs identified through the mixed linear model (MLM), with 6 SNPs are associated with multiple traits and 4 SNPs are associated with all 6 traits. Furthermore, we pinpoint 21 candidate genes located in proximity to or within these significant SNPs. Among them, Scarb1, acetoacetyl-CoA synthetase and HIVEP3 were implicated in bone formation and rarely encountered in livestock body measurement traits, emerge as potential candidate genes regulating body measurement traits in Wenshan cattle.Discussion: This investigation provides valuable insights into the genetic mechanisms underpinning body measurement traits in this unique cattle breed, paving the way for further research in this domain. The nutritional value of beef, characterized by its high-quality animal protein content, underscores its significance in the meat market . With anp = 0.0004) (p < 0.05)  . The gro 0.0004) . At 12 m < 0.05) . These dWith the rapid development of single nucleotide polymorphism (SNP) arrays and the reduction of the genotyping costs, genome-wide association study (GWAS) has been widely used to identify genomic regions that control economic traits in domestic animals . GWAS emIn this study, the GWAS was conducted using the Bovine 50K SNP Array to identify significant SNPs associated with six body measurement traits of 1060 Wenshan cattles, including body height (BH), body length (BL), hip height (HH), back height (BAH), waist height (WH) and ischial tuberosity height (ITH). To enhance the accuracy of our analysis and control for population structure, we employed the Mixed Linear Model (MLM) and used population structure as covariates. The primary objective of this study was to map significant SNPs, with a particular focus on common SNPs, and to identify candidate genes that play a role in body measurement-related traits. Besides, Mixed Linear Model (MLM) was used for GWAS analysis, and population structure was used as covariates. The objective of this study was to map significantly associated SNPs, especially common SNPs, and to identify candidate genes involved in body measurement related traits. This study represents the first comprehensive genome-wide investigation of body measurement-related traits in Wenshan cattle, with the aim of identifying candidate genes and potential markers. The findings of this research provide valuable insights and pave the way for further exploration of the genetic mechanisms underlying body measurement traits in Wenshan cattle. This study offers valuable insights for the further investigation of potential genetic mechanism of body measurement traits in Wenshan cattle.All of the animals employed in this research were handled following the guidelines established by the Ministry of Agriculture and Rural Affairs of China for use of experimental animals. The ethics committee of Yunnan Agricultural University  approved of the entire research.According to the date of birth, 1,060 Wenshan cattles  aged around 12-monthold (±15 days) were collected from Yunnan Guduo Agriculture and Animal Husbandry Co., Ltd. in Yunnan Province, China. These cattles were reared under the homologous management conditions and similar environments. In this study, 6 phenotypic traits including BH, BL, WH, BAH, ITH and HH were measured simultaneously for each individual by the method as in Ref. . Ear tisSPSS25 software was used to make descriptive statistics, including the number, minimum, maximum, mean, standard deviation, and coefficient of variation for our 6 body measurement traits in Wenshan cattle. Phenotypic correlations among the body measurement traits were calculated within the R statistical environment and used to determine whether they reflected the relationships between the GWAS results.p > 10–6 were retained. All markers located on sex chromosomes or in unmapped regions were excluded. Missing genotypes were imputed using the Beagle software  kit according to the manufacturer’s instructions. After that, the DNA was quantified and genotyped using the Bovine 50K SNP Array and the PLINK v1.90 Software was used for quality control . Brieflysoftware . After tTo investigate population structure of Wenshan cattle, population stratification was detected using eigenvalues and eigenvectors calculated by GCTA software  based onpost hoc Fisher’s least significant difference (LSD) test using the Agricolae package (https://www.R-project.org/). The log-transformed phenotypic data were eventually used in the GWAS analysis. The single variable mixed linear model (MLM) of GEMMA software , followed by  package  in R 4.2software  was emplWhere y is the phenotypic value vector; γ is the regression coefficient; PCA is a covariate vector; β is the fixed effect vector of birth year, birth month and sex; α is SNP effect value vector; μ is the residual polygenic effect value vector, which follows the [a∼MVN plots of GWAS were drawn using the R package “qqman”.Using the Bonferroni correction, the genome-wide significant thresholds and suggestion threshold were set as https://uswest.ensembl.org/index.html), was used to annotate the significant SNPs identified for the 6 body measurement traits in Wenshan cattle. Candidate genes were then selected according to their biological function.Candidate genes were selected within 0.25 Mb upstream or downstream of the significant SNPs based on LD value for Wenshan cattle population. The major genome browser, Ensembl , ranging from 0.603 to 0.961. The highest correlation was found between BH and BAH .The phenotypic correlation coefficients for the 6 body measurement traits were shown in After elimination of the monomorphic loci and loci with minor allele frequency (MAF) below 1%, from the total of 63,791 SNP markers, 46,284 SNPs remained for associations with body measurement of Wenshan cattle. The density distributions of the filtered SNPs are shown in In our study, in order to investigate the genetic structure of 1,060 Wenshan cattle, quality-controlled SNP markets were utilized for principal component analysis (PCA). The analysis revealed that there were no distinct genetic clusters among the samples, as shown in p-value ranging from 1.48 × 10−5  to 2.3 × 10−68 , and the MAF ranging from 0.012  to 0.401 .In total, 8 SNPs (listed in p = 1.53 × 10−64), which located on BTA13: 5,348,767 bp. For BL trait, a total of 6 SNPs were detected, with two located on chromosome 17 and the others on chromosomes 2, 13, 22 and 24, respectively. Among them, the most significant SNP was Affx-41,315,554 (p = 1.58 × 10−45), which located on BTA17: 52,029,736 bp. For HH, WH and BAH trait, 6 identical SNPs were detected for every one of them, with two located on chromosome 17 and the others on chromosomes 2, 3, 13 and 22, respectively. For HH, WH and BAH, Affx-41,315,554 was the most significant one among their selected SNPs, which located on BTA17: 52,029,736 bp, and the p-values were 2.52 × 10−64, 6.26E×10−68 and 2.3 × 10−68, respectively. For ITH trait, a