Oryza sativa, 284 Solanum tuberosum and 247 Sus scrofa and 544 Manihot esculenta Crantz were used. The original SNPs were filtered and screened to remove the SNPs with deletion number more than 1% or the homozygous genotype 0/0 and 1/1 number less than 2. The correlation between SNPs were calculated, and the two adjacent SNPs with correlation R2 > 0.95 were retained. The genetic algorithm program was developed to convert the genotype format and randomly combine SNPs to calculate a set of a small number of SNPs which could distinguish all varieties in different species as fingerprint data, using Matlab platform. The successful construction of three sets of fingerprints showed that the method developed in this study was effective in animals and plants. The population structure analysis showed that the genetic algorithm could effectively obtain the core SNPs for constructing fingerprints, and the fingerprint was practical and effective. At present, the two-dimensional code of Manihot esculenta Crantz fingerprint obtained by this method has been applied to field planting. This study provides a novel idea for the Oryza sativa, Solanum tuberosum, Sus scrofa and Manihot esculenta Crantz identification of various species, lays foundation for the cultivation and identification of new varieties, and provides theoretical significance for many other species fingerprints construction.With the rapid development of molecular breeding technology and many new varieties breeding, a method is urgently needed to identify different varieties accurately and quickly. Using this method can not only help farmers feel convenient and efficient in the normal cultivation and breeding process but also protect the interests of breeders, producers and users. In this study, single nucleotide polymorphism (SNP) data of 533  A DNA molecular marker is a kind of genetic marker based on DNA molecular polymorphism, which directly reflects genetic variation at the DNA level . With th, GBS  refers to the use of restriction endonucleases to interrupt genomic DNA to carry out the high-throughput sequencing of specific fragments and obtain a large number of genetic polymorphism marker sequences, thereby fully representing the whole genome information of the target species sequencing strategy. The method is simple in experimental steps and low in cost, and it can obtain genetic polymorphism markers of the whole genome without referring to the genome. It has been widely used in ecology, evolution and genomics. Reduced genome can obtain representative genetic diversity SNPs in the whole genome, so it is widely used to construct population evolution, population structure, population history, genetic mapping and linkage map. At present, the commonly used RRGS technologies includin RAD , GBS  is a search algorithm that simulates natural evolution to explore the optimal solution , which bSolanum tuberosum varieties (Solanum tuberosum varieties (Solanum tuberosum L. subsp. tuberosum) listed in the Latvian plant genetic resources database. The variety fingerprint, genetic distance evaluation and cluster analysis were carried out  is the most recommended marker for constructing DNA fingerprinting . This tearieties . A., Zhuried out . Zhao S ried out . Li L etried out .Oryza sativa, polyploid Solanum tuberosum and mammalian Sus scrofa were used. After a series of algorithm screening and population structure analysis, 100 core SNPs were finally selected to construct fingerprints that can distinguish different varieties. These SNPs are expected to be applied to variety specificity and authenticity identification and seedling purity identification, which will provide new technical basis for the further excavation and utilisation of genetic resources of various species and registration and protection of varieties.How to simply identify new materials and varieties is not only the need of research classification but also an important basis for the protection and utilisation of knowledge output. Therefore, a method that can accurately identify various varieties in different species is urgently needed. Population structure analysis plays a major auxiliary role in variety identification. Cluster analysis is used to observe the genetic relationship of varieties in the population for the further analysis of the origin and variety situation of the varieties. In this study, the SNP data of Gramineae Solanum tuberosum accessions from the Academy of Agriculture and Forestry Sciences of Qinghai University  were collected, and DNA of all samples was extracted by the modified CTAB method. Through 1% agarose gel electrophoresis detection and concentration measurement, the working solution diluted to 100 ng/μL according to the mother liquor concentration was stored at −20°C. The EcoRI-MspI and EcoRI-HpaII libraries of 284 Solanum tuberosum DNA samples were constructed by the AFSM method, and two mixed gene pools of EcoRI-MspI and EcoRI-HpaII were purified by OMEGA’s E.Z.N.A.Cycle Pure Kit. After PCR amplification and monoclonal detection met the requirements, the EcoRI-MspI and EcoRI-HpaII libraries were mixed into a library at a ratio of 1:1 for Hiseq 2,500 sequencing.The fresh leaves of 284 Solanum tuberosum, the original Illumina sequencing data were optimised. The artificial data errors were reduced by a Perl script (http://afsmseq.sourceforge.net/), and the total number of sequencing reads was obtained. All the reads were assigned to each individual through the designed tags, and the number of reads of each individual was counted. Optimised sequencing reads were compared with the Solanum tuberosum PGSC_DM_v4.03 reference genome by Bowtie2 software, and subsequent SNPs were identified by the two software programs of SAMtools (http://samtools.sourceforge.net/) and VCFtools (http://vcftools.sourceforge.net/). SNP data of Oryza sativa and Sus scrofa were from the RiceVarMap v2.0 database (http://ricevarmap.ncpgr.cn/) (https://bigd.big.ac.cn/), respectively. SNPs of Manihot esculenta Crantz was from our laboratory (For pgr.cn/)  and the boratory .2 value among SNPs, and the adjacent SNPs with R2 > 0.95 were reserved for subsequent calculation.SNPs were processed in the obtained samples. Genotypes were converted by using online editing tools of awk and sed under the Linux system. The SNPs with the deletion rate over 99%, as well as the homozygous genotype 0/0 and 1/1 number less than 2, were removed. The remaining SNPs were obtained as candidate SNPs. For the candidate SNPs, VCFtools was used to calculate the RThe read_SNP.m program independently developed in the Matlab platform was used to transform the format of the selected SNPs and pre-process the data, remove all the labelling information in the vcf file and transform the genotype format. The obtained SNPs were used for the calculation of the subsequent GA.1) For linear ordering:2) For nonlinear ordering:With the main.m program in Matlab in the same folder, data.mat obtained in the last step was imported to the program automatically. The number of SNPs and the maximum number of iterations in the parameter area were set, then the program was run after deployment . In the Nind is the number of individuals in a population. sp is the selected differential pressure. pos is a position in an ordered population.Solanum tuberosum L. individuals were displayed on the interface. The calculation result was saved in the result file. The detailed information of all the SNP combinations were included in the result file. The SNP combinations were ranked from high to low according to the number of individuals identified. Therefore, a group was selected from several SNP combinations according to the distribution of SNPs on chromosomes for fingerprint construction. The main information obtained from the result file included the ID number of all the SNPs and their positions corresponding to total SNP database. With this information, the core SNP genotype data was collected and arranged.After several iterations, the population number increased. The population with a high fitness was selected for cross-mutation with a new population, then a new population was generated again. The process was repeated until the maximum number of iterations was reached. Then, the procedure was ended. All SNP combinations that can be used in distinguishing The specific operation process of genetic algorithm in this study is as follows:Encoding parameters: the genotypes at SNP loci were transformed into the following four forms by encodingN  loci of I  individuals, and a two-dimensional matrix S of N*I  was obtained.The raw data were obtained from P, and each individual randomly selects C SNP loci from the N SNP loci to form an initial population G, which is a two-dimensional matrix of P*C.Setting of initial population: We set the population size to be C SNP sites randomly selected by it. Assuming that the individual may be any one of the I individual samples, data of the corresponding C SNP sites in the I samples are collected respectively.Setting of adaptive function: For the individual of each population, we currently have Thus, we can obtain the genotype number coding data e of the randomly selected SNP loci corresponding to each individual in the population.C genotypes that each individual may have, namely, the valuable SNPs that can be used to distinguish species. The more unique SNPs that an individual may have, the more individuals that it can identify. We believe that the higher the fitness is, and the individual fitness array F of the population can be obtained.Next, we find out the unique ones in the S of the fitness of P individuals. Then the fitness/total fitness of each individual is used to calculate the selection probability of a specific individual in the inheritanceSelection probability:We find the sum F(j) required for the selection of the j-number individual is as followsCumulative probability: In a roulette-like manner, from 0 to 1, the selection range of each individual is added with the selection probability of the individual from the sum of the selection ranges of all the individuals before, and finally a number in the range of 0–1 is generated to see which range the number falls in, so as to determine which individual is selected finally. for example, the range of the random number R which is consistent with uniform distribution on the numbers of 0–1Select action: Randomly generating a number of 0–1, selecting individual serial numbers corresponding to two ranges which are closest to the number, and generating a random number Obtaining the serial numbers of the two mated individuals and then mating the twoB1 needs to be randomly generated in the range Interlace operation: It is determined whether or not to perform a crossover operation according to a given crossover probability, and if the crossover operation is performed, a crossover bit I SNP loci and sequentially replaced, if the cross operation is not carried out, the offspring keep the original genotype unchanged.Taking the crossing position as a dividing line, and carrying out cross exchange on two groups of genes participating in mating, wherein if the error that the SNP locus repeatedly appears in a new genotype after crossing occurs, the SNPs not appearing in the new genotype can be listed from all the C mutation bits need to be randomly generated in the  range and counted as B2 arrayMutation operation: According to the variation probability, if the mutation operation is performed, 0.3*The mutation site are first emptied, Then listing all the SNP sites which do not currently appear in the variant genotype and sequencing in ascending order, and selecting the SNP sites which do not appear as the variant SNP to be inserted into the variant genotype by taking the value of the variant site as a subscript.P constant, then the highest fitness, the average fitness and the best SNP individuals of each generation are recorded, and The X (maximum number of iterations) generation is cycled in such a way that the last generation is the best combination of SNP loci with the highest fitness, average fitness, and legacy from elimination.Analog result: As a result, a new population is