total of 4 SNPs were detected, with two located on chromosome 17 and the others on chromosomes 13 and 22, respectively. Among them, the most significant SNP was Affx-41,315,554 (p = 8.96 × 10−64), which located on BTA17: 52,029,736 bp.For BH trait, a total of 6 SNPs were detected, with two located on chromosome 17 and the others on chromosomes 3, 13, 16 and 22, respectively. Among them, the most significant SNP was Affx-257,095,832 (The GWAS results for the 6 body measurement traits were illustrated by Manhattan plots and Quantile-Quantile (Q-Q) plots in Particularly, we found that 4 significant SNPs including BTA13:5348767bp, BTA17:50965063bp, BTA17:52029736bp and BTA22:39829835bp were associated with all the 6 body measurement traits . BTA3:10HIVEP3, which located on BTA3, was associated with WH, BAH, BH and HH, respectively. Through reviewing relevant research reports, we found that Scarb1, AACS and HIVEP3 as candidate genes for body measurement traits according to their biological functions.To identify candidate genes associated with the 6 body measurement traits of Wenshan cattle, we searched for genes within a 0.5 Mb of the identified SNPs using the UCSC genome browser. In total, 21 genes were identified within or overlapping the candidate regions of 8 SNPs . And, 1,p-value threshold for statistical significance must be adjusted to control the overall false positive rate. In order to control false positives or ensure the accuracy of analysis results, the Bonferroni correction approach which may be a little strict, and is often applied in GWAS  on BTA17 was related to all the 6 body measurement traits. Scarb1 produced by the SCARB1 gene, is the major receptor for high-density lipoprotein (HDL). The role of Scarb1 in bone formation has been the subject of conflicting findings in recent research. Scarb1 KO mice was osteopenic relative to the wild type. Morever, osteoblast and osteoclast-related mRNAs of KO mice greatly decreased compared to WT mice, suggesting that Scarb1 is required for normal bone differentiation. More strangely, the latest research from Scarb1 KO mice and thought Scarb1 did not contribute to bone homeostasis. However, it is established that HDL, via its main protein component apolipoprotein A1, is essential for normal bone formation by affecting osteoblast , is a big zinc finger protein and an essential regulator of bone formation. HIVEP3−/− mice reveled osteosclerosis due to increased osteoblast activity and increased bone mass. Further research found that, HIVEP3 regulated the bone mass by controlling the protein levels of Runx2 which was the principal transcriptional regulator of osteoblast differentiation  activity, particularly downstream of WNT signaling in osteoblasts. Mutations in the HIVEP3 gene can disrupt this interaction, leading to abnormal activation of ERK and hyperactivity of osteoblasts in vivo  and Hip Geometry  gene is known for the synthesis of biologically important lipogenic substances (AACS in vivo decreases peroxisome proliferator-activated receptor γ (PPARγ) and CCAAT/enhancer binding protein α (C/EBP α), which can enhance bone resorption and have a critical role in relationship between obesity and bone loss  and adult femur epiphysis. Collectively, these findings indicated that AACS may be involved in bone homeostasis through its impact on adipogenic transcription factors such as the C/EBP family and PPARγ. Then, we speculate that AACS plays an important role associated with the bone homeostasis, which is worthy of further study and can be selected as a candidate gene for body measurement traits.The acetoacetyl-CoA synthetase (bstances . Previoubstances . In 1999bstances  demonstrone loss . In obesntiation . In situScarb1, AACS and HIVEP3 have been indicated to be related to bone formation and are also discovered rarely in livestock body measurement traits, which can be used as candidate genes for Wenshan cattle. This study offers valuable insights for the further investigation of potential genetic mechanism of body measurement traits in Wenshan cattle.To sum up, 8 SNPs were detected to be associated with 6 body measurement traits. Interestingly, 6 of these SNPs were detected in more than one of our 6 body measurement traits, which was consistent with their phenotype correlations. Meanwhile, we found 21 candidate genes located nearby or within the associated SNPs. Among them,


Body size is a significant economic trait for dairy cows due to its significant influence on the production, health, selection, and environmental adaptation of dairy cattle. However, measurements of body size are widely used to predict body weight. Managers of dairy farms also use them to assess the development and growth of the animals during the rearing process. Therefore, understanding the genetic basis of inter-individual variation of body size might accelerate future efforts aimed at dairy cattle improvement. Our genome-wide association study identified several genes and functional pathways associated with body size traits in Chinese Holstein cattle. Results of this study might serve as a foundation for genetic improvement programs for dairy cattle that are based on molecular genetics.−7) using the fixed and random model circulating probability unification (FarmCPU) model. Several genes within 200 kb distances (upstream or downstream) of the significant SNPs were identified as candidate genes, including MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L. Moreover, genes within 200 kb of the identified SNPs were significantly enriched (p ≤ 0.05) in 25 Gene Ontology terms and five Kyoto Encyclopedia of Genes and Genomes pathways. We anticipate that these results provide a foundation for understanding the genetic architecture of body size traits. They will also contribute to breeding programs and genomic selection work on Chinese Holstein cattle.Body size is one of the most economically important traits of dairy cattle, as it is significantly associated with cow longevity, production, health, fertility, and environmental adaptation. The identification and application of genetic variants using a novel genetic approach, such as genome-wide association studies (GWASs), may give more insights into the genetic architecture of complex traits. The identification of genes, single nucleotide polymorphisms (SNPs), and pathways associated with the body size traits may offer a contribution to genomic selection and long-term planning for selection in dairy cows. In this study, we performed GWAS analysis to identify the genetic markers and genes associated with four body size traits  in 1000 Chinese Holstein cows. We performed SNPs genotyping in 1000 individuals, based on the GeneSeek Genomic Profiler Bovine 100 K. In total, we identified 11 significant SNPs in association with body size traits at the threshold of Bonferroni correction (5.90 × 10 Body size in humans, cattle, and other domestic animals has been widely investigated ,2,3,4. BFor over a decade, GWAS has become an effective approach for detecting genetic markers that are associated with multiple economic traits in animal production. Some GWASs on body size have been carried out across different populations in order to understand the genetic mechanisms of growth traits, such as Chinese Holstein cattle , SimmentThe entire procedures involving animal care, collecting samples of hair follicles, and measurement of phenotypic traits were performed in strict compliance with the guidelines provided by the China Council on Animal Care and the Agricultural Ministry, China. This research was also accepted by the Institutional Animal Care and Use Committee of School of the Yangzhou University Animal Experiments Ethics Committee (License Number: SYXK (Su) IACUC 2020-0910), Yangzhou University. During the collection of samples and data, no animals were uncomfortable or malnourished.Four farms located in the province Jiangsu, China, were used to select 1000 Chinese Holstein cows for the experiment. Body height (BH), body depth (BD), chest width (CW), and angularity (ANG) were measured by three trained technicians. They were recorded on a point scale from 1 to 9 in accordance with the China National Standard Code of the practice of type classification in Chinese Holstein (GB/T 35568-2017). In addition, 50 hair follicle samples were obtained from each cow for genotypic analysis.The computer-based software IBM-SPSS, , was used to estimate the pairwise Pearson correlation coefficients and to determine the descriptive statistic of phenotypic traits .ijklmY is the phenotype in the jth year, kth season, and lth parity of the mth individual from ith herd; u is overall mean of the population, iHerd is the herd effect according to a cow’s origin from one of the four herds; jYear is the jth year effect, kSeason is the kth season effect, and lth parity; a is the additive effect of mth individual, and e is the residual in the jth year, kth season, and lth parity of the mth individual from ith herd. All effects were treated as fixed except the additive effect. There are at least three generations of pedigree data (age) available for the cows (2009–2020), their parities range from 1 to 4, and four seasons during the measurement time were June–August, September–November, December–February, and March–May [Genetic correlations and heritability among the four traits of body size were estimated using the DMU software   with thearch–May ,22.http://www.neogenchina.com.cn), accessed on 28 June 2020), based on ARC-UCD1.2/bosTau9 as the genome reference. Quality control was performed using Plink 1.90 software [p < 1.0 × 10−6). A total of 984 cows and 84,406 SNPs were kept for subsequent analyses following quality control.Hair follicle samples have been used to extract genomic DNA. DNA extraction and genotyping was carried out using the GeneSeek Genomic Profiler Bovine 100 K SNP Chip at Neogen Biotechnology  Co., Ltd.  model . Iteratip-value after substitution. The p-values and the associated marker map are used to update the selection of pseudo QTNs using the SUPER algorithm (Settlement of MLM Under Progressively Exclusive Relationship) [Markers have their own ionship)  in a REMp-value (5.9 × 10−7) of the significance for SNPs was determined based on the Bonferroni correction method  rate was set at 5%. The (0.05/N) , where NThe UCSC genome browser via an Asian server for cow assembly (April 2018) (UCSC Genome Browser Gateway) (accessed on 29 May 2022) and the full National Center for Biotechnology Information gene (NCBI) database  (accessed on 29 May 2022) were used to identify genomic regions and candidate genes. The linkage disequilibrium (LD) analysis performed in [p value was set at p ≤ 0.05. The online Search Tool for the Retrieval of Interacting Genes (STRING) database, v11.0 [We submitted the GWAS candidate genes to the Database for Annotation, Visualization, and Integrated Discovery (DAVID)  for the e, v11.0 , was useThe descriptive statistics of phenotypic measurements of body size traits , including mean, maximum, and minimum values, are shown in x-axis represents genomic position, while the y-axis displays the negative of the log p value.The GWAS analyses were performed based on the Farm-CPU model. Quantile–quantile plots suggested no inflation or clear systematic bias in the study . Because−7) after Bonferroni correction, as shown in The results of the GWAS analyses identified 11 significant SNPs related to body size conformation traits in Chinese Holstein cattle, which statistically passed the threshold (s 5.9 × 10MYH15), the SNP rs109930583 on Chr. 6 is located within LOC112447047 gene and chromosome 6 C4orf17 homolog gene (C6H4orf17), the SNP rs109824125 on Chr. 14 is close (100 kb) to KH RNA binding domain containing gene (KHDRBS3), and the SNP rs42188649 on Chr. 29 is located within aryl hydrocarbon receptor interacting protein gene (AIP). Two SNPs were associated with BD trait; the SNP rs133735152 on Chr. 24 is located near (100 kb) to the DCC netrin 1 receptor gene (DCC), and the SNP rs43286429 on Chr. 1 is located close (100 kb) to the LOC112447004 gene. Moreover, three SNPs were associated with CW; the SNP rs110355602 on Chr. 10 is positioned close (200 kb) to sulfide quinone oxidoreductase gene (SQOR), the SNP rs43615333 on Chr. 10 is located within ubiquitin associated protein 1 like gene (UBAP1L), and SNP rs42095998 on Chr. 26. is positioned within vesicle transport through interaction with t-SNAREs 1A gene (VTI1A). In addition, one SNP was related to the ANG trait; the SNP rs135918869 on Chr. 5 is located close (100 kb) to the coiled-coil domain, containing 59 gene (CCDC59)  .p ≤ 0.05) by the set of genes, may have been associated with body size involving 10 pathways .To gain a deeper understanding of the biological functions shared by the trait-associated genes, we analyzed 105 genes  within tA PPI analysis was conducted using the STRING database for all the genes previously analyzed as part of the functional analysis. MYH15 gene, which belongs to the myosin heavy chain gene family. Myosins are superfamily genes of eukaryotic motor proteins that bind actin and use ATP hydrolysis energy to contribute significantly to a wide range of biological processes, such as muscle contraction, cytokinesis, cell motility and contractility, and intracellular trafficking [MYH15 gene is listed as a plausible candidate gene for detecting pleiotropic polymorphisms for body height, reproduction, and fatness in beef cattle [MYH15 gene has been reported in several studies in chickens, such as RNA sequencing results, which validated that the MYH15 gene was associated with muscle structures [MYH15 gene was also related to skeletal muscle myoblast proliferation and differentiation among layer and broiler chickens [KHDRBS3 gene, which is a member of the signal transduction and the activator of RNA (STAR) family proteins. It has been