generated, the previous generation and the new generation should be integrated now, after discarding the SNP with repeated genotypes, the SNP information table of all the existing genotypes is updated, and then the population is eliminated according to the descending order of the fitness of all the genotypic individuals in the current population to ensure that the population number is In the calculation process of GA, the number of SNPs was set as 80, 100, 200 and 300, and multiple sets of fingerprints were constructed. The correlation between each matrix of the fingerprint and the original SNP data matrix was calculated in the form of a matrix. The utility of fingerprints composed of different SNPs was evaluated, and fingerprints composed by the best SNP combinations were selected for subsequent verification and analysis.Given that SNP molecular markers have dimorphism, the data in the fingerprint database can be converted into binary coded data. Homozygous genotype 0/0 was expressed as 1, homozygous genotype 1/1 was expressed as 2, heterozygous genotype 0/1 was expressed as 0 and deletion base locus was marked as ‘-’. The data of each sample were statistically sorted, so that the genotype data of each sample were integrated to form a unique code, that is, the fingerprint code to distinguish each sample.https://itol.embl.de/upload.cgi). The results were visualised, and the clustering effect was observed.ADMIXTURE software was used to evaluate the population structure, and each sample in the population was divided into corresponding subgroups. Firstly, the file format required by the software was obtained by using PLINK software . Secondlhttps://www.r-project.org/), and the SNP distribution variations before and after GA calculation were observed. At the same time, VCFtools were used to calculate the allele frequency of vcf file containing SNP data. The calculated allele frequency was imported into PIC_CALC 0.6 (https://github.com/luansheng/PIC_CALC) software to calculate the PIC value of each SNP locus, count the PIC value of SNP on each chromosome and verify the consistency of data distribution before and after GA calculation. The clustering relationship between the fingerprint database composed of core SNPs and the filtered SNP database was compared. Whether the genetic distance and genetic relationship between each individual are consistent with the obtained results were observed and compared as well  was estimated to be a certain value between 2 and 14, and the cross-validation error rate (CV error) under each K value was calculated. When K was from 1 to 9, CV error decreased rapidly. When K was greater than 9, CV error increased gradually and tended to be flat  was on chromosome 1. By contrast, there were 38 SNPs, the least number of SNPs, on chromosome 2. At the whole genome level of Solanum tuberosum, the average polymorphism information content was 0.249. The polymorphism information content of most SNPs was between 0.24 and 0.26, and the polymorphism information content of very few SNPs was between 0.2 and 0.22. These values indicated that the polymorphism information content of Solanum tuberosum SNPs was relatively even. After selection and GA calculation, a fingerprint constructed by 100 core SNPs was obtained, and the SNPs were distributed evenly on 12 chromosomes of Solanum tuberosum. Solanum tuberosum. The statistical results showed that the number of SNPs on chromosome 1 was the largest (36). However, the one with the least number of SNPs occurred on chromosome 5, followed with chromosome 2. These observations showed that the distribution of sites was relatively even and consistent with the distribution of the original calculated data filtered. The selected core locus was at the whole genome level of Solanum tuberosum, and the average polymorphism information content was 0.255. The PIC values of 100 SNPs were mostly distributed between 0.24 and 0.26, with a total of number of 39. The average polymorphism information content of the SNPs on each chromosome was between 0.241 and 0.268. The highest average polymorphism information content was on chromosome 9, reaching 0.268, while the lowest was on chromosome 2, which was only 0.2411. After the calculation of GA, the obtained 100 core SNPs were consistent with the original filtered data.A total of 1,127 SNPs were obtained, after two-step filtration, which were distributed evenly on 12 chromosomes of Solanum tuberosum population structure analysis of all 1,127 high-quality SNPs. The maximum number of cluster subsets (K) was inferred to be a certain value between 1 and 10, and the CV error under each K value was calculated. When K was from 1 to 7, CV error decreased rapidly. After K was greater than 7, CV error increased gradually and tended to be flat  was inferred to be a certain value between 1 and 12, and the CV error under each K value was calculated. When K was from 1 to 9, CV error decreased rapidly. When K was greater than 9, CV error increased gradually and tended to be flat . Every two individuals in these two groups of individuals were similar in origin, and they all came from CIP. There were two groups of individuals (Sebago-1 and Ziyun and CIP09-29 and zhongshu18) when the genetic distance was the maximum value of 0.65. These two groups of individuals had different origins. The origin of Sebago-1 was unknown, while Ziyun came from China, CIP09-29 came from CIP and zhongshu18 came from China. We compared the genotypes of 100 SNP loci and found that the number of different loci between R3R4 and CIP09-29 is 28, the number of different loci between KW-59 and KW-11 is 25, and that between Sebago-1 and Ziyun The number of different loci is 38, and the number of different loci between CIP09-29 and zhongshu18 is 42, which is significantly more than the previous two groups. These findings showed that the fingerprint could show the origin and genetic relationship of the Solanum tuberosum population, and the results were reliable and available.To determine the genetic relationship among DNA fingerprinting is an applied genetic germplasm analysis method established with the development of molecular biology. There are different traits and DNA sequences among various species and varieties, and varieties can be distinguished according to these differences .At the end of the 1970s, the technology of studying the differences in DNA among different individuals was proposed and applied. The differences in DNA molecules can directly reflect the differences between individuals and are not affected by the external environment. Therefore, it gradually replaces the previous method of observing external phenotypic differences with the naked eyes to distinguish individuals. With the development of molecular biology, various DNA molecular marker technologies have been widely studied and applied, from RFLP, AFLP to later developed SNP. These molecular markers have played a critical role in the analysis and research of fingerprint constructions.However, the previous molecular markers have some shortcomings. Relatively speaking, RFLP technology is cumbersome to operate and has low polymorphic information content, which has limitations in individual recognition. The RAPD technique has poor repeatability and unstable results, so it can obtain reliable results only by optimising the reaction conditions repeatedly. AFLP technology has high requirements on genome purity and reaction conditions. Compared with the previous ones, SSR technology has the advantages of high polymorphism detection rate, large information content, simple experimental operation and stable and reliable results, but it also has the disadvantages of low detection efficiency and low repeatability . Li et aSolanum tuberosum, which makes the study of genetic identification or genetic variation time-consuming and laborious. SNP is a double allele genotype marker, which is more abundant than SSR, easy to be automated, highly repetitive and can be shared among laboratories  Set the fitness as Fi, and the fitness of each individual is:GA simulates the genetic evolutionary mechanism of human beings and organisms, which is mainly based on Darwin’s theory of biological evolution: natural selection and survival of the fittest. The specific implementation process is as follows: ①The individuals that adapt to the environment and perform well are selected from the initial generation population. ②Using genetic operators, the screened individuals are combined, crossed and mutated, and the second-generation population is generated. ③Individuals with good environmental adaptability are selected from the second-generation population and combined, crossed and mutated to form the third-generation population. The evolution continues in this way until the final population is generated, that is, the approximate optimal solution of the problem . The priUi represents the number of samples that the population can uniquely identify, and N represents the total number of samples. Let the probability be Pi, and the P value of each individual is:P values and calculate sort (P). 3) Individuals with high P value are selected and hybridised to generate new individuals. 4) The generated sub-individuals are placed into the original population, and the individual fitness and probability are recalculated through step 2. The corresponding number of individuals with low probability are eliminated. 5) Steps 2, 3 and 4 are repeated until the set number of iterations of the population is reached. 6) When the iterations are completed, the individual with the highest probability in the population is selected, which is the best SNP combination to be found.Order the The program written by GA can find the SNP combination that can distinguish all samples by computer language calculation. It has the advantages of high efficiency, convenience and high reliability, and it is a highlight of this study.Oryza sativa, 284 Solanum tuberosum and 247 Sus scrofa accessions, 100 core SNPs for fingerprint construction were selected from the original SNP data by GA calculation. All varieties of each species could be distinguished and identified by using these core SNPs. Each variety had its own fingerprint code, by which different varieties could be classified and identified. The two-dimensional code of Manihot esculenta Crantz fingerprint obtained by this method has been applied to field planting. Therefore, this method can be used for various species, has wide application and high practicability. In addition, the fingerprints obtained by GA were tested by the population structure. A small number of core SNPs used for fingerprint construction could basically restore the population structure of the original SNP data. Better restoration results were obtained in the populations of model crop Oryza sativa, autotetraploid Solanum tuberosum and mammalian Sus scrofa. For example, original SNPs from 533 Oryza sativa were to divide the population into nine subgroups, while the subgroup classifications of core SNPs were to divide the population into nine subgroups; the population structures were also highly consistent. Through the program written by GA, the core SNPs calculated had high accuracy; this set of core SNPs combination may provide new ideas and methods for identification analysis and genetic diversity analysis of each species (,38].In this paper, based on the SNP data of 533  species ,38].Oryza sativa, olyploid crop Solanum tuberosum and mammal Sus scrofa were obtained to construct the fingerprint. The proposed method is innovative and widely applied; it provides a new idea for genetic research and animal and plant breeding and has good application prospect.In this paper, using SNP data of three representative species and the concept of GA, the SNPs with a high deletion rate and high single variation rate were finally screened out. A total of 100 SNPs that could distinguish all varieties in the population for constructing the model crop


A total of 36 gene ontology (GO) terms and 3 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to metabolic processes, immune response, and cellular and amino acid catabolic processes. Eleven genes including MMS22L, E2F8, CSRP3, CDH11, PEX26, HAL, TAMM41, HIVEP3, SBF2, MYO16 and STXBP6 were selected as candidate genes that might play roles in the teat traits of cows. These results identify SNPs and candidate genes that give helpful biological information for the genetic architecture of these teat traits, thus contributing to the dairy production, health, and genetic selection of Chinese Holstein cattle.In the dairy industry, mammary system traits are economically important for dairy animals, and it is important to explain their fundamental genetic architecture in Holstein cattle. Good and stable mammary system-related teat traits are essential for producer profitability in animal fitness and in the safety of dairy production. In this study, we conducted a genome-wide association study on three traits—anterior teat position (ATP), posterior teat position (PTP), and front teat length (FTL)—in which the FarmCPU method was used for association analyses. Phenotypic data were collected from 1000 Chinese Holstein cattle, and the GeneSeek Genomic Profiler Bovine 100K single-nucleotide polymorphisms (SNP) chip was used for cattle genotyping data. After the quality control process, 984 individual cattle and 84,406 SNPs remained for GWAS work analysis. Nine SNPs were detected significantly associated with mammary-system-related teat traits after a Bonferroni correction ( Mammary system traits play an important economic role in cattle . In dairGenerally, mammary system-related teat shape confirmation trait variation causes mastitis, reducing milk production and affecting milk flow and quality on dairy cattle farms. Long and thick teats have been identified as a possible risk factor for the development of mastitis. Milk somatic cell count and mastitis resistance were found to have a relationship with udder and teat conformation traits ,4. Teat Genome-Wide Association Study (GWAS) has confirmed that the single-nucleotide polymorphisms (SNPs) are used as genetic markers to identify the complete genome for target genes that are associated with phenotypic traits in genetic breeding programs. GWAS was used to enhance genetic breeding in various animals, i.e., cattle, sheep, pigs and chickens ,15. GWASThe objective of our study is to identify the significant SNPs associated with the anterior teat position (ATP), posterior teat position (PTP), and front teat length (FTL) confirmation traits in Chinese Holstein cattle by using the GWAS approach, and to find the SNP position of candidate genes and pathways that affect these teat traits.The Ministry of Agriculture and the China Council on Animal Care provided guidelines for collecting hair follicle samples and measuring phenotype traits. It was also accepted by the School of Yangzhou University Animal Research Group Institutional Animal Care under the research group license number: SYXK (Su) IACUC 2012-0029. Anesthesia was not used for experimental animals during this study.One thousand Chinese Holstein cattle were selected from 4 animal farms in the area of Jiangsu Province, China. The studied Holstein cattle population was raised on four dairy farms, Sihong farm: 199, Xuyi farm: 214, Xuzhou farm: 224, and Huaxia farm: 363 animals. The primary data were measured within two months, from November to December 2019. The three mammary system teat traits were measured individually, while teat traits were recorded on a scale from 1 to 9. Three mammary system teat-related traits, anterior teat position (ATP), posterior teat position (PTP), and front teat length (FTL), of the 1000 cattle were measured according to the China National Standard (GB/T 35568-2017); three specialists completed the phenotype trait measurements for each cow. For genotyping analysis, hair follicle samples were collected from each cow and were stored in special paper envelopes to prevent environmental and DNA contamination; thus, a minimum of 50 hairs were present in each paper envelope.Pairwise Pearson coefficients and descriptive statistics of the phenotypic datasets were analyzed by using computer-based software, SPSS software . Genetic analysis was performed by using DMU software  to estimate individuals’ heritability and genetic correlation between pairs of traits in Holstein cattle . The pheThe three teat trait scores were used for the following analysis, and the following two steps were used for all calculations, adjusted with fixed environmental factors. The pedigree data were measured back to three generations (2010–2020); the parities of the cattle were between 1 and 4.Steps 1. Multiple-trait animal models were used to estimate genetic parameters:n et al. . The herSteps 2: We used the following model for adjusted phenotype:http://www.neogenchina.com.cn, accessed on 25 July 2021), and the ARS-UCD1.2 (bosTau9) was used as a genome reference. The GGP Bovine Chip contained approximately 100,000 high polymorphic SNPs that were used for individual genotyping data. Meanwhile, quality control (QC) was conducted by using Plink software  to remove the markers that did not meet the following standards [p < 1.0 × 10−6). After quality control, there remained 984 cows and 84,406 SNP variants for further association analysis.The DNA of the hair follicles was extracted and genotyped by using the GGP (Geneseek Genomic profiler) Bovine 100K SNP Chip by Neogen Biotechnology  theWe used Plink software  to detect principal component analysis (PCA) on 984 cows genotyping , with 84p value. The associated marker map and p values are utilized to update the selection of pseudo-QTNs by using the SUPER algorithm in a REM, as follows [−7 .We conducted GWAS analysis; the fixed and random model circulating probability unification (FarmCPU) model used a multi-locus linear mixed model to carry out the association analysis between SNPs and traits . In a fi follows :(4)y=u+errection . The genhttp://genome.ucsc.edu, ARC-UCD1.2/bos Tau9, accessed on 25 July 2021) [http://www.ncbi.nim.nih.gov/gene/) database (accessed on 25 July 2021).We determined the candidate gene and genomic region by using the UCSC genome browser from cow assembly April 2018 (ly 2021)  and the https://www.genome.jp/kegg, accessed on 25 July 2021 ) were conducted on the candidate gene by using the cluster profiler package in R software [In this study, we submitted the candidate genes attained by GWAS into the profiler. The gene ontology (GO)  and the Zealand) .Phenotypic data of 1000 animals were used for description data analysis. The mean and standard error (S.E) of ATP were 5.34 (0.33), those of PTP were 6.50 (0.51) and those of FTL were 5.72 (0.52), while the estimated range of the mean (SD) and standard error was1.04 to 1.63 for teat traits. The distribution of teat trait score is shown in Similarly, the directions of pairwise phenotypic correlations were estimated by the Pearson correlation coefficient in mammary system-related teat traits. The estimates of positive phenotypic correlations were 0.16 (FTL and ATP), 0.27 (PTP and ATP), and 0.03 (FTL and PTP) for teat traits . The paiAfter quality control, 984 cattle and 84,406 SNPs were used to perform GWAS, and the filter SNPs were distributed on 29 chromosomes. After quality control, the minor allele frequency of all SNPs was recalculated due to its low value, and only MAF remained above 5% in the data. The distribution of the SNP information on chromosomes is shown in The PC analysis was used to formulate the population structure of the 984 animals in four farms: farm 1: 197, farm 2: 206, farm 3: 220, and farm 4: 361 animals. The population structure  showed ap value of each SNP) by chromosome position. Large peaks of SNPs in the Manhattan plot showed a strong association with traits.GWAS analysis was conducted by using the FarmCPU model in this study. The quantile–quantile plots  showed t−7 (0.05/84406). All nine significant SNPs were associated with mammary system-related teat traits. Four significant SNPs were located on chromosomes Chr9 (ARS-BFGL-NGS-101241), Chr29 (ARS-BFGL-NGS-43147), Chr18 (BovineHD1800006781) and Chr5 (BovineHD0500031672), which were detected to be associated with trait ATP. For posterior teat position, three significant SNPs were detected on chromosomes Chr22 (ARS-BFGL-NGS-16048), Chr3 (Hapmap58721-rs29026738), and Chr12 (12-88054488-G-A-rs4235240); these SNPs were related to trait PTP. For front teat length (FTL), two SNPs were located on the chromosomes Chr15 (BovineHD1500023818) and Chr21 BovineHD2100009187, which were detected to be associated with trait FTL , mitochondrial trans-locator assembly and maintenance protein 41 homolog (TAMM41), HIVEP zinc finger 3 (HIVEP3), myosin 16 (MYO16), and SET binding factor 2 (SBF2), respectively (E2F8), cysteine and glycine rich protein 3 (CSRP3), cadherin 11 (CDH11) and syntaxin binding protein 6 (STXBP6), respectively, whereas the four SNPs were found on different chromosomes.In this study, of the genes that are located at a distance within this region, 200 kb of significant SNPs are known as candidate genes. All nine significant SNPs were associated with traits. The five SNPs, BovineHD0500031672, ARS-BFGL-NGS-16048, Hapmap58721-rs29026738, 12-88054488-G-A-rs42352402, and BovineHD1500023818, were located within the genes peroxisome biogenesis factor 26 (ectively . The fouUCSC Genome browser and the NCBI database were used for identifying genes through cow assembly in April 2018 (ARC-UCD1.2/bosTau9). A total of 185 genes were found within a 200 kb region on significant SNPs for the mammary system-related teat traits, and these functional genes were used for further analysis, gene ontology terms and KEGG pathway analysis.p < 0.05) in teat traits, and gene ontology enrichment analysis derived 36 GO terms, which included 28 biological processes, one cellular component and seven molecular functions, as shown in For a deeper understanding of the functions of the nine significant SNPs that are related to indicators of the mammary system performance of cattle, the genes within 200 kb of significant SNPs for each mammary system-related teat shape trait were selected for functional enrichment analysis. A total of 185 genes were used for gene enrichment analysis. GO and KEGG analysis were conducted by the Cluster Profiler Software Package in R . Gene ontology results were significantly enriched , in which all pathways are closely related to mammary system-related teat traits. A KEGG pathway dot plot is shown in the KEGG results  identifyMammary system-related teat traits are a type of linear trait or functional trait in dairy cattle. These traits are associated with cattle longevity, health and welfare. In this study, the heritability of mammary system-related teat traits of Holstein cattle was of a medium level . PreviouThe relationship between phenotypic and genotypic correlations has been researched in animal studies . Front tPopulation stratification is a major challenge faced in GWAS studies. Population stratification and family structure can cause several false positive results in GWAS . In GWASAfter the correction of population stratification, the inflation factor mean lambda value should be close to 1 . AccordiThe principal component analysis method  detectedMMS22L gene within TAMM41. These genes were associated with ATP and PTP traits. MMS22L may help in homologous recombination-mediated genome stability maintenance during DNA replication [MMS22L genes might play an essential role in river buffalo’s heat adaptability [MMS22L gene has been detected in esophageal cancers and clinical lungs of humans. Kutik et al. [TAMM41 gene is the mammalian equivalent of a yeast protein; the deletion of the Tamm41 gene resulted from the high content of phosphatic acid in mitochondria. The first metabolite was involved in the biosynthesis of cardiolipin, with a virtual absence of phosphatidylglycerol in most cardiolipin species.In the present study, GWAS analysis found nine significant SNPs that are associated with three traits  in Chinese Holstein cows by using the FarmCPU model. The two most highly significant SNPs (ARS-BFGL-NGS-101241 and ARS-BFGL-NGS-16048) occurred among them; two SNPs were located nearby: the lication . River btability . Nguyen tability  