reported that this gene is associated with the 305-day milk yield trait in two cattle breeds: Girolando crossbreed cattle [Body size plays a crucial role in dairy cattle’s production, health, selection, and environmental adaptation. However, body size measurements are widely used to predict body weight. In addition, they are commonly used by dairy farmers to monitor growth and track the development of their animals’ developmental progress during the rearing period. Therefore, mapping and detection of significant SNPs and candidate genes influencing body size traits have high economic benefits for dairy cattle breeding. In the current work, we identified 11 SNPs and their associated candidate genes , which sfficking . A previf cattle , as wellf cattle . Over thructures , as wellructures . The MYHchickens . The SNPchickens . This SNd cattle  and primd cattle . Gene ned cattle , as welld cattle . Deng etd cattle  reportedd cattle ,46. Reced cattle , and a gd cattle . Anotherd cattle .DCC gene. Several studies, including that of Sunirmal Sheet et al. [DCC gene to be probably associated with morphological, milk, meat, healthy, and reproductive traits in three indigenous Iranian river buffaloes [In this study, SNP rs133735152 was significantly associated with the BD trait, which is close to the t et al. , identifp = 9.45 × 10−11), rs110355602, was located on Chr. 10, which is associated with BD trait; the rs110355602 SNP is located close to the SQOR gene. The SQOR is a mitochondrial inner membrane-coding gene in humans that plays an essential role in catalyzing and metabolizing hydrogen sulfide (H2S.) [SQOR gene has been also associated with the growth and muscle development traits in Chinese Simmental beef cattle [UBAP1L gene; this gene was also significantly associated with BD trait in our results.The most significantly associated SNP in the present analysis (e (H2S.) . Similarf cattle . On the p ≤ 0.05). Among them, five pathways  were reported to be associated with residual feed intake in Australian Angus cattle [Various metabolic activities occur during an animal’s body growth and development, so it is reasonable that the metabolic pathways are one of the significantly enriched KEGG pathways in this study. In the present work, many GO terms  and KEGGs cattle . Moreoves cattle . In conts cattle . In addis cattle .ALDH3B1 gene, three members of ADHs genes  were displayed in the top six significantly enriched GO terms . Moreover, these genes are also revealed in most KEGG pathways in the current study. Zhao et al. [ADH6 in three enriched KEEG pathways  of the functional analysis of differentially expressed genes in the down and up group are based on transcriptome analysis of ruminal epithelia related to the gradual high fermentable dietary transition in beef cattle. The ADH1C gene has an interactive effect with vitamin A supplementation level on intramuscular fat content in beef steers [ADH1C gene to be downregulated in the lipolysis process in poultry based on the integration of RNA-Seq and microarray data approach. The ADH1C gene also has been reported in another study using text mining technique to identify genes associated with meat quality and carcass traits in Hanwoo cattle [ALDH3B1 gene in a GWAS work for backfat thickness in Nellore cattle [Besides the o et al.  reportedf steers , while Gf steers  reportedo cattle . In theio cattle  found tho cattle . This geo cattle . Rafael e cattle .There is some interest in the fact that the most significant genes in this study were related to growth and body development traits of beef cattle in several previous studies, which might be attributed to the importance of body size as one of the main breeding selection criteria in beef cattle breeding.MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L); in addition, five KEGG pathways and 25 GO terms were significantly enriched. These results provide novel insights into the molecular breeding basis and highlight useful information for understanding the genetic architecture of body size traits in dairy cattle. Thus, they contributed to the genomic selection of Chinese Holstein cows. These findings offer valuable information. However, further studies will be required to investigate the biological functions and the molecular regulatory network of the candidate genes.In summary, we identified 11 significant SNPs associated with four body size traits  using FarmCPU-based GWAS in Chinese Holstein cattle. This study revealed the six most promising candidate genes (


LEMD3 and HMGA2 genes, which were previously reported as being responsible for dwarfism in different species. In conclusion, MERI and MUG breeds seem to share a genetic basis of dwarfism, which differentiates them from the small PPP breed.The identification of the dwarf phenotype in chicken is based on body weight, height, and shank length, leaving the differentiation between dwarf and small breeds ambiguous. The aims of the present study were to characterize the sequence variations associated with the dwarf phenotype in three Italian chicken breeds and to investigate the genes associated with their phenotype. Five hundred and forty-one chickens from 23 local breeds  were sampled. All animals were genotyped with the 600 K chicken SNP array. Three breeds were described as “dwarf”, namely, Mericanel della Brianza (MERI), Mugellese (MUG), and Pepoi (PPP). We compared MERI, MUG, and PPP with the four heaviest breeds in the dataset by performing genome-wide association studies. Results showed significant SNPs associated with dwarfism in the MERI and MUG breeds, which shared a candidate genomic region on chromosome 1. Due to this similarity, MERI and MUG were analyzed together as a meta-population, observing significant SNPs in the  GHR)  during routine health controls by the public veterinary service.® tubes containing EDTA as an anticoagulant. All studied breeds were collected from at least three different poultry farms and conservation centers to ensure a representative sampling of the breed.The dataset was previously described in Cendron et al.  and consDNA extraction and genotyping were performed at Neogen  using a commercial kit and the Affymetrix Axiom 600 K Chicken Genotyping Array, representing 580,961 SNPs. The Gallus_gallus 5.0 chicken assembly (accession number: GCA_000002315.3) was used in this study as the reference genome  includinhttps://github.com/BGI-shenzhen/VCF2Dis, accessed on 1 November 2022), and visualized with R package “treeio” [The population structure of the final dataset was estimated with principal component analysis (PCA) and multidimensional scaling plot (MDS). First, we used the PLINK command “--keep” to extract the MERI, MUG, and PPP breeds and the control population  from the .ped file. This subset was used for the population structure analysis performed through PCA, MDS, and Neighbor-Joining Tree (NJ). PLINK 1.9 was used for this purpose and to generate eigenvectors and eigenvalues, and the ggplot2 (v3.1.0) package of R software was used to visualize the results of PCA . The 1-i“treeio” . Pair-wiA genome-wide association study (GWAS) was performed, comparing the case (supposed dwarf breeds) and control populations. The MERI, MUG, and PPP breeds do not have normal-size chicken counterparts. The breeds used in the control population were chosen based on phenotypic data such as live body weight, wingspan, body length, breast width, shank width, and shank length . Particup-value (0.05) was divided by the number of tests performed as follows: cutoff = −log10(0.05/number of variants). A genomic control inflation factor, the lambda was calculated in each study to evaluate the confounding due to population stratification. Variants were then annotated using snpEFF version 5.1 with default parameters in vCard File (VCF) [p-values were taken into consideration for further analyses. The VCF file from the previous step was then used as input in Ensembl Variant Effect Predictor (VEP) to evaluate the effect of SNPs on the genes, transcripts, and protein sequence. Fst values for single SNP loci observed between the experimental and control populations were calculated with PLINK 1.9. In order to compare the linkage disequilibrium (LD) around the HMGA2 gene, we computed the r2 between the markers of this region against the highest associated variant. Moreover, Haploview software [HMGA2 gene in the studied populations. The SNPs used for this purpose had MAF >0.10 and the Haploview software calculated the block  if 95% of informative  comparisons were in strong LD.We performed GWAS using a single dwarf breed as well as all of the dwarf breeds combined, compared against the control population. GWAS was performed in GEMMA software v0.94.128 with linear-mixed models, adjusting for the effect of sex and relatedness, using a previously calculated matrix . Upon thle (VCF) . Only SNsoftware  was usedIn GHR gene located on chromosome Z, no significant SNPs in or around this gene were detected (−8), of which 215 were associated with known genes  annotated in 43 genes . Only MUG showed a unique block in HMGA2  gene was commonly shared among all groups  in this region (5:26600000–27300000). The RGS6 gene negatively regulates G protein signaling by activating GTPase activity. Although the precise physiological roles of RGS6 are unknown, this gene is linked to cellular stress responses and induces cell cycle arrest and apoptosis [DPF3 in MM, MERI, and PPP, and SIPA1L1 in MM and MERI. SIPA1L1 is the signal induced proliferation associated 1-like 1 gene that has been implicated in osteogenesis in vitro [DPF3), which has a role in heart development. The DPF3 gene is specifically expressed during heart development in mice, chickens, and zebrafish, and the knockdown of DPF3 in zebrafish resulted in myocardial contractility and incomplete heart formation; thus, DPF3 was categorized as a major gene for heart development, but it is also involved in skeletal muscle [IGF1R, which was annotated with upstream genetic variation in the MM and MUG analyses. Reduced IGF1R expression has been reported in dwarf Holstein calves [Single breeds showed similar signals on GGA5 compared to the control population. All GWAS analyses shared a common annotated gene , which regulates the downstream signaling of many genes related to cell growth [HMGA2 gene were responsible for modification in the translated protein. Indeed, they were all intronic or intergenic variants. In another study in humans, the role of LEMD3 in the pathogenesis of osteopoikilosis and short stature was highlighted [LEMD3 is located in the same genomic region as HMGA2 and was identified in MM and MUG analyses in the present study. Carneiro et al. [LEMD3 functional mechanism is an antagonist to transforming growth factor-β signaling (TGF-β) [LEMD3 has also been reported in the modulation of ear size in pigs [LEMD3, the WIF1 gene has been reported in pigs, rabbits, and dogs as a candidate gene for the modulation of growth [WIF1 binds to Wnt proteins and inhibits the activity of the Wnt/b-catenin pathway. The Wnt/b-catenin pathway may regulate proliferation and differentiation in many tissues including controlling the growth of connective tissue by regulating connective tissue growth factor (CTGF) and fibroblast cells in the skin [WIF1 gene was reported with an intronic variant at position 1:34008227 in the MM and MERI analyses.The most important findings in the present study come from the identified region on chromosome 1. The  chicken , rabbit  chicken , and in  chicken . Moreove chicken  reportedl growth . In thishlighted . LEMD3 io et al.  found th (TGF-β) . LEMD3 h in pigs , underlyf growth . WIF1 bithe skin ,77,81. RHMGA2 gene, suggesting a strong selection in that part of the genome. These results were in line with those reported by the Italian Association of Fancy Breeders for these breeds . In fact, the MERI is the only breed to be officially recognized both as an official breed and at the same time as a dwarf. This result underlines that, over time, crossbreeding has led to selective pressure in MERI birds, precisely in the identified genes on chromosome 1. Regarding the PPP birds, these candidate genes were not identified in our analyses. In fact, it is reported that PPP is not an officially recognized breed because of its diverse phenotypic variability. Finally, the MUG breed seems to have different variants with respect to breeds with normal size. Based on the LD results, however, the chromosome 1 locus does not appear to be as significant as it seems to be for the MERI breed. Generally, the SNP arrays were designed with variants mostly mapped to non-coding genomic regions. Indeed, in the 600 K Affymetrix® Axiom® array for chickens, only 3.7% of SNPs out of 580,954, were in the coding regions [HMGA2), which may lead to a change in the final protein. However, this work has highlighted candidate genes for the dwarfism phenotype in MUG and MERI chickens. Further studies with deeper or targeted sequencing of the candidate regions could shed light on the main features of genes in the studied breeds.Our results highlight that the Fst values of the variants on GGA1 segregate in the control population. The Fst analysis evaluated the important differences that exist between the populations for the studied genomic region on GGA1. A different view of what happens on chromosome 1 was provided by the LD results.  regions . This coIn conclusion, we identified candidate genes for dwarfism in MUG and MERI chickens and highlighted the importance of genes on chromosomes 1 and 5, while we showed that the PPP population does not appear to be a true dwarf breed when we consider the candidate genomic loci and morphological data. Further studies should focus on the validation of results obtained in the present manuscript through a larger sample size and the genotyping of identified candidate genes.