reportedPEX26, MYO16, HIVEP3 and SBF2). These candidate genes of teat traits have effects on mammary structure and milk production in dairy animals. Expression of PEX26 gene mutations have occurred in Chinese hamster ovary cells and human cell lines and can cause disorders of peroxisome biogenesis (PBDs) in humans [MY016 gene affects milk production yield, fat yield and protein yield in Buffalo animals [MY016 gene (BTA12) has been identified at significant SNPs within position in Holstein cattle [SBF2 gene may refer to the cause of thrombocytopenia in humans [HIVEP3 gene belongs to the enhancer-binding protein family of the human immunodeficiency virus type 1 that encodes large zinc finger proteins and regulates transcription via the kappa B enhancer motif [HIVEP3 gene functions as an oncogene [The study recognized the other genes that are significantly associated with anterior teat position, posterior teat position and front teat length traits  was located on Chr18 and had a position within 100 kb of the cadherin (CDH11) gene. Specifically, the highly associated CDH11 gene belonging to this module has been identified in the development of mastitis and growth of mammary glands in dairy animals [STXBP6 has important roles in the development of multiple diseases, including neurological disorders, diabetes, and cancer in humans and animals [E2F8 is important for orchestrating genes essential for cell cycle progression and proliferation [E2F8 gene has been found in the follicular development of cattle [CSRP3 gene is found on chromosome 29, and these findings suggest CSRP3’s high potential as a marker gene for growth improvement and body traits in the selection program of Chinese Holstein cattle [CSRP3 genes have been functionally characterized and play vital roles in the development of tissue-specific cell growth in sheep [The  animals ,56. STXB animals . The sigferation . The pref cattle . In cattn cattle . CSRP3 gin sheep .MMS22L, E2F8, HAL and STXBP6) were clustered in biological processes, and these genes were associated with our significant SNPs. MMS22L gene terms were related to DNA-dependent DNA replication (GO: 0006261) and recombination DNA repairing (GO: 0000725); E2F8 gene terms were related to embryo development (GO: 0009790) and utero embryonic development (GO: 0001701); HAL gene terms were related to cellular amino acid catabolic processes (GO: 0009063), cellular amino acid metabolic processes (GO: 0006520), and organic acid metabolic processes (GO: 0006082); and STXBP6 gene terms were related to proteins containing complex subunit organization in biological processes. This is important for cell division during the growth and repair of damaged tissues; in mammals, the disruption of DNA replication can lead to a loss of genome integrity and an increase in cancer susceptibility [HAL gene was found in gene ontology and has an effect on amino acid metabolism in animals and humans; dietary histidine is related to factors that improve metabolic syndrome and affect ion absorption in human populations. In ruminant animals, histidine is a limiting factor for milk protein synthesis, and it may be the first limiting amino acid for growth [HAL gene was found to be significantly associated with milk protein yield and milk yield. Additionally, the HAL gene has been reported within QTLs for milk production traits [In our study, gene ontology terms enrichment analysis and KEGG pathway analysis identified a number of GO terms and KEGG pathways. The identified significant genes were related to our traits under observation; for example, the four candidate genes . The UDP-glucose 6-dehydrogenase (UGDH) gene had been confirmed to be related to biological processes, metabolic processes, growth performance, and milk production in Holstein cattle [The n cattle .FASLG, is commonly found in the first three KEGG pathways. The FASLG gene has a role in apoptosis in normal human mammary cells [BoLA–DRA genes influences the immune function of lactating cows and decreases milk yields [HAL gene was found in both gene ontology terms and KEGG pathways, histidine metabolism is a limiting factor for milk protein synthesis, and it may be the first limiting amino acid for growth in animals  [The top four significant KEGG analysis pathways were related to immune response, e.g., allograft rejection (KEGG: bta5330), which has three candidate genes; apoptosis (KEGG: bta04210), which consists of seven genes; cytokine–cytokine receptor interaction (KEGG: bta04060), which comprises eight genes; and histidine metabolism (KEGG: bta00340), which is a non-significant pathway that has two genes. All candidate genes are involved in the regulation of pathways and mammary development; one candidate gene, ry cells . The expk yields . While t humans) . TherefoMMS22L, E2F8, CSRP3, PEX26, CDH11, HAL, TAMM41, HIVEP3, MYO16, SBF2 and STXBP6, that contribute to metabolism, immune response, biological information, and genetic improvement. Our study highlighted useful biological information to understand genetic architecture and provided fundamentals for molecular breeding programs that can lead to genetic improvement programs regarding ATP, PTP and FTL traits in Holstein cattle. Further research is needed to confirm the biological activities of these genes.GWAS analysis was performed on three mammary system-related teat traits, which were chosen as indicators of mammary system performance in Chinese Holstein cattle. This study identified nine significant SNPs associated with ATP, PTP and FTL traits in Chinese Holstein cattle. We also found several candidate genes,


LTRs with lower LTR similarity were generally more frequent in the population than LTRs with higher LTR similarity, though high-frequency insertions with very high LTR similarity were observed. LTR similarity and frequency estimates of nested elements and the outer elements in which they insert revealed that most nesting events occurred very near the timing of the outer element insertion. TEs within genes were at higher frequency than those that were outside of genes and this is particularly true for those not inserted into introns. Many TE insertional polymorphisms observed in this population were tagged by SNP markers. However, there were also 19.9% of the TE polymorphisms that were not well tagged by SNPs (R2 < 0.5) that potentially represent information that has not been well captured in previous SNP-based marker-trait association studies. This study provides a population scale genome-wide assessment of TE variation in maize and provides valuable insight on variation in TEs in maize and factors that contribute to this variation.Intact transposable elements (TEs) account for 65% of the maize genome and can impact gene function and regulation. Although TEs comprise the majority of the maize genome and affect important phenotypes, genome-wide patterns of TE polymorphisms in maize have only been studied in a handful of maize genotypes, due to the challenging nature of assessing highly repetitive sequences. We implemented a method to use short-read sequencing data from 509 diverse inbred lines to classify the presence/absence of 445,418 nonredundant TEs that were previously annotated in four genome assemblies including B73, Mo17, PH207, and W22. Different orders of TEs ( Transposable elements (TEs) are present in all eukaryotic genomes , white wTEs are classified into two classes depending on how they replicate and from there into superfamilies and families by sequence similarity . Long tebronze locus in multiple maize lines found that different lines differed in not only the gene order and content but also in TE content  (last accessed 2021-07-21). Adapter sequence and low-quality base trimming were done using cutadapt version 1.18 (https://github.com/najoshi/sickle) (last accessed 2021-07-21) both with default parameters. Sequence reads were aligned to the B73 v4 . Mean coverage over the 10 bp window internal to the start and end of each TE was calculated using multicov within bedtools version 2.29.2  did not result in a present classification the TE was considered recalcitrant to accurate calls for short-read data and was removed from downstream analysis. Across all samples mapped to a reference genome assembly if there was greater than 25% ambiguous data for a TE the TE was removed from downstream. Presence, absence, and ambiguous scores for each TE that was retained can be found in Supplementary File S1 for B73, Supplementary File S2 for Mo17, Supplementary File S3 for PH207, and Supplementary File S4 for W22.A final model was trained for 15x coverage and 30x coverage using resequencing data from all four genomes mapped to all four genome assemblies as described above using 500,000 TEs for training per model. These models were used to estimate the probability of the presence of a TE based on the two internal coverage windows and the TE order for all of the resequenced genomes. If the realized coverage for a sample was ≥25x depth the 30x model was used and if the realized coverage for a sample was <25x depth the 15x model was used . If the probability of presence from the model was ≥0.7 a TE was classified as present in the sample. If the probability of presence from the model was ≤0.3 a TE was classified as absent in the sample. All other TEs were classified as ambiguous. For any TE where the resequencing reads mapped to its cognate genome  for the TE in the reference genome it was first identified in was used in downstream analyses using a previously described order for adding in TEs from each of the reference genome assemblies  any one of those filters was removed from the dataset. Finally, vcftools (v 0.1.13) , –ld-window 1000000 –ld-window-kb 1000 to calculate LD within 1mb windows, and –r2 dprime with-freqs to report both D’ and r2 and to display minor allele frequencies in the output. If more than one SNP had the same highest LD in either the r2 or D’ analysis, the SNP that was physically closest to the SNP was used for downstream analyses. Principal components analysis of SNPs and TEs were conducted using Plink v1.90b6.18  analysis with the TE presence/absence scores described above. For this analysis, TE presence/absence data from only the alignments to B73 reference genome assembly were used. Any SNPs located within TEs were removed. LD between SNPs and TEs was calculated using plink v1.90b6.16  and those with high levels of ambiguous calls ] provide a high-quality set of TE presence/absence calls that allowed for analysis of TE variation on a genome-wide scale in maize. It should be noted, however, those rare alleles will be under-represented in this set due to the fact that only those TEs that were previously annotated in at least one of four de novo assembled maize genomes are included in this study.These models and the subsequent filtering methods , demonstrating the consistency of this pipeline, and enabling classifications to be combined into a nonredundant set of TEs. For downstream analysis of these redundant TEs, the mean was calculated across frequencies obtained from all genomes for which a homologous TE was present , a subset of the TEs were present in all or nearly all of the diverse lines included in this study for LTRs, Helitrons, and TIRs (de novo assembled and annotated.Different orders of TEs have different mechanisms of replication (and TIRs . Howevern = 4) of genotypes  of LTR insertions that had moderately LTR similarity  or high LTR similarity , and are at high frequency (>80%) in the population  of the fixed insertions with high LTR similarity that were located within previously defined low recombination regions , 15 had a higher than expected proportion of elements that had high LTR similarity and were at high frequency in the population . The majority of these enriched families are within the Gypsy superfamily, including RLG00001, a large Cinful-Zeon family (The elements in the high-frequency group (not just fixed) with high LTR similarity were from 494 different families. We sought to test if there was an enrichment within these families for elements that were at high frequency with high LTR similarity relative to the frequency of this class compared