The snap bean is the most commonly grown vegetable legume worldwide, and its pod size is both an important yield and appearance quality trait. However, the improvement of pod size in snap beans grown in China has been largely hindered by a lack of information on the specific genes that determine pod size. In this study, we identified 88 snap bean accessions and evaluated their pod size traits. Through a genome-wide association study (GWAS), 57 single nucleotide polymorphisms (SNPs) significantly associated with pod size were detected. Candidate gene analysis showed that cytochrome P450 family genes, WRKY, and MYB transcription factors were the predominant candidate genes for pod development, and eight of these 26 candidate genes showed relatively higher expression patterns in flowers and young pods. A significant pod length (PL) SNP and a single pod weight (SPW) SNP were successfully converted into kompetitive allele-specific polymerase chain reaction (KASP) markers and validated in the panel. These results enhance our understanding of the genetic basis of pod size, and also provide genetic resources for the molecular breeding of pod size in snap beans. Phaseolus vulgaris L.) (2n = 2x = 22) is the most commonly grown grain and vegetable legume in the world. The common bean belongs to the Leguminosae family, which includes tropical legumes such as common beans (P. vulgaris L.), cowpeas [Vigna unguiculata (L.) Walp.], and soybeans (Glycine max L.), and cool season legumes such as peas (Pisum sativum L.) and faba beans (Vicia faba L.). It was recognized that the common bean was independently domesticated in two regions, now Mexico and South America, and formed the Middle American and Andean gene pools   , the str. Over 4,900 common bean accessions have been collected in China, showing notable diversity . Wu et aUromyces appendiculatus)  . All accFor each accession, at least 10 representative immature pods were collected for pod size evaluation. These pods were grown for about 15 days after pollination, which was when they reached the commercial maturity stage and could be used only as vegetables. The PL was measured using a ruler, PW and PT were measured on the middle section of pods using a vernier caliper, and the single pod weight (SPW) was measured using a scale. The phenotype data of two replications on each trait were averaged. The data analyses, including frequency distributions, correlation analyses, and variance analyses, were conducted using Origin software (Version 2018). Multivariate analysis of variance (MANOVA) was conducted using SPSS software (v.22) under a general linear model (GLM).P. vulgaris v2.1 (https://phytozome-next.jgi.doe.gov/info/Pvulgaris_v2_1) using Burrows-Wheeler Aligner (BWA) software (http://bio-bwa.sourceforge.net/). Subsequently, the aligned files were converted to binary alignment map (BAM) files using SAMtools software, and, finally, the valid BAM files were used for SNP calling using the genome analysis toolkit (GATK) “UnifiedGenotyper” function (http://www.broadinstitute.org/gatk/). A total of 20,175,784 SNPs were identified. The criteria of a missing call rate > 0.5 and minor allele frequency (MAF) < 0.05 were used to filter the original SNPs. Finally, 603,910 high-quality SNPs were retained for population structural analysis and GWAS.The genotype data of the 88 accessions were retrieved from the study by R2 values. The SNPs showing logarithm of the odds (LOD) values of ≥3.5 or 6.0 were regarded as significant SNPs. If two significant SNPs were located in the same linkage disequilibrium (LD) block  under a compressed mixed linear model (MLM) that accounted for population structure. The genetic effect of each SNP to the total phenotypic variation was calculated based on the marker D) block , they weP. vulgaris v2.1 (https://phytozome-next.jgi.doe.gov/info/Pvulgaris_v2_1) to determine their physical position under an e-value cut-off of 1e−10. By comparing the physical positions of the known pod size QTLs/genes and new QTLs detected in this study, their syntenic relations were determined, and if only two QTLs/genes were located in a LD block they were considered to represent a single gene/locus.The reported pod size QTLs/genes in common bean were first identified through a literature search, and then the sequences of the linked markers to these genes were blasted against the common bean reference genome P. vulgaris v2.1 genome. Based on their annotation information, the genes with functions in developmental processes were considered as possible candidate genes. In addition, the gene expression data in different tissues were retrieved from P. vulgaris v2.1 and analyzed using Tbtools software (v1.098).According to the estimated LD decay distance for common bean , the potTo facilitate the molecular breeding of pod size in the common bean, the sequence of the significant SNP was used to design kompetitive allele-specific polymerase chain reaction (KASP) markers using a local script. The KASP genotyping for the 88 accessions was performed on an IntelliQube workstation  following the PCR process described in the study by As shown in In a previous study, the 88 accessions were genotyped by re-sequencing and used for detecting SNPs significantly associated with rust resistance . The resOf the significant SNPs found, nine were for PL, seven for PT, 22 for PW, and 19 for SPW Table 2.Phvul.004G005700- Phvul.004G006100) formed a cluster from 368,531 bp to 403,445 bp on chromosome Pv04, which has a 24.471 kb distance to the PL SNP Pv_0285901. We also identified two MYB transcription factor family gene models  and three WRKY protein gene models  that play crucial regulatory roles in plant developmental processes , the auxin response factor (Phvul.003G128800), cytochrome P450 gene , the hormone biosynthesis and regulation genes (Phvul.011G015300 and Phvul.006G159300), the MYB transcription factor Phvul.010G075500 and the WRKY gene Phvul.008G090300, suggesting that hormonal and nutritional signaling are also important mechanisms related to pod size control.The results of an elopment Figure 4CC and TT genotypes for 88 accessions, and the haplotype Pv_0026128-CC showed a longer PL  has a 92.501 kb distance to the PW SNP Pv_0203965 , indicating that they may represent a single locus. This pleiotropic effect on PW and PT was also investigated in a previous study, in which a pod width QTL PWI4 and a pod thickness QTL PT4.1 were mapped on the same interval . We alsoPhvul.004G005400 and Phvul.004G005800, showed higher expression levels in flowers and young pods (BnaC7.ROT3 and BnaA9.CYP78A9 both mediate silique length by affecting cell elongation in rapeseed through the unknown auxin biosynthesis pathway (NOA1) Phvul.011G015300 was considered as a candidate gene of the PL SNP Pv_1016502, which is involved in growth regulation and hormonal signaling in plants  affects the seed weight and silique length by regulating cell growth in polyploid rapeseed (As a fruit organ, pod development is a complex process that is regulated by plant hormones and transcription factors . Cytochrung pods Figure 4 pathway . Thus, in plants . The auxrapeseed . In addiIn summary, we detected 57 genomic regions that were significantly associated with pod size in Chinese snap bean germplasms, which provides genomic resources for molecular breeding of pod size. The two developed KASP markers could be used directly in the marker-assisted selection of pod size traits. The results also enhanced our understanding of the molecular mechanism of pod development and established a springboard for future pod size gene cloning and its application in snap bean cultivation.All sequencing data of the 88 bean accessions are available from NCBI under BioProject accessions no. PRJNA934100.XYW, GL, and JY conceived and designed the research; ML performed the experiments, analyzed the data, and wrote the draft manuscript; BW, XHW, and ZL contributed to germplasm collection; YW, JWa, YS, and WD contributed to phenotype evaluation. JD and JWu helped analyzing the data. XYW analyzed the data and revised the manuscript. All authors contributed to the article and approved the submitted version.


However, the genetic basis of this selection response is poorly understood. To better address this, we employed the Pekin duck Z2 pure line, in which the breast muscle weight was increased nearly 3-fold after 10 generations of breeding. We UTP25 and FBRSL1 were most likely to contribute to breast muscle weight improvement. The major allele frequencies of these 2 loci increased gradually with each generation following the same trend. Additionally, we found that a copy number variation region containing the entire EXOC4 gene could explain 1.9% of the variance in breast muscle weight, indicating that the nervous system may play a role in economic trait improvement.We identified 53 selected regions between the first and tenth generations, and 93.8% of the identified variations were enriched in regulatory and noncoding regions. Integrating the selection signatures and genome-wide association approach, we found that 2 regions covering 0.36 Mb containing Our study not only provides insights into genomic dynamics under intense artificial selection but also provides resources for genomics-enabled improvements in duck breeding. Artificial selection experiments can help to understand the mechanism that allows populations to adapt to strong selection pressure , 2. In cAnas platyrhynchos) (NCBI:txid8839) are among the most economically important waterfowl; they can provide meat, eggs, and down for humans and show important characteristics, such as a short generation interval, high reproductive ability, and a long but traceable history of artificial selection [Studies of 2 representative bidirectional selected resource populations in which chicken body weight  and abdoelection . MoreoveHigh-quality reference genomes are the foundation for genetic research and molecular marker breeding, which can support innovations in sustainable animal production , 26. To Herein, we utilized a Pekin duck pure line selected for breast muscle weight for 10 generations. The foundation stock was the local conserved population in Beijing, China. After 10 generations of high-intensity artificial selection, the breast muscle weight of the Pekin duck Z2 line increased from 80 g to 220 g. These data were investigated to elucidate the dynamic response patterns in the Pekin duck genome under artificial selection.To understand the genetic basis of short-term, intense artificial selection, we utilized a Pekin duck pure line selected for breast muscle weight at 6 weeks of age. We implemented the whole-genome resequencing of 119 ducks across 10 generations  and mapped the resequencing data to the high-quality reference genomes assembled in this study. Using 8.60 million of single-nucleotide polymorphisms (SNPs), we tested for dynamic changes in population structure under short-term intense selection, and we assessed the genome for signatures of short-term intense selection associated with breast muscle weight.de novo assembly of the Pekin duck genome, we adopted a combination of PacBio long-read sequencing, BioNano optical mapping, and Hi-C technologies. We first generated 65.9 Gb of PacBio long reads with 50× genome coverage, 49 Gb of BioNano high-quality reads with 41× genome coverage, and 106 Gb of Hi-C reads with 82× genome coverage  [t test, P < 2.22e-16) when using the new assembly.We next evaluated the quality of the new assembly. BUSCO (015 008)  (v5.1.2)To track the dynamic change process in the Pekin duck genome driven by intense selection, we conducted the resequencing of 30 individuals  from G1 to G10 in intervals of 3 generations. We mapped all of the paired-end reads to the GCA_003850225.1 assembly with an average coverage rate of 94.77% and an average depth of 7.08× (6.26–8.01×) . These sBreast muscle weight was calculated according to the following equations. First, breast muscle volume (BMV) = BB × KL × BMT, and BMW = 0.6228 × BMV + 17.042 , where BTo examine genetic differentiation at the whole genome level in 10 generations, we performed principal component analysis (PCA)  using thAF) between the G1 and G10 generations for each SNP and sorted these values into 5% bins . We evaluated the enrichment of the SNPs in each bin in exons, introns, and untranslated regions (UTRs) to illustrate the numbers and distributions of sites that played a major role during artificial selection in the sequenced genomes. We observed a large number of allele frequency shifts in the entire data set, but no SNPs with ΔAF > 0.55 were identified  in both UTRs and introns , whereas in exons, the excess was only 2 SNPs  [Fst (Fst > 0.09) and XP-EHH  analysis values, we identified a total of 187 regions as potentially containing selective signals   values aFst)  values  of the top SNPs  and surrounding SNPs within these candidate regions. Then, we detected corrected signals (r2 > 0.4) in the 0.34 to 0.43 Mb region of chromosome 3 and the 3.46 to 3.49 Mb region of chromosome 16, which were associated with breast muscle weight  were identified in the 2 putative selected regions  revealed that individuals carrying the variant T alleles exhibited heavier breast muscles Fig. . The topons Fig. . Combinein ducks . We thent muscle .EXOC4 and TRPA1  show higher mutational rates than SNPs , typicalPA1 Fig. . In addiPA1 Fig. , and thePA1 Fig. .EXOC4 gene was completely encompassed by CNV1. Combining this analysis with transcriptome data showed that EXOC4 was widely expressed in various periods and tissues, and the expression level in breast muscle, sebum, and brain was higher than that in other tissues  in bovine [Interestingly, the  tissues . Similarn bovine . In PekiSequencing technology and comparative genomics have furthered our understanding of selection and variation. Based on long-term selection and phenotypic differentiation in livestock populations, several major genes controlling economically important traits in livestock have been identified . Howevercis-regulatory elements of pleiotropic loci are now considered the main source of phenotypic differentiation [Based on our new assembly, we analyzed the SNP information of each generation. We found that noncoding sequences have played a prominent role during Pekin duck breeding. This is can potentially be explained as, on the one hand, compared with coding sequences, ntiation . And, onntiation . After antiation . Thus, mWe did not find completely fixed loci in the genome of the Pekin duck Z2 line, although this population was subjected to high-intensity artificial selection for 10 generations. However, we detected allele frequency shifts at many loci throughout the Pekin duck genome. Two possible explanations may account for this finding. First, it is more common for complex quantitative traits to involve a combination of multiple standing variations at many loci. Such polygenic adaptive patterns achieve fast phenotypic optimization through allele frequency shifts at many loci but do not necessarily lead to the fixation of any variation , 35. OncUTP25 and FBRSL1, significantly associated with breast muscle weight. FBRSL1 belongs to the Polycomb group (PcG) gene and is essential for many biological processes in mammals, including stem cell maintenance and differentiation [FBRSL1 in the breast muscle of Pekin duck Z2 line decreased with the increase of age. This may hint that FBRSL1 was strongly selected to promote the development of Pekin duck breast muscle. UTP25  reportedly affects digestive organ expansion by regulating the p53 pathway [UTP25 has not been characterized in animal muscles. UTP25 was strongly selected in the continuous breeding process of the Pekin duck Z2 line, and further investigation would be necessary to elucidate its functions.In this study, we identified 2 genes, ntiation , 54. Our pathway , 56. HowEXOC4 and TRPA1, respectively. EXOC4 may