to all other LTRs. Indeed, for those families with at least 20 elements in the family , it is expected that some will be common as they have had time to rise in frequency in the population and others will be rare as they are being lost over time, which was the case in this population. Within the class of TEs that have low LTR similarity, 14.28% were at low frequency in the population, 30.92% were at high frequency in the population, and the remaining 54.80% were at moderate frequency in the population .P-value < 2 × 10–16). A nested TE could be the product of an insertion very soon after the outer element inserted or it could be the product of a spectrum of much younger insertions that happened well after the insertion of the outer element. Genome-wide we see that nested elements have higher LTR similarity than nonnested elements .Nested TEs exhibited higher levels of moderate frequency (20–80%) and low frequency (<20%) TEs as compared to the nonnested TEs . In all r2 = 0.52; To further explore the difference in frequency of nested and nonnested elements we looked at specific nested TEs and the nonnested element into which they inserted. Within these pairs, the nested insertion should be at the same or lower frequency compared to the outer element, as the nested insertion cannot exist in a genotype without the outer element. This was true for 67.38% of the pairs, and an additional 20.33% had less than 5% higher frequency in the nested element. Overall, there was a strong correlation between the nested TE frequency and the frequency of the TE into which it was nested (Pearson’s correlation Within the set of outer elements that were fixed or nearly fixed in the population (frequency > 0.95), there was a continuous range of frequencies for inner elements that likely represent nested insertions with a range of ages. Using LTR similarity as a proxy to LTR age, we tested if there is a relationship between the LTR similarity of the nested element contained within fixed elements and their frequency in the population. This analysis could only be done for LTR nested elements as the other orders do not have an accurate metric to approximate age. As hypothesized, those nested elements that were at lower frequency in the population had higher LTR similarity than those that were at high frequency in the population , and thiTo further address this question of the timing of nested insertions into outer elements we focused on the subset of nested-outer element pairs in which both elements were LTRs and therefore had LTR similarity information. As with frequency, the quality of the LTR similarity metric was assessed with the expectation that the similarity of the nested insertions should be higher than that of the outer element. Across the LTR nesting pairs, 84.12% had a higher LTR similarity for the inner element, indicating an overall high quality of this data . Using the combination of frequency and LTR similarity we tested the extent to which an outer element and nested element insertion occurred at a similar time or wheather the nested insertions occur over a range of time based on the LTR similarity . The majZmCCT9 in cis and promotes flowering under long days . This finding is somewhat unexpected given the potential deleterious functions that TE insertions could have on the expression of a gene and the integrity of the encoded protein, and likely indicates a positive effect of these insertions allowing them to rise in frequency relatively quickly.There are relatively few genes proximal TEs that are actually within the gene body  to high (r2 > 0.9) LD with a nearby SNP marker, but there was a subset of 49,382 (19.9%) TEs that were in low LD (r2 < 0.5) with all SNPs within a 1 Mb window of the TE (We sought to test the extent to which the extensive number of polymorphic TE insertions that were classified in this study were in linkage disequilibrium (LD) with genome-wide SNP markers to begin to understand the extent to which phenotypic variation that is caused by TEs has or has not been accounted for in previous studies that used only SNP markers. For this analysis, all SNPs within 1 Mb plus or minus a TE were evaluated and the SNP that was in the highest LD was recorded. The majority of TEs were in moderate . Only 357 TEs had a D' value of less than 0.9 to a SNP within 1 Mb of the TE, and a relationship with population frequency was no longer observed. The average distance between the SNP and TE decreased relative to r2 for the SNP in highest LD with a TE , there are TE insertional polymorphisms that are not tagged by SNPs across different metrics  likely occur near the same time as the insertion of the outer element. Finally, analysis of LD between genome-wide SNP variants and TE insertional polymorphisms revealed that over 19.9% of TE insertional polymorphisms are not well tagged (R2 > 0.5 by nearby SNPs. This result has major implications when interpreting the results of genome-wide association studies (GWAS) that have been conducting using only SNP markers. Future work utilizing insertional polymorphism information may shed light into unexplained phenotypic variation in diverse germplasm such as this population.TE insertional polymorphisms can play a crucial role in reshaping the phenotype of plants. In this study, we used the whole genome resequencing data, four high-quality reference genomes with de novo annotated TEs, and random forest machine learning models, to generate a high-quality set of genome-wide insertional polymorphism data for 509 diverse maize lines. The majority of TE insertions (both nested and nonnested) were at the low to moderate frequency in the population. Within the LTR retrotransposon insertions, we observed a strong negative relationship between the frequency of the insertion in the population and the LTR similarity within the inserted element. Population frequency information coupled with LTR similarity also allowed us to determine that the majority of nested insertions (


Detection of CNVs (copy number variants) and ROH (runs of homozygosity) from SNP (single nucleotide polymorphism) genotyping data is often required in genomic studies. The post-analysis of CNV and ROH generally involves many steps, potentially across multiple computing platforms, which requires the researchers to be familiar with many different tools. In order to get around this problem and improve research efficiency, we present an R package that integrates the summarization, annotation, map conversion, comparison and visualization functions involved in studies of CNV and ROH. This one-stop post-analysis system is standardized, comprehensive, reproducible, timesaving, and user-friendly for researchers in humans and most diploid livestock species. Genome-wide data have been accumulated for large numbers of individuals of various species as the cost of single nucleotide polymorphism (SNP) genotyping continues to decrease. In addition to using these data for GWAS (genome wide association study) or GS (genomic selection), interesting genomic information about copy number variant (CNV) and runs of homozygosity (ROH) can be inferred from these genotypes, and a range of software products [such as PennCNV , CNVPartThere are several common “pitfalls” we have observed when conducting CNV analyses using SNP genotyping data. The most frequent is to annotate the candidate genes in a CNVR (copy number variation region) without considering the frequency of the CNVs: this can result in undue weight being given to rare CNVs that affect only one or two samples. A second issue is comparing CNVs between different studies, and making comparisons only at the population level, and not at the individual sample level. Comparison at the population level could reflect the ubiquitous nature of CNVs, but at the individual level it also provides information about the robustness of CNV detection algorithms. A third issue arises when comparing CNVRs that have been detected using different reference genomes, which requires converting the coordinates of the regions between the two genomes. Making these conversions requires careful consideration, as the order of SNPs on chromosomes might differ between two different reference assemblies, such that the lengths or even chromosomal orders of CNVs can change, which might lead to meaningless comparisons between CNVRs. A fourth common problem is get the incorrect number of overlapping CNVRs when presenting comparison results via Venn diagram. Since the number of overlapping regions is relative to the results, and a single long interval generated using one approach might overlap multiple shorter intervals detected using another approach, in which case representing the results via Venn diagram requires special annotation.There are also some steps that may be easily forgotten performing ROH analysis on SNP genotyping data. For example, the SNP density distributions may not have been carefully examined prior to inference of ROH. The density of SNPs may differ across the chromosome on different SNP chips, but ROH detection methods are highly affected by characteristics such as SNP density, window size, tolerance of occasional heterozygosity in the run, and the presence of missing values in the detection window. Knowing SNP density can therefore help us to select better parameters when performing ROH detection. Moreover, while reporting the candidate genes by functional annotation of genes that located in ROH regions, we may not examine the frequencies of haplotypes within these interesting genes, but this step could provide valuable information about the high frequency genotypes of these genes, which is useful on designing the further validation experiments and can provide the valuable reference to others when they comparing the genes using the same SNP chips on different populations.There are several common requirements in studying CNV and ROH patterns in a new species or population. These include: the need for preparing summary tables, making summary figures, generating CNVRs and plotting CNVR distribution maps with gene annotations, comparing CNVs and CNVRs between studies, converting genome coordinates and map files from one reference to another, finding high frequency abnormal genomic regions, creating consensus gene lists, producing custom visualization of results, and identifying haplotypes in regions of interest. Therefore, we built this open-source tool to provide a standardized, reproducible, time-saving and widely available one-stop post-analysis system to make research more simple, practical and efficient while avoiding common “pitfalls” that can affect the accuracy and interpretability of these studies.The functions provided by this package can be categorized into five sections: Conversion; Summary; Annotation; Comparison; and Visualization. The most useful features provided are: integrating summarized results, generating lists of CNVRs, annotating the results with known gene positions, plotting CNVR distribution maps, and producing customized visualizations of CNVs and ROHs with gene and other related information on one plot . This paconvert_map is designed to compare SNP map files for two different reference genomes, matching by SNP name, and produce SNP maps in a format suitable for use by convert_coord. The function also reports the density of SNPs by chromosome. convert_coord is designed to convert the physical positions of genomic intervals based on a given SNP map file. Currently, the function is limited to inputs generated by convert_map, and can only convert the coordinates for intervals on the same type of SNP chip. Converting coordinates may change the total length of the intervals, as the positions and orders of the SNPs on the chromosome will potentially differ between various reference genomes; therefore, the function produces a table that summarizes how many intervals were converted successfully, and reports on the differences in length between the converted and original intervals.The conversion section handles the conversions of genomic positions between two reference genomes, and provides two functions. The summary section contains a group of functions to summarize CNV results, generate CNVRs, and make CNVR distribution maps from CNV results. There is also a collection of functions to summarize ROH results, report frequencies of ROH regions, inbreeding coefficient by different length groups and to generate haplotypes on interesting ROH regions.clean_cnv, summary_cnv_plot, and call_cnvr. clean_cnv takes a CNV list from PennCNV and CNVPartition and reformats it into a standard format for use in the functions listed below. cnv_summary_plot generates a range of summary plots, aggregating CNV results by length group, CNV type, chromosome, and individual. call_cnvr generates CNV regions as the union of sets of CNVs that overlap by at least one base pair /(∑Lauto)  , where ∑ormation .get_refgene will automatically download a reference gene list and invoke clean_ucsc and clean_ensgene from UCSC , CNVRs (compare_cnvr), gene frequency lists (compare_gene), and other intervals . These functions were implemented using the foverlaps function in the data.table R package  on a population and individual basis.The comparison section consists of functions for comparing sets of CNVs , the CNVR list (“cnvr”), and the reference gene list (“human_hg19”); the gene frequency list  and the high frequency ROH regions file that was generated by roh_window. Since we have the reference gene list, we can visualize ROH region with genes  using the “filter” and “select” functions  as input and assigning the gene position to the “target_region” argument to present the plot  was downloaded from the UCSC website  by get_rions see  were annenes see  via roh_unctions , then usplot see . We can plot see  of visuashell  that was generated by Beagle, and set the “convert_letter” argument as “TRUE” to convert the genotype file into the standard format used by HandyCNV (returned as “geno_chr1”). Second, we use closer_snp to extract the gene’s position (returned as “GABPB1_pos”) from the SNP map file, which requires the SNP map file (provided using the “phased_input” argument), and to assign the gene’s physical position we got from reference gene list to the “chr,” “start,” and “end” arguments, respectively. Finally, we use get_haplotype to get the haplotype information (see GABPB1 gene by assigning the formatted phased genotype list (“geno_chr1”) to the “geno” argument and assigning the gene’s position (“GABPB1_pos”) to the “pos” argument.Finally, we take tion see  for the Here we present a freely available and open source R package called HandyCNV, which provides a comprehensive set of functions to summarize and visualize the CNVs and run of homozygosity results detected from SNP genotyping data.Many good software packages have been developed for the detection of CNV and ROH from SNP chip data [such as PennCNV , CNVPartplot_cnvr_panorama function needs to read genotype data to plot BAF and LRR information: this can require larger amounts of storage. We have tested it on 150 k SNP chip with 2,100 samples on a desktop windows system and it performs well; however, it may not be suitable for higher density chips and very large data sets. The get_haplotype function is also limited, as it currently only accepts phased genotypes produced by Beagle 5.1 .The current release of HandyCNV is version 1.1.6, which can be installed in the R environment using the following code: “remotes::install_github (repo = ‘JH-Zhou/HandyCNV@v.1.1.6’).” The current development version can be found at the GitHub repository (10.1590/1678-4685-GMB-2019-0218). The genotype data used in Example 2 can be found in Brandon D. Velie’s study which was public available via Figshare (doi: 10.6084/m9.figshare.3145759).Publicly available datasets were analyzed in this study. This data can be found here: The human CNV lists used in Example 1 can be found in “Table S1 – Detailed information about all CNVs analyzed” at Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Ethical review and approval was not required for the animal study because no animal sampling, experiments or phenotype measurement applied in this study. The genotype data used in this analysis are from previous studies.JZ conceived the analysis, compiled the package, and wrote the manuscript. LL contributed to code writing and testing, and reviewed the manuscript. TL contributed to package testing, proofreading of the manuscript, and vignette. DG and YS provided instruction for analysis, reviewed the manuscript, manual, and vignette. All authors contributed to the article and approved the submitted version.TL is employed by Livestock Improvement Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Reproduction plays a pivotal role in dairy cow farming. Good reproductive performance in cows can decrease the elimination rate of cows, increase the success rate of breeding, and thereby enhance milk production. Identification of the genetic variants in reproduction-related traits helps to increase the genetic improvement of cows’ reproductive performance. In this study, we estimated the genetic parameters of three indicators of reproductive ability, namely, Loin Strength (LS), Rump Angle (RA), and Pin Width (PW), and conducted a genome-wide association study of them. The heritability of these three traits was medium, and in total, 11 significant single-nucleotide polymorphisms (SNPs) were detected. Through a bioinformatics analysis of the genes adjacent to these variants, 16 candidate genes were identified as being associated with these three traits. We expect that the results could help with the genetic improvement of Chinese Holstein cows’ reproductive performance.CDH12, TARP, PCDH9, DTHD1, and ARAP2 were selected as candidate genes that might affect LS. The six genes LOC781835, FSTL4, ATG4C, SH3BP4, DMP1, and DSPP were selected as candidate genes that might affect RA. The five genes USP6NL, CNTN3, LOC101907665, UPF2, and ECHDC3 were selected as candidate genes that might affect the PW of Chinese Holstein cows. Our results could provide useful biological information for the improvement of body shape traits and contribute to the genomic selection of Chinese Holstein cows.Reproduction is an important production activity for dairy cows, and their reproductive performance can directly affect the level of farmers’ income. To better understand the genomic regions and biological pathways of reproduction-related traits of dairy cows, in the present study, three body shape traits—Loin Strength (LS), Rump Angle (RA), and Pin Width (PW)—were selected as indicators of the reproductive ability of cows, and we conducted genome-wide association analyses on them. The heritability of these three traits was medium, ranging from 0.20 to 0.38. A total of 11 significant single-nucleotide polymorphisms (SNPs) were detected associated with these three traits. Bioinformatics analysis was performed on genes close to the significant SNPs (within 200 Kb) of LS, RA, and PW, and we found that these genes were totally enriched in 20 gene ontology terms and six KEGG signaling pathways. Finally, the five genes  Reproductive performance is an economically important feature of livestock. Successful pregnancy and birth of offspring can maintain the scale of a farm, increase the productivity and profitability of animal production, and improve farmers’ income . Many reBody-shape linear scoring is an indispensable activity in dairy cattle breeding that is generally carried out as part of the national breeding program, and the level of linear scores expresses the specificity of dairy cow muscle and bone development and function . The curMHC class II genes, which were significantly associated with pregnancy success in Nellore cattle [LOC511981, KIF1A, and EPRS genes were related to the age at first calving of Xinjiang Brown cattle [A genome-wide association study (GWAS) is a powerful method to screen a whole genome for genetic factors related to phenotypic traits by using single-nucleotide polymorphisms (SNPs) as genetic markers, and has been widely applied in domestic animals. Many GWAS studies have been carried out on dairy cows in recent years, but previous studies mainly focused on the important economic and disease traits of cows, including milk production, milk protein, body height, body weight, and ketosis ,12,13,14e cattle . The 44 e cattle , and LOCn cattle . Althougn cattle ,19, therIn this study, we conducted GWAS studies on LS, RA, and PW traits to identify the significant SNPs and candidate genes related to these traits of Chinese Holstein cows. We expect our results to become valuable resources for genetic evaluation and provide a theoretical basis for improving the genomic selection of reproductive performance in dairy cows.The collection of hair-follicle samples and the measurement of traits in this study were conducted in accordance with the Institutional Animal Care and Use Committee of the School of the Yangzhou University Animal Experiments Ethics Committee (License Number: SYXK (Su) IACUC 2012-0029), and no animals were anesthetized or euthanized during the study.A total of 1730 healthy Chinese Holstein cows from four dairy farms in Jiangsu Province, China were used in this study . Three body-shape traits, Loin Strength (LS), Rump Angle (RA), and Pin Width (PW), of 1730 cows were measured according to the China National Standard (GB/T 35568-2017); at least three professionals performed the measurement of traits for each cow, and the average of the measurements taken by the different technicians was used as the phenotype of each trait to ensure the accuracy of the data. All cows were in the dry period when they were measured. The parities of cows were between 1 and 4, and the pedigree of the cows could be traced back at least three generations. The phenotype distribution of the three traits across the farms is shown in The three body-type traits used for subsequent analysis were all adjusted with fixed environmental factors using the following two steps:I is a matrix for unit vector, Step 1: We estimated genetic parameters in the following multiple-trait animal model:e (v5.6) . Finallyximation .Step 2: We adjusted the phenotypes using the following model:http://www.neogenchina.com.cn, accessed on 20 March 2021), and the ARS-UCD1.2 (bosTau9) was used as the reference genome. Then, the following quality-control criteria were implemented for the variants detected and individuals by Plink software (v 1.90) [p > 1.0 × 10−6); (3) SNP information on sex chromosomes had to be eliminated; and (4) the call rate of individual genotypes had to exceed 95%. Then, the variants and individuals that did not meet the quality-control requirements were removed. In this study, the SNPs on the sex chromosomes were also removed for the following three reasons: (1) the inheritance pattern of sex chromosomes is more complicated than that of autosomes [Of the 1730 cows measured for body-type traits, hair-follicle samples of 999 cows were collected . DNA was extracted and genotyped using the GGP Bovine 100K SNP Chip by Neogen Biotechnology  Co., Ltd.  oneutosomes ,26. In t2. Principal component analysis (PCA) was conducted using the FactoMineR package in the statistical software program R (v4.0.4) using the 84,407 variants of the 984 cows to estimate the population structure [Plink software (v1.90)  was usedtructure . Then, ttructure .p-Values calculated using Formula (3) for one trait. (2) For each bin (segment) on a chromosome, we chose one SNP with the lowest p-Value as the representative for the bin. Then, we selected the most influential bins to build kinship. The size and number of bins chosen were treated as parameters to maximize the restricted maximum likelihood for a trait. The selected SNPs (each representing a bin) were then used as a base of a SNP pool to define individual relationships for the later association test. (3) We excluded the SNPs in the SNP pool that were in LD (r2 > 0.7) with the testing SNP to derive a complementary trait-specific kinship. The random-effect model is as follows [The multilocus linear mixed model was used to conduct the association analysis between the SNPs and traits by the fixed- and random-model circulating probability unification (FarmCPU) method . The Faree steps : (1) We  follows ,30:(4)y=The SNP genotypes coded for the association analyses were 0, 1, and 2, which were converted by Plink software (v 1.90) . The exp−7 (0.05/84407).We set the significance threshold for selecting the significant SNPs using the Bonferroni correction method . The typhttps://www.ensembl.org/Bos_taurus/Info/Index” (accessed on 20 March 2021), and ARS-UCD1.2 (bosTau9) was used as the reference genome in this process. Then, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis  was conducted on the candidate genes using the cluster profiler package in R (v4.0.4) [Genes within 200 Kb (LD > 0.35) upstream and downstream of the significant SNPs detected from the three indicators of reproductive ability  were selected as candidate genes at “(v4.0.4) .t-Test in SPSS (v26.0) software . Because these 214 cows were all from same farm (Farm 4), they were all in their first lactation at the time of measurement, the technicians were the same, and the measurement work was all finished in one week, we used the raw measured scores (not the adjusted phenotypes) to check the relationship between LS, RA, and PW and reproductive traits.The relationship between the phenotype traits studied in this article  and the reproductive and calving traits of cows in the first parity, including the occurrence of pregnancy after one breeding, the occurrence of premature birth, and the ease of calving, were evaluated using the method of Independent Sample p < 0.05), the LS and PW scores were significantly associated with the occurrence of premature birth in the parity (p < 0.05), and the LS and PW scores were significantly related to the ease of calving of cows (p < 0.05).As shown in The adjusted body-type traits of the 984 cows including LS, RA, and PW presentedapproximately normal distributions in this study . The des2 was lower than 0.35 when the average distance between SNPs was around 200 Kb.After quality control, 84,407 SNPs on 29 chromosomes remained for subsequent marker analysis. The distribution of the SNP information within 200 Kb windows on the different chromosomes is shown in The three highest principal components (PCs) were used to determine the population stratification level. As shown in p-Value of each SNP. The vast majority of the variants did not deviate from the expected p-Value, which revealed that the models and methods for GWAS analysis were reasonable  plots of the three traits were drawn according to the asonable .−7 (0.05/84407). Four SNPs  located on chromosome 4, 6, 12, and 20, respectively, were detected to be associated with trait LS, and the genes nearest to the four SNPs were TARP (5 Kb), DTHD1 (within), PCDH9 (within), and CDH12 (within), respectively. Four SNPs  located on chromosome 3, 3, 6, and 7, respectively, were detected to be associated with trait RA, and the genes nearest to the four SNPs were ATG4C (200 Kb), SH3BP4 (50 Kb), LOC781835 (within), and FSTL4 (within), respectively. Three SNPs  located on chromosome 12, 22, and 29, respectively, were detected to be associated with trait PW, and the genes nearest to the three SNPs were USP6NL (within), CNTN3 (200 Kb), and LOC101907665 (200 Kb), respectively : endocytosis, other glycan degradation, ECM-receptor interaction, autophagy—other, mRNA surveillance pathway, and RNA transport; 7 of 45 candidate genes were involved in pathway regulation , the genes within 200 Kb of significant SNPs for each trait were selected for enrichment analysis, and a total of 45 genes were obtained, of which 11 belonged to LS, 23 to RA, and 11 to PW . These cgulation .Reproduction is a key factor in dairy cows’ postpartum lactation and herd expansion. Cows with good reproductive performance show pregnancy symptoms on time and can become pregnant after the first mating, which can directly increase production performance and the economic situation of dairy farms . Due to Studies have reported that body-type traits could be the indicator traits of reproduction of pigs and cattle. The vulva score categories (VSC) had the potential to improve reproductive efficiency in the first parity performance, and had been proposed as an indicator trait of efficient reproductive performance in sows . The estHowever, some studies also presented the limitations of using body-type traits as indicator traits to improve reproductive performance, such as the environmental factors that would influence the score of measurement ,42, althIn this study, the heritability of reproductive traits of cattle was medium . The her2) between SNPs decreased as the distance increased, and the R2 was lower than 0.35 when the average distance between SNPs was around 200 Kb (The linkage disequilibrium (LD) analysis of the population is the basis of association studies. In the present study, the level of LD  transporting, and affected the success rate of mouse reproduction and litter size [FSTL4 played a role in the expression of follicle-stimulating hormone (FSH), which in turn affected the ovarian follicular and corpus luteum dynamics, reproductive-hormone secretion, and estrus behavior of dairy cows [ATG4C in endometrium was closely related to pregnancy status and affected the reproductive efficiency of beef heifers [To gain insight into the function of the significant SNPs, genes that were in linkage disequilibrium regions (LD > 0.35) with these significant SNPs were used for further analysis. Of the 11 genes closest to the significant SNPs, some had been confirmed to be related to bone and muscle-tissue growth. y weight ; the unun humans ; SH3BP4 l growth ; USP6NL n humans ; CNTN3 wd rabbit . Interes chicken ; TARP pater size ,63; FSTLiry cows ,64; the  heifers . We suspARAP2 in the endocytosis pathway was a candidate gene that could affect the carcass traits, including carcass weight, eye-muscle area, back-fat thickness, and marbling of Korean cattle [ARAP2 might be a candidate gene that could affect the LS of cows.In the present study, a total of 45 genes within 200 Kb upstream and downstream of the significant SNPs of the three traits were found . The cann cattle , and we DMP1 and DSPP, were important genes that could regulate bone development and growth [The candidate genes of RA were mainly involved in the protein-deglycosylation process and protein-modification process b. Studied growth ,78,79, aUPF2 gene participated in both pathways simultaneously, but there are few studies on the effect of UPF2 on animal growth and development. ECHDC3 is one of the candidate genes for PW (ECHDC3 is a kind of testis-tissue sperm-binding protein-encoded gene, and studies have shown that it was mainly related to metabolic disease and insulin sensitivity, and might affect the growth of animals [UPF2 and ECHDC3 might be key candidate genes that affect the PW of dairy cows.Two KEGG pathways; namely, the mRNA surveillance pathway and the RNA transport pathway, were enriched by the candidate genes of PW , and thes for PW , and ECH animals ,82. We sThis study focuses on the three body-shape traits, LS, RA, and PW, which were selected as indicators of the reproductive ability of Chinese Holstein cows. We estimated the genetic parameters of these three traits and performed genome-wide association analyses on them. The heritability of these three traits was of medium size, and a total of 11 significant SNPs were associated with them. We also found some candidate genes associated with LS, RA, and PW, and these genes might also play roles in these three traits of dairy cows. Bioinformatics analyses of candidate genes were also performed, and the pathways and the biological processes they enriched were presented. In general, our study found that the three body traits, LS, RA and PW, were closely related to the reproductive performance of dairy cows, and we detected some new variants and candidate genes that might affect these traits from a genetic perspective. Our findings provide useful biological information for the improvement of body shape traits and reproductive performance, and therefore will contribute to the genomic selection of Chinese Holstein cows.


Precision medicine is a phrase used to describe personalized medical care tailored to specific patients based on their clinical presentation and genetic makeup. However, despite the fact that several single nucleotide polymorphisms (SNPs) have been reported to be associated with increased susceptibility to particular anesthetic agents and the occurrence of perioperative complications, genomic profiling and thus precision medicine has not been widely applied in perioperative management.We validated six SNP loci known to affect perioperative outcomes in Japanese patients using genomic DNA from saliva specimens and nanopore sequencing of each SNP loci to facilitate allele frequency calculations and then compared the nanopore results to those produced using the conventional dideoxy sequencing method.Nanopore sequencing reads clustered into the expected genotypes in both homozygous and heterozygous cases. In addition, the nanopore sequencing results were consistent with those obtained using conventional dideoxy sequencing and the workflow provided reliable allele frequency estimation, with a total analysis time of less than 4 h.Thus, our results suggest that nanopore sequencing is a promising and versatile tool for SNP genotyping, allowing for rapid and feasible risk prediction of perioperative outcomes.The online version contains supplementary material available at 10.1186/s40981-022-00506-7. The most critical role of any anesthesiologist is to ensure the safety of their patient during the perioperative period and provide the skills and expertise to ensure a smooth surgical experience. A preoperative medical assessment is used to ensure the safety and comfort of patients undergoing surgery. However, even with preoperative testing, it is difficult to predict individual susceptibility to particular anesthetic drugs and the risk of potential postoperative complications such as postoperative nausea and vomiting , 2.Precision medicine is an emerging approach designed to facilitate improved prevention and treatment of human diseases . This apPrevious studies have shown that there are a range of specific genetic variations associated with changes in susceptibility to specific anesthetic agents and perioperative outcomes . Single Given this, we designed this study to evaluate a streamlined workflow for SNV/SNP genotyping using nanopore sequencing technology, with a view to enabling genetic-based perioperative risk evaluations in preoperative patients. The MinION nanopore sequencer is a portable DNA/RNA sequencing platform that provides on-site genetic analysis with rapid and affordable deployment , 6. We dg for 5 min before being resuspended in 0.3 mL of PBS and subjected to DNA extraction using the Maxwell RSC Blood DNA Kit  on the Maxwell RSC automated nucleic acid purification platform . Briefly, the sample (0.3 mL) was mixed with lysis buffer (0.3 mL) and proteinase K solution (30 μL), incubated at 56 °C for 20 min, and transferred to a Maxwell RSC Cartridge for magnetic bead-based DNA extraction. DNA was eluted in 50 μL elution buffer and quantified using the QuantiFluor ONE dsDNA System .This study was approved by the institutional review board at Kansai Medical University Hospital  and saliva samples were collected from five willing participants. Approximately 1 mL of saliva was mixed with 4 mL of phosphate-buffered saline  and centrifuged at 1800×A detailed protocol of the 2-step polymerase chain reaction (PCR) for nanopore amplicon sequencing is available at protocols.io (10.17504/protocols.io.bwr5pd86). Briefly, six SNP loci were used for amplicon sequencing  with 0.2 μM of each inner primer in a total volume of 25 μL. Amplification conditions were as follows: initial denaturation at 94 °C for 2 min, 35 cycles of 94 °C for 15 s, 60 °C for 15 s, 68 °C for 30 s, followed by a final extension at 68 °C for 1 min. The resultant amplicons (1 μL) were subjected to a second PCR to introduce the indices (barcodes) and the 5′ tags required for adapter attachment. These reaction mixtures (25 μL) contained KAPA2G Robust HotStart ReadyMix  and the barcoded outer primers (0.5 