affect the development of breast muscle in Pekin ducks by affecting glucose transport and insulin synthesis. Studies have found that EXOC4 was involved in insulin synthesis and glucose transport in skeletal muscle [EXOC4 is required for targeting of Glut4 to the plasma membrane by insulin [EXOC4 gene increases in the population of Pekin ducks. We speculate that the duplication makes fold increase of Exoc4 proteins, and a large number of Exoc4 proteins facilitate glucose transport to cells. Since cells become more efficient at taking up glucose, the excess glucose can be converted to fat and amino acids and stored by the body. Therefore, this may be one of the factors affecting the change of breast muscle weight of Pekin ducks. However, this requires confirmation. There is solid evidence demonstrating that TRPA1 is expressed throughout the mammalian body and has potential beneficial effects on systemic metabolism, including glucose metabolism [TRPA1 plays an important role in weight gain, obesity, and insulin secretion [TRPA1 in breast muscle, but the body weight increased with the increase of breast muscle weight. Previously, our group has also demonstrated that there is a high genetic and phenotypic correlation between breast muscle weight and body weight in Pekin ducks (0.83 and 0.80) [TRPA1 indirectly affects the breast muscle weight of Pekin ducks by affecting body weight.It can be anticipated that CNVs may be more important hotspots to reveal selection-induced molecular changes in Pekin ducks. First of all, CNVs are widespread in domesticated animals, such as pigeon, sheep, pig, chicken, cattle, horse, and dog . Moreovel muscle . As a co insulin . Our restabolism . Growingecretion , 77–81. nd 0.80) . We surmIn summary, our research enabled us to understand the genetic variation mechanism of farm animal genomes under intense artificial selection and will provide useful information for the establishment of an efficient molecular breeding system for livestock.in vivo at the age of 6 weeks of each generation and selected ducks with the heavier breast muscle weight as parents to produce the next generation. Roughly 750 individuals in each generation were retained for breeding, with a 35% to 40% retention rate for female ducks and a 7% to 8% retention rate for male ducks. The inbreeding was strictly avoided by calculating the inbreeding coefficient of each generation. The breast muscle weight (BMW) trait was estimated by breast muscle volume (BMV), breast width (BB), keel length (KL), and breast muscle thickness (BMT)  it is bred in a closed group, with a pure pedigree and a clear genetic background; (ii) the selection pressure of this line is constant, which is favorable for the accumulation of alleles gradually; (iii) at the age of 6 weeks each generation, 15 male and 15 female ducks were randomly selected from a large population for a slaughter test to measure their breast muscle weight and retain blood samples; and (iv) the breeding of all generations was completed in Pekin duck breeding base, Changping District, Beijing, and the performance measurement was done in spring, resulting in little difference in environmental effect between generations. Furthermore, all ducks were kept in a similar environment and had free access to water and feed pellets . We measss (BMT) . The corIn this study, we collected the blood of 30 ducks and phenotypic data of each individual  in the first, fourth, seventh, and tenth generations, respectively. A total of 120 duck samples were obtained (one sample was lost in the fourth generation), and a total of 119 samples were obtained finally.RRID:SCR_016385) was used to sequence the paired-end sequencing libraries with an inserted fragment length of approximately 500 bp in 8× .In addition, we randomly selected an adult female Pekin duck from the Pekin duck Z2 line to collect its liver for PacBio sequencing. We also collected breast muscle tissue from a male Pekin duck for BioNano and Hi-C sequencing. Furthermore, phenotype and pedigree data from all samples were collated for subsequent analysis. All individuals used for resequencing were collected wing vein blood and rapidly frozen at −20 °C. The phenol–chloroform method was used to extract blood DNA. The quality and quantity of the DNA were examined via Nanodrop and agarose gel electrophoresis. Then, the Illumina  HiSeq X Ten platform (RRID:SCR_017988) [RRID:SCR_015880) [RRID:SCR_014731) [We used the combined strategy of long-reads single-molecule sequencing  , 83, opt_017988) , 85, and_017988) , which i_017988) . We firs_014731)  (v1.23) _014731)  (v1.02) RRID:SCR_017643) [RRID:SCR_012091) [Then we adopted Hi-C technology to anchor the scaffolds near the chromosome level. We first used bowtie2 to align the clean Hi-C raw reads to scaffolds. We generated ∼1.06 Gb pair-end reads, and ∼593.5 million were uniquely mapped to the scaffolds . After f_017643)  (v2.10.0_017643)  (v0.8.1)The genome assembly was annotated by the NCBI Eukaryotic Genome Annotation Pipeline , an autoRRID:SCR_010910) [RRID:SCR_002105) [RRID:SCR_006525) to avoid any influence on variant detection. After the comparative evaluation of the depth and coverage of the results, we used the HaplotypeCaller program of GATK (RRID:SCR_001876) [RRID:SCR_001235) [The raw reads from Illumina sequencing were filtered before downstream analyses by removing adapter sequences, contaminated reads, and low-quality reads. Then the reads were mapped to the assembly (GCA_003850225.1) with Burrows–Wheeler alignment (_010910)  (v0.7.17_002105)  (v1.13–1_002105)  (RRID:SC_001876)  (v1.90) _001235)    softwareThe Smartpca program of EIGENSOFT (RRID:SCR_004965)  (v4.2) sAF) between the G1 and G10 generations was then calculated using the following formula: ΔAF = abs (RefAFG10 − RefAFG1). We next binned SNPs by ΔAF in steps of 0.05  and intersected these binned SNPs with coding exons, introns, and UTRs.For estimations of allele frequencies of single SNPs, we used VCFTools  , X is the matrix corresponding to the fixed effect, and b is the fixed effect size. Fixed effects include sex effects. G is the genetic matrix corresponding to the population kinship, and e is the random residual. PCA was performed based on all SNPs, and the top 3 components were set as fixed effects in the mixed model to correct for population stratification. We defined a Bonferroni correction threshold of 0.01/N (−log10P = 8.94) to identify the significant loci of the GWAS results, where N was the number of whole-genome SNPs.where For the identified associated genes, we also referred to the Pekin duck Panoramic transcription map  established by our previous study  to checkFst between G1 and G10 generations by selecting parameters of 10-kb windows with a 5-kb step size. We used selscan software [We used the VCFtools  (v0.1.14software  (v1.2.0ar2) as the coefficient to measure the linkage disequilibrium between the leader SNP and the surrounding SNPs. The parameters settings were “–ld-window 99999 –ld-window-kb 1000 –ld-window-r2 0 –r2.” Then we integrated and plotted the LD data with GWAS results   to analyBased our new assembly, we used the CNV caller  to identgiad016_GIGA-D-22-00268_Original_SubmissionClick here for additional data file.giad016_GIGA-D-22-00268_Revision_1Click here for additional data file.giad016_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad016_Reviewer_1_Report_Original_SubmissionGene Ng -- 11/15/2022 ReviewedClick here for additional data file.giad016_Reviewer_1_Report_Revision_1Gene Ng -- 2/17/2023 ReviewedClick here for additional data file.giad016_Reviewer_2_Report_Original_SubmissionSurya kanta Mishra -- 12/11/2022 ReviewedClick here for additional data file.giad016_Supplemental_Figures_and_TablesClick here for additional data file.