μL) supplied in the PCR Barcoding Kit . Cycling conditions were as follows: initial denaturation at 95 °C for 3 min; 15 cycles of 95 °C for 15 s, 62 °C for 15 s, 72 °C for 30 s, followed by a final extension at 72 °C for 1 min. Amplified DNA was then purified using AMPure XP  and quantified using a QuantiFluor ONE dsDNA System.The purified barcoded amplicons were then pooled, and 100 fmol was applied as template in the library preparation which was completed using the PCR Barcoding Kit. These libraries were loaded onto the R9.4.1 flow cell  and sequenced on the MinION Mk1C with MinKNOW software version 21.05.21 (Oxford Nanopore Technologies). Base-calling was performed in real time via Guppy version 5.0.13 (Oxford Nanopore Technologies) using the following settings: fast basecalling model, trim_barcodes=on, require_barcodes_both_ends=off, detect_mid_strand_barcodes=on, min_score=60. Called reads (FASTQ format) were filtered to generate pass reads, with a minimum Phred quality score of 8.The first round PCR products described above were purified using an AMPure XP and sequenced on both strands using the following primers: forward TTTCTGTTGGTGCTGATATTGC and reverse ACTTGCCTGTCGCTCTATCTTC, which correspond with the 5′ tails described above. Sequencing was performed on a 3130xl Genetic Analyzer using a BigDye Terminator v3.1 Cycle Sequencing kit (Thermo Fisher Scientific).The average Phred quality scores of the nanopore sequencing reads were analyzed using NanoPlot ver. 1.27.0 and the allele frequencies for each of the target SNP sites were determined using the following bioinformatics pipeline in SeqKit version 0.13.2. Step 1: Reads with a perfect match to the probe sequence specific to each SNP site , have enabled rapid and precise analysis of large amounts of genomic data . This haOPRM1), which has been extensively studied as an example of a genetic variant affecting perioperative outcomes. The μ-opioid receptor is the primary site of action for most perioperative opioids, including morphine and fentanyl [Recent reports have described several genetic variations that may affect a patient’s susceptibility to particular anesthetic agents and the occurrence of perioperative complications . These ifentanyl . This SNfentanyl , 22. It fentanyl –11; howefentanyl , 8 and rfentanyl , 13 alsofentanyl , rs33985fentanyl , and rs1fentanyl  are quitConventional NGS generates vast quantities of highly accurate sequencing data on a large scale , 23. HowDespite the obvious advantages presented by this approach, our study did suffer from several limitations. First, the reliability of this method was only evaluated for a very small number of known SNPs in a very small population. Second, this genotyping workflow limits the identification of novel or unknown SNPs. Third, the clinical relevance of each SNP has not yet been thoroughly validated. Accumulating evidence has shown the potential of several SNPs to act as genetic markers for risk prediction around perioperative outcomes. However, the utility of SNP genotyping data in anesthetic management remains unclear. Further studies are required to elucidate the underlying mechanisms linking these genetic variations to the clinical phenotype and to establish a basis for the practice of precision medicine in perioperative care.Nanopore sequencing reads were reasonably well clustered within their expected genotypes, and this simplified workflow provided reliable allele frequency estimation with a total analysis time of less than 4 h. This suggests that nanopore sequencing may be a promising and versatile tool for SNP genotyping, allowing for rapid and reliable perioperative risk prediction in clinical settings.Additional file 1:. Bioinformatics analysis.Additional file 2: Table S1. Allele frequency data for selected SNPs associated with perioperative outcomes.Additional file 3: Figures S1-S6. SNP genotyping using dideoxy sequencing.Additional file 4: Table S2. Statistics of the nanopore sequencing data.Additional file 5: Tables S3-S8. Allele frequencies for each SNP as determined by nanopore sequencing.


Cattle production is dependent upon fertility because it results in producing offspring to offset production costs. A number of semen attributes are believed to affect fertility and are frequently measured as part of routine breeding soundness exams or semen collection procedures. The objective of this study was to perform a single-step genome-wide association study (ssGWAS) for beef bull semen attributes. Beef bull fertility phenotypes including volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC) were obtained from two artificial insemination (AI) centers. A total of 1819 Angus bulls with 50,624 collection records were used for ssGWAS. A five-generation pedigree was obtained from the American Angus Association and consisted of 6521 sires and 17,136 dams. Genotypes on 1163 bulls were also obtained from the American Angus Association and utilized in ssGWAS.10P-value threshold greater than 4.0. Volume, CONC, NSP, IMot, PTMot, 3HRPTMot, %NORM, PRIM, and SEC have five, three, six, seven, two, six, six, and two genome-wide significant SNP, respectively.A multi-trait animal model was used for the estimation of single nucleotide polymorphism (SNP) effects. Significant SNP were those with a -logSeveral significant SNP were determined to be near or within quantitative trait loci (QTL) associated with beef bull semen attributes. In addition, genes associated with fertility were found to contain or be near the significant SNP found in the study. The results indicate there are regions of the genome that impact fertility, proving inclusion of genomic information into genetic evaluation should be advantageous for genetic improvement of male fertility traits. Fertility is a complex trait which is affected by management –3, nutriHowever, thousands of semen records from beef bull semen collection facilities provide an opportunity to identify polymorphisms which may affect spermatogenesis, morphology, and motility of spermatozoa . One wayPhenotypic observations for 1819 Angus bulls were obtained from two bull semen collection facilities. The phenotypic observations for beef bull semen attributes included volume (VOL), concentration (CONC), number of spermatozoa (NSP), initial motility (IMot), post-thaw motility (PTMot), three-hour post-thaw motility (3HRPTMot), percentage of normal spermatozoa (%NORM), primary abnormalities (PRIM), and secondary abnormalities (SEC). Trait definitions are listed in Table n = 3921) and minor allele frequency of < 0.05 . Animals with a call rate of < 0.90 were removed (n = 3). Imputation was performed by genotyping providers to the American Angus Association, so that all genotyped bulls had 54,609 SNP. After quality control filtering, a total of 38,515 SNP from 1160 animals were available for analysis. The American Angus Association also provided the SNP positions and the reference genome used by the American Angus Association was UMD3.1.The American Angus Association provided pedigree information for 1819 bulls and SNP data for 1163 bulls. The pedigree contained 6521 sires and 17,136 dams. The maximum number of SNP per bull was 54,609. Data were edited to remove SNP with a call rate of < 0.90  model. In a ssGBLUP model, the numerator relationship matrix (A) is replaced with H, which augments A with the genotype information. The inverse of the H matrix is generated as follows:ividuals .The following algorithm was used to back solve for the SNP effects for the ssGWAS :\documena is a vector of breeding values for the genotyped individuals generated from BLUPF90, Z is a matrix relating individuals to phenotypes, and u is a vector of SNP marker effects.where The variance for the genotyped animal effects is as follows :\documenG is the genomic relationship matrix, I is an identity matrix, and λ is the ratio of the SNP marker effect variance and the breeding value variance.where u is a vector of SNP marker effects, I is an identity matrix, Z is a matrix relating individuals to phenotypes, and a is a vector of breeding values for the genotyped individuals. Each SNP was assumed to have an equal allele substitution effect variance, and it was assumed the SNP affects followed the infinitesimal model. Thus, an unweighted ssGBLUP was performed.The SNP effects were predicted utilizing the following equation:P-values associated with the SNP effects were obtained from the POSTGSF90 program within the BLUPF90 software suite as detailed in [p-value for the SNP effect is obtained by [SNP effects were obtained from one trivariate and six bivariate analyses. VOL, CONC, and NSP were included in a trivariate analysis. Due to convergence issues, bivariate analyses were performed among the remaining groups of traits. Initial motility, PTMot, and 3HRPTMot SNP effects were generated from three different bivariate analyses, and %NORM, PRIM, and SEC from an additional three bivariate analyses. For traits where a bivariate analysis was performed, significant SNP from both bivariate analyses are reported. Utilizing multi-trait analyses allows for multiple, genetically correlated traits to be included in the model and improves the predicative capability for each trait. The ailed in . The p-vained by :\documenαi is the estimate of the marker effects, sd is the standard deviation, and Φ is the cumulative standard normal function.where P-values are generated by back solving for SNP effects from the breeding value estimates. This approach is possible because the fitting of animal as a random effect to generate breeding value estimates is an equivalent model to fitting all SNPs as random effects and solving for these effects directly [The directly .P-values, Manhattan plots for all nine traits were generated utilizing the CMplot package in R [10P-values were converted to p-values so that the false discovery rate (FDR) could be calculated for each P-value. While there were no significant SNP identified at an FDR threshold of < 0.0001, < 0.001, < 0.01, < 0.025, or < 0.05, several significant SNP existed with a -log10P-value threshold of 4.0 , which contributes to the structural integrity of the extracellular matrix and heparin binding . It is iAlthough not within ±250,000 kilobases of the significant SNP, a pertinent gene nearby was the major facilitator superfamily domain containing 14A (MFSD14A) gene. Also known as HIAT1, it was 300,000 kilobases upstream of the SNP. The MFSD14A gene is important for spermatid nucleus differentiation and sperm mitochondrion organization. It has been reported that mice with a disruption to the MFSD14A gene are infertile due to incomplete acrosome formation and round head defects .Primary abnormalities had a significant SNP in the vicinity of a gene on chromosome 20 Table . PrimarySeveral QTL associated with fertility traits were identified in this study and validated by previously published literature. In addition, genes within the QTL regions for beef bull semen attributes were discussed. While a few gene functions could be associated with beef bull semen attributes, some of the genes found in the current study do not have any known association with fertility traits reported in previous literature and have unknown related physiological association. Results from the current study, paired with findings in previous literature, validate that bull fertility traits are controlled by genetic factors. Identification of the QTL regions and SNP positions associated with these traits provides the knowledge necessary to enrich these regions in future iterations of SNP chips used within the cattle industries, and thus increase genomic prediction accuracies for these traits should the industry add them to routine genetic evaluation. The increased use of genomic testing in tandem with the number of phenotypes routinely recorded by bull semen collection facilities makes identification of beef bulls with superior genetic merit for fertility traits feasible. This advancement would increase profitability of beef producers by improving fertilization rate, increasing overall herd reproductive rate, and providing producers another genetic tool to use in